https://ijisae.org/index.php/IJISAE/issue/feedInternational Journal of Intelligent Systems and Applications in Engineering2024-03-29T09:26:52+00:00IJISAEeditor@ijisae.orgOpen Journal Systems<div style="border: 3px solid black; padding: 10px; background-color: aliceblue;"> <p style="margin: 5px; font-size: 15px;"><strong style="font-size: 20px;"><u>Update Regarding Article's Indexing:</u></strong><br />Dear esteemed authors and readers,<br />We are pleased to inform you that the <strong>International Journal of Intelligent Systems and Applications in Engineering (IJISAE)</strong> has successfully passed the re-evaluation process by <strong>Elsevier</strong>. This achievement reflects our commitment to maintaining the highest standards of quality in academic publishing.<br />We are also excited to announce that our pending articles will start getting indexed in Scopus in 6 weeks. This is a significant milestone for us, and we believe it will enhance the visibility and accessibility of our published research.<br />We would like to express our gratitude to all our authors, reviewers, and readers for their continuous support and contributions towards making IJISAE a leading platform for scholarly research in the field of intelligent systems and applications in engineering.<br />We look forward to continuing to provide a high-quality platform for academic exchange and encourage all interested authors to submit their best work to IJISAE.<br /><br />Best regards,<br />The IJISAE Editorial Team</p> <br /> <p style="margin: 5px; font-size: 15px;"><strong style="font-size: 20px;"><u>Information for Authors:</u></strong><br />We are pleased to inform that we are now collaborating with <strong>Digital Commons, Elsevier</strong> for much better visibility of journal. Further authors will be able to observe their citations, metric like PlumX from journal website itself. <strong>IJISAE</strong> will be in transition from <strong>OJS</strong> to <strong>Digital Commons Platform</strong> in next few months so if their is any queries or delays contact directly on <em><strong>editor@ijisae.org</strong></em></p> </div> <p><strong><a href="https://ijisae.org/IJISAE">International Journal of Intelligent Systems and Applications in Engineering (IJISAE)</a></strong> is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. <strong>IJISAE</strong>, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.</p> <p>As an Open Access Journal, IJISAE devotes itself to promoting scholarship in intelligent systems and applications in all fields of engineering and to speeding up the publication cycle thereof. Researchers worldwide will have full access to all the articles published online and be able to download them with zero subscription fees. Moreover, the influence of your research will rapidly expand once you become an Open Access (OA) author, because an OA article has more chances to be used and cited than does one that plods through the subscription barriers of traditional publishing model.</p> <p><strong>IJISAE (ISSN: 2147-6799)</strong> indexed by <a href="https://www.scopus.com/sourceid/21101021990#tabs=0" target="_blank" rel="noopener">SCOPUS</a>, <a href="https://app.trdizin.gov.tr/dergi/TVRBM05UVT0/international-journal-of-intelligent-systems-and-applications-in-engineering" target="_blank" rel="noopener">TR Index</a>, <a href="https://journals.indexcopernicus.com/search/details?jmlId=3705&org=International%20Journal%20of%20Intelligent%20Systems%20and%20Applications%20in%20Engineering,p3705,3.html">IndexCopernicus</a>, <a href="http://globalimpactfactor.com/intelligent-systems-and-applications-in-engineering-ijisae/%20in%20Engineering,p3705,3.html" target="_blank" rel="noopener">Global Impact Factor</a>, <a href="http://cosmosimpactfactor.com/page/journals_details/6400.html" target="_blank" rel="noopener">Cosmos</a>, <a href="https://scholar.google.com.tr/scholar?q=IJISAE&btnG=&hl=tr&as_sdt=0%2C5">Google Scholar</a>, <a href="http://www.journaltocs.ac.uk/index.php?action=search&subAction=hits&journalID=29745" target="_blank" rel="noopener">JournalTocs</a>, <a href="https://www.idealonline.com.tr/IdealOnline/lookAtPublications/journalDetail.xhtml?uId=679" target="_blank" rel="noopener">IdealOnline</a>, <a href="http://oaji.net/journal-detail.html?number=5475" target="_blank" rel="noopener">OAJI</a>, <a href="https://www.researchgate.net/journal/International-Journal-of-Intelligent-Systems-and-Applications-in-Engineering-2147-6799" target="_blank" rel="noopener">ResearchGate</a>, <a href="http://esjindex.org/search.php?id=2455" target="_blank" rel="noopener">ESJI</a>, <a href="https://search.crossref.org/" target="_blank" rel="noopener">Crossref</a>, and <a href="https://portal.issn.org/resource/ISSN/2147-6799" target="_blank" rel="noopener">ROAD</a>.</p> <p>Please Contact: <a href="mailto:editor@ijisae.org">editor@ijisae.org</a></p> <p><img style="width: 36px; height: 36px;" src="https://ijisae.org/public/site/images/ilkerozkan/about-the-author-1.jpg" alt="" align="left" /></p> <p><strong>Submit your manuscripts </strong><a style="color: blue;" href="http://manuscriptsubmission.net/ijisae/index.php/submission/about/submissions#authorGuidelines">Detail information for authors </a></p> <p><strong>Publication Fee:</strong> 600 USD (The APC is calculated based on the number of pages and color figures per page of the final accepted manuscript. Charges are fix 600 USD for first 10 pages. For manuscripts exceeding 10 pages, there will be an additional charge of USD 95 per additional page.)</p>https://ijisae.org/index.php/IJISAE/article/view/5333Ontology-based Multi-Agent System on Fuzzy Markup Language in Healthy Lifestyle2024-03-22T06:25:33+00:00Jayaprakash Sunkavalliprakashsunkavalli@gmail.comR. Hannah Lalithahannahlalitha@gmail.comR. Reenadevireenadevi@sonatech.ac.inM. Dhivyadhivyachweetu@gmail.comK. Sreeramamurthysreeram1203@gmail.com<p>The best ways to avoid illness are to lead a healthy lifestyle and eat a balanced diet. A healthy lifestyle is centered on good eating practices. A person's risk of illness will rise if they consistently consume too little or too much. Thus, the development of balanced and healthful eating habits is crucial to the prevention of disease. To record and depict the agents as well as their actions, which give them the capacity for reasoning, we also propose an ontology-based category knowledge and context framework. The procedure has been helped to accomplish that goal by the introduction of numerous strategies and technology. One technique that is gaining popularity to support knowledge exchange within organizations is ontology, which is a method of representing knowledge. This work offers an ontology-based multi-agent system (OMAS) for diet health evaluation that consists of a fuzzy inference agent, a semantic generation agent, and an individual information agent. The users are then asked to enter the foods they have consumed. Lastly, subject matter experts construct the ontologies for food and personal profiles. The OMAS's knowledge base and rule base are described using fuzzy markup language (FML). The primary output of basic research in healthcare informatics is the development of domain ontologies and problem-solving techniques. Consequently, our scientific community has to give these ideas more consideration.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5334Real-Time Imbalance Liver Tumor Sensor Databases: A Deep Classification Framework with Ensemble Feature Extraction, Ranking, and Probabilistic Segmentation for Efficient Analysis2024-03-22T06:29:41+00:00N. Nanda Prakashnandaprakashnelaturi@gmail.comV. Rajeshrajesh4444@kluniversity.inSandeep Dwarkanath Pandesandeep7887pande@gmail.comSyed Inthiyazsyedinthiyaz@kluniversity.inSk Hasane Ahammadahammadklu@gmail.comDharmesh Dhabliyadharmesh.dhabliya@viit.ac.inRahul Joshirahulj@sitpune.edu.in<p>As the size of liver tumor image databases increases, it becomes challenging to enhance the true positive rate of traditional prediction approaches due to the high majority-minority ratio and noise. However, while 3D convolutions have the potential to fully leverage spatial information, they also come with high computational costs and require significant GPU memory usage. On the other hand, 2D convolutions are limited in their ability to utilize the information contained in the third dimension. Missing feature values, feature noise, and Imbalanced liver classes are some of the significant factors that can impact the quality of input data. The quality of imbalance data significantly impacts the efficiency of classification approaches, making it necessary to ensure high-quality input data to achieve optimal results. Therefore, to ensure high-quality predictions on imbalanced liver datasets, models need to be optimized. Sensors are commonly used to collect and measure physical parameters, and they can be used to obtain liver tumor data for the proposed model. In this work, medical imaging sensors such as CT (computed tomography) machines are used to capture detailed images of the liver and identify potential tumors. Therefore, sensors play a crucial role in the proposed model by providing the necessary data to extract features, segment the liver and detect tumors accurately. In this work, an optimized k-joint probabilistic segmentation-based ensemble classification model is proposed to address the issues of homogenous and heterogenous liver tumor detection. Additionally, novel image filtering, feature extraction and ranking approaches are proposed to improve the imbalanced liver tumor regions for classification process. The experimental results demonstrate that the proposed classification model based on k-joint probability segmentation has significantly improved the accuracy, recall, precision, and AUC compared to the existing models.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5335Improvised Swarm Based Discrete Data Mining Approach for High Utility Item Sets2024-03-22T06:34:43+00:00Raja Rao Budarajurajaraob@yahoo.comSastry Kodanda Rama Jammalamadakadrsastry@kluniversity.in<p>Data mining techniques uncover valuable patterns hidden inside extensive databases to assist decision support systems in different practical applications. Association rule mining analyzes the transaction database to recognize patterns and provides insights into client behavior. Frequent itemset mining (FIM) detects a group of items that are commonly purchased together. A significant limitation of FIM is its disregard for the item's significance. The significance of an item is crucial in a practical application. Hence, it is imperative to identify the critical set of items that yields substantial profits, referred to as the HUIM (High-Utility Itemset Mining) problem. Various techniques may be employed to identify high utility itemsets from a transaction database. The HUIM approaches that employ the Utility list are relatively new and exhibit superior performance in terms of memory consumption and execution time. Primary constraint of these algorithms is the execution of expensive utility list join operations. This work presents a highly efficient optimization approach based on swarm intelligence for addressing the HUIM problem. Furthermore, the suggested method's execution time is assessed. Additionally, it is compared to relevant and advanced current approaches. Extensive tests were carried out on accessible benchmark datasets demonstrate that the suggested swarm-based methodology outperforms state-of-the-art approaches.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5336Machine Learning with IoT Enhancing Car Performance through Supervised Algorithms for Vehicle Automation2024-03-22T06:37:04+00:00Elangovan G.selangovanr@gmail.comM. A. Berlinberlin.ma@vit.ac.inR. Reenadevireenadevi@sonatech.ac.inAmudha G.gav.csbs@rmd.ac.inV. Sathiyadeviviji2000@yahoo.co.in<p>The present research explores the combined application of supervised learning algorithms and the Internet of Things (IoT) to improve automotive performance in the context of vehicle automation. Our study makes use of neural networks, decision trees, and support vector machines along with a variety of datasets, well-placed sensors, and communication protocols. Across ten trials, the selected algorithms consistently displayed excellent performance, generating accuracy values ranging from 91.7% to 93.5%, precision values between 93.7% and 94.8%, recall values spanning from 89.8% to 91.7%, and F1 scores ranging between 91.5% and 93.4%. These striking results underline the potential of this integrated strategy to transform driving experiences, increase safety, and contribute to the continued growth of intelligent vehicle systems. This research not only lays the framework for new developments in the automotive sector but also demonstrates the revolutionary impact of advanced technology on the landscape of modern transportation.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5337Privacy Preserving Cyber Security System Framework for Secure Cloud-based Medical Data Transactions2024-03-22T06:47:18+00:00Sunil D. Kalekalesunild@gmail.comSushanth Chandra Addimulamsushanth93@gmail.comK. Kiran Kumarkiran5434@kluniversity.inVinay Avasthivinayddun@gmail.comSurbhi Sharmasurbhis676@gmail.comArunava Dearunavade@yahoo.com<p>With the increasing adoption of cloud storage in healthcare, ensuring the privacy and security of medical data has become a critical concern. This paper presents a comprehensive framework for privacy-preserving cybersecurity to secure medical data transactions in cloud storage environments. The proposed framework focuses on preserving the confidentiality and integrity of medical data while facilitating secure transactions. It integrates encryption methods to safeguard sensitive data both at rest and during transmission, guaranteeing that only authorized individuals have the ability to access and decrypt the information. Access controls are implemented to enforce fine-grained permissions, restricting data access to authorized personnel based on roles and privileges. Additionally, the framework includes mechanisms for secure authentication and identity verification to prevent unauthorized access and mitigate the risk of data breaches. Compliance with regulatory requirements such as HIPAA and GDPR is also addressed to ensure that the framework meets the necessary standards for protecting patient privacy. By integrating privacy preservation principles with robust cybersecurity measures, the proposed framework provides a comprehensive solution for securely managing medical data in cloud storage, enhancing trust and confidence in digital healthcare systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5338An Automatic Multi-Variate Multi-Class Feature Extraction, Ranking Based Joint Probabilistic Segmentation and Classification Framework for Multi-Class Liver Tumor Detection2024-03-22T06:52:33+00:00N. Nanda Prakashnandaprakashnelaturi@gmail.comV. Rajeshrajesh4444@kluniversity.inSandeep Dwarkanath Pandesandeep7887pande@gmail.comSyed Inthiyazsyedinthiyaz@kluniversity.inSk Hasane Ahammadahammadklu@gmail.comDharmesh Dhabliyadharmesh.dhabliya@viit.ac.inRahul Joshirahulj@sitpune.edu.in<p>Automatic multivariate and multi-class tumor detection plays a crucial role in the diagnosis and treatment of large heterogeneous liver databases. However, existing liver segmentation models often encounter challenges such as multi-modal tumor detection, detecting tumors with varying shapes, liver over-segmentation, and difficulty in identifying tumors with different orientations and shapes. Moreover, the presence of noise in excessively segmented border regions can further complicate the segmentation and classification process, leading to inconsistent and inaccurate results. In this work, we propose novel approaches for multivariate liver filtering, multivariate feature extraction, and ranking. We employ efficient multivariate segmentation-based classification methods to enhance the overall detection of multi-modal tumors on large databases. The proposed Multi-variate Liver and Tumor Segmentation and Classification (MCMVLTSC) model efficiently classifies key tumor segmented regions with a high true positive rate and runtime efficiency (ms). To evaluate the performance of our proposed MCMVLTSC model compared to existing models, we utilize various statistical measures on diverse liver imaging databases. Experimental results demonstrate that the proposed model outperforms conventional models in terms of different statistical classification metrics and runtime efficiency.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5339A Hybrid Approach Using AES-RSA Encryption for Cloud Data Security2024-03-24T06:44:13+00:00Juvi Bhartijuvibansal@gmail.comSarpreet Singhersarpreetvirk@gmail.com<p>This paper provides an in-depth analysis of cloud vulnerabilities and explores the role of encryption techniques in addressing security challenges within cloud services. We compare a range of symmetric and asymmetric encryption algorithms and discuss recent advancements in modified encryption models. The study highlights the importance of understanding the encryption landscape, selecting appropriate techniques based on specific requirements and threat models, and keeping up to date with the latest developments in cryptography to ensure data security in the cloud.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5340Algorithmic Insights into Predicting Hypertension Using Health Data in Cloud-Based Environments2024-03-24T06:49:21+00:00S. V. N. Sreenivasudrsvnsrinivasu@gmail.comMaytham N. Meqdadmaytham.meqdad@uomus.edu.iqM. Ravi Kishoremrks@aitsrajampet.ac.inHarendra Singh Negiharendrasinghnegi@geu.ac.inKamal Sharmakamal.sharma@gla.ac.inA. L. N. Raodean.engineering@liet.inAmit Srivastavaamit@lloydlawcollege.edu.inAnurag Shrivastavaanuragshri76@gmail.com<p>This exploration examines the use of cutting-edge calculations for anticipating hypertension inside cloud-based health conditions. Utilizing assorted health information sources, including electronic health records and wearables, we investigated the prescient abilities of four key calculations: Strategic Relapse, Random Forest, Backing Vector Machine (SVM), and Neural Network (Multi-facet Perceptron). Our exploratory arrangement included thorough information preprocessing, highlight extraction, and model preparation on an extensive dataset. The Neural Network arose as the best calculation, accomplishing an exactness of 90%, accuracy of 92%, review of 88%, F1 score of 90%, and an AUC-ROC of 0.94. Random Forest and SVM likewise exhibited hearty execution with a precision of 88% and 87%, individually. Calculated Relapse, however less difficult, displayed cutthroat dependability with a precision of 85%. Correlations with related work highlighted the adaptability of the calculations, reaching out past unambiguous medical services spaces. This exploration adds to the more extensive talk on prescient medical services examination, stressing the reconciliation of cutting-edge calculations in cloud-based conditions. Our findings set the stage for subsequent research, which may include the continuous observation of IoT devices and the improvement of profound learning designs, all while recognizing specific constraints like the representativeness of the dataset and the model's interpretability.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5341Historical Data Mining and Cultural Heritage Inheritance Path Modeling of Traditional Architecture in the Guangfu Region2024-03-24T06:55:09+00:00Jin Lingherelingjin@163.comLinhui Huhlh@gdut.edu.cn<p>Historical data mining plays a crucial role in cultural heritage inheritance by uncovering insights from vast repositories of historical information. Through advanced data analysis techniques, such as machine learning algorithms and pattern recognition, historical data mining enables the extraction of valuable knowledge from historical documents, artifacts, and archaeological findings. in historical data mining for cultural heritage inheritance include data quality and accessibility, cultural sensitivity, and interpretation challenges. Historical datasets may suffer from inconsistencies, incompleteness, or biases, posing challenges to the accuracy and reliability of mining results. Additionally, accessing historical data, especially from remote or protected cultural sites, can be challenging due to legal, logistical, or ethical considerations. Cultural sensitivity is crucial, as historical data may contain sensitive or contentious information that requires careful handling and interpretation to avoid misrepresentation or offense. This study explores the application of historical data mining techniques in the context of cultural heritage inheritance, focusing on traditional architecture in the Guangfu region. Leveraging Software-Defined Hierarchical Clustering Path Modeling (SDHCPM), the research aims to uncover underlying patterns and pathways in the evolution of traditional architecture, shedding light on its historical significance and cultural heritage preservation. By analyzing historical datasets related to architectural styles, construction techniques, and socio-cultural influences, SDHCPM facilitates the construction of a path model that traces the development of traditional architecture over time. Through this approach, the study seeks to enhance our understanding of the cultural heritage of the Guangfu region and provide valuable insights for heritage conservation and revitalization efforts. the average clustering coefficient for traditional architecture in the region is found to be 0.75, indicating a high level of architectural coherence and cultural continuity.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5342Rural Landscape Pattern Analysis and Optimization Model Construction Based on Remote Sensing Technology2024-03-24T06:57:44+00:00Shuai Xiao15105575323@163.com<p>Rural landscape pattern analysis involves the examination of the spatial arrangement and composition of land cover types in rural areas. in rural landscape pattern analysis encompass challenges such as data availability, scale discrepancies, and methodological complexities. Limited access to high-resolution spatial data, particularly in remote or developing regions, can impede accurate analysis and interpretation. Scale discrepancies between the spatial extent of data sources and the ecological processes being studied can also affect the reliability of findings. Hence, this paper proposes Genetic Optimized Stimulated Annealing Multi-Spectral (GSA-MS) for the pattern analysis. The proposed GSA-MS model uses multi-spectral features for the analysis of the images and processing. With the GSA-MS model features are extracted in the rural images for the estimation of patterns. With the GSA-MS model the features in the multi-spectral images are estimated and classified. The estimated features are optimized with the stimulated annealing model for the estimation and classification of patterns in rural images. Based on the computed and estimated features the LSTM-based deep learning model is implemented for the pattern classification in the rural area. By utilizing multi-spectral data, the model captures a broader range of information, enabling a more comprehensive analysis of rural landscapes. Specifically, the GSA-MS model optimizes the extracted features using a simulated annealing algorithm, which iteratively refines the feature set to improve pattern estimation and classification accuracy in rural images. Additionally, the paper proposes the integration of a Long Short-Term Memory (LSTM) based deep learning model for further enhancing pattern classification accuracy in rural areas. Simulation results demonstrated that the proposed GSA-MS model achieves a higher classification accuracy of 99% for the estimation of patterns in the images with a minimal loss of 0.09.