This book features the proceedings of the 7th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2024). The book features detail on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on technologies from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platforms for Privacy Preserving Data Science, and Internet-Based Cognitive Platforms.

Contains proceedings from 7th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing Features topics ranging from Data Science for Cognitive Analysis to Internet-Based Cognitive Platforms Relevant for researchers, professionals, academics, and students in big data for sustainable cognitive computing

Autorentext

Dr. Anandakumar Haldorai is a Professor in the Department of Computer Science and Engineering and the Associate Dean of Research at Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He obtained his master’s in software engineering from PSG College of Technology, Coimbatore, and earned his Ph.D. in Information and Communication Engineering from PSG College of Technology under Anna University, Chennai. His research interests include Big Data, Cognitive Radio Networks, Mobile Communications, and Networking Protocols. Dr. Haldorai has published over 176 research papers in reputed international journals and IEEE conferences. He is the author of 14 books and numerous book chapters with leading publishers such as Springer and IGI Global. He serves as the emeritus Editor-in-Chief of the International Journal of Intelligent Networks (KeAi – Elsevier), Associate Editor for IEEE Access, and Area Editor for EAI Energy Web. He is also a guest editor for several journals published by Elsevier, Springer, and Inderscience and has served as a reviewer for journals from IEEE, IET, Springer, Inderscience, and Elsevier. Additionally, he has contributed as General Chair, Session Chair, and Panelist at various international conferences. Dr. Haldorai is a Senior Member of IEEE, ACM and IET, and a Fellow of the EAI research Group.

Dr. Arulmurugan Ramu is Associate Professor, School of Mathematical and Computer Sciences Engineering, Heriot-Watt University, Kazakhstan. His research focuses on the automatic interpretation of images and related problems in machine learning and optimization. His main research interest is in vision, particularly high-level visual recognition. He has authored more than 72 papers in major computer vision and machine learning conferences and journals. He is the recipient of Ph.D. degrees in Information and Communication Engineering from the Anna University at Chennai, M. Tech in Information Technology Anna University of Technology and B. Tech degree in Information Technology. He is guided many Ph.D. research scholar under the area of Image Processing using Machine Learning.

Dr. Suriya Murugan has received her B.E. in Information Technology from Avinashilingam University, Coimbatore in 2008 and master’s in computer science and engineering from Anna University of Technology, Coimbatore in 2010. She is pursuing Ph.D. in Information and Communication under Anna University, Chennai. She is having about 12 years of teaching experience and her areas of interest include Artificial Intelligence, Machine Learning and Wireless Communication. She has authored more than 30 research papers including 4 SCI and 25 Scopus indexed publications with high impact Journals like IEEE Access, Elsevier, Springer and Taylor and Francis.

Inhalt

Introduction.- Real-time analytics for instantaneous decision-making, network optimization, and quality of service enhancement in NGNs .- Big data analytics in identifying and mitigating security threats .- Quality monitoring to elevate the overall user experience within NGNs .- Challenges and opportunities in capacity planning, resource allocation, and dynamic adaptation of NGNs .- Machine learning algorithms with big data to automate decision-making processes .- Fuzzy systems and uncertainties in big data analytics .- Big data Analytics Tools and Machine Learning for Data Analysis.-Big data Analytics in Industries and Ubiquitous Data Science .- Blockchain Security and Health Privacy Protection.- Green Technologies in Smart City Computation .- Self-supervised learning for multi-modal biomedical data .- Smart Internet of Things and Vehicular Communications .- Bioinformatics and Visualization of data .- Big Algorithms for Software Development .- Image Computing and Data Science Technology .- Natural Language Generation and Clean Data .- Natural language processing and knowledge discovery .- Text analytics and natural language processing .- Big Enterprise for Future Retail .- Data lakes and Open Source Bigdata Software .- Scalable Data Management Systems .- Future Advancements .- Conclusion.