Big Data, IoT, and Machine Learning

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features - Addresses the complete data science technologies workflow - Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning - Covers data processing and security solutions in IoT and Big Data applications - Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems - Presents security issues and data migration techniques of NoSQL databases

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 Poznan, 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.

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