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5343Hidden Feature Weighted Deep Ranking Model (Hfwdr): A Novel Deep Learning Approach to Investigate the Nuanced Aesthetic Value of the Elderly Furniture Design & Cultural Identity2024-03-24T06:59:49+00:00Jing Luwangjinycg@163.comMusdi bin Hj Shanatmzf7968@163.com<p>The aesthetic value of elderly furniture design transcends mere functionality, embodying a rich tapestry of cultural heritage and historical significance. Rooted in traditional craftsmanship and informed by generations of cultural evolution, elderly furniture design carries with it a sense of timelessness and authenticity. Cultural identity and the evolution of the times are intertwined forces that shape societies, influencing everything from art and architecture to social norms and values. Cultural identity encompasses the unique customs, traditions, and beliefs that define a community or group, providing a sense of belonging and continuity across generations. This study investigates the aesthetic value of elderly furniture design, exploring its connection to cultural identity and the evolving socio-cultural landscape. By employing the Hidden Feature Weighted Deep Ranking Model (HFWDR), a novel deep learning approach, the research delves into the nuanced features of elderly furniture designs that resonate with cultural heritage and contemporary sensibilities. Through an analysis of design elements, material choices, and cultural motifs, the study uncovers the intrinsic relationship between furniture aesthetics and cultural identity, shedding light on how design evolves over time while retaining cultural authenticity. The HFWDR model, with its ability to capture hidden features and prioritize their significance in ranking, offers a comprehensive framework for evaluating and understanding the aesthetic evolution of elderly furniture design within the context of changing cultural dynamics. the HFWDR model assigned numerical values to hidden features such as symmetry, material quality, and historical relevance, with scores ranging from 0 to 100, indicating the degree of importance in determining the aesthetic value of elderly furniture designs.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5344Real-Time Optimization and Fault Diagnosis Algorithms for State Event Analysis in Elevator Group Control Systems2024-03-24T07:02:43+00:00Jie YuZhuoji_sh@163.comBo Hubohu@fudan.edu.cn<p>Real-time optimization and fault diagnosis algorithms are essential components of elevator group control systems, ensuring efficient operation and timely detection of malfunctions. These algorithms continuously analyze the state events within elevator systems, such as passenger demand, elevator positions, and system performance metrics, to optimize elevator dispatching and minimize passenger waiting times. Moreover, they employ fault diagnosis techniques to detect anomalies or failures in elevator components, such as motors, sensors, or control systems, enabling prompt maintenance interventions and ensuring system reliability. By combining real-time optimization with fault diagnosis, elevator group control systems can enhance operational efficiency, passenger safety, and overall system performance, contributing to a seamless and reliable vertical transportation experience. This paper presented an approach to real-time optimization and fault diagnosis in elevator group control systems, integrating Trickle Transition State Event Analysis (TTSEA) techniques. Elevator group control systems require efficient management of state events to optimize elevator dispatching and ensure passenger satisfaction. The proposed methodology utilizes TTSEA to analyze state events in real time, considering factors such as passenger demand, elevator positions, and system performance metrics. This analysis enables dynamic optimization of elevator operations, minimizing passenger waiting times and enhancing overall system efficiency. Additionally, the incorporation of fault diagnosis algorithms allows for the timely detection of anomalies or malfunctions within elevator components. By combining TTSEA with fault diagnosis, the system can promptly identify and address issues, ensuring continuous operation and passenger safety. The integration of real-time optimization and fault diagnosis with TTSEA offers a robust framework for improving the reliability and performance of elevator group control systems in various operational scenarios. in simulation experiments, the TTSEA algorithm reduced average passenger waiting times by 20% compared to traditional methods. Additionally, fault diagnosis algorithms detected anomalies within elevator components with an accuracy of over 95%, facilitating timely maintenance interventions and ensuring system reliability.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5345Bibliometric Cluster Analysis and Classification Algorithm for Questioning Effectiveness in Elementary School Classrooms2024-03-24T07:05:33+00:00Rong Wangsxyc12342022@163.comFadzilah Amzahfadzilahamzah@usm.my<p>Bibliometric cluster analysis and classification algorithms offer a systematic approach to evaluating the effectiveness of questioning techniques in elementary school classrooms. By analyzing a vast array of academic literature and educational resources, bibliometric methods identify key themes, trends, and patterns related to questioning effectiveness. This analysis enables the classification of questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. By leveraging advanced algorithms, educators can gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. This paper presented a novel approach to assessing the effectiveness of questioning techniques in elementary school classrooms through Bibliometric Cluster Analysis and a Classification Algorithm, integrating Centroid Clustering Deep Learning (CCDL). By analyzing a diverse range of scholarly literature and educational resources, the bibliometric analysis identifies key themes, trends, and patterns related to questioning effectiveness. Subsequently, CCDL is employed to cluster and classify questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. Through this integrated approach, educators gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. the algorithm identifies three primary clusters of questioning techniques: low-engagement (average student participation rate below 30%), moderate-engagement (30-60% participation rate), and high-engagement (above 60% participation rate).</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5346Gaussian Markov Chain Deep Neural Network Investigation for College Graduates' Initial Employment and Long-Term Career Development from an Economic Perspective2024-03-24T07:08:16+00:00Xinyue Zhangwinnie_2024@126.comMuhammad Hussinmuhsin@ukm.edu.myMohamad Z uber Abd Majidmzuber@ukm.edu.my<p>The initial employment and long-term career development of college graduates are critical topics from an economic perspective, with implications for both individuals and society as a whole. Examining graduates' entry into the labor market provides insights into broader economic trends, such as job availability, wage levels, and skill demands. Several issues affect the initial employment and long-term career development of college graduates. These include mismatched skills and job requirements, resulting in underemployment or unemployment among graduates. This study examines the initial employment and long-term career development of college graduates from an economic perspective, employing the Gaussian Markov Chain Deep Neural Network (GMC-DNN) for analysis. By integrating economic theory with advanced machine learning techniques, the research aims to elucidate the complex dynamics underlying graduates' labor market outcomes and career trajectories. Through the GMC-DNN model, which combines the capabilities of Gaussian Markov Chains for time series analysis and Deep Neural Networks for nonlinear pattern recognition, the study explores factors influencing graduates' employment transitions, wage growth, and career advancement prospects over time. Additionally, the model provides insights into the impact of economic factors, such as GDP growth, industry trends, and labor market conditions, on graduates' career trajectories. Simulation results demonstrated that the average starting salary for college graduates is found to be $50,000, with variations across fields of study and geographic regions. Furthermore, the GMC-DNN model predicts a median wage growth rate of 3% per year for the first five years of employment, with graduates in STEM fields experiencing higher wage growth rates compared to those in the humanities. Additionally, the simulation reveals that economic recessions lead to temporary setbacks in wage growth, with an average decrease of 2% observed during recessionary periods.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5348Optimization of Security Algorithms for Digital Authentication and Electronic Signatures in International Electronic Commerce Regulations2024-03-24T07:14:01+00:00Zixiao Lulzx0303@163.com<p>Digital authentication and electronic signatures play a crucial role in international electronic commerce laws, facilitating secure and legally binding transactions across borders. These technologies enable parties to authenticate the identity of individuals or entities involved in electronic transactions and provide assurance regarding the integrity and non-repudiation of electronic documents. International laws and regulations, such as the United Nations Commission on International Trade Law (UNCITRAL) Model Law on Electronic Commerce, provide a framework for the recognition and enforcement of electronic signatures and authentication methods across jurisdictions. This paper focuses on optimizing security algorithms for digital authentication and electronic signatures in the context of international electronic commerce laws, utilizing the Homomorphic Elliptical Cryptograph Deep Neural network (HMEC-DNN). In an increasingly digitalized global economy, ensuring the security and integrity of electronic transactions is paramount. The HMEC-DNN framework combines the robust cryptographic properties of homomorphic elliptical cryptograph algorithms with the powerful pattern recognition capabilities of deep neural networks. Through rigorous optimization and training, the HMEC-DNN model enhances the efficiency and reliability of digital authentication and electronic signature processes, mitigating risks associated with identity fraud, data breaches, and transaction tampering. The results demonstrate significant improvements in security and efficiency compared to conventional methods. For instance, the HMEC-DNN model achieved an authentication accuracy of over 99.5% in verifying electronic signatures, ensuring the integrity and authenticity of electronic documents. Additionally, the framework reduced computational overhead by 30%, enabling faster transaction processing times and enhancing user experience.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5403Crop Yield Maximization Using an IoT-Based Smart Decision2024-03-24T13:10:38+00:00Amita Shukla, Krishna Kant Agrawalauthor@email.com<p>This paper conducted a comprehensive analysis of the integration between sensor technologies and machine learning algorithms in terms of crop yield prediction for precision agriculture. Appreciating the role of precision yield estimate in mitigating global food challenges, this paper discusses various sensor technologies including NPK sensors among others; their strengths and weaknesses are highlighted. An in-depth analysis of machine learning algorithms such as Decision Trees, Naïve Bayes, Support Vector Machines, K-Nearest Neighbors and Ensemble Learning reveal their comparative performances with regards to adopting them into agricultural practices. In addition, the use of Multiple Linear Regression for planning rainfall enables an interdisciplinary approach to precision agriculture based on both soil characteristics and climatic conditions. The discussion covers the emerging trends, patterns and gaps in previous research evidence on this topic along with possible implications for future studies or concrete implementation. Through identifying the challenges and limitations, including periodic sensor calibration as well as algorithm interpretability furthered by the review our complex reality of precision farming.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5408Indian Stock Market Sell and Buy Indication using Technical Indicators and Enhanced Bidirectional Long Short-Term Memory2024-03-24T17:05:24+00:00Bhagyashree Pathak, Snehlata Baradeauthor@email.com<p>This study introduces an innovative approach to signal generation for sell and buy decisions in the Indian Stock Market, leveraging Novel Technical Indicators and an Enhanced Bidirectional Long Short-Term Memory (BiLSTM) model. We evaluated various machine learning models, including Random Forest, Gradient Boosting, XGBoost, DenseNet, CNN-BiLSTM, LSTM, BiLSTM, and DANN, on their predictive performance using metrics such as MAPE, MAE, and computation time. Our proposed BiLSTM model, optimized with novel technical indicators, demonstrated superior performance with the lowest MAPE and competitive MAE, while maintaining a rapid computation time. These results highlight the efficacy of BiLSTM models in handling the sequential nature of stock data and the advantage of novel technical indicators in capturing intricate market trends. The proposed system holds the potential to revolutionize decision-making processes for traders and investors by providing highly accurate, real-time market predictions. The comparative analysis across diverse machine learning techniques showed that the proposed method significantly surpasses conventional models like Random Forest, Gradient Boosting, and XGBoost in terms of accuracy and efficiency. It achieved a remarkable reduction in Mean Absolute Percentage Error (MAPE) to nearly 0.03%, drastically lower Mean Absolute Error (MAE) at 10.45, and exhibited the fastest execution speed at 0.984 ms, highlighting its substantial advancement over existing approaches.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5409Analysis of SNR of Physical Layer of 802.16e WiMAX under AWGN Channel and Doppler impact for Rician Blurring Channel with various Digital Modulation Techniques2024-03-24T17:07:26+00:00Mahesh Pasari, Santosh Pawarauthor@email.com<p>The drive to provide high-speed, cost-effective, and broadly accessible broadband wireless access (BWA) technologies has been a cornerstone in the evolution of the 802.16 standards, known as WiMAX. This standard outlines the air interface specifications for fixed BWA systems, operating within the 10-66 GHz frequency range. A significant body of research, including the model discussed in this context, has been devoted to evaluating the performance of the WiMAX Physical Layer (PHY) across various channel conditions, employing different digital modulation techniques. This analytical approach specifically focuses on measuring the system's efficacy by observing the impact on packet and bit loss rates under the IEEE 802.16 standard. Through such in-depth analysis, the model aims to shed light on the potential and limitations of WiMAX technology in delivering robust and efficient wireless broadband services.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5410A Model to Automate the Development of Computer Science Curriculum Syllabi2024-03-24T17:09:10+00:00Ritu Sodhi, Jitendra Choudhary, Anil Patidar, Laxmikant Soni, Ritesh Joshi, Kuber Datt Gautamauthor@email.com<p>Creating curricula and syllabuses is a crucial aspect of education. How well students are trained in computer science determines how good the students will be in the subject.A given course's syllabus is determined by several factors, including the objectives of the course, the resources available, the amount of time allotted, feedback from previous students, employers, and alumni, the aptitude of the learners, etc. Before developing a syllabus, course experts use a manual method that takes into account some variables and updates the syllabus as needed. Automation of the syllabus creation process is important because the needs of the software sector are always changing.This paper put out a model to make the process of creating syllabuses easier. The capability to design, modify, and store curricula will be offered by the model. Our model takes into account two factors: input from the industry and open-source course curricula from different universities. It will provide recommendations to the person who prepared the syllabus regarding its content based on these factors. The syllabus creator will finalize the contents by considering the suggestions given by the model and other attributes upon which it depends.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5411An Empirical Evaluation of Clustering Techniques for the Oral Cancer Prediction2024-03-24T17:10:53+00:00S. Sivakumar, T. Kamalakannanauthor@email.com<p>Data Mining is now widely used in healthcare applications to predict various cancers such as breast, kidney, thyroid, Colorectal, ovarian and many others. Clustering in Data Mining offers a solution for determining the prediction of Oral Cancer. This research explores K-means algorithm and introduces a new novel algorithm, the Kohonen map with K-means (Koho K-means). The experimental findings are based on 3004 oral cancer datasets, focusing on the time complexity and accuracy of the algorithms. The comparative study is then conducted with varying cluster points. The experimental results prove that Koho K-means outperforms K-means in predicting oral cancer, particularly in terms of accuracy.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5412A Hybrid Probabilistic Graph and Link Prediction Model for Complex Social Networking Data2024-03-24T17:12:42+00:00Rajasekhar Nennuri, S. Iwin Thanakumar Joseph, B. Mohammed Ismail , L. V. Narasimha Prasadauthor@email.com<table width="100%"> <tbody> <tr> <td> <p><em><sup>1</sup></em><em> Research scholar, Department of computer science and engineering, Koneru Lakshmaiah education foundation, Vaddeshwaram, AP, India</em></p> <p><em><sup>2</sup></em><em>Assistant Professor, Koneru Lakshmaiah Education Foundation, Vaddeshwaram, AP, India</em></p> <p><em><sup>3</sup></em><em>P.A. College of Engineering Mangalore, Affiliated to Visvesvaraya Technological University Belgum.</em></p> <p><em><sup>4</sup></em><em>Professor, Institute of Aeronautical Engineering, Hyderabad.</em></p> <p> </p> </td> </tr> </tbody> </table> <p>In this complex datasets of social networking, the possibility based graph community identification acts a prominent role. As many of the traditional models are intricate in estimating the novel link prediction type by utilizing benchmark graph community grouping measures. Besides conventional clustering measures utilize measures of nearest-neighbour regardless of contextual identicality for estimating the association amongst diversified nodes of graph. For optimizing contextual clustering of node & estimation of link, the hybrid scalable measure has been projected for clustering the community on intricate networks. Hence, in this research, the hybrid clustering graph & link prediction models have been projected on intricate social-networking dataset for effective patterns of decision-making. The simulation outcomes assist that projected contextual probabilistic-graph-clustering & link estimation model is having better effectiveness when compared to traditional approaches on intricate datasets of social-networking.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5413Prioritized Compressed Data Acquisition Framework for Securing the Data Integrity in the Medical Wireless Sensor Networks2024-03-24T17:14:42+00:00B. Naresh Kumar, M. Srinivasauthor@email.com<p>MWSNs are essential in enhancing healthcare systems by delivering efficient and effective solutions. These allow elderly and disabled individuals to live independently while ensuring their well-being and safety. In hospitals, the data collected by the sensors deployed in these networks can be analyzed in real time. To ensure the safety and confidentiality of the data collected by MWSNs, it is important that the information is properly aggregated and transmitted to the appropriate central servers. This process should be performed with the utmost security to avoid unauthorized access and manipulation of the data. This paper proposes a prioritized compressed data acquisition framework that is designed to increase the efficacy of gathering various health information types. The framework utilizes a sampling technique known as compressed sensing to reduce the transmission overhead and power consumption. Data is then encrypted, and its integrity is protected through a cryptographic hash protocol. The priority of the information is then considered, and the transmission is performed according to the usual method. The security aspects of the proposed framework are analyzed to ensure that the data collected by the MWSNs is protected. In addition, we performed an experiment on the system's performance. The findings of the study indicate that the proposed system can help improve the quality of healthcare by allowing more accurate and timely information.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5414Simulation of Artificial Intelligence based Robotic Arm for Patients with Upper Limb Amputations2024-03-24T17:29:39+00:00Rajkumar Chougale, Vinay Mandlik, Asit Kittur, Vikas Patil, Ranjeet Suryawanshiauthor@email.com<p>A myo-electric controlled prosthetic extremity is a prosthetic limb that seems to be controlled but is really controlled by electrical signals that the muscle structure itself automatically delivers. Electromyography is a novel method for recording and analysing electrical activity generated by muscles. Computerized reasoning and machine learning are particularly impressive in the mechanical and biological sciences. The purpose of this work is to apply artificial intelligence to predict and comprehend prosthetic hand movements using muscle training data. This idea already exists in the mechanical world, but it is prohibitively expensive and unavailable to non-industrialized countries. As a result, the primary goal of our research is to develop the much more precise intelligent bionic hand. In this research, also used MyoWare Muscle Sensor data, a tool that continually analyses information from eight sensors are also employed. Artificial intelligence and the informative index were used to anticipate finger, finger-close, round grip, and satisfactory-squeeze impulses. We It is next applied a few Artificial intellogence computations to the statistics verified with the 8-terminal superficial Electromyography MyoWare Strength Detector, including K-closest Neighbor (KNN), Support Vector Machine (SVM), and a mixture of SVM and KNN. In this research it is further characterised the four demonstrations of our prosthetic hand with a unceasing test accuracy of 98.33 percent by merging SVM and KNN. This report also includes a 3D visualisation of the robotic finger and its control strategy using Autodesk 3D's Max software design, an EMG MyoWare Muscle Sensor, Artificial intelligence.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5415BERT Model Based Identification and Classification of Web Vulnerabilities Using Deep Learning Approach2024-03-24T17:36:57+00:00Manjunatha K. M., M. Kempanna, Pushpa G., Rangaswamy M. G.author@email.com<p>In recent years, researchers have been focused upon machine learning and machine language based models to predict and identify effects of their researches. In this research the vulnerabilities in web, using the machine learning model BERT (Bidirectional Encoder Representations from Transformers) with additional layers have been attempted. The datasets used for the model’s prediction and classification are <em>SQLInjection</em> (SQLI) (namely: attacks and benign) and Cross Site Scripting (XSS) datasets respectively. The developed BERT model predicts the vulnerabilities in the data and classifies them accordingly. The loss is estimated through cross entropy loss technique. The performance of the model is evaluated through metric evaluation method namely binary accuracy. The analyses and findings shows that the developed advanced BERT obtained higher accuracy (SQLI with 98% and XSS with 97% accuracies respectively), than the standard BERT model (SQLI with 87% and XSS with 84% accuracies respectively). The research concludes stating that an increased BERT layers based model performs significantly with higher accuracy in classification than the standard BERT as a transformer model.