Cognitive Big Data Intelligence with a Metaheuristic Approach

1st Edition - November 9, 2021
Editors: Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Arun Kumar Sangaiah, Gyoo-Soo Chae
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 1 1 7 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 1 1 8 - 3

Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies… Read more

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Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity.

This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. A discourse on metaheuristics techniques for solving clustering and semisupervised learning models
1. Introduction
2. Overview of clustering
3. Conclusion
Chapter 2. Metaheuristics in classification, clustering, and frequent pattern mining
1. Introduction
2. Metaheuristics in classification
3. Metaheuristics in clustering
4. Metaheuristics in frequent pattern mining
5. Conclusion
Chapter 3. Impacts of metaheuristic and swarm intelligence approach in optimization
1. Introduction
2. Concepts of Metaheuristic
3. Metaheuristic techniques
4. Swarm intelligence techniques
5. Impacts of metaheuristic and swarm intelligence approach in optimization
6. Conclusion
Chapter 4. A perspective depiction of heuristics in virtual reality
1. Introduction to virtual reality
2. Heuristics in brief
3. Virtual reality–enabled case studies
4. Performance evaluation and discussion
5. Conclusion
Chapter 5. A heuristic approach of web users decision-making using deep learning models
1. Introduction
2. Analysis of user online behavior using deep learning models
3. Greedy algorithm as the heuristic
4. Background study
5. Description of the dataset
6. Implementation and discussion
7. Conclusion
Chapter 6. Inertia weight strategies for task allocation using metaheuristic algorithm
1. Introduction
2. Related work
3. Standard PSO
4. Model of task allocation in VM
5. Inertia weight strategy
6. Performance evaluation
7. Conclusion and future work
Chapter 7. Big data classification with IoT-based application for e-health care
1. Introduction
2. State of the art
3. Big data in health care
4. Classification techniques
5. IoT-based smart biomedical data acquisition and processing system
6. Multiagent system for biomedical data processing
7. Detection of cardiac abnormalities
8. Results and discussion
9. Conclusion
Chapter 8. Study of bio-inspired neural networks for the prediction of liquid flow in a process control system
1. Introduction
2. Related work
3. Experimental setup
4. Preliminary details of the algorithm
5. Proposed model
6. Results and discussion
7. Conclusions and future work
Chapter 9. Affordable energy-intensive routing using metaheuristics
1. Introduction
2. Literature survey
3. Problem description
4. Routing
5. Routing algorithms
6. Routing table
7. Metaheuristics
8. Metaheuristics for efficient routing
9. Proposed solution using metaheuristics
10. Conclusion
Chapter 10. Semantic segmentation for self-driving cars using deep learning: a survey
1. Introduction
2. Semantic segmentation for autonomous driving
3. Deep learning
4. Related work
5. Experimental results
6. Conclusion
Chapter 11. Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms
1. Introduction
2. Cognitive computing technologies for E-health care
3. Cognitive big data analytics for E-health care
4. Need for cognitive big data analytics in E-health care
5. Advantages of cognitive big data analytics in E-health care
6. Challenges of cognitive big data analytics in E-health care
7. Metaheuristic approach for optimization of cognitive big data healthcare
8. Cognitive big data analytics use cases in E-health care
9. Future of cognitive big data analytics in E-health care
10. Market analysis of cognitive big data analytics in E-health care
11. Cognitive big data players in E-health care
Chapter 12. Multicriteria recommender system using different approaches
1. Introduction
2. Related work
3. Working principle
4. Proposed approaches
5. Experimental data analysis
6. Result
7. Conclusion
Chapter 13. Optimization-based energy-efficient routing scheme for wireless body area network
1. Introduction
2. Related work
3. Case study on an energy-efficient hybrid C-means donkey-smuggler optimization-based routing technique for a wireless sensor network
4. Analysis of the previous approach
5. Conclusion
Chapter 14. Livestock health monitoring using a smart IoT-enabled neural network recognition system
1. Introduction
2. System architecture
3. Recognition of a diseased bird by the central monitoring unit using Raspberry Pi
4. Results and discussion
5. Conclusion
Chapter 15. Preserving healthcare data: from traditional encryption to cognitive deep learning perspective
1. Introduction
2. Related works
3. Encryption algorithms
4. Performance evaluation
5. Future challenges of cognitive encryption models in healthcare
6. Conclusion
Index

Sushruta Mishra

Dr. Sushruta Mishra is working as an Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He pursued his M.Tech from IIIT, Bhubaneswar in 2012 and has completed his Ph.D in Computer Science from KIIT University, Bhubaneswar, Odisha, India in 2017. Dr. Mishra has more than 7 years of teaching experience in various educational institutions. He has handled many subjects such as Computer networks, Data mining, Software engineering, Machine learning etc during his academic experience. His research interest includes Image processing, Machine Learning, Internet of Things and Cognitive computing. He has published several research articles in reputed scopus indexed International Journals, Edited books and Conferences.

