5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (eBook, PDF)

BDCC 2022
Redaktion: Haldorai, Anandakumar; Mohanram, Sudha; Ramu, Arulmurugan

• Format: PDF

Produktbeschreibung

This book features the proceedings of the 5^th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2022). The papers feature 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, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 228
• Erscheinungstermin: 5. Juni 2023
• Englisch
• ISBN-13: 9783031283246
• Artikelnr.: 68177708

Autorenporträt

Dr. Anandakumar Haldorai, Professor and Research Head in Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He has received his Master's in Software Engineering and PhD in Information and Communication Engineering from PSG College of Technology under, Anna University, Chennai. His research areas include Big Data, Cognitive Radio Networks, Mobile Communications and Networking Protocols. He has authored more than 148 research papers in reputed International Journals and IEEE conferences. He has authored 12 books and many book chapters with reputed publishers such as Springer and IGI. He is Editor in Chief of KeAi - Elsevier IJIN, Associate Editor - IEEE Access, Academic Editor - PLOS ONE, Academic Editor - Applied Computational Intelligence and Soft Computing, Academic Editor - Computational Intelligence and Neuroscience, Associate Editor- International Journal of Pervasive Computing and Communications, AreaEditor - EAI Energy Web and served as a reviewer for IEEE, IET, Springer, Inderscience and Elsevier journals. He is also the guest editor of many journals with Elsevier, Springer, Inderscience, etc. He has been the General Chair, Session Chair, and Panellist in several conferences. He is senior member of IEEE, IET, ACM and Fellow member of EAI research group. Dr. Arulmurugan Ramu is a Professor, CMR University, Bengaluru, India. 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 52 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 guidedmany Ph.D. research scholar under the area of Image Processing using Machine Learning. He is an Associate Editor of Inderscience IJISC journal. He is awarded as Best Young Faculty Award 2018 and nominated for Best Young Researcher Award (Male) by International Academic and Research Excellence Awards (IARE-2019). Dr. Sudha Mohanram has graduated from Government College of Engineering, Salem and has obtained her master's in engineering from Coimbatore Institute of Technology. She has completed her PhD in Electrical Engineering in Anna University Chennai in the year 2010. She started her teaching profession as a Lecturer in Government College of Technology, Coimbatore. She possesses 20 years of teaching experience. When she was about 13 years into teaching profession, her family founded Sri Eshwar College of Engineering in 2008. She has been playing the role of Secretary till 2011 and became the Principal of the institution in 2011. She has steered the institution to be oneof the most sought after institutions in Coimbatore, within a short span of time through its laudable achievement in Academic excellence and Placement. She has published many papers in leading journals. She is a member of IEEE and EAI research group.

Inhaltsangabe

Part I. Bigdata Services.- Chapter 1. Modelling Cognitive Scores for Alzheimer's Disease Progression Prediction Using Longitudinal MRI D.- Chapter 2. Various Physiological Methods to Identify Sleep Onset.- Chapter 3. IoT Adoption: Challenges among Small and Medium Enterprises.- Chapter 4. Performance evaluation of Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Cloud Service Provider.- Part II. Bigdata and Security.- Chapter 5. Online Covid-19 Risk Analysis System for Early Detection of Possible Infection.- Chapter 6. Application of Multi focused and Multi Modal image fusion using Guided Filter on Bio-Medical Images.- Chapter 7. Experimental Comparative Analysis on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on Aspect Level Sentiment Analysis.- Part III. Bigdata Emerging Applications.- Chapter 8. Data hiding in binary images for secret and secure communication using decision tree.- Chapter 9. Private and Secure Blockchain-based mechanism for an Online Voting System.- Chapter 10. AI-Enabled Pregnancy Risk Monitoring and Prediction: A Review.- Chapter 11. Finger Knuckle Print Recognition using Complex Conjugate Feature Vector.- Part IV. Bigdata and Technology.- Chapter 12. What Your Tweets Say about You - A case study of Extraversion and word usage.- Chapter 13. An Automated Cervical Cancer Detection mechanism using Pap smear images.- Chapter 14. A Systematic Literature Review on Data Freshness for reinforcing mutual authentication in Wireless Body Area Networks.- Chapter 15. Detection of Non-Technical Losses in Power Utilities Using Machine Learning.- Part V. Bigdata in Medical Applications.- Chapter 16. A Novel Real Time 3D Object Detection Network in Autonomous Driving using reformed rs-resnet network.- Chapter 17. Solar radiation prediction using the random forest regression algorithm.- Chapter 18. Vehicular Support System for User and Vehicle Accident Prevention.