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5416Naïve Bayes Classification of Sentiments on Subset using Tweets-during Covid-192024-03-24T17:40:00+00:00V. Geetha, N. Sujatha, Latha Narayanan Valliauthor@email.com<p>Now a day, Social Media create a platform for almost all people for sharing and communicating with one another. Most of the business people and the organizations avail the social media conversation for their product promotion or predicting people behavior. The popular Social Media Networks are Facebook, Twitter, LinkedIn of Social Networks, Instagram, YouTube of Media Sharing Networks, Whatsapp, Pinterest and tripAdvisor of Consumer Review Networks. A Text Mining tool, Sentiment Analysis can help us to predict and classify the susceptible text used in the social media conversation. Even though having lots of advantages, unfortunately we have many risks in the usage of the social media content. Any individual must follow the rules and regulations for accessing the content in the social media networks. The objective of this research paper is to understand the various techniques involved in Sentiment Analysis process and choose to apply naïve bayes machine learning model in the subset level using twitter data to classify the sentiments of people in a best way.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5417An Adapted Moth Search with Convolutional Neural Network with Replicator Neuron-Based Leaf Disease Detection 2024-03-24T17:42:06+00:00Majed Aborokbahauthor@email.com<p>Crop quality and yield can be significantly impacted by plant diseases, and even though plants may be examined for indicators of illness by trained biologists or farmers, this is typically an inexact and labor-intensive process. This study employs IoT and AI-based monitoring strategies to design and develop a smart method for classifying leaf illnesses. So as to measure the effectiveness of these two approaches, simulation results are compared in this work. In the first section, the data of photos of plants from the Plant Village data set augmented using a Hybrid CNN (Convolutional Neural Network) with RNN (Replicator Neural Network) and named as HCRNN, and deep features mined from these images. So as to enhance the precision of the segmentation procedure, the plant images undergo preliminary processing with an adaptive kaun filter. Next, a Glowworm Swarm Optimization based Clustering (GSOC) technique is used to isolate the plant region in the processed image. The HCRNN was then used to classify the plant disease based on the retrieved features. The projected method uses the Adapted Moth Search (AMS) Algorithm to fine-tune the CNN's (Convolutional Neural Network) hyperparameter in order to enhance its classification accuracy. Two branches of the model are used to learn from the T2- and Diffusion-weighted MRI data: one employs a ten-layer CNN After applying HCRNN to classify the relevant characteristics, and then assesses the quality of the classification in terms of precision, recall, and f-score. Extensive field testing indicates that the technique is useful in hot and humid environments and that it is more accurate than other categorization schemes at recognizing classes of disease in leaves.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5418Smart Agriculture: IoT and Machine Learning for Crop Monitoring and Precision Farming2024-03-24T17:43:40+00:00Sri Lakshmi Chandana, Jayasri Kotti, Vinod Motiram Rathod, Elangovan Muniyandy, Mylapalli Ramesh, Amit Verma, Ankur Guptaauthor@email.com<p>Through the implementation of cutting-edge technology like the Internet of Things (IoT) and machine learning, smart agriculture, which is also referred to as precision agriculture, is bringing about a revolution in the conventional agricultural techniques that have been used for generations. The purpose of this article is to present an overview of how the Internet of Things (IoT) and machine learning are utilised in crop monitoring and precision farming in order to improve production, maximise resource usage, and reduce environmental consequences. The Internet of Things (IoT) devices that are connected with sensors are placed throughout agricultural fields in order to collect real-time data on a variety of environmental characteristics. These parameters include soil moisture, temperature, humidity, and nutrient levels. These sensors are connected to one another over wireless networks, which enables the transmission of data to centralised cloud-based platforms for statistical analysis in a smooth manner. In order to recognise patterns, correlations, and anomalies in the data that has been collected, machine learning algorithms are applied to the subject matter. The development of predictive models allows for the forecasting of agricultural yields, outbreaks of pests and diseases, and the implementation of ideal irrigation schedules. In order to enable farmers to make educated decisions about irrigation, fertilisation, pesticide application, and crop management methods, decision support systems offer them with recommendations and alerts that may be put into action. The report also discusses the Internet of Things (IoT) and machine learning for crop monitoring. In addition to that, challenges associated with precision farming are discussed in this research.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5419Decentralized and Trustworthy Connectivity in IoT through Blockchain-Enabled Secure Data Sharing over Wireless Networks2024-03-24T19:06:05+00:00K. Seshadri Ramana, Veera Talukdar, Manisha Mittal, Elangovan Muniyandy, V V S Sasank, Amit Verma, Dharmesh Dhabliyaauthor@email.com<p>The proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generated and shared over wireless networks, posing significant challenges in terms of security, privacy, and trustworthiness. This paper proposes a novel approach to address these challenges by leveraging blockchain technology for secure data sharing in IoT ecosystems. By decentralizing data storage and enabling tamper-resistant transaction records, blockchain provides a robust framework for ensuring the integrity, confidentiality, and accountability of IoT data. This paper outlines the key principles and mechanisms underlying blockchain-enabled secure data sharing in IoT networks, including cryptographic techniques, consensus algorithms, and smart contracts. Furthermore, it discusses the potential benefits and challenges of adopting blockchain in IoT deployments, as well as future research directions to overcome existing limitations and realize the full potential of decentralized and trustworthy connectivity in IoT environments.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5420Enhancing Financial Fraud Detection in Banking Systems: Integrating IoT, Deep Learning, and Big Data Analytics for Real-time Security2024-03-24T19:08:36+00:00B R Celia, Shahanawaj Ahamad, Manisha Mittal, Elangovan Muniyandy, Aruna Kolukulapalli, Amit Verma, Dharmesh Dhabliyaauthor@email.com<p>Financial fraud poses a significant threat to banking systems, with increasingly sophisticated attacks targeting sensitive customer data and financial transactions. This paper proposes an innovative approach to enhance financial fraud detection in banking systems by integrating Internet of Things (IoT), deep learning, and big data analytics for real-time security. By leveraging IoT devices to gather real-time transaction data and user behavior patterns, coupled with advanced deep learning algorithms and big data analytics techniques, banks can detect and prevent fraudulent activities more effectively. This paper outlines the key principles and mechanisms underlying the integration of IoT, deep learning, and big data analytics in financial fraud detection. Furthermore, it discusses the potential benefits and challenges of adopting this approach, as well as future research directions to improve real-time security in banking systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5421A Novel Fuzzy C—Mean Based Segmentation Technique for Spinal Cord Tumors from MR Images2024-03-24T19:10:33+00:00Alam N. Shaikh, Nisha A. Auti, B. K. Sarkarauthor@email.com<p>Image processing plays a crucial role in extracting meaningful information from images to enhance their utility and effectiveness. Among various techniques, image segmentation stands out as an efficient method for extracting and isolating specific features within images. This research focuses on optimizing the Fuzzy C Means (FCM) algorithm for accurately identifying the axial and coronal planes in MRI brain images, considering both the algorithm's accuracy and computational efficiency.</p> <p>The preprocessing phase involves converting MRI brain images from DICOM format to a standard image format. To enhance image quality, a Gaussian filter technique is applied to eliminate noise. Subsequently, the FCM algorithm is implemented to segment regions affected by brain tumours in MR images. The evaluation of algorithmic efficiency and accuracy involves comparing histogram values of images before and after segmentation with the cluster center values determined by the FCM algorithm.</p> <p>The results provide insights into the algorithm's performance, with a focus on computational time as a key metric. By identifying the best fit of the FCM algorithm for both axial and coronal planes, this research contributes to advancing the field of image segmentation in the context of brain tumor detection. In conclusion, the study underscores the significance of FCM algorithm in accurately delineating tumor-affected regions in MRI brain images, thereby aiding in the diagnosis and treatment of brain tumors. The identified optimal parameters showcase the potential of FCM as a valuable tool in the realm of medical image analysis.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5422Scale-Invariant Feature Extraction for Skin Image Detection2024-03-24T19:12:25+00:00Sami Hussein Ismael, Adel Al-Zebari, Shahab Wahab Kareemauthor@email.com<p>In many applications, including dermatology, biometrics, and medical diagnostics, skin image detection is essential. Because of the differences in lighting, positions, and scales, it is difficult to identify skin regions in images. The article presents a new method for scale-invariant feature extraction-based skin image detection. The proposed strategy makes use of scale-invariant features to improve the skin image detection's resilience at various scales. Scale-invariant feature transform (SIFT) is used to extract key points from skin images, enabling the identification of unique patterns regardless of their size. The skin portions in the image are then reliably represented by using these key points. The incorporation of machine learning methods to improve the skin image recognition procedure is also explored in this research. A model is trained on a broad dataset of skin photos to enable the system to learn and adapt to different skin kinds, circumstances, and image scales. The evaluation's findings show how well the suggested scale-invariant feature extraction technique works to recognize skin images with reliability and accuracy.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5423Architecture Patterns Clustering using a Machine Learning Approach2024-03-24T19:14:04+00:00Omar Al Huniti, Khawla Al-Tarawneh, Esra Alzaghoul, Fawaz Ahmad Alzaghoulauthor@email.com<p>Architecture patterns are frequently employed in software development to address prevalent design challenges. The identification and classification of architecture patterns have become crucial in optimizing the design process due to the increasing complexity of software systems. Clustering has emerged as a widely adopted technique to categorize comparable entities. Recently, machine learning algorithms have been employed to automate and enhance the precision of clustering.</p> <p>This study proposed using k-means clustering to group the architectural patterns like repository, client-server, broker, microkernel, publisher-subscriber, model view controller, REST, and space-based patterns together.</p> <p>That was done on one of the benchmark dataset (Architectural Patterns dataset ) by using different Ks to perform the clustering, demonstrating the connections between architecture patterns. Then extract the related patterns and propose valid splitting for some patterns.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5424Masked Face Detection and Recognition System Using HOG Algorithm2024-03-24T19:15:37+00:00Maryam Sarmad M. Ali, Fattah Alizadeauthor@email.com<p>The emergence of COVID-19 pandemic at the end of 2019 has introduced new problem to the face detection and recognition systems as faces are covered with masks that in return lead to reduction in the accuracy of face identification. For this reason, new adaptation to the previously available methods are becoming a necessary work in this area. In this paper, at first we are going to create a masked dataset to be used in our work that will mainly be divided into two parts, first to propose an algorithm for mask detection using Viola Jones algorithm, and second to suggest a face recognition algorithm that would use two methods namely (using pre-trained convolutional Neural Network CNN architectures Resnet-50 and Mobilenet, and building a customized CNN). The evaluation of this method was done on the dataset that we created at the beginning of our work based on the use of FEI dataset. The mask detection algorithm used has provided and accuracy of 79% whereas the face recognition methods has shown accuracy ranging between 75% and 99%.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5425Developing a Vehicle Monitoring and Tracking System using the Internet of Things (IoT)2024-03-24T19:17:20+00:00Zina Balani, Naska Ismael Mustafaauthor@email.com<p>Public transportation plays a vital role in daily life, serving as a key mode of transportation for individuals traveling between their homes, workplaces, and educational institutions. However, the inconvenience of extended waiting times for transportation often leads to time wastage. Many students frequently experience prolonged waiting periods at bus stops, eager to know the real-time location of buses and their estimated arrival times. To address these challenges, this research introduces the creation of an online system designed to visualize the routes taken by each bus during its journey. Integrated with Google Maps, this system enables students to easily access bus routes and schedules. The platform provides comprehensive details, including up-to-date information on the live location of buses displayed on the map interface. Additionally, the system furnishes supplementary information about bus drivers, encompassing their names, contact numbers, bus identifications, and the start and end times of their shifts. Users have the flexibility to access this data from any location, whether it's from their residence, workplace, or educational institution, using the web-based application and an internet connection. Moreover, a QR code scanning feature is available at bus stops, facilitating swift access to the desired information. By implementing this system, users, especially students, benefit from improved visibility into bus routes and schedules, enabling effective planning and minimizing wait times. The online platform delivers convenience and easy access, empowering users to make well-informed decisions regarding their journeys. The integration of real-time bus tracking, driver details, and QR code scanning significantly enhances the overall user experience, presenting a comprehensive solution for public transportation users.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5426CMOS Transceiver Epiretinal Vision Restoration Retina Chipset using Wireless Inductive Coupling2024-03-24T19:18:51+00:00Hima Bindu Katikala, Telagathoti Pitchaiah, Gajula Ramana Murthyauthor@email.com<p>In medical implants the inductive coupling is the most common topology used for power transfer. Because of design complexity a wireless power transfer is mostly adaptable in the medical implants. In this paper, for the data progressing inside the implant a complementary metal oxide semiconductor transceiver (CMOS TRx) on-chip prototype is proposed with an inductive coupling power transfer for retina application. The CMOS TRx is an integrated device contains the data modulators, configured inductive, demodulator & epiretinal electrode array used to generate an action potential inside the retina. The binary phase shift modulator with class-E power amplifier is designed to transfer the data within two carrier cycles at the transmitter side to maintain high modulation rate than other modulation techniques. The proposed inductive coupling tuned to be resonant at the frequency of 13.56MHz as same as bpsk carrier frequency with more parallel receiver resonance (ωp) by considering the retina permittivity.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5427Machine Learning Mastery: Leveraging Convolutional Neural Networks to Classify Skin Cancers as Benign or Malignant in the ISIC Database2024-03-24T19:20:29+00:00Upendra Singh, Krupa Purohit, Chitralekha Dwivedi, Ritu Patidar, Sanjay Patidarauthor@email.com<p>This research tackles the urgent need for enhanced precision in the detection of skin cancer, a common yet potentially deadly disease. Traditional diagnostic techniques frequently fall short in accuracy, prompting unnecessary and invasive medical interventions. Previous attempts to employ machine learning for distinguishing among different types of skin cancer have not been fully successful in achieving effective differentiation. To address these challenges, the study proposes an innovative approach utilizing Convolutional Neural Networks (CNN) for the autonomous identification of skin cancer. The designed CNN architecture incorporates three hidden layers, with the number of channels in each layer progressively increasing from 16 to 32, and then to 64. The model leverages the AdamW optimization algorithm with a learning rate set at 0.001, a choice that has proven to be highly effective. In evaluations conducted using the International Skin Imaging Collaboration (ISIC) dataset, which involved classifying skin lesions as either benign or malignant, the proposed CNN methodology demonstrated a remarkable accuracy rate of 96%. This level of precision indicates a significant advancement in the field of skin cancer diagnostics, highlighting the potential of CNN-based models to revolutionize the early detection and treatment of this condition.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5428The Interactive Visualization Gap in Initial Exploratory Data Science and Analysis2024-03-24T19:22:07+00:00Md Shahid Ahmad, Ravi Kumar, Md. Talib Ahmadauthor@email.com<p>Complexity is one of the defining features of data scaling. When it comes to big data and data integration, heterogeneous data is a major factor. Both are necessary, but enormous amounts of data processing and storage make it hard to see and understand big databases. Data extraction in a way that the human brain can understand is a major challenge in this data-driven era of exponential data growth. This study summarizes and offers a description of heterogeneous distributed storage, data visualization, and the difficulties associated with it, drawing on a variety of approaches from prior studies. In addition, we compare the findings of the examined research works and talk about the major change happening in the field of virtual reality huge data visualization.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5429 Acquiring the Ability to Identifying Covid19 using Deep CNN from Impulse Noise in Chest X-Ray Pictures2024-03-24T19:35:46+00:00Sandeep Kumar Mathariya, Mahaveer Jain, Piyush Chouhan, Manoranjan Kumar Sinha, Jayesh Suranaauthor@email.com<p>Utilizing CNNs, COVID19 is identified in X-ray pictures. Deep CNNs may have a harder time identifying things in noisy X-ray pictures. We provide a unique CNN technique that eliminates the need for preprocessing of noise in X-ray pictures by using adaptive convolution to enhance COVID19 detection. A CNN will therefore be more resistant to erratic noise. This method adds an adaptive convolution layer, an impulsive noise-map layer, and an adjustable scaling layer to the standard CNN architecture. Additionally, we employed a learning-to-augment technique with X-ray pictures that were noisy in order to enhance a deep CNN's generalization. The 2093 chest X-ray photos are divided into 1020 images showing a healthy image, 621 images showing pneumonia other than COVID-19, and 452 images showing COVID19. The architectures of pre-trained networks have been modified to increase their resilience to impulsive noise. Validation on noisy X-ray pictures showed that the proposed noise-robust layers and learning-to-augment strategy incorporated ResNet_50 led to 2% better classification accuracy than the present-day method..</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5430Implementation of FinFET based 14T SRAM Memory Cell using Modified Lector Technique & Dual Threshold2024-03-24T19:37:54+00:00Ramesh Gullapally, N. Siva Sankara Reddy, P. Chandra Sekharauthor@email.com<p>Static Random Access Memory (SRAM) can't become any smaller because to the sorts of materials and leakage testing used in today's large-scale silicon MOSFETs. This study uses a double edge and modified LECTOR method to create new tradeoffs in 14-semiconductor SRAM cells. Because of their outstanding transportation capabilities and the possibility for usage on large-scale processing and production, FinFETs are great replacements replacing outdated CMOS electronics. This test especially investigates a double edge value-based SRAM cell design. There are 14 semiconductor materials used in the design. According to the simulation's findings, FinFET-based circuits are more energy-efficient and reliable than scaled circuits. They are more susceptible to variances and flaws nevertheless. These findings suggest that in terms of power efficiency, FinFETs are far superior to CMOS. </p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5431An Ensemble Approach for Comprehensive Brain Tumour Detection Using MRI-Based Machine Learning Models2024-03-24T19:39:43+00:00Kavita Jain, Deepali R. Vora,Teena Varma, Harshali Patil, Adit Anil Deshmukh, Asad Shaikh, John Baby, Shivam Goswamiauthor@email.com<p>In the realm of medical imaging, the rapid evolution of techniques and exponential growth of data have emphasised the significance of automatic and reliable tools for brain tumour detection. This project proposes a system designed to detect brain tumours utilising Magnetic Resonance Imaging (MRI) data. Two distinct models leveraging advanced machine learning algorithms, particularly Convolutional Neural Networks (CNNs), are developed using multiple datasets. The BraTS dataset is used for segmentation tasks, providing detailed information about specific brain tumour regions. Concurrently, the Br35H dataset is used for binary classification, distinguishing the presence or absence of tumours. Furthermore, the brain tumour dataset from Kaggle adds another dimension to this study, offering diverse data samples. The proposed system encompasses a two-step approach. First, a segmentation model is fine-tuned on the BraTS dataset to identify specific regions within brain scans. Subsequently, a classification model is trained using both the Br35H and Kaggle datasets. Ensemble learning techniques, involving an ensemble of CNN architectures such as ResNet and VGG, along with the exploration of ensemble methods like AdaBoost, are employed for effective classification.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5432Enhancing Session-Based Recommendations with GRU4Rec and ReChorus2024-03-24T19:41:33+00:00Drashti Shrimal, Harshali Patilauthor@email.com<p>Recommender systems have evolved from basic item-to-item recommendations to sophisticated, session-based algorithms. A pivotal model in this transition is GRU4Rec, which employs Recurrent Neural Networks (RNNS) for session-based recommendations. While GRU4Rec has shown marked improvements over traditional methods, its effective deployment necessitates a robust training frame- work. This paper leverages ReChorus, a PyTorch frame work designed for top-K recommendation with implicit feedback, to train the GRU4Rec model. ReChorus offers a streamlined model design process, high efficiency, and flexibility, making it well-suited for achieving state-of-the art metrics, specifically NDCG and Hit Rate. Empirical evaluations across multiple datasets confirm that this approach successfully matches existing state-of- the-art metrics in the field of Recommender Systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5433An Optimized Integer Representation through a Novel Numeric Encoding for Textual Data Compression2024-03-24T19:44:04+00:00Kanak Pandit, Harshali Patil, Poonam Joshi,Tarunima Mukherjeeauthor@email.com<p>The objective of this paper is to introduce a new variable sized integer encoding technique for file compression. The paper aims to compare the performance of the proposed method with established codes like Elias Gamma, Elias Delta, and Golomb. The study also seeks to examine the impact of varying log base values on compression ratio and runtime efficiency. The proposed method utilizes radix conversion and the Burrows Wheeler Transform for file compression. Performance comparison is conducted on the Calgary corpus, which includes both text and binary files. Existing codes like Elias Gamma, Elias Delta, and Golomb are executed on the files before evaluating the proposed code. Graphs are used to analyze the impact of log base values on compression ratio, while runtime efficiency is assessed. The proposed compression code achieves varied compression ratios (1.67 to 1.87) at radix r=4, highlighting its effectiveness over existing algorithms. A non-linear relationship between the log base and compression ratio is observed, plateauing as the log base increases. Runtime varies among files, with 'bib1' at the longest time (6.41 seconds) and 'obj1' the shortest (0.09 seconds). A positive correlation exists between the number of data points (n) and runtime, while a negative correlation is seen between 'n' and compression ratio, indicating lower ratios for larger 'n' files. Comparing its performance with established codes provides a benchmark for evaluation. Analyzing compression ratio trends and runtime efficiency offers insights into the effectiveness of the proposed method, adding to its novelty.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 Kanak Pandit, Harshali Patil, Poonam Joshi,Tarunima Mukherjeehttps://ijisae.org/index.php/IJISAE/article/view/5435Pictorama: Text based Image Editing using Diffusion Model2024-03-24T19:45:42+00:00Teena Varma, Harshali Patil, Kavita Jain, Deepali Vora, Akash Sawant, Vishal Mamluskar, Allen Lopes, Nesan Selvanauthor@email.com<p>This research aims to pioneer image modification through text, integrating natural language descriptions with advanced computer vision and NLP techniques. The primary objective is to bridge human language and image editing, empowering users to convey creative visions effortlessly, revolutionizing the field of image modification. The study employs stable diffusion models, leveraging PyTorch and Python. It builds on prior works like Imagic, LEDITS, and Instructpix2pix, integrating a novel Vector Quantized Diffusion (VQ-Diffusion) model. The model is trained on a dataset of 436 GB containing 3 features an input image, an editing instruction, and an output edited image. Test samples include real images subjected to diverse text prompts for image edits, with disentanglement properties explored. The approach combines text inversion and Box-Constrained Diffusion (BoxDiff) for personalized and conditional image synthesis. The research showcases that stable diffusion models exhibit disentanglement properties, enabling effective modifications without extensive fine-tuning. The introduced BoxDiff and VQ-Diffusion models demonstrate superior performance in spatially constrained and complex scene synthesis, outperforming traditional methods. We are able to observe greater quality in output images with good cohesiveness throughout the image. Runing the model with greater number of steps allows for upheaval in quality. Here we have used 100 steps for greater image quality. The effect of number of steps on the time taken for inference is also studied. Due to the large amount of video memory required for inferencing, we recommend a GPU with >11 GB of video memory. The study adds value by addressing biases, achieving higher speeds, and enhancing image quality, contributing to the evolving landscape of text-to-image synthesis. This research introduces novel approaches in disentanglement, spatially constrained synthesis, and rapid image generation, pushing the boundaries of text-to-image synthesis beyond existing limitations.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5436Enhancing User Recommendations through Context-Driven Natural Language Processing (NLP) and Strategic Feature Selection2024-03-24T19:47:33+00:00Akanksha Pal, Abhishek Singh Rathoreauthor@email.com<p>The surge in popularity and significance of social networks in recent years is undeniable, with social networking sites experiencing an exponential increase in user engagement. These platforms enable users to connect with others, establishing friendships and facilitating communication. A notable trend among most social network websites is leveraging the social graph's proximity for recommending potential friends to users. The study in question introduces a user recommendation system that employs various algorithms to identify similarity factors among users, thereby enhancing the precision of friend suggestions. A key technique utilized in this system is feature selection, which effectively extracts pertinent information from both text and hypertext data sources. Among the various algorithms explored, the Context-Driven Network (CDN) stands out for delivering superior performance in generating user recommendations, indicating its effectiveness in harnessing contextual information to improve the relevance and quality of connections suggested on social networking sites.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5437Integrating Long Short-Term Memory and Reinforcement Learning in Federated Learning Frameworks for Energy-Efficient Signal Processing in UAV-Assisted Wireless Communication Networks2024-03-24T19:49:17+00:00Mahesh Y. Sumthane, Kirti Saraswatauthor@email.com<p>This paper presents a comprehensive study of signal processing algorithms designed for enhancing the energy efficiency of UAV-aided wireless communication networks. We explore a sequence of advanced machine learning techniques, each tailored to address specific challenges within the network. We begin by detailing the application of Long Short-Term Memory (LSTM) networks, which are adept at uncovering patterns in data with unknown objectives or constraints. Echo-State Networks (ESNs) are then introduced for their proficiency in sequence and pattern detection, essential for classification and regression prediction problems in signal processing. We further examine the role of Reinforcement Learning (RL) in actively engaging with prediction problems and NP-hard problems, leveraging a reward-based system to facilitate active learning. In addressing the critical concerns of data privacy and excessiveness, Federated Learning (FL) is proposed as a decentralized solution that promotes local training on UAVs, significantly reducing the need for data centralization. Through the methods outlined, we achieve a novel optimization framework that integrates the aforementioned techniques, commencing with the identification and mitigation of unwanted vehicles in the network, which is processed into a Data Traffic Matrix. This feeds into an LTE DIC algorithm based on correlation and culminates in an optimization process that considers specific network parameters 'P' and 'B'. The results, derived from the comparative analysis using the established techniques, indicate a significant improvement in network efficiency. The proposed framework demonstrates a marked enhancement in energy efficiency, with an observed improvement percentage over existing methods. This substantiates the efficacy of the integrated approach, suggesting that the application of machine learning algorithms can lead to superior performance in UAV-assisted networks, providing a significant step forward in the development of autonomous and efficient wireless communication systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5438Secure Model of Access Control for Cloud Computing using Key Generation Based Public Cyclic Key Generation Method2024-03-24T19:51:37+00:00Ranjeet Osari Rahul Singhaiauthor@email.com<p>Cloud computing is a big platform of service-oriented applications over the internet. The primary access control of cloud services using login credentials for users. The growing rate of malicious software breaks the security credentials of users and theft data, and blocks the services. To prevent security threats, cloud service providers and NIST design various access control using cryptography algorithms. However, the role-based access control mechanism has limitations and breaks the security bridge between users and service providers. This paper proposed key generation-based access control methods for accessing services and data over cloud computing. The proposed key generation approach is a public key generation algorithm, a cyclic key generation algorithm. The proposed key generation methods are implemented in the Java RMI model and MYSQL database. The proposed algorithm compares with RSA based key authentication approach. The experimental results suggest that the proposed algorithm is better than the existing algorithm of access control of cloud computing.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5439Deep Learning-Infused Cascading Regression Approach to Predict the Academic Performance of Immigrant Students2024-03-24T19:54:06+00:00 Selvaprabu Jeganathan, Arun Raj Lakshminarayananauthor@email.com<p>The academic performance of immigrant students is governed by a diverse range of resources and contexts, including the families of the students, the immigrant communities from which the students originate, and the social and educational attitudes that are held toward immigrants in the countries in which the students are currently residing. The Program for International Student Assessment, is an educational research initiative that is used to assess the knowledge and skills of students who are 15 years old. In this paper, the performance of immigrant students is predicted using the PISA dataset. There are a total of 35 attributes present in the dataset. Among these, the proposed method chooses three attributes(maths, science and reading) as target variables for performance prediction. This research constitutes a novel cascading regression framework designed to accurately forecast academic performance. Sequentially integrating CatBoost Regressor, Bidirectional Recurrent Neural Network (Bi-RNN), and Random Forest Meta Regressor optimizes predictive accuracy. Initiated by the CatBoost Regressor, its outputs serve as inputs for a Bi-RNN model, exploiting bidirectional sequential information. The ensuing predictions from Bi-RNN inform a Random Forest Meta-Regressor, refining the final outcome. Evaluation metrics, comprising MAPE, RMSE, and R2, substantiate the superior accuracy of the cascading model. The cascading ensemble significantly outperformed all individual models, achieving a MAPE reduction of 3.74%, an RMSE reduction of 20.70%, and an R-squared increase of 0.96.This research highlights the efficacy of cascading ensemble techniques for predicting student performance with enhanced accuracy. The method being proposed demonstrates the capacity to capture both fixed and changing characteristics, which may result in enhanced interventions and educational decision-making.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5440Performance Optimization of Long-Haul Optical Transmission Link with Optical-OFDM 2024-03-24T19:56:32+00:00Jyoti Prashant Singh, Deepak Kumar Singh, B. B. Tiwariauthor@email.com<p>This research focuses on improving communication system performance by implementing optical orthogonal frequency division multiplexing (OFDM). OFDM is utilized to extract data from high-capacity systems involved in extensive information transmission. Advanced computer programs and strategic methodologies are employed to develop a measurable and highly efficient communication system. The study delves into both theoretical and practical applications of these techniques. Using MATLAB simulations, the investigation into OFDM capabilities offers a robust platform for comprehensive analysis and evaluation. Researchers can easily adjust subcarrier separation, modulating schemes, and other key parameters to gauge their impact on system performance. This adaptability facilitates thorough exploration of various scenarios and optimization strategies. The findings of this study provide valuable insights into the effectiveness and feasibility of optical multiplexing with OFDM for high-capacity transmission in optical communication systems. Optical OFDM, through simulations, demonstrates outstanding spectrum utilization in the light domain compared to conventional radio-frequency (RF) OFDM, owing to its ability to tightly allocate subcarriers.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5441Cyber Security Framework for Manufacturing Industry with Robotic Process Automation integration2024-03-24T19:59:24+00:00 Murugappan K., T. Sree Kalaauthor@email.com<p>Cyber Security is becoming one of the business enablers in the current Industry. It is a necessity rather than added item as there were various security breaches that occurred in the past that brought many businesses to their knees. Especially it is a challenge in the Manufacturing industry because of the lack of security consideration in Operational Technology (OT) equipment and its surrounding processes. OT is a hardware and software-integrated platform that shall be used to monitor and control the physical device’s operation. A major portion of it is mechanical, and those that use digital controls have closed, proprietary protocols. However now the trend is moving towards smart technology, and convergence of Information Technology and OT is unavoidable. This paves the way for typical cyber-attacks in OT. It shall lead to critical infrastructure damages or malfunctioning, and those results in key services failure, and put human life at risk. Hence in this paper, we explored cyber security requirements in the manufacturing industry, robotic process automation integration and recommend a proposal to build an end-to-end Cyber Security framework.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5442Apple Disease Detection Using Convolutional Neural Networks2024-03-24T20:01:53+00:00A. S. Lalitha, K. Nageswararao author@email.com<p>Cultivation of a crop is a pertinent aspect in the agricultural sector. The infection of crops with a disease is one of the bottlenecks leading to reduction in the yield of the crop. This decreases the production rate and thereby creating problems in maintaining the crop throughout the year. Manual identification of the disease is laborious and time consuming. It is laborious in the sense that, it requires lot of expertise and monitoring and this problem is going to be much more, especially, when the crop is big enough to manage. In order to minimize the effort needed, A deep learning model has been developed to identify particular disease. Using this model it facilitates the farmer to detect the disease accurately in a real time. The model developed in this research is a Residual Neural Network model as it helps us in implementing the feature extraction and classification of a particular disease of a fruit. A database of 505 apples was considered and 385 apples were used for training the model and 120 apples were considered for testing. The proposed model has generated an accuracy of 78.76% with a loss value of 0.6818 in detecting the disease of an apple. This computer based model will enhance usability of the model in detecting the disease of a fruit in a feasible manner and at an early stage of the disease. This in turn enables the farmer to detect the disease at an early stage of the disease and can take the precautionary measures to cure the disease in a more effortless manner. </p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5443Pemantic Segmentation in Medical Imaging using U-Net Convolutional Neural Networks2024-03-25T04:08:31+00:00Mahmoud AbouGhaly, Shashi Rathore, Ravindra Sadashivrao Apare, Vipashi Kansal, A Kakoli Rao, Akhil Sankhyan, Saloni Bansal, Anurag Shrivastavaeditor@ijisae.org<p>This research investigates the application of U-Net convolutional neural systems in the semantic division for restorative imaging, centering on brain tumor distinguishing proof and kidney tumor division. Four cutting-edge calculations, specifically U-Net, DeepLabv3, Mask R-CNN, and LinkNet, were comprehensively assessed. Through thorough experimentation on assorted therapeutic imaging datasets, the study uncovers that Veil R-CNN shows superior division exactness, accomplishing an amazing IoU of 91.3% and a Dice coefficient of 94.2%. Comparative investigations with related work illustrate the competitiveness of the proposed approach in comparison to state-of-the-art strategies. In addition, the investigation digs into computational productivity contemplations, generalization over modalities, and factual importance testing, advertising a comprehensive appraisal of algorithmic choices. Patterns such as privacy-preserving combined learning and the integration of worldly data in dynamic imaging modalities were also investigated. The results of this investigation not as it were contribute to the headways in the semantic division for therapeutic imaging but also give practical experiences for the improvement of precise and productive apparatuses with real-world clinical applications.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5444Image Super-Resolution with Deep Learning: Enhancing Visual Quality using SRCNN2024-03-25T04:10:16+00:00Sesha Bhargavi Velagaleti, Shailaja Sanjay Mohite, Ravindra Sadashivrao Apare, Vipashi Kansal, A L N Rao, Amit Srivastava, Saloni Bansal, Anurag Shrivastavaeditor@ijisae.org<p>This research digs into the space of Image Super-Resolution, particularly centring on the assessment and comparison of four noticeable calculations: SRCNN, FSRCNN, ESPCN, and VDSR. The study utilizes a different dataset enveloping different spaces, from common scenes to restorative imaging and satellite applications. Quantitative measurements, counting Top Signal-to-Noise Proportion (PSNR) and Structural Similarity Index (SSIM), and nearby visual quality evaluations were utilized to gauge the execution of each algorithm. SRCNN developed as the frontrunner, showing the most elevated PSNR of 28.7 and a commendable SSIM of 0.89. FSRCNN and ESPCN were closely taken after with PSNR values of 27.9 and 28.3, and SSIM scores of 0.88 and 0.87, separately. VDSR illustrated competitive execution with a PSNR of 27.5 and an SSIM of 0.86. These quantitative results were complemented by visual quality appraisals, where SRCNN got the most elevated rating of 9.2, followed by FSRCNN (8.8), ESPCN (8.7), and VDSR (8.5). This research contributes to the continuous exchange of computer vision, emphasizing the qualities and trade-offs of each calculation and giving important bits of knowledge into their pertinence over differing picture super-resolution scenarios.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5445Genetic Algorithms and Machine Learning for Optimal Power Flow Solutions2024-03-25T04:11:36+00:00Rajesh Chandra Chokkara, Mainak Saha, Ravindra Sadashivrao Apare, Vipashi Kansal, Arun Pratap Srivastava, Akhilesh Kumar Khan, Arti Badhoutiya, Anurag Shrivastavaeditor@ijisae.org<p>This research examines the application of Genetic Algorithms (GAs) and Machine Learning (ML) in tackling the Optimal Power Flow (OPF) issue inside control frameworks. The study points to playing down operational costs whereas assembly operational imperatives through the optimization of control factors. Tests were conducted comparing GAs, Particle Swarm Optimization (PSO), Bolster Vector Machines (SVM), and Neural Networks (NN). The comes about uncovered that GAs reliably outflanked other calculations, illustrating predominant merging speed and accomplishing lower add up to costs. The research contributes experiences into the viability of GAs in exploring the complex and non-convex arrangement space of the OPF issue. Comparative investigations with related works assist fortified the competitive execution of the proposed Genetic Algorithm approach. This consideration not only propels the understanding of control framework optimization but also gives profitable suggestions for the broader application of GAs and ML procedures over differing spaces. The research highlights the potential for crossover approaches and the integration of real-time information to enhance versatility and vigor within the setting of keen networks and feasible vitality systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5446Deep Learning Applications in Medical Image Analysis: U-Net for Diagnosis2024-03-25T04:12:44+00:00Shanthi H J, Hamza Mohammed Ridha Al-Khafaji, Salwan Mohammed Shaheed, Rajasekhar Pittala, Harendra Singh Negi, Arun Pratap Srivastava, Navneet Kumar, Anurag Shrivastavaeditor@ijisae.org<p>This research investigates the application of U-Net engineering in restorative image investigation for enhanced symptomatic capabilities. Leveraging a different dataset comprising MRI, CT scans, and X-rays, we methodically compare U-Net with conventional CNN, SegNet, and state-of-the-art DeepLabv3. The U-Net show showcases predominant execution, accomplishing a Dice coefficient of 0.85, an Intersection over Union (IoU) of 0.75, and a pixel exactness of 0.92. The incorporation of skip associations in U-Net demonstrates instrumental in protecting spatial data, driving more exact division comes about. Moreover, our examination amplifies to particular therapeutic conditions, illustrating U-Net's flexibility with a Dice coefficient of 0.87 for tumor division and 0.83 for organ outline. The results confirm U-Net as a vigorous and dependable instrument for exact medical picture division, with suggestions for improved demonstrative precision over different imaging modalities.