This book provides a comprehensive coverage of machine learning techniques ranging from fundamental to advanced. The content addresses topics within the scope of the book from the ground up, providing readers with a trustworthy source of theoretical and technical learning content. The book emphasizes not only the theoretical features but also their practical and implementation aspects in real-world applications. These applications are crucial because they provide comprehensive experimental work that supports the validity of the offered approaches as well as clear instructions on how to apply such models in comparable and distinct settings and contexts. Furthermore, the chapters shed light on the problems and possibilities that researchers might use to direct their future research efforts. The book is beneficial for undergraduate and postgraduate students, researchers, and industry personnel.

Offers a diverse set of machine learning applications Is a trustworthy source of theoretical and technical learning content Establishes a foundation for future research

Autorentext

Dinesh K. Sharma is a Professor of Quantitative Methods and Computer Applications in the Department of Business, Management, and Accounting at the University of Maryland Eastern Shore, USA. He earned his MS in Mathematics, MS in Computer Science, Ph.D. in Operations Research, and a second Ph.D. in Management. Professor Sharma has over twenty-nine years of teaching experience, has served on several committees to supervise Ph.D. students, and acts as an external Ph.D. thesis examiner for several universities in India. Dr. Sharma's research interests include mathematical programming, artificial intelligence and machine learning techniques, supply chain management, healthcare management, and portfolio management. He has published over 250 refereed journal articles and conference proceedings, two forthcoming edited books (Springer and Taylor & Francis), and has also won sixteen best paper awards. Professor Sharma has collaborated on many funded research grants. Professor Sharma is the Editor of the Journal of Global Information Technology and the Review of Business and Technology Research, is on the editorial board of several journals, and is a paper reviewer for several additional journals and conferences. Additionally, he is a member of Decision Sciences, USA, and a life member of the Operational Research Society of India. He has served as a program chair and coordinator of several international conferences in many countries.

H.S. HOTA is a Professor of Computer Science in the Department of Computer Science and Application and Dean Faculty of Science at Atal Bihari Vajpayee University in Bilaspur, India. Prior to joining Atal Bihari Vajpayee University, he worked as an Associate Professor at the same university and as an Assistant Professor at Guru Ghasidas Central University, Bilaspur, India. He earned his Ph.D. from Guru Ghasidas Central University in Bilaspur, India. His research interests include Artificial Intelligence and Machine Learning, Business Intelligence, Information Security including Cloud Security, Data Mining, and Evolutionary Computing, and he has published over 75 refereed journal articles in reputable journals. In the conference proceedings, more than 60 articles have been published. He has also won five best paper awards at international conferences. He has also been to several countries and spoken at international conferences as a keynote speaker. He has three patents related to the application of machine learning in different domains in his credit, and he has published one book and three edited books are in the pipeline from internationally reputed publishers. He is also actively involved in various administrative activities of the university. He is also a member of the editorial boards of five international journals as well as a reviewer for many reputed journals of Elsevier and Inderscience. He is a member of many academic bodies, including the Computer Society of India (CSI) and the Indian Science Congress.

Aaron Rasheed Rababaah is an Associate Professor at the College of Engineering and Applied Sciences at the American University of Kuwait (Jan 2016 - Present). He held the following academic positions in the USA: Associate Professor and Assistant Professor at the University of Maryland Eastern Shore, Post-Doc researcher and Instructor at Tennessee State University, and Adjunct Instructor at Indiana University of South Bend. He finished a B.Sc. in Industrial Engineering from the University of Jordan, Jordan, a M.Sc. in Applied Computer Science from the University of Indiana, USA, and a Ph.D. in Computer Information and Systems Engineering from Tennessee State University, USA. He has 5 years of experience in IE and 12 years in teaching at the college level. He has a research interest in Machine Intelligence and Computing Education He has published 4 books, 90 refereed journal and conference papers, and 50 professional presentations. He was awarded several awards in education, research, andindustry. He has supervised, advised, and referred senior projects, master theses, doctoral dissertations, and a number of journals. He has been funded by the USA Department of Defense, the US Army, NASA, USA NSF, and the MD Department of Education.

Inhalt

1. Artificial Intelligence to Meet Sustainability and Strategic Goals: Evidence from India.- 2. Development of a Sustainable Smart City Measurement Framework: A Study of the City of Bandung, Indonesia.- 3. Drive Harmony: An AI-based safety assistant driving System.- 4. Effective Integration of Clustering and Classification or Regression Machine Learning Algorithm.- 5. Sentiment Analysis of Textual Reviews Using Deep Learning Techniques.- 6. Machine Learning Based Detection of Parkinson's Disease Using Voice and Handwriting Analysis.- 7. Machine Learning Algorithmic model for Pairs Trading.- 8. BT-CAP: Brain Tumor detection from MRI images using Capsule network with Template-based K-means clustering and Discrete Wavelet Transformation.- 9. Application of Neural Network based Techniques to Network Intrusion Detection.- 10. Novel Adaptation Algorithms and Schemes.- 11. A Hybrid Machine Learning Techniques for Intrusion Detection.- 12. Forecasting the Covid-19 End in India Using MachineLearning and Population Density Clustering.- 13. A Machine Learning based Analysis of Internet Addiction among Children and Adolescents during Covid-19 Lockdown.- 14. Neural Computing for the Pandemic's Biological Waste Management.- 15. Deep learning-based Speech recognition systems for Sylheti and Bishnupriya Manipuri languages.- 16. Classification of Diabetic Retinopathy Using Modified Convolutional Neural Network.- 17. Building Architectural Styles Classification Using Convolutional Neural Networks Models.- 18. Synthetic Data Generation for SAR Vehicular Signature Modeling and ATR Deep Learning Training.- 19. Automatic Target Recognition (ATR) in Multi-object SAR Images based on Simulated Data.- 20. Fruit detection using pre trained and customized model with Deep Learning approach.