Affiliations and expertise

Assistant Professor, School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

Hrudaya Kumar Tripathy

Dr. Hrudaya Kumar Tripathy is presently working as Associate Professor in School of Computer Engineering at KIIT University, Bhubaneswar, Odisha, India and Program Head of M.Tech(CSE) courses. He has received M.Tech degree in CSE from IIT Guwahati in 2006, and obtained Ph.D degree in Computer Science from Berhampur University, Berhampur, Odisha, India in Computer Science in 2010. Dr. Tripathy had been a visiting faculty in Asia Pacific University, Kuala Lumpur, Malaysia and University Utara Malaysia, Sintok, Malaysia. He is having 20 years of teaching experience with Post Doctorate from Malaysia. He has handled various subjects like Software engineering, Machine learning, Business intelligence etc. His research interest includes Neural Networks, Pattern Recognition, Software Engineering, Machine learning and Big Data. He has published several research papers in various journals and conferences. Besides, academic experience he had been actively involved in various administrative responsibilities in his previous job positions. Dr. Tripathy is a senior member of IEEE, life member of CSI & having membership in other different professional bodies such as IET, IACSIT, IAENG.Dr. Hrudaya Kumar Tripathy is presently working as Associate Professor in School of Computer Engineering at KIIT University, Bhubaneswar, Odisha, India and Program Head of M.Tech(CSE) courses. He has received M.Tech degree in CSE from IIT Guwahati in 2006, and obtained Ph.D degree in Computer Science from Berhampur University, Berhampur, Odisha, India in Computer Science in 2010. Dr. Tripathy had been a visiting faculty in Asia Pacific University, Kuala Lumpur, Malaysia and University Utara Malaysia, Sintok, Malaysia. He is having 20 years of teaching experience with Post Doctorate from Malaysia. He has handled various subjects like Software engineering, Machine learning, Business intelligence etc. His research interest includes Neural Networks, Pattern Recognition, Software Engineering, Machine learning and Big Data. He has published several research papers in various journals and conferences. Besides, academic experience he had been actively involved in various administrative responsibilities in his previous job positions. Dr. Tripathy is a senior member of IEEE, life member of CSI & having membership in other different professional bodies such as IET, IACSIT, IAENG.

Affiliations and expertise

Associate Professor, School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

Pradeep Kumar Mallick

Dr. Pradeep Kumar Mallick is currently working as Senior Associate Professor in the School of Computer Engineering , Kalinga Institute of Industrial technology (KIIT) Deemed to be University, Odisha, India .He has also served as Professor and Head Department of Computer Science and Engineering , Vignana Bharathi Institute of Technology, Hyderabad . He has completed his Post Doctoral Fellow (PDF) in Kongju National University South Korea , PhD from Siksha Ó’ Anusandhan University, M. Tech. (CSE) from Biju Patnaik University of Technology (BPUT), and MCA from Fakir Mohan University Balasore, India. Besides academics, he is also involved various administrative activities, Member of Board of Studies, Member of Doctoral Research Evaluation Committee, Admission Committee etc. His area of research includes Algorithm Design and Analysis, and Data Mining, Image Processing, Soft Computing, and Machine Learning. Now he is the editorial member of Korean Convergence Society for SMB .He has published 9 books and more than 70 research papers in National and international journals and conference proceedings in his credit

Affiliations and expertise

Senior Associate Professor, School of Computer Engineering, Kalinga Institute of Industrial technology (KIIT) Deemed to be University, Odisha, India

Arun Kumar Sangaiah

Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.

Affiliations and expertise

National Yunlin University of Science and Technology, Taiwan

Gyoo-Soo Chae

Professor Gyoo-Soo Chae works in the Division of ICT at Baekseok University, Cheonan in South Korea.

Affiliations and expertise

Professor, Division of ICT, Baekseok University, Cheonan, South Korea

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Cognitive Big Data Intelligence with a Metaheuristic Approach

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Sushruta Mishra

Hrudaya Kumar Tripathy

Pradeep Kumar Mallick

Arun Kumar Sangaiah

Gyoo-Soo Chae

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