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5447An Illustrative Review Cryptographic Algorithms for Scada Application in Networking Ntru for Security2024-03-25T04:13:53+00:00Nitin Sudhakar Patil, Shailaja Sanjay Mohite, Ravindra Sadashivrao Apare, Rajesh Bhatt, Akanksha Kapruwan, Manish Saraswat, Akhil Sankhyan, Anurag Shrivastavaeditor@ijisae.org<p>The computational applications and network protection capabilities of mobile and wireless networks have grown exponentially. Cyberattacks and other unlawful activity on commercial and personal networks have increased in recent years. Firewalls and security code fail to secure computer networks. Personal gadget users, company employees, and military personnel realise network defence is crucial. SCADA systems are commonly utilised in Critical Infrastructure Systems to autonomously monitor and control industrial activities. Security concerns are more likely in SCADA design due to its reliance on computers, networks, applications, and programmable controllers. The huge demand for computers among corporations and other organisations has led to the creation of several networks. Computer network attacks have increased in recent years. Cryptographic solutions of these requirements have performance issues in SCADA systems. NTRU, a faster and lighter public key technique for end-to-end security, is used in this research to improve SCADA security requirements.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5448Machine Learning Algorithms for IOT Services in Big Data and Cloud Computing2024-03-25T04:15:14+00:00Sesha Bhargavi Velagaleti, Suma T, Shubhangi N. Ghate, Harendra Singh Negi, G. Charles Babu, Arun Pratap Srivastava, Navneet Kumar, Anurag Shrivastavaeditor@ijisae.org<p>The phrase "cloud computing" refers to a kind of data management system in which mobile devices are not used for either the processing nor the storing of user data. The Internet of Things (IoT), a brand-new technology that is only now entering its formative years, is also becoming more widespread in the networks and telecommunications sectors. The "modern" sector of wireless telecommunications networks is where the majority of the emphasis of application for the Internet of Things is now being directed. In the most recent part of our line of research, we investigated the relationships and interactions that exist between the many different entities and equipment that communicate across wireless networks. They need to achieve the goal that has been set for them as a group in order to make the atmosphere more conducive to the use of big data. This will help create a more favourable environment for the use of big data. This article discusses the Internet of Things (IoT) and Cloud Computing technologies, with a particular focus on the security challenges that each of these technologies has experienced. In the field of medicine, for instance, big data is being put to use in order to bring down the costs of treatment, anticipate the arrival of pandemics, prevent sickness, and carry out a variety of other related activities. This article provides a comprehensive introduction to the approach of big data analytics, which is crucial in a variety of fields of work and businesses. First, we will present a brief overview of the concept of big data, which refers to the quantity of data that is generated on a daily basis, as well as its characteristics and facets.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5449 “Hybrid MAC Methodology for Improving the Qos in Fiber Wireless Network”2024-03-25T04:16:27+00:00Prabhjot Kaur, Hardeep Singh Sainieditor@ijisae.org<p>The Fi-Wi (Fiber-Wireless) approach stands out as a crucial element in the realm of networks, demonstrating superiority over various technologies. With the exponential growth in Internet users, significant strides have been made in the evolution of Fi-Wi networking systems in recent years. This mechanism offers broader bandwidth and network stability, ensuring high-speed connectivity with "Anytime Anywhere" availability for end users. However, the escalating energy demand in networking systems poses a constraint on the network's lifespan, impacting transmission.</p> <p>Over the years, researchers have proposed and tested various Media Access Control (MAC) protocols to address transmission and energy consumption issues. Despite these efforts, existing protocols have encountered challenges such as overheating, delays, throughput issues, and collisions. This research paper introduces a combination of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) to tackle associated challenges. The primary objective is to enhance throughput and reduce delays in Fi-Wi networks.</p> <p>To achieve this goal, the study employs techniques that involve an Utilizing an Orthogonal Frequency Division Multiplexing (OFDM) modulator, a free-space optical (FSO) communication channel, an OFDM demodulator, and Opti-system for the analysis and enhancement of received signals, our study demonstrates that the proposed MAC protocol surpasses conventional MAC protocols in terms of delay, data throughput, and transmission efficiency.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5450Artificial Neural Networks (ANNs) used for change detection in remotely sensed images2024-03-25T04:17:26+00:00Annu Sharma, Praveena Chaturvedi, Sakshi Kathuria, Amit Verma, Elangovan Muniyandy, Mohd Navededitor@ijisae.org<p>This paper examines the application of semi-supervised Artificial Neural Networks (ANNs) in the change detection of remotely sensed images. Relying on the analysis of multi –temporal satellite images to detect altercations caused by natural or human activities is crucial for change detection for monitoring environmental changes and urban expansion. Recent advancements in Artificial Intelligence (AI) particularly semi-supervised ANNs, have significantly improved the accuracy and efficiency of change detection processes. This review highlights various methodologies and techniques employed in the field, including the integration of Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) for enhanced feature extraction and classification. The paper discusses the application of these methods across different scenarios such as agricultural yield prediction, urban growth monitoring and environmental surveillance underlining the importance of ANNs in advancing remote sensing capabilities.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5451Machine Learning and Ai in Marketing–Connecting Computing Power to Human Insights2024-03-25T04:18:41+00:00Pooja Nagpal, C. Vinotha, Lucky Gupta, Gunjan Sharma, Khyati Kapil, Vijay Kumar Yadav, Akhil Sankhyaneditor@ijisae.org<p>Researchers' interest in artificial intelligence (AI) agents that are driven by machine learning methods has been piqued as a result of the rapid changes that these technologies are creating in the marketing sector. In the framework of this article, we investigate and argue in favour of the use of methods related to machine learning to marketing research. We provide a comprehensive overview of the common aims and methodologies of machine learning and compare them to the traditional statistical and econometric approaches that are employed by marketing professionals. In this research, we claim that machine learning approaches can analyse vast volumes of unstructured data, make accurate predictions, and have model structures that are adaptable. In addition to being difficult to understand, these methodologies are also unclear with reference to the models. We provide scalable and automated decision support capabilities, which are essential for business managers. We investigate the most important business trends and practices that are being driven by AI, as well as academic marketing research that combines machine learning approaches. Most significantly, we provide both a detailed plan for further research as well as an extensive conceptual framework.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5452Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential2024-03-25T04:19:58+00:00Shilpa Pathak Thakur, Sridevi R, Ashulekha Gupta, Gunjan Sharma, A. Deepak, Arun Pratap Srivastava, Akhilesh Kumar Khan, Anurag Shrivastavaeditor@ijisae.org<p>Machine learning (ML) and artificial intelligence (AI) have the ability to save expenses and increase the efficacy of corporate operations. On the other hand, they also have the capacity to devalue a company's assets, which may sometimes have extremely catastrophic effects. It's possible that some managers won't accept new technologies because they can't fully understand and effectively manage the risks associated with doing so. This will prevent them from realising their maximum potential. The findings of this study provide a fresh paradigm for detecting and limiting the value-reducing potential of artificial intelligence and machine learning for businesses. In addition to outlining the components of an AI solution, this research also recommends this paradigm. The paradigm might be used to map the components of an artificial intelligence system. The concepts of value-generation process and content are then used to illustrate how the aforementioned dangers have the potential to obstruct the creation of value or even result in the loss of that value. In the interest of shedding some light on the topic of the commercial activation of artificial intelligence, this study does an in-depth and careful examination of the existing body of literature on the topic. In addition to that, a clear and succinct explanation of what constitutes artificial intelligence at the present time will be provided. The Implications, Applications, and Methods model (also known as the IAM model) has uncovered a total of six topics that are associated with these three primary topics of discussion. It is possible that academics and practitioners will find our study beneficial in that it provides an overview of the body of knowledge and research agenda. This will allow for artificial intelligence to be used as a strong facilitator in the process of producing business value.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5453A Comprehensive Multimodal Approach to Assessing Sentimental Intensity and Subjectivity using Unified MSE Model 2024-03-25T04:21:22+00:00Mohd Usman Khan, Faiyaz Ahamadeditor@ijisae.org<p>In the dynamic realm of multimodal learning, where representation Learning serves as a pivotal key, our research introduces a groundbreaking approach to understanding sentiment and subjectivity in audio and text. Illustration from self-supervised learning, we've innovatively combined multi-modal and Unified--modal tasks, emphasizing the crucial aspects of consistency and distinctiveness. Our training techniques, likened to the art of fine-tuning an instrument, harmonize the learning process, prioritizing samples with distinctive supervisions. Addressing the pressing need for robust datasets and methodologies in combinational text and audio sentiment analysis, we offer the dataset for Multi-modal sentiment intensity assessment at the Opinion Level (MOSI). This meticulously annotated corpus offers insights into subjectivity, sentiment intensity, text features, and audio nuances, setting a benchmark for future research. Our method not only excels in generating Unified-modal supervisions but also stands resilient against benchmarks like MOSI and MOSEI, even competing human curated annotations on the challenging datasets. This pioneering work paves the way for deeper explorations and applications in the burgeoning field of sentiment analysis.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5454Fuzzy Intrusion Detection Method and Zero-Knowledge Authentication for Internet of Things Networks2024-03-25T04:22:34+00:00Elangovan Muniyandy, Iratus Glenn A. Cruz, Mansoor Farooq, Yeruva. Jaipalreddy, Rakesh Kumar, Vivek Kumar Pandeyeditor@ijisae.org<p>One of the encouraging trends that has contributed to the exponential rise in human progress over the last decade is the Internet of Things (IoT). Interconnection of physical objects for the purpose of data exchange is the next frontier of the internet, known as the Internet of Things (IoT). Everything from household appliances to cars to buildings to animals is part of the Internet of Things (IoT). The Internet of Things (IoT) has become the de facto standard because to its many useful uses in business, medicine, agriculture, and other fields. The Internet of Things (IoT) integrates wireless, pervasive, and ubiquitous technology to solve problems. Things with sensors implanted in them and linked over the internet make it up. Data is collected and shared by these networked devices. Many applications rely on the monitoring and analysis of data originating from heterogeneous devices. Fuzzy logic is used in this study to create a new, lightweight intrusion detection system (IDS) for Internet of Things (IoT) applications based on the MQTT protocol. Using fuzzy variables, the IDS detects network irregularities. In conclusion, this study offers a fresh security framework to solve the problems with current algorithms in an Internet of Things setting. This study also provides an application layer security that smart environments may use to avoid DoS attacks</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5455Identifying Fake News on ISOT Data using Stemming Method with a Subdomain of AI Algorithms2024-03-25T04:23:50+00:00Madhura Hemant Kulkarni, Ravindra Sadashivrao Apare, Gururaj L. Kulkarni, Mukesh Singh, Arun Pratap Srivastava, Krishna Kant Dixit, A. Deepak, Anurag Shrivastavaeditor@ijisae.org<h1 style="text-align: justify; text-justify: inter-ideograph; line-height: 115%; margin: 0cm 0cm 12.0pt -.25pt;"><span lang="EN-US" style="font-size: 9.0pt; line-height: 115%; font-weight: normal;">Nowadays, social media platforms have played a significant role in disseminating information throughout the world without any hindrance. Some people take this opportunity to propagate fake news in order to make money, by damaging the reputations of others. To tackle this issue, we proposed a methodology for detecting fake news on social media. This methodology extracts a feature of TF-IDF using N grams and Word2Vec in two ways i) with stemming method ii) without stemming method. Both of the process is performed and they fed an into supervised machine learning algorithms (ML) such as logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting (Grad), adaptive boosting (Adaboost), and stochastic gradient descent (SGD) to detect a fake information. Evaluation shows that the unigram gives a better result with random forest when compared to the bigram and trigram. All classification algorithms were outperformed by Trigram. Unigram is more exact both with and without a stemming method. Word2vec has lower accuracy to detect fake information in the given dataset.</span></h1>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5456Machine Learning for Quantum Computing Bridging the Gap between AI and Quantum Algorithms2024-03-25T04:25:18+00:00B. J. Dange, Kaustubh Manikrao Gaikwad, H. E. Khodke, Santosh Gore, S. N. Gunjal, Kalyani Kadam, Sayali Karmodeeditor@ijisae.org<p>This study explains how machine learning techniques are applied to enhance quantum algorithms and examines the interplay between machine learning and quantum computing. It explores quantum data analysis, quantum machine learning, and hybrid quantum-classical techniques, emphasizing their contributions to bridging the gap between artificial intelligence and quantum algorithms. Additionally, it analyzes how quantum data production, quantum-assisted optimization, and quantum neural networks could influence the direction of AI-quantum integration in the future.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5457Human Activity Detection using Profound Learning with Improved Convolutional Neural Networks2024-03-25T04:26:39+00:00S. Anthonisamy, P. Prabhueditor@ijisae.org<p>Human Activity recognition (HAR) is an interesting area of research mainly due to the availability of low cost sensors and accelerometers live streaming of data and advances in technology. HARs involve identifying various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, opening the door, abnormal activities, etc. are recognized. The data can be collected from wearable sensors or accelerometer. HARs can be extensively used in medical diagnostics for keeping track of elderly people, HARs approaches analyze data acquired from sensing devices, including vision and embedded sensors. HARs are assistive technologies mainly used for taking care of elders in healthcare. Approaches of HARs attempt to predict people’s movements often indoors and based on sensor data like accelerometers of smart phones. In terms of classifications, HARs are challenging tasks as they involve time series data where Deep Learning Techniques (DLTs) like CNNs (Convolution Neural Networks) have the ability to correctly engineer features from these raw data while building their learning models. This paper proposes Human Activity Detections using Profound Learning (HADPL) based on CNNs which detects HARs from captured accelerometer data. HADPL was tested on WISDM_Act_v1.1 dataset and evaluated for its performances in terms of precisions, accuracies, recalls and F1-scores where it achieved a decent level of accuracy by scoring up to 95 percent. The proposed technique can be implemented for monitoring elderly people based on captured or stored HAR data.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5458Intelligent Advanced Model Implementation of Green Financing Concept in the Financial Monitoring System for Enterprises Activity based on Sustainable Development2024-03-25T04:27:38+00:00Sushil Kumar Gupta, S. Prabakar, Pratibha Giri, Debi Prasad Satapathy, Gunjan Sharma, Praveen Singh, Anurag Shrivastavaeditor@ijisae.org<p>According to the information that was presented in the article, financial monitoring is used as a critical control point observation tool to minimise the threat to the nation's overall financial stability. It is explained how the company monitoring system is compatible with the goals of a sustainable and environmentally friendly economy. Internal financial monitoring is required in order to put green finance initiatives into action, and it is also used to evaluate how well these efforts are being carried out. According to the reports, the organization's financial monitoring system acts as a tool for accomplishing the goals of sustainable development and a green economy through resource efficiency, the institutions of production, financial resources, and human resources. This is the case because of how the system was designed. This assertion is backed up by the idea that the company's financial monitoring system acts as a tool for the purpose of accomplishing the aforementioned goals. It is essential to underline that the State Financial Monitoring Service has tight linkages to both the national and global sustainable development plan and that, in its fight against financial crime, it takes into account international norms. Both of these points are crucial to emphasise. The term "green financing" refers to an emerging subfield within the field of finance that is concerned with the question of how to strike a balance between the competing goals of increasing economic activity and increasing environmental protection. It is essential to place equal focus on each of these features. In addition to establishing the function of internal and governmental financial monitoring in green finance, the essay underlines the need to investigate the investment environment for the green economy. This requirement comes after the article outlined the importance of internal and governmental economic growth in green finance. The essay also underlines how essential it is to take into account the business climate while pursuing a green economy.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5459Machine Learning for Alzheimer's Disease Detection and Categorization in Brain Images2024-03-25T04:28:56+00:00Mandeep Kaur, Anupama Arora, Sakshi Kathuria, Muhammad Waqas Arshad, Surya Pratap Singheditor@ijisae.org<p>Alzheimer's disease (AD) is a devastating form of dementia characterised by advanced symptoms in affected individuals' later years. Significant intellectual deficiencies, memory loss, and other cognitive impairments characterise the lives of Alzheimer's patients. Diagnosing Alzheimer's disease may be difficult and time-consuming due to the multitude of mental and physical tests neurologists often use. MCI, or mild cognitive impairment, is a kind of dementia that occurs in the early stages of Alzheimer's disease. The last stage of MCI is called late-MCI, and it is sometimes mistaken for the first stages of Alzheimer's disease (EAD). Correctly classifying EAD is also crucial for preventing or delaying the start of AD. The most fundamental modification in terms of AD's physical presentation is the degeneration of brain cells. Critical biomarkers related with the illness may be uncovered by careful analysis of brain images. The use of magnetic resonance imaging, commonly referred to as an MRI, is a common diagnostic tool used in the medical imaging area during clinical investigations. A large quantity of MRI data was collected from a number of publicly available sources in order to conduct this investigation. All the photos that were taken have had the "skull stripped" effect added to them. The skull and other non-brain pixels carry very little information, hence this is necessary</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5460The Intelligent Technical Influence in Chat Generative Pre-Trained among Students for Modern Learning Traits2024-03-25T04:30:02+00:00Kathiravan Ravichandran, B. Anita Virgin, Lucky Gupta, Aby John, Santiago Otero-Potosi, Álvaro Vargas-Chavarrea, Anurag Shrivastavaeditor@ijisae.org<p>This paper explores the influence of Chat Generative Pre-Trained, which is an Artificial Intelligence tool which provides text-based responses to user queries, on students' learning motivation. The study involved 500 participants who were students in Medan, Indonesia. The research employed a quantitative approach, using surveys and questionnaires to gather data from the respondents. Previous research instruments were adapted with some modifications, which resulting in 10 items for the dependent and independent variables for each. Hypotheses were tested using linear regression, and classical assumption tests, such as multicollinearity, heteroscedasticity, and normality, were conducted. Descriptive statistics, like mean scores, were utilized to assess the extent of Chat GPT usage among students. The results showed that male students displayed a greater propensity for utilizing Chat GPT when compared to their female counterparts. Interestingly, younger students exhibited a higher degree of engagement with Chat GPT in contrast to their older peers. Additionally, the study uncovered a notable, positive, and statistically significant influence of Chat GPT usage on students' motivation to learn.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5461Prejudge: A Predictive Analytics System for Crime and Legal Judgments2024-03-25T04:32:42+00:00Aastha Budhiraja, Kamlesh Sharmaeditor@ijisae.org<p>The recent era has seen a substantial inflow of legal documents in the electronic format. Given the fact that data mining can be employed in the world of textual data to extract relevant knowledge, it is being prominently exploited in the domain of criminology and legal matters. With increasing crime rates day-by-day, it has become essential to readily impart justice to the victims. It takes a considerable amount of time for the lawyers to go through previous judgments for their research. The judicial process can be accelerated by decreasing the time spent on research work. Smart legal systems have enormous potential for providing significant insights to the legal community and the general public through the use of legal data. As a result, these systems can assist in the analysis and mitigation of a variety of societal concerns. By extracting numerous things from legal decisions, such as dates, case numbers, reference cases, person names, and so on, this work takes the first step toward realizing a smart legal system. The major research issues in the area of applying machine learning in jurisprudence are information extraction and analysis of legal texts. This study proposes an Machine Learning based framework to improve the user's query for retrieval of precisely relevant legal judgments in order to overcome these limitations. This work has been carried out in order to act as an aid to the legal advisors and the lawyers in framing arguments to make strong standpoints based on predictions given on their case pertaining to previous judicial outcomes for similar such cases. Logistic regression-based classification enables efficient retrieval and prediction by allowing inferences based on domain knowledge collected during the dataset development. According to empirical results obtained, the proposed methodology generates finer results than other traditional approaches.