Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing
Keywords:
Machine Learning, Next-Generation Computing, Artificial Intelligence, Edge Computing, Cybersecurity, Deep Learning, BlockchainAbstract
This research investigates how new digital sciences merge with cutting edge computing. We're exploring the big impact of quantum computing, AI, high performance computing, and edge computing across various fields. We see them blending with other fresh advances in digital science. We talk about quantum computing's challenges like security and operation. Quantum computing could make solving tough problems way faster. We also look at how important AI, including machine learning and deep learning, is for sorting data and making predictions. The study digs into the growth of mighty supercomputers and exascale computing. We're interested in how they manage many tasks at once, use less power, and stay secure. Security and privacy concerns are brought up in relation to the real-time analytics offered by edge computing in IoT applications. This paper highlights the need for interdisciplinary collaboration, education, scalability, efficient data management, and workforce development in the context of cybersecurity in computational science, while also highlighting the fundamental importance of cybersecurity, the changing threat landscape, and best practices. The study elucidates the potential of these tendencies and their ethical and security dimensions, providing direction for future research and highlighting the inextricable link between computational science, innovation, and security in the modern digital era.
Downloads
References
K. Wang et al., "Task Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks," in IEEE Journal on Selected Areas in Communications, vol. 41, no. 2, pp. 306-319, Feb. 2023, doi: 10.1109/JSAC.2022.3227102.
Z. Lv and W. Xiu, "Interaction of Edge-Cloud Computing Based on SDN and NFV for Next Generation IoT," in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5706-5712, July 2020, doi: 10.1109/JIOT.2019.2942719.
M. Yang, W. Yang, X. Wang, J. Liao and J. Chen, "Performance flexibility architecture of core service platform for next-generation network," in Tsinghua Science and Technology, vol. 13, no. 1, pp. 85-90, Feb. 2008, doi: 10.1016/S1007-0214(08)70014-2.
M. Y. -K. Chua, F. R. Yu and S. Bu, "Dynamic Operations of Cloud Radio Access Networks (C-RAN) for Mobile Cloud Computing Systems," in IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1536-1548, March 2016, doi: 10.1109/TVT.2015.2411739.
J. Wu, M. Dong, K. Ota, J. Li and Z. Guan, "FCSS: Fog-Computing-based Content-Aware Filtering for Security Services in Information-Centric Social Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 4, pp. 553-564, 1 Oct.-Dec. 2019, doi: 10.1109/TETC.2017.2747158.
Y. -Y. Shih, C. -Y. Wang and A. -C. Pang, "Fog Computing Service Provision Using Bargaining Solutions," in IEEE Transactions on Services Computing, vol. 14, no. 6, pp. 1765-1780, 1 Nov.-Dec. 2021, doi: 10.1109/TSC.2019.2905203.
D. Liu et al., "An Energy-Efficient Mixed-Bit CNN Accelerator With Column Parallel Readout for ReRAM-Based In-Memory Computing," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 12, no. 4, pp. 821-834, Dec. 2022, doi: 10.1109/JETCAS.2022.3212314.
A. M. Rashwan, A. -E. M. Taha and H. S. Hassanein, "Characterizing the Performance of Security Functions in Mobile Computing Systems," in IEEE Internet of Things Journal, vol. 1, no. 5, pp. 399-413, Oct. 2014, doi: 10.1109/JIOT.2014.2360217.
S. Ranadheera, S. Maghsudi and E. Hossain, "Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game," in IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 688-691, Oct. 2018, doi: 10.1109/LWC.2018.2810292.
Z. Wang, L. Yang, Q. Wang, D. Liu, Z. Xu and S. Liu, "ArtChain: Blockchain-Enabled Platform for Art Marketplace," 2019 IEEE International Conference on Blockchain (Blockchain), Atlanta, GA, USA, 2019, pp. 447-454, doi: 10.1109/Blockchain.2019.00068.
Daniel Minoli; Benedict Occhiogrosso, "Current and Evolving Applications to Network Cybersecurity," in AI Applications to Communications and Information Technologies: The Role of Ultra Deep Neural Networks , IEEE, 2024, pp.347-405, doi: 10.1002/9781394190034.ch6.
D. Xu, N. Ren and C. Zhu, "High-Resolution Remote Sensing Image Zero-Watermarking Algorithm Based on Blockchain and SDAE," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 323-339, 2024, doi: 10.1109/JSTARS.2023.3329022.
H. Du, Z. Che, M. Shen, L. Zhu and J. Hu, "Breaking the Anonymity of Ethereum Mixing Services Using Graph Feature Learning," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 616-631, 2024, doi: 10.1109/TIFS.2023.3326984.
Y. Zhang, Z. Ma, S. Luo and P. Duan, "Dynamic Trust-Based Redactable Blockchain Supporting Update and Traceability," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 821-834, 2024, doi: 10.1109/TIFS.2023.3326379.
Qi Xia; JianbinGao; Isaac AmankonaObiri; Kwame OmonoAsamoah; Daniel AduWorae, "Subscriber Data Management System Based on Blockchain," in Attribute-based Encryption (ABE): Foundations and Applications within Blockchain and Cloud Environments , IEEE, 2024, pp.215-228, doi: 10.1002/9781119989387.ch13.
D. R. Kothadiya, C. M. Bhatt, A. Rehman, F. S. Alamri and T. Saba, "SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning," in IEEE Access, vol. 11, pp. 47410-47419, 2023, doi: 10.1109/ACCESS.2023.3274851.
F. Afza, M. A. Khan, M. Sharif, S. Kadry, G. Manogaran, T. Saba, et al., "A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection", Image Vis. Comput., vol. 106, Feb. 2021.
P. Linardatos, V. Papastefanopoulos and S. Kotsiantis, "Explainable AI: A review of machine learning interpretability methods", Entropy, vol. 23, no. 1, pp. 18, Dec. 2020.
K. V. Dudekula, H. Syed, M. I. M. Basha, S. I. Swamykan, P. P. Kasaraneni, Y. V. P. Kumar, et al., "Convolutional neural network-based personalized program recommendation system for smart television users", Sustainability, vol. 15, no. 3, pp. 2206, Jan. 2023.
M. Baldeon Calisto and S. K. Lai-Yuen, "AdaEn-net: An ensemble of adaptive 2D–3D fully convolutional networks for medical image segmentation", Neural Netw., vol. 126, pp. 76-94, Jun. 2020.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, "DeepLab: Semantic image segmentation with deep convolutional nets atrous convolution and fully connected CRFs", IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 834-848, Apr. 2018.
J. Ganesan, A. T. Azar, S. Alsenan, N. A. Kamal, B. Qureshi and A. E. Hassanien, "Deep learning reader for visually impaired", Electronics, vol. 11, no. 20, pp. 3335, Oct. 2022.
Y. He et al., "Backdoor Attack Against Split Neural Network-Based Vertical Federated Learning," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 748-763, 2024, doi: 10.1109/TIFS.2023.3327853.
Q. Xia et al., "PRIDN: A Privacy Preserving Data Sharing on Named Data Networking," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 677-692, 2024, doi: 10.1109/TIFS.2023.3327660.
S. Hajra, M. Alam, S. Saha, S. Picek and D. Mukhopadhyay, "On the Instability of Softmax Attention-Based Deep Learning Models in Side-Channel Analysis," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 514-528, 2024, doi: 10.1109/TIFS.2023.3326667.
H. Du, Z. Che, M. Shen, L. Zhu and J. Hu, "Breaking the Anonymity of Ethereum Mixing Services Using Graph Feature Learning," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 616-631, 2024, doi: 10.1109/TIFS.2023.3326984.
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.