An Improvised Mechanism for Optimizing Fault Detection for Big Data Analytics Environment
Keywords:
Fault Detection, Optimisation, Big DataAbstract
In the applications of fault detection, the inputs are the data reflected from health state of the observed system. A major challenge to finding errors is the nonlinear relationship between the data. Big data has other drawbacks, and the volume and speed with which it is generated are reflected in the data streams themselves. In this paper, we develop a deep learning model that aims to provide fault detection in big data analytics engine. This investigation develops an approach for fault detection in large datasets using unsupervised learning. In this research, an unsupervised method of learning is developed specifically for the task of classifying large datasets. To discover regular textual patterns in large datasets, this research use data visualization methods. In this virtual environment, we employ an unsupervised learning method of machine learning that does not require human oversight. Instead, the system should be allowed some leeway to work and find things on its own. The unsupervised learning approach utilizes data that has not been tagged. In contrast to supervised learning, this approach can handle complex tasks.
Downloads
References
Sarangi, S., Sahu, B. K., & Rout, P. K. (2022). An Advanced Fault Detection Technique for DG Integrated Microgrid Using Fast Fourier Discrete Orthonormal Stockwell Transform-Based Hybrid Optimized Kernel Extreme Learning Machine. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 46(2), 329-351.
Alissa, K., H. Elkamchouchi, D., Tarmissi, K., Yafoz, A., Alsini, R., Alghushairy, O., ... & Al Duhayyim, M. (2022). Dwarf Mongoose Optimization with Machine-Learning-Driven Ransomware Detection in Internet of Things Environment. Applied Sciences, 12(19), 9513.
Zhang, J., Qu, Z., Chen, C., Wang, H., Zhan, Y., Ye, B., & Guo, S. (2021). Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Computing Surveys (CSUR), 54(7), 1-36.
Uppal, M., Gupta, D., Juneja, S., Dhiman, G., & Kautish, S. (2021). Cloud-based fault prediction using IoT in office automation for improvisation of health of employees. Journal of Healthcare Engineering, 2021.
Danthuluri, S., & Chitnis, S. (2021). Energy and cost optimization mechanism for workflow scheduling in the cloud. Materials Today: Proceedings.
Ning, P., Zhou, Z., Cao, Y., Tang, S., & Wang, J. (2021). A Concurrent Fault Diagnosis Model via the Evidential Reasoning Rule. IEEE Transactions on Instrumentation and Measurement, 71, 1-16.
Thilagaraj, M., Dwarakanath, B., Pandimurugan, V., Naveen, P., Hema, M. S., Hariharasitaraman, S., ... & Govindan, P. (2022). A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data. Computational and Mathematical Methods in Medicine, 2022.
Sreedevi, A. G., Harshitha, T. N., Sugumaran, V., & Shankar, P. (2022). Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review. Information Processing & Management, 59(2), 102888.
Sahoo, S. (2021). Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. International Journal of Production Research, 1-29.
Zhang, Z., Huang, W., Liao, Y., Song, Z., Shi, J., Jiang, X., ... & Zhu, Z. (2022). Bearing fault diagnosis via generalized logarithm sparse regularization. Mechanical Systems and Signal Processing, 167, 108576.
Rao, G. S., Armstrong Joseph, J., Dhiman, G., Mohammed, H. S., Degadwala, S., & Bhavani, R. (2022). Novel big data networking framework using multihoming optimization for distributed stream computing. Wireless Communications and Mobile Computing, 2022.
Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R. S., & Malik, M. (2021). Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics, 5(4), 66.
Yao, Y., & Shekhar, D. K. (2021). State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field. Building and Environment, 200, 107952.
Zhou, X., Xu, X., Liang, W., Zeng, Z., & Yan, Z. (2021). Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT. IEEE Internet of Things Journal, 8(16), 12588-12596.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