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5462Artificial Intelligence, Content Recommendation, Biases, and Consumer Behavior: An Analysis of the Impact of Artificial Intelligence on Consumer Behavior2024-03-25T04:33:45+00:00Sanghamitra Das, Ankit Garg, Neha Verma, Deepak Jha, Ritesh Kumar Singhal, Manupriya Gaur, Rahul Singhaleditor@ijisae.org<p>Purpose: Information plays very important role in decision making. Awareness of brand, price, discounts, post sales activities like guarantee, warrantee, and maintenance must be advertised to influence the buying behaviour. But what if this information creates a bias. Does any type of bias generated by this information, in the form of advertisement, influence the buying behaviour? The present research is exploring the fact that how artificial intelligence-based advertisement suggestions and content recommendations create certain type of bias and how it affects the buying behaviour. This research is based upon a survey of consumers.</p> <p>Design/methodology/approach: The methodology emphasised to eliminate the errors in measurement. Respondents were approached twice, in a gap of 30 days for collecting data. They were asked to retake the survey and data in both the attempts have been examined for any major deviation. The average of scores have been consolidated as final data of the research analysis.</p> <p>Findings: The linear regression equation coefficients for the various model variables. The "B" values are the coefficients for each variable. In model 04 we could predict buying behaviour as BB (y) = 0.589 + .403 Anchoring bias + .284 Conformity Bias + .259 Heuristic Bias+ .233 FOMO.</p> <p>Originality: Researchers have emphasis on exploring a new set of influencing factors for consumer behaviour rather following the key factors in systematic review of previous works. Thus, the work ensures the originality in research.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5463Role of Computer Mapping in the Strategies of Water Conservation in Green Buildings as per IGBC guidelines- A Case study2024-03-25T04:35:08+00:00Sushma R, Nuthana N, Lakshmi Ceditor@ijisae.org<p>Green buildings are sustainable or eco-friendly buildings. They conserve natural resources, reduce reliance on fossil fuels and minimize negative environmental impacts. Green building technologies with the help of computers can improve the quality of life for residents and mitigate the environmental and economic challenges associated with rapid urbanization and resource depletion. Conservation of water is a mandatory requirement of green buildings. The amount of water that is suitable for human consumption is only 0.3%. Many reports predict that by 2030, India's water demand will be double the supply. Groundwater, which constitutes 40% of the country's water supply, is depleting rapidly, with 54% of India's groundwater sources in decline. Different strategies are suggested to reduce the water requirements in buildings and landscaping the areas. The Indian Green Building Council (IGBC) claims that techniques including rain gardens, green roofing, rainwater harvesting, recycling, and the reuse of treated wastewater are employed to lower the portable water demand in green buildings. The study discusses the role of computer mapping in water management. The latest graphics processing and display functions are powerful tools and are used in several fields including water conservation. The study put forth that computer intelligence, automation and other characteristics can effectively improve soil and water conservation. The study emphasises the role of computer applications like mapping in water conservation methods followed in green buildings as per IGBC guidelines. </p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5464Retracted2024-03-25T04:36:23+00:00Retractededitor@ijisae.org<p>Retracted</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5465Interpretation of Change in Stress Measurement using Strain Gauge during Stressing of Pre-stressing Cables in a Bridge Span2024-03-25T04:53:45+00:00Partha Pratim Roy, Amitava Sileditor@ijisae.org<h2 style="margin-top: 0cm; text-align: justify; line-height: 115%;"><span lang="EN-US" style="font-size: 9.0pt; line-height: 115%; font-weight: normal;">This paper presents a comprehensive study of the interpretation of changes in stress measurements using strain gauges during stressing of prestressed cables in a bridge span. The use of prestressed concrete in bridge construction has become prevalent owing to its ability to counteract tensile stresses and enhance structural integrity. Strain gauges are commonly employed to monitor the stress levels within the prestressing cables during the stressing process. This paper employs computer simulations to analyze and interpret the changes in stress measurements obtained from strain gauges during cable stressing, contributing to a deeper understanding of the structural behavior and load distribution. The study employs a finite element analysis (FEA) to simulate the cable stressing process, providing insights into the stress redistribution within the bridge span. The findings highlight the significance of accurate stress interpretation in ensuring the safety and efficiency of pre-stressed concrete bridge structures.</span></h2>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5466Sentiment Analysis using a Multinomial LR-LSTM Model2024-03-25T04:54:51+00:00Seema Rani, Jai Bhagwan, Sanjeev Kumar, Yogesh Chaba, Sunila Godara, Sumit Sindhueditor@ijisae.org<p>Sentiment analysis (SA) refers to a technique utilized to ascertain the emotional state conveyed in information or text. It involves categorizing the text into three classes: positive, negative, or neutral. For instance, when someone says "the aqi of the city is good," they are expressing a positive opinion about the aqi of a specific place, while the statement "the aqi is bad" reflects the opposite. The introduction of social media increased the amount of content on the internet of sentiment data. Users on various social media platforms have been able to offer their opinions on various products, services, etc. These opinions are often expressed on social media in the form of movie reviews, product reviews, user comments, posts, etc. In light of this context, one of the captivating research areas in Natural Language Processing (NLP) is Twitter sentiment analysis. The paper proposes a stacked Multinomial-LR-LSTM model for the classification of tweets into three classes. Tweets are re-annotated using Text Blob. Twitter Sentiment dataset was used for experiments with accuracy of 97%.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5467Ai-Powered Insights into Diabetes Mellitus: A Comprehensive Systematic Review2024-03-27T02:51:41+00:00Vikas J. Magar, Sachin B. Bhoite, Rajivkumar S. Mente, Tulashiram B. Pisaleditor@ijisae.org<p>This comprehensive systematic review delves into the current landscape of artificial intelligence (AI) applications to illuminate the intricate metabolic processes and facets of diabetes mellitus. The primary objective is to thoroughly scrutinize and assess the existing body of studies to uncover potential benefits that AI may offer in identifying diabetes mellitus. This study delves into AI's potential for diabetes management, from early detection and personalized therapy to predictive modeling. It critically assesses both the benefits and drawbacks of AI integration, paving the way for responsible future advancements in this complex field. By uncovering AI's potential in diabetes research and exploring its impact on healthcare, this analysis ignites a transformation in how technology shapes both research and treatment.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5468Optimizing Diabetes Prediction: LDA Pre-processing & ANN Classification in Healthcare`2024-03-27T02:53:19+00:00Soumya K N, Raja Praveen K Neditor@ijisae.org<p>Diabetes mellitus (DM) is a chronic disease that poses significant health risks if not well managed. The current healthcare system is overwhelmed by the impact of DM. Modern machine learning and deep learning methods have a hard time correctly predicting the stages of diabetes and often encounter decreased classification accuracy when dealing with massive datasets. In this work, we provide a novel approach to address these problems by integrating pre-processing with LDA and ANN for classification. By combining the LDA and ANN probability distribution functions by back propagation with initialized weights, our method enhances the accuracy of diabetes categorization. After pre-processing data from the PIMA and NCSU datasets using min-max normalization, bivariate filter-based feature selection is used to identify crucial characteristics. Pearson correlation is used to improve the feature set according to a threshold value, further refining the selected qualities. Our experimental results demonstrate the efficacy of the proposed approach, surpassing even the most cutting-edge methods. By integrating a robust classification model with advanced pre-processing techniques, our strategy produces encouraging outcomes in the accurate prediction of diabetes, which in turn helps to improve healthcare management methods.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5469New Artificial-Based Automated Quality Risk Prediction Methodology for College Students with Disabilitie’s Entrepreneurial Schemes2024-03-27T02:54:12+00:00Hengyun Shen, Zhiyuan Lv, Siti Nisrin Binti Mohd Aniseditor@ijisae.org<p>Evaluating and predicting the risk of entrepreneurial projects among college students with disabilities is a critical endeavor that requires a multifaceted approach. This process involves assessing various factors such as the nature of the business idea, the skills and capabilities of the student, potential market demand, and external environmental factors. the issues surrounding entrepreneurial projects among college students with disabilities require a nuanced understanding of the unique challenges they face. Accessibility barriers, societal stereotypes, limited support networks, and lack of inclusive resources are among the key issues hindering their entrepreneurial endeavors. To foster an inclusive environment, it's essential to implement targeted interventions, provide accessible resources and mentorship, raise awareness, and advocate for policy changes that promote equity and accessibility in entrepreneurship for individuals with disabilities. This paper proposed an Automated Quality Risk Prediction (AQRP). The proposed AQRP model uses the Quality assessment of the project at each stage with the ranking-based classification model. The AQRP estimates the process of ranking at every stage of the project and performs the assessment and evaluation of risk. Factors such as the quality of human features in the project and practical features are examined to estimate the features through the process of ranking. With the AQRP model, the features are ranked and integrated for the extraction and classification of features in the projects. With AQRP model the deep learning model is implemented for the classification of features in the projects. Simulation analysis demonstrated that social factors contribute significantly to the project quality assessment. Through the ranking, it is observed that ranking features comprise a higher feature value of 0.98 than the other features. The classification accuracy is achieved as 99% which is 12% higher than the conventional SVM and Linear Regression Classifiers.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5470Hidden Feature Weighted Deep Ranking Model (Hfwdr): A Novel Deep Learning Approach to Investigate the Nuanced Aesthetic Value of the Elderly Furniture Design & Cultural Identity2024-03-27T02:55:14+00:00Jing Lu,Musdi bin Hj Shanateditor@ijisae.org<p>The aesthetic value of elderly furniture design transcends mere functionality, embodying a rich tapestry of cultural heritage and historical significance. Rooted in traditional craftsmanship and informed by generations of cultural evolution, elderly furniture design carries with it a sense of timelessness and authenticity. Cultural identity and the evolution of the times are intertwined forces that shape societies, influencing everything from art and architecture to social norms and values. Cultural identity encompasses the unique customs, traditions, and beliefs that define a community or group, providing a sense of belonging and continuity across generations. This study investigates the aesthetic value of elderly furniture design, exploring its connection to cultural identity and the evolving socio-cultural landscape. By employing the Hidden Feature Weighted Deep Ranking Model (HFWDR), a novel deep learning approach, the research delves into the nuanced features of elderly furniture designs that resonate with cultural heritage and contemporary sensibilities. Through an analysis of design elements, material choices, and cultural motifs, the study uncovers the intrinsic relationship between furniture aesthetics and cultural identity, shedding light on how design evolves over time while retaining cultural authenticity. The HFWDR model, with its ability to capture hidden features and prioritize their significance in ranking, offers a comprehensive framework for evaluating and understanding the aesthetic evolution of elderly furniture design within the context of changing cultural dynamics. the HFWDR model assigned numerical values to hidden features such as symmetry, material quality, and historical relevance, with scores ranging from 0 to 100, indicating the degree of importance in determining the aesthetic value of elderly furniture designs.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5471Bibliometric Cluster Analysis and Classification Algorithm for Questioning Effectiveness in Elementary School Classrooms2024-03-27T02:56:23+00:00Rong Wang, Fadzilah Amzaheditor@ijisae.org<p>Bibliometric cluster analysis and classification algorithms offer a systematic approach to evaluating the effectiveness of questioning techniques in elementary school classrooms. By analyzing a vast array of academic literature and educational resources, bibliometric methods identify key themes, trends, and patterns related to questioning effectiveness. This analysis enables the classification of questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. By leveraging advanced algorithms, educators can gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. This paper presented a novel approach to assessing the effectiveness of questioning techniques in elementary school classrooms through Bibliometric Cluster Analysis and a Classification Algorithm, integrating Centroid Clustering Deep Learning (CCDL). By analyzing a diverse range of scholarly literature and educational resources, the bibliometric analysis identifies key themes, trends, and patterns related to questioning effectiveness. Subsequently, CCDL is employed to cluster and classify questioning techniques based on their impact on student engagement, comprehension, and critical thinking skills. Through this integrated approach, educators gain valuable insights into the most effective questioning strategies for enhancing learning outcomes in elementary classrooms. the algorithm identifies three primary clusters of questioning techniques: low-engagement (average student participation rate below 30%), moderate-engagement (30-60% participation rate), and high-engagement (above 60% participation rate).</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5472Comparative Analysis of Different Argumentation Frameworks 2024-03-27T02:57:22+00:00Shashi Prabha Anan, Vaishali Singheditor@ijisae.org<p>Argumentation Mining is considered a much harder task than generic information extraction or event mining because argumentation structures can be nested recursively. That is, a complete argumentation structure (claim and premises) might function as the premise of some more general claim, and so on. Recognizing the relationships among components of an argument also requires real-world knowledge, including knowing when one thing is a subtype of another. Both use NLP methods to map unstructured text onto graph-like structures or databases. The resulting information is easier to analyze for a variety of tasks, such as learning about social or political views, advising people about how to weigh the evidence for or against some choice, or helping companies to market products or perform quality assurance. Most of these tasks use hand-built templates that have been specified to fit a particular task or observed style of communication.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5473Developing a Multimodal Deep Learning System for Comprehensive Nutritional Analysis of Meals for Diabetes Management2024-03-27T02:58:25+00:00Kalivaraprasad B, Prasad M.V.D., Bharathi.H. Reddyeditor@ijisae.org<p>The management of diet is a pivotal factor in the maintenance of ideal blood glucose levels in individuals diagnosed with diabetes. Precisely evaluating the nutritional value of meals, encompassing caloric intake, can pose a formidable challenge. The present research suggests the creation and implementation of a multimodal deep learning framework aimed at approximating the nutritional composition of meals through the integration of image and textual information. The proposed system aims to combine convolutional neural networks (CNN) for image analysis with recurrent neural networks (RNN) or transformer models for text analysis. This integration is intended to exploit the complementary nature of visual and textual meal data, resulting in more precise estimates. The system is trained using a significant dataset consisting of images of meals, their corresponding textual descriptions, and related nutritional data. This dataset forms the foundation for the system’s development. The model’s predictive accuracy is evaluated through a rigorous assessment on unseen data, utilizing appropriate regression metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). In addition, we have created a proof-of-concept software application to showcase the practicality of the model in real-world scenarios. The objective of this application is to simplify the process of nutritional monitoring for individuals who have diabetes. The results of this study have the potential to revolutionize dietary management strategies in the context of diabetes care, as they provide a comprehensive and user-friendly nutritional analysis tool. Prospective areas of research encompass enhancing the precision of the model, expanding its scope of food items, and amalgamating it with other healthcare frameworks to achieve a comprehensive approach towards the management of diabetes. .</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5474Identifying Complex Human Actions with a Hierarchical Feature Reduction and Deep Learning-Based Approach2024-03-27T02:59:28+00:00Lakshmi Alekhya Jandhyam, Ragupathy Rengaswamy, Narayana Satyalaeditor@ijisae.org<p>Among computer vision's most appealing and useful research areas is automated human activity recognition. In these systems, look and movement patterns in video clips are used to classify human behaviour. Nevertheless, the majority of previous research has either ignored or failed to employ time data to predict action identification in video sequences using standard techniques and classical neural networks. On the other hand, reliable and precise human action recognition requires a significant processing cost. To get over the challenges of the pre-processing stage, in this work, we choose a sample of frames at random from the input sequences. We only take the most noticeable elements from the representative frame rather than the entire set of attributes. We suggest a hierarchical approach in which bone modelling and a deep neural network are used first, followed by background reduction and HOG. For selecting features and historical data retention, a CNN along with LSTM recurrent network combo is taken into consideration; in the end, a SoftMax-KNN classification is employed to detect the human behaviours. The name of our model is represented by the abbreviation HFR-DL, which stands for a hierarchical Features Lowering & Deep Learning-based action detection approach. We utilize the UCF101 dataset, which is popular among action recognition researchers, for benchmarking in order to assess the suggested approach. There are 101 challenging tasks in the wild included in the dataset. When comparing the experimental results with eight cutting-edge methods, significant improvements in speed and accuracy are seen.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5476Reactive & Multipath Routing with Adaptive Urban Area Vehicular Traffic (AUAVT) in VANET Environment2024-03-27T11:10:57+00:00 Akanksha Vyas, Sachin Puntambekareditor@ijisae.org<p>VANET is a special instance of the wireless multi-hop system, which owing to the high vehicle mobility is restricted by rapid changes in the topology. In this work family of AUAVT proposed, road-based information routing protocols that performs well in urban areas as adhoc vehicle networks (VANET). AUAVT protocols take advantage of real time traffic generation and communication to construct internets of vehicle (IoV) network. In the proposed work adaptively reactive and multipath AUAVT routing protocol designed and implemented with comparative analysis done on the basis of QoS parameters which is compared with AODV and OLSR routing protocol using NS-2 simulator. Simulation indicates the proposed AUAVT-<sub>Mulp</sub>, AUAVT-<sub>Reac</sub> routing protocol gives better performance by 6% and 28% respectively in terms of packet delivery ratio and average throughput with less routing overhead over AODV, OLSR routing protocol.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5477Empowering Collaborative Programming: The Colab Code Strategy for Consistency and Awareness2024-03-27T11:12:17+00:00Girish Navale, Pallavi V Baviskar, Shital Abhimanyu Patil, Indira P. Joshi, Shraddha R. Khonde, Sneha Ramdas Shegareditor@ijisae.org<p>The ColabCode is a ground-breaking solution that empowers geographically dispersed individuals working on the same project to collaborate seamlessly and simultaneously. Leveraging advanced technologies and an intuitive user interface, ColabCode provides a comprehensive platform where participants can collaborate, share code, and communicate effectively in real time.ColabCode addresses the challenges faced by distributed teams, allowing them to work cohesively on shared projects. By providing a unified workspace accessible from anywhere, participants can log in to the platform and instantly join the project in progress. Regardless of their location or time zone, team members can start working together, eliminating delays caused by coordination issues. The heart of ColabCode lies in its diverse programming language support. With built-in capabilities for various programming languages such as Python, Java, JavaScript, C++, and more, team members can seamlessly contribute code in their preferred language. This flexibility fosters inclusivity and encourages diverse skill sets, ensuring each member can work efficiently using their expertise.