Applied Soft Computing Techniques: Theoretical Principles and Practical Applications

Soft computing techniques have the ability to handle complex, uncertain, and imprecise information to create usable solutions to convoluted problems, or those just too time-consuming to solve with current hardware. This new book details the use and applications of soft computing technology in several fields, exploring the use of these techniques in biomedical applications, communication technologies, data analytics and applications, image processing and natural language processing.

The chapters in the section on biomedical applications explore soft computing techniques for cancer data analysis, depression and mental health analysis, heart disