Big Data, IoT, and Machine Learning (eBook, PDF)

Tools and Applications
Redaktion: Agrawal, Rashmi; Gupta, Neha; Paprzycki, Marcin

• Format: PDF

Produktbeschreibung

This book discusses the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. It describes essential technical knowledge, building blocks, processes, design principles, and implementation of big data tools and machine learning techniques and applications for IoT projects.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 337
• Erscheinungstermin: 29. Juli 2020
• Englisch
• ISBN-13: 9781000098280
• Artikelnr.: 59807844

Autorenporträt

Rashmi Agrawal is a PhD and UGC-NET qualified, with 18-plus years of experience in teaching and research. She is presently working as a Professor in the Department of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad. She has authored/co-authored more than 50 research papers, in various peer-reviewed national/international journals and conferences. She has also edited/authored books and chapters with national/international publishers (IGI global, Springer, Elsevier, CRC Press, Apple academic press). She has also obtained two patents in renewable energy. Currently she is guiding PhD scholars in Sentiment Analysis, Educational Data Mining, Internet of Things, Brain Computer Interface, Web Service Architecture and Natural language Processing. She is associated with various professional bodies in different capacities, a Senior Member of IEEE, a Life Member of Computer Society of India, IETA, ACM CSTA and a Senior Member of Science and Engineering Institute (SCIEI). Marcin Paprzycki is an Associate Professor at the Systems Research Institute, Polish Academy of Sciences. He has an MS from Adam Mickiewicz University in Poznä, Poland, a PhD from Southern Methodist University in Dallas, Texas, and a Doctor of Science from the Bulgarian Academy of Sciences. He is a senior member of IEEE, a senior member of ACM, a Senior Fulbright Lecturer, and an IEEE CS Distinguished Visitor. He has contributed to more than 450 publications and was invited to the program committees of over 500 international conferences. He is on the editorial boards of 15 journals. Neha Gupta has completed her PhD at Manav Rachna International University, and she has a total of 14-plus years of experience in teaching and research. She is a Life Member of ACM CSTA, Tech Republic and a Professional Member of IEEE. She has authored and co-authored 34 research papers in SCI/SCOPUS/peer reviewed journals (Scopus indexed) and IEEE/IET conference proceedings in the areas of Web Content Mining, Mobile Computing and Cloud Computing. She has published books with publishers such as IGI Global and Pacific Book International and has also authored book chapters with Elsevier, CRC Press and IGI Global USA. Her research interests include ICT in Rural Development, Web Content Mining, Cloud Computing, Data Mining and NoSQL Databases. She is a Technical Programme Committee (TPC) member in various conferences across the globe. She is an active reviewer for the International Journal of Computer and Information Technology and in various IEEE Conferences.

Inhaltsangabe

Section I: Applications of Machine Learning 1. Machine Learning Classifiers
2. Dimension Reduction Techniques 3. Reviews Analysis of Apple Store
Applications Using Supervised Machine Learning 4. Machine Learning for
Biomedical and Health Informatics 5. Meta-Heuristic Algorithms: A
Concentration on the Applications in Text Mining 6. Optimizing Text Data in
Deep Learning: An Experimental Approach Section II: Big Data, Cloud and
Internet of Things 7. Latest Data and Analytics Technology Trends That Will
Change Business Perspectives 8. A Proposal Based on Discrete Events for
Improvement of the Transmission Channels in Cloud Environments and Big Data
9. Heterogeneous Data Fusion for Healthcare Monitoring: A Survey 10.
Discriminative and Generative Model Learning for Video Object Tracking 11.
Feature, Technology, Application, and Challenges of Internet of Things 12.
Analytical Approach to Sustainable Smart City Using IoT and Machine
Learning 13. Traffic Flow Prediction with Convolutional Neural Network
Accelerated by Spark Distributed Cluster