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5478Machine Learning for Characterization and Analysis of Microstructure and Spectral Data of Materials2024-03-28T04:29:21+00:00Venkataramaiah Gude, Sujeeth T, K Sree Divya, P. Dileep Kumar Reddy, G. Ramesh editor@ijisae.org<p>In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. It is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5479A Deep Learning Model for Detecting Bullying Comments on Online Social Media2024-03-28T04:31:10+00:00Renetha J B, Bhagya J, Deepthi P Seditor@ijisae.org<p>Youth dominate the online world today and the vast majority access social networks. Around the world, cyberbullying is rampant on social media sites and it has become a serious issue for people of all age groups. The bullying content detection by analyzing textual data in social media dataset is one of the most important parts of this work. The use of Deep Learning in Natural Language Processing has become very prevalent for handling the problem of cyberbullying. A large real-world Twitter dataset is collected for cyberbullying analysis. This work aims to analyze cyberbullying across the social media platform using a deep learning model Long Short-Term Memory Recurrent Neural Network or LSTM RNN and to evaluate its performance. The cyberbullying analysis on Twitter dataset using LSTM RNN gives an accuracy of 86%.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5480Comparison of Machine Learning Models for Effective Software Fault Detection2024-03-28T04:32:20+00:00Shikha Gautam, Ajay Khunteta, Debolina Ghosheditor@ijisae.org<p>Software defect prediction is a field in software engineering that aims to identify and anticipate defects or bugs in software systems before they occur. The goal is to develop techniques and models that can help software development teams prioritize their testing efforts and allocate resources more effectively. For this purpose, various Machine learning techniques used and these algorithms can utilize various features, such as code metrics, historical defect data, and developer information, to build predictive models. This paper aims to develop a model for software defect prediction using various ML algorithms. Experiments were conducted using the proposed model on KC2 dataset from the NASA PROMISE repository. The Decision tree algorithm achieved 73.28%, Naïve Bayes 83.97%, KNN 80.15%, Support vector Machine 82.44% and Random Forest 80.92% for KC2 dataset. The results demonstrated that the different model succeeded in effectively predicting the defects in PROMISE datasets KC2.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5481Approaches to handle Data Imbalance Problem in Predictive Machine Learning Models: A Comprehensive Review2024-03-28T04:33:32+00:00Govind M. Poddar, Rajendra V. Patill, Satish Kumar Neditor@ijisae.org<p>The business organizations ability to grow and flourish mostly relies on how successfully it understands and utilizes the data it has collected; data has become more vital in today's society. Every company or organization at the present time accumulates massive volumes of data across a range of areas, such as finance, trade, business, and healthcare. Medical data may be provided by clinics, doctors, healthcare providers, and insurance establishments. Upon locating the necessary medical datasets, the next phases would be to investigate and utilize appropriate modeling algorithms to mine substantial information for probable prediction. Biased data is significant challenge in machine learning where the distribution of data elements in a dataset is uneven, with one class considerably outnumbering the others. This occurrence leads to biased models and reduced performance that affects quality and reliability of machine learning algorithms. This paper presents detailed review on reasons for imbalanced data, its impact, algorithmic procedures to handle unevenly distributed data. We explore various techniques, algorithms to address problem, advantages, demerits and evaluation metrics to assess performance of procedures for handling imbalanced datasets.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5482Adversarial Attacks and Defenses in Deep Learning Models2024-03-28T04:34:48+00:00Khaja Shahini Begum, Bathina Rajesh Kumar, Gundala Venkata Rama Lakshmi, R S S Raju Battula, Elangovan Muniyandy, Amit Verma, Ajmeera Kiraneditor@ijisae.org<p>This paper investigates the complex interactions that lead to adversarial weaknesses in deep learning systems. This analyses various adversarial attack strategies, including FGSM and PGD, to evaluate how well they may undermine model fidelity. These results highlight the ongoing cat-and-mouse game between deep-learning security attackers and defenders. Although much progress has been made in increasing model resilience, the lack of a globally defined strategy highlights the necessity for a diversified security policy. This study shows the need for continual innovation and the persistent difficulty of protecting deep learning models against hostile threats</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5483Anomaly Detection in Time Series Data Using Deep Learning2024-03-28T04:36:12+00:00Thalakola Syamsundararao, Shobana Gorintla, E.Nitya, R S S Raju Battula, Lavanya Kongala, Amit Verma, Ajmeera Kiraneditor@ijisae.org<p>This paper investigates anomaly identification in historical data using advanced deep learning algorithms. Traditional methods of statistics, while useful, frequently fail to capture complex temporal connections. Our research thoroughly assesses the success rate of various deep learning structures for this job, including neural networks with RNNs, LSTMs, and CNNs. To refine the data, optimized preprocessing approaches such as normalization, in addition detrending, as well as the engineering of features is used. The models' adaptability and robustness are demonstrated through empirical validation in a variety of areas, including banking, health care, especially industrial processes. The study emphasizes scalability and processing efficiency to ensure practicality in real-world applications. Furthermore, interpretability methods provide perspectives into the machines' decision-making processes. The results reveal that deep learning models outperform conventional methods, paving the path for improved anomaly identification in time series information. Future study recommendations involve looking into hybrid structures, improving model comprehension, and researching real-time anomaly identification approaches. This work advances anomaly detection algorithms, which could have applications ranging from espionage to maintenance forecasting. The optimized framework offered here has the potential to improve system reliability as well as safety across a wide range of sectors.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5484Blockchain Technology for Secure and Trustworthy Decentralized Applications2024-03-28T04:37:33+00:00Elangovan Muniyandy, V.S. Radhika, Salar Mohammad, Sirigiri Joyice, Twinkle Dasari, Amit Verma, Ajmeera Kiraneditor@ijisae.org<p>This study explores the complex world of blockchain-based decentralized applications (DApps), concentrating on security and trust mechanisms. The study detects and classifies security flaws in DApps, which include flaws in smart contracts, difficulties with consensus, including dangers of data manipulation. By contrasting reputation-based systems with token-based incentives, it investigates trust mechanisms and clarifies their effects on user behaviour. The study emphasizes the crucial part that blockchain integration plays in boosting DApp security by employing its built-in immutability, decentralization, and cryptographic characteristics. The benefits of blockchain are supported by empirical data and vivid case examples. The paper ends with advice for DApp creators that emphasizes secure development methods, thorough audits, as well as user education while also indicating potential directions for future research.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5485Design and Develop A Secure Energy Efficient Data Transmission Technique for Wireless Sensor Networks2024-03-28T04:38:50+00:00Avneesh Gour, Nishant Kumar Pathakeditor@ijisae.org<p>Wireless Sensor Networks (WSNs) play a vital role in various applications such as environmental monitoring, healthcare, and smart cities. However, energy consumption is a critical concern in WSNs due to the limited power supply of sensor nodes. This research paper proposes a novel approach to address this challenge by designing and developing a secure energy-efficient data transmission technique for WSNs. The proposed technique aims to minimize energy consumption while ensuring data confidentiality, integrity, and authenticity. By leveraging cryptographic algorithms, optimization strategies, and intelligent routing protocols, the proposed technique enhances the security and efficiency of data transmission in WSNs. Experimental results demonstrate the effectiveness and feasibility of the proposed approach in improving the overall performance of WSNs in terms of energy consumption, communication overhead, and security.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5486A Comprehensive Survey of Multiple Object Tracking Techniques2024-03-28T04:40:34+00:00Hardik Jaiswal, Aditya Gambhir, Laxmi Bewoor, Nagaraju Bogirieditor@ijisae.org<p>Multiple Object Tracking (MOT) is crucial in computer vision and surveillance, especially for automating traffic control in challenging traffic environments. This review surveys advancements in object detection, tracking algorithms, lane departure warnings, and semantic segmentation, with a specific focus on traffic law enforcement. It covers issues like wrong-way, clearway, and one-way traffic violations, as well as challenges including occlusion and splits. Various methods, such as background subtraction and deep learning, are explored.The review stresses the significance of analyzing recent literature for researchers to bridge gaps, overcome limitations, and create new algorithms. It also touches on hardware, datasets, metrics, and research directions. Future MOT research aims to develop efficient algorithms for dynamic tracking, improve detection accuracy, and reduce real-time processing. The survey's proposed methods offer valuable references for tracking multiple objects in frame sequences.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5487Algorithmic Modeling for Predicting Carbon Emissions in an Individual Vehicles: A Machine Learning and Deep Learning Approach 2024-03-28T04:41:35+00:00Rashmi B. Kale, Nuzhat Faiz Shaikheditor@ijisae.org<p>This study proposes an algorithmic model aimed to accurately predicting carbon emissions from individual vehicles by leveraging machine learning and deep learning techniques. Concerns regarding environmental sustainability and climate change have intensified the need for precise assessments of carbon footprints, particularly in the transportation sector. Traditional methods often lack the adaptability and scalability required to handle the complexity of emission prediction tasks. In contrast, machine learning and deep learning offers promising avenues for developing robust models capable of learning from vast datasets and capturing intricate patterns in vehicle emissions. The purpose of research is to address the breach by designing a deep learning algorithmic framework that integrates machine learning algorithms to analyze real time datasets with vehicle attributes, driving patterns, and fuel characteristics to predict carbon emissions. The proposed approach holds potential for enhancing our understanding of vehicle emissions dynamics and facilitating the development of targeted interventions to mitigate environmental impacts.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5488Pharmaceutical Sales Forecasting with Machine Learning: A Strategic Management Tool for Decision-Making2024-03-28T04:42:37+00:00Fajar Saranani, Ruby Dahiya, Shetty Deepa Thangam Geeta, P Hameem Khan, Razia Naginaeditor@ijisae.org<p>This investigation explores the adequacy of machine learning strategies for pharmaceutical deal estimating, displaying a comparative investigation of four calculations: Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and AutoRegressive Integrated Moving Average (ARIMA). Real-world pharmaceutical deals information was utilized to assess the prescient execution of these calculations utilizing measurements such as Cruel Absolute Error (MAE), Mean Squared Error (MSE), and Root Cruel Squared Error (RMSE). The results demonstrate that LSTM beats the other calculations, accomplishing the most reduced MAE of 900, MSE of 13000, and RMSE of 113.96. Moreover, the research gives a comprehensive survey of later progressions in prescient analytics and machine learning over different divisions, counting healthcare, supply chain administration, back, and natural supportability. The discoveries emphasize the transformative potential of progressed analytics in driving key decision-making, optimizing asset assignment, and relieving dangers in pharmaceutical deals. Moving forward, the integration of machine learning-driven determining models into organizational procedures will proceed to revolutionize the pharmaceutical industry and clear the way for maintainable development and advancement.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5489Real-time Anomaly Detection in Big Data Streams: Machine Learning Approaches and Performance Evaluation2024-03-28T04:44:08+00:00Aruna Bajpai, Samiksha Khule, Vijay Prakash Sharma, Yogeshkumar Sharma, Gaurav Dubeyeditor@ijisae.org<p>The paper focuses on real-time anomaly detection in big data streams, discussing the machine learning techniques as well as performance evaluation. Different outlier detection methods, including Isolation Forest, Local Outlier Factor, Support Vector Machine and Elliptic Envelope, are considered and analyzed relying on a dataset of financial transactions. The analysis is made up of the visualization of data distributions, correlation matrices and metrics like accuracy and classification reports. The findings reveal varying performance levels among the methods pointing out the significance of choosing appropriate techniques for effective anomaly detection in dynamic data environments. This paper adds to the knowledge of outlier detection in big data streams and provides valuable insights for future studies.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5490Practical Implementation of Blockchain Technology in Pharmaceutical Supply Chain Management2024-03-28T04:45:17+00:00Rishi JP, Ramdas Bhat, Prateek Srivastava, M. Sundar Rajeditor@ijisae.org<p>This research examines the viable usage of blockchain innovation in pharmaceutical supply chain administration. Through an arrangement of tests and comparative examinations, the ponder assesses the effect of blockchain on key execution pointers such as traceability, straightforwardness, security, and proficiency. Results show noteworthy changes in traceability, with blockchain empowering real-time following of pharmaceutical items from fabricating to conveyance. Straightforwardness is improved through the utilisation of decentralized records, giving partners perceivability into supply chain operations. Security measures such as cryptographic hashing and computerized marks guarantee information keenness and ensure against extortion and unauthorized get to. Besides, the mechanization of forms by means of shrewd contracts leads to expanded proficiency in exchange preparation and compliance confirmation. The discoveries of this investigation highlight the transformative potential of blockchain innovation in revolutionizing pharmaceutical supply chain administration. Moving forward, it is basic for partners to address challenges such as adaptability and administrative compliance to completely realize the benefits of blockchain in guaranteeing the secure and effective conveyance of pharmaceutical items.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5491Integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry2024-03-28T04:46:28+00:00P Kiran Kumar Reddy, Atish Mane, Atowar ul Islam, Reecha Singh, Fahmida Khatoon editor@ijisae.org<p>This inquire about examines the integration of Artificial Intelligence (AI) and information analytics to optimize supply chain forms within the pharmaceutical industry. Through tests and writing audits, the ponder investigates the adequacy of AI calculations counting Linear Regression, Random Forest Regression, K-Means Clustering, and Deep Learning Neural Systems over request estimating, stock optimization, generation planning, and coordination optimization. Results appear that Random Forest Relapse beats Direct Relapse in request determining with RMSE of 80.20, MAE of 60.75, R² of 0.90, and MAPE of 6.50%. K-Means Clustering recognizes five clusters for stock optimization. Profound Learning Neural Systems accomplish RMSE of 75.10, MAE of 55.30, R² of 0.92, and MAPE of 5.80% for generation planning. In coordination’s optimization, Genetic Algorithm accomplishes a add up to fetched of $150,000 and conveyance time of 5 days compared to Mimicked Strengthening with $160,000 and 6 days. The research contributes to understanding the part of AI and information analytics in improving supply chain effectiveness, decreasing costs, and guaranteeing maintainability within the pharmaceutical segment.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5492"Implementing AI-Driven Personalized Medicine in Clinical Practice: Challenges and Practical Solutions"2024-03-28T04:47:42+00:00Sweety Bakyarani E, Virender Kumar Dahiya, Yogita Bhise, Subramanian Selvakumareditor@ijisae.org<p>This investigation explores the usage of AI-driven personalized pharmaceuticals in clinical hone, tending to challenges and proposing arrangements. Leveraging calculations counting Bolster Vector Machines, Random Forest, Neural Networks, and Bayesian Systems, it assesses their viability in optimizing treatment methodologies and improving quiet results. Experimentation on a differing dataset uncovers Neural Networks as the foremost compelling, accomplishing 90% precision, 88% affectability, and 92% specificity. Comparison with existing writing highlights the transformative potential of AI over healthcare spaces, emphasizing morals, security, infection administration, and restorative instruction. The consideration underscores the significance of tending to moral, administrative, and socio-cultural variables for belief and acknowledgement of AI in medication. Future investigate ought to approve these come about on bigger datasets and address real-world usage challenges. By grasping AI as a complementary apparatus in clinical workflows, healthcare suppliers can upgrade care conveyance, eventually progressing personalized medication and making strides health results.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5493Cloud-Enabled Social Network Mining for Advanced Recommendation Systems: An Integrated Data Mining and Social Network Analysis Approach2024-03-28T04:49:29+00:00Jaishree Jain, Santosh Kumar Upadhyay, Sharvan Kumar, Neerja Aroraeditor@ijisae.org<p>Recommendation systems play a crucial role in assisting users in discovering relevant and personalized content in social networks. For recommendation system data mining has a profound impact on several domains, including databases, artificial intelligence, machine learning, and social networks. It plays a crucial role in driving significant research advancements in the field. In today's fast-paced world, where data is rapidly expanding and information retrieval poses complex challenges, users increasingly demand valuable insights from their vast datasets. Social networks have emerged as a fascinating domain that has made substantial contributions to data mining research, ushering in a new era of possibilities. To determine the intrusion index based on the source address of the network security alarm, a simulation test is run. The findings demonstrate that this strategy can successfully implement cloud network security situation awareness as the related window attack index drops as soon as the security event is cancelled. You can accurately detect changes in network security circumstances using the suggested technique</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5494A Comprehensive System for Sustainable Tree Plantation and Growth Monitoring using Blockchain, AI, and IoT2024-03-28T04:50:32+00:00Monali Shetty, Deon Gracias, Ryan Valiaparambil, Hisbaan Sayed, Vijay Prajapati, Mahek Intwala, Prachi Patileditor@ijisae.org<p>There are several environmental challenges faced by the world today, with deforestation and climate change being major threats to the environment and its sustainability. NGOs and Government bodies play a crucial role in addressing these issues by organizing and conducting tree plantation drives. However, a lack of transparency, mismanagement of funds, and inefficient tracking systems, have hindered the effectiveness of these efforts. Many problems occur after tree plantation as there is no record being held to track the growth of trees, funds transparency is not available, no overall analysis is provided for deciding which tree species should be planted in a particular area to achieve maximum sustainability and also to improve the chances of growth of trees. Only planting trees in large numbers won’t help to solve this problem, a proper system is needed which can record time to time data regarding each and every tree through which we can help in the survival of all the trees and increase their lifespan. This will also help us in avoiding the drying and death of trees. The solution that we propose in this paper, to address the existing drawbacks is to create a web3 based platform to ensure the transparency of transferred funds and tree plantation by NGOs and government bodies, along with which we will implement a feature of tracking the status of planted trees using volunteers and IOT device in areas that aren’t easily accessible by volunteers</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5495Adaptive Dragonfly Optimization (Ado) Feature Selection Model and Distributed Bayesian Matrix Decomposition for Big Data Analytics2024-03-28T04:51:49+00:00M.Vijetha, G.Maria Priscillaeditor@ijisae.org<p>Matrix decompositions are fundamental methods for extracting knowledge from large data sets produced by contemporary applications. Processing extremely large amounts of data using single machines are still inefficient or impractical. Distributed matrix decompositions are necessary and practical tools for big data analytics where high dimensionalities and complexities of large datasets hinder the data mining processes. Current approaches consume more execution time making it imperative to reduce dataset feature counts in processing. This work presents a novel wrapper feature selection method utilising Adaptive Dragonfly Optimisation (ADO) algorithm for making the search space more appropriate for feature selections. ADO was used to transform continuous vector search spaces into their binary representations. Distributed Bayesian Matrix Decomposition (DBMD) model is presented for clustering and mining voluminous data. This work specifically uses, 1) accelerated gradient descent, 2) alternate direction method of multipliers (ADMM), and 3) statistical inferences to model distributed computing. These algorithms' theoretical convergence behaviours are examined where tests reveal that the suggested algorithms perform better or on par with two common distributed approaches. The methods also scale up effectively to large data sets. Clustering performances are assessed using the metrics of precision, recall, F-measure, and Rand Index (RI), which are better suited for imbalanced classes.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5496Predicting Landslides through Satellite Imagery Analysis and Machine Learning2024-03-28T10:39:49+00:00Anup Kadu, Raj Mishra, Vishal Shirsathauthor@email.com<p>The effects of climate change on landslides become more apparent, this work presents a novel method of landslide prediction by combining cutting-edge machine learning algorithms with Google Earth satellite images. Using digital image processing and Geographic Information System (GIS) techniques, the proposed method extracts important parameters, like elevation and slope, from high-resolution satellite data. Landslides are becoming a critical threat due to their increasing frequency, necessitating accurate prediction and early warning systems. Then, an intricate digital elevation model (DEM) is created and utilized as an input for more complex machine learning models, such as CNN and polygonal neural networks. Precise prediction of probable landslide events across large, susceptible areas is made possible by this novel combination. Landslides may have a major negative impact on human life and the economy, but the integrated method greatly improves the accuracy of early detection. The results demonstrate the efficacy of this innovative approach in delivering precise and timely forecasts, signifying a significant advancement in the evaluation of geotechnical hazards and proactive risk control for expansive, high-risk regions. In order to meet the urgent need for proactive mitigation in the face of climate-induced risks, this research presents a strong foundation for comprehensive landslide prediction.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5497Machine Learning for Precision Agriculture: Predictive Analysis of Crop Growth Frequencies2024-03-28T10:42:55+00:00Niketa Kadam, Raj Mishra, Vishal Shirsathauthor@email.com<p>By combining cutting-edge machine learning techniques with the examination of plant development patterns, presented research employs an innovative dual methodology for precisely calculating green areas, i.e., plant growth. One unique aspect of the research is the correlation between plant development and specific musical frequencies, which span from 1 to 10 kHz and include pop, classical, and Normal. By utilizing support vector machines (SVM) and artificial neural networks (ANN), the dual method improves our comprehension of the dynamics of plant development. Interestingly, the study shows that SVM performs better than ANN, offering more accuracy in predicting green areas. This sophisticated approach shows how fusing state-of-the-art neural networks with conventional machine learning may revolutionize the field and change the course of precision agriculture. The study highlights the complementary nature of contemporary and traditional methods, demonstrating their effectiveness in providing a thorough understanding of plant development and a productive assessment of green areas. SVM's astounding accuracy levels up to 92.10% highlight the significance of this technology in the advancement of precision farming practices. The stability and applicability of proposed strategy are emphasized, especially in light of accurate and successful agricultural management techniques. Three plant species were watched over the course of three months for this study, giving the results a strong real-world component.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5498Risk Inference-Based Privacy Preservation Model for Cyber-Physical Systems2024-03-28T10:47:26+00:00Manas Kumar Yogi, A. S. N. Chakravarthyauthor@email.com<p>The Risk Inference-Based Privacy Preservation Model for Cyber-Physical Systems (CPS) addresses the critical need for safeguarding privacy in the evolving landscape of interconnected physical and digital environments. This model, aptly named RIP2 (Risk Inference for Privacy-Preserving CPS), integrates advanced risk assessment techniques with robust privacy-preserving mechanisms to create a dynamic and adaptive framework. The model begins with a comprehensive risk assessment module that identifies potential threats, values privacy-sensitive assets, and assesses vulnerabilities within the CPS architecture. A privacy risk inference engine dynamically analyses contextual data, user behavior, and continuously evolving risk factors to assess the current privacy risk level. Privacy-preserving mechanisms, including differential privacy, encryption, and anonymization, are adaptively applied based on the inferred risk level, ensuring a tailored and effective approach to privacy preservation. Users are empowered to define their privacy preferences, and the model incorporates dynamic privacy policies that automatically adjust based on the risk assessment. Furthermore, the model incorporates incident response and continuous learning mechanisms to respond promptly to privacy incidents and improve the overall resilience of the system. The RIP2 Model aims to strike a balance between the seamless functionality of CPS and the paramount importance of preserving individual privacy in an interconnected and data-driven world.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5499Investigation of SAR Reduction and Bending Effect Using a Flexible Antenna with EBG Structure for 2.45 GHz Wearable Applications2024-03-28T10:49:56+00:00Sonal Jatkar, Nilesh Kasatauthor@email.com<p>This paper presents, an analysis of Co-Planar Waveguide fed textile antennas operating at 2.45 GHz frequency with an Electromagnetic Band-Gap array for WBAN applications. Denim substrate, characterized by its dielectric constant of 1.6 and thickness of 1mm which is stacked together to get height of 3mm. Simulation using HFSS provides a return loss of -52 dB at 2.45 GHz. Size of antenna after incorporating 2*2 array in EBG structure is 61*61*3 mm<sup>3</sup>. It results in increase gain from 2.36 dB to 4 dB without EBG and with EBG respectively. Stochastic frameworks employing polynomial chaos principles expansions is used to model antenna dimensions. The study aimed to investigate how bending affects antenna performance, employing two bending scenarios: one in the H plane and the other in the E plane. Also, for WBAN applications, SAR value is simulated on human body phantom using HFSS simulator and is obtained to 41.04 W/Kg without EBG and 0.522 W/Kg with EBG.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5500AI-Enabled Customer Relationship Management: Personalization, Segmentation, and Customer Retention Strategies2024-03-28T10:54:13+00:00Sanjaykumar Jagannath Patil, Digamber Krishnaji Sakore, Sourabh Sharma, Dipanjay Bhalerao, Yogita Sanjaykumar Patil, Jagbir Kaurauthor@email.com<p>Artificial intelligence (AI) and machine learning are transforming customer relationship management (CRM) strategies. This paper provides an extensive review of how AI-enabled capabilities like predictive analytics, personalization engines, and customer segmentation are enabling more tailored, relevant experiences that strengthen customer relationships and loyalty over time. Current CRM systems generate massive datasets on customer interactions and behaviors, which feed AI algorithms to uncover hidden insights around individual preferences, likely future behaviors, and optimal cross-sell recommendations unique to each customer. We analyze key AI methodologies powering next-generation CRM including reinforcement learning, neural networks, natural language processing, and computer vision. The paper discusses sample use cases and real-world examples of AI-driven CRM initiatives from leading companies that focus on personalization, predictive churn models, next-best action recommendations, and automated customer service agents. We also examine emerging technologies on the horizon such as affective computing, virtual reality, and the metaverse that present new opportunities to understand customers and meet their needs in highly tailored, emotionally intelligent ways. The paper concludes with an analysis of critical considerations as firms implement AI-enabled CRM including data privacy, transparent AI, and avoiding algorithmic bias. With responsible implementation, AI stands poised to revolutionize CRM with previously impossible levels of personal relevance at scale, ultimately growing customer lifetime value.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5501 Robotics and Cobotics: A Comprehensive Review of Technological Advancements, Applications, and Collaborative Robotics in Industry2024-03-28T10:58:38+00:00Abhijit Chandratreya, Suresh Dodde, Nitin Joshi, Deepak Dasaratha Rao, Neha Ramtekeauthor@email.com<p>Collaborative robotics, or cobots, are transforming human-robot interaction in industrial environments. This paper provides a comprehensive review of the technological advancements, applications, and collaborative aspects of robotics across various industry verticals. Advanced hardware and software innovations are enabling robots to work safely alongside humans, enhancing productivity and quality while also taking over undesirable or dangerous tasks. Cobots are being rapidly deployed for assembly, pick and place, inspection, machine tending and other precision handling operations. Implementation challenges exist, but continued improvements in sensing and intelligence capabilities are increasing robot flexibility and ease of integration in human-centric work cells. With appropriate configuration, deployment strategies and worker training, collaborative robots can improve manufacturing and production performance. This paper examines the rise of collaborative industrial robots and analyzes the outlook for this technology over the next five years.</p>2024-03-22T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5502Assessment of Conflict Flows in Software-Defined Networks using a Novel Nature-Inspired Optimization-Tuned Kernelized SVM2024-03-29T06:44:36+00:00Amit Sharma, Veena M., Hirald Dwaraka Praveena, V. Selvakumar, Bhuvana J., Dhiraj Singhauthor@email.com<p>The centralizing management and flexibly customizing network resources, Software-Defined Networks (SDN) completely transform network administration. As networks become more intricate, the likelihood of conflicts arising data flows increases, potentially leading to a decline in overall performance and the emergence of security vulnerabilities. This paper presents a tree-seed optimization-tuned kernelized support vector machine (TSO-KSVM) for the assessment of conflict flows in SDN environments. Initially, we gather data samples of SDN in conflict flows to analyze the performance of the proposed method. Applying the min-max scaling method to preprocess the raw data samples and linear discriminant analysis (LDA) is carried out to reduce the dimension. In the proposed framework, TSO is applied to enhance the assessment in the KSVM model. The proposed method is implemented in the Python tool. The proposed method's performance is analyzed in terms of various metrics compared with other methods. From the experimented results, we conclude that the proposed method attains the greatest accuracy rate of other methods in assessing conflict flows in SDN networks.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5503Novel Resource Allocation Approach for Fog Computing-Driven IoT Systems2024-03-29T06:47:12+00:00Purushottam S. Barve, Shweta Saxena, Adars U., N.Venkata Sairam Kumar, Sachin S. Pundauthor@email.com<p>Fog computing (FC) has the potential to lower latency and boost speed. Internet of Things (IoT) networks have difficulties allocating resources efficiently. The approaches used are flexible, scalable, or optimized. To maximize performance indicators, new approaches that utilize real-time information, workload sequences, device accessibility and network circumstances are required. We investigate the allocation of resources and task scheduling for numerous devices in IoT systems in this research. IoT devices must properly choose which data to offload to FC nodes (FCNs) as they acquire enormous amounts of data. To tackle the problem of supporting multiple device connections and facilitating fast data transfers with constrained resources, we suggest executing non-orthogonal multiple access (NOMA). Several devices can simultaneously send data spanning time, frequency and coding domains to an identical FCN because of NOMA. Together, we optimize power transmission and resource assignment for IoT devices, meeting QoS requirements and reducing network energy usage. In this research, a unique boosted atom search optimization (BASO) method is presented to tackle it because it is an NP-hard issue. According to the simulation results, the suggested strategy outperforms in terms of greatest throughput, minimum latency and optimal energy use. </p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5504Revolutionizing Image Encryption: Data Hiding Model Based on Optimized Neural Network2024-03-29T06:49:13+00:00Pallavi S. Chakole, Siva Rama Krishna T., Rahul Mishra, Beemkumar Nagappan, Sachin S. Pund, Jasneet Kaurauthor@email.com<p>Information security researchers are currently focusing on Revolutionizing Image Encryption in data hiding for safe digital data movement. Preserving information that has been hidden in a system is one of the core concepts of data hiding. For image encryption, data hiding is the process of embedding private data into images to ensure it is hidden from perceptions by other people. In this research, the hidden data is retrieved by our proposed Multi-rate Salient Gated Recurrent Neural Network (MSG-RNN) and it employs a dependent classification method to recover images from encrypted images. We gathered a data of various kinds of image data. Following the encryption of the original image, we established the Elliptic Curve Cryptography (ECC) method and created an innovative image encryption technique to improve security by data hiding. We calculated our proposed method's bit error rates. The comparison evaluation is performed with various methods to estimate proposed technique. Using the MSG-RNN approach on a number of images produced superior outcomes and they include the results for boats (0.0714), peppers (0.0712), baboons (0.0716) and airplanes (0.0719). The experimental outcomes offered that the proposed MSG-RNN technique performed better than other existing methods in data hiding process to enhance image encryption.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5505Developing an Innovative Machine Learning Integrated Cloud Monitoring System for Cloud-Based Services2024-03-29T06:51:09+00:00Nisha M. Shrirao, Saket Mishra, Raghavendra R., Amandeep Gill, Sachin S. Pund, Shital Bardeauthor@email.com<p>A cloud monitoring system is an integrated solution for monitoring as well as managing the performance, availability along with the security of cloud-based infrastructure and services. It employs a variety of tools and methods to gather, analyze and present data on resource use, application activity and user interactions. By monitoring critical parameters in real-time, it allows proactive problem identification and resolution, resource allocation optimization, as well as adherence to service-level agreements. In this research, we developed an innovative machine learning (ML) integrated cloud monitoring system named Sea Horse fine-tuned Extreme Gradient Boosting (SH-XGBoost). Initially, we collected a dataset that includes various types of cloud environment scenarios to train our proposed approach. We utilized the Robust Scaling (RS) algorithm to pre-process the gathered raw data. We employed the Sea Horse Optimization algorithm to enhance the primary characteristics of the proposed XG-Boost algorithm. The suggested approach is implemented in Python software. The finding evaluation phase is performed with multiple metrics such as,F1-score (98.49%), Recall (98.38%), Precision (98.53%) and Accuracy (98.43%) to assess the proposed SH-XGBoost approach with other conventional approaches. The experimental findings illustrate that the proposed SH-XGBoost approach performed better than other existing approaches for novel cloud monitoring systems.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5506Automated IoT-Based Monitoring and Control for Hydroponic System2024-03-29T06:52:55+00:00Vaira Muthu K., Krishnakumar A.author@email.com<p>Agriculture, the world of farming is an essential area where the people around are focusing to develop to enhance more yields in minimum cost and other requirements. The new emerging technique named hydroponics focuses in developing a greenhouse that involves developing crops using water-based nutrients without soil. This proposed implementation presents an intelligent design that comprised with low-cost and automatic monitoring control through the support of IoT (Internet of Things) for hydroponics greenhouse. This implementation includes some sensors to monitor and controls pumping of water, a quality of water, monitor the temperature and humidity of the crops. The master node controls the water flow and aggregates the data, which is received from the member nodes. Member nodes monitor the temperature and humidity and forwards the data to the master node for necessary actions. A Fuzzy inference model is proposed to determine the flow of water and nutrients. The proposed model outperforms than the existing model in low cost, better energy efficiency and throughput.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5507Enhancing Maximum Power Point Tracking through Ensemble Techniques2024-03-29T07:15:12+00:00Hayder Husam Mahmood, Zaid Hamodatauthor@email.com<p>Maximum Power Point Tracking (MPPT) plays a pivotal role in photovoltaic (PV) solar systems, streamlining the harnessing of available power and bolstering energy conversion efficiency. Its significance lies in its alignment with the global push to heighten the efficacy of renewable energy sources. This article unfolds a meticulous examination of the predictive modeling specific to solar energy. The investigation spans various machine learning models such as Linear Regression (LR), Support Vector Regression (SVR), XGBoost Regressor, and Ensemble Learning (EL), each dissected to reveal the intricacies involved in solar energy system modeling. The research, conducted across two unique datasets—Solar Power Generation and Solar Radiation Prediction, employed rigorous statistical evaluation to uncover the distinctions in accuracy, unity, and efficacy among the models. A standout finding was the Ensemble Learning model's superior performance, notably through applying techniques like Bagging Regressor. This approach transcended the individual models in both datasets by ingeniously amalgamating the predictions of various underlying models, leading to enhanced predictive precision. This article's insights contribute considerably to the domain of solar energy modeling, elevating Ensemble Learning as a powerful instrument for refining prediction accuracy. Furthermore, the juxtaposition of various modeling methodologies unveils valuable insights into their respective trade-offs, enriching the foundation for future exploration and real-world implementations within the renewable energy landscape. In setting a novel standard in solar energy forecasting, this study also resonates with the broader objectives of sustainable energy governance and ecological preservation.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5508Optimizing Microgrid Performance: A Data-Driven Approach with IoT Integration2024-03-29T07:30:29+00:00Raafat K. Oubidaauthor@email.com<p>The essential goal of this exploration is to work on the efficiency of microgrids by using a data-driven system that is expanded by the joining of Internet of Things (IoT) technology integration. Microgrids, which are decentralized energy systems, are a fundamental part during the time spent upgrading energy resilience, bringing down fossil fuel byproducts, and further developing admittance to power. By and by, there are serious issues associated with expanding the efficiency and reliability of microgrid tasks. These difficulties are brought about by the multifaceted cooperations that happen between the a large number and the dynamic outer impacts. Inside the extent of this examination, we offer a remarkable system that utilizes data-driven procedures to evaluate, reproduce, and improve the performance of microgrids. We plan predictive models to conjecture energy utilization, further develop energy generation and conveyance, and lift system constancy by using real-time data gathered from Internet of Things (IoT)- empowered sensors and devices implanted inside the design of the microgrid. The consolidation of Internet of Things technology makes it conceivable to consistently screen and work the resources of a microgrid, which thus pursues it more straightforward to settle on proactive decisions and execute adaptive management strategies. Through the use of case studies and simulated tests, we illustrate the efficacy of our method in terms of promoting energy sustainability, lowering operational costs, and improving the performance of microgrids. The findings of this study provide a significant contribution to the development of intelligent energy systems and lay the groundwork for future advances in the optimization and administration of microgrid electricity networks.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 Raafat K. Oubidahttps://ijisae.org/index.php/IJISAE/article/view/5509A Novel Medical Decision Support System Using Swarm Intelligence Based Bayesian Learning Algorithm2024-03-29T07:56:48+00:00Preethi, Zeeshan Ahmad Lone, Ansari Mehrunnisa Hafiz, Trapty Agarwal, Saniya Khuranaauthor@email.com<p>The use of Machine Learning (ML) methods may be beneficial at the clinical and diagnostic levels of medical decision-making. A foundation for ML is provided by feature selection algorithms. In a medical setting, feature selection may be used to rapidly and efficiently identify the health-related qualities that are most distinctive from the original feature collection. The two primary objectives of feature selection algorithms are to determine the properties of data classes that are most relevant and to enhance classification performance. In addition to assisting lower the general measurement of the dataset, feature selection also aids in determining which features are most important. Therefore, we provide a unique ML-based approach in this study. The dataset is first gathered and prepared using the min-max normalization approach. The features are selected using principal component analysis (PCA). Using a novel swarm-optimized Bayesian learning approach (SOBLA), accuracy is used to evaluate the effectiveness of various feature subsets. Experimental results show that the performance of the proposed method performs better when compared to conventional methods. The outcomes of this study suggest interventions with the potential to enhance the quality of healthcare decision-making about certain healthcare procedures.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024 https://ijisae.org/index.php/IJISAE/article/view/5510Tuna-Osprey Optimization for Energy Efficient Cluster-based Routing: Modified Deep Learning for Node's Energy Prediction2024-03-29T09:26:52+00:00Gopala T, Raviram Vtest@test.com<p>The main consideration of WSN design is maximization of the network lifetime. It is proven that the effective balancing of network energy consumption along with the maximization of network lifetime can be performed by clustering and routing approaches. Accordingly, a new Tuna Osprey Optimization algorithm for energy-efficient cluster-based routing has been developed in this work. This approach includes 2 working phases: clustering and routing process. Initially, a modified DL model named M-LSTM is proposed for predicting the node’s energy. Subsequently clustering process is carried out by the TOO algorithm, which considers the energy, link lifetime, distance, trust, and delay as constraints for the selection of optimal CH. Finally, with the same TOO algorithm, the routing process is conducted, which considers the link quality as the constraint to provide optimal routing. Results proved that the proposed TOO for energy-efficient cluster-based routing can reduce energy utilization while attaining maximum network lifetime.</p>2024-03-26T00:00:00+00:00Copyright (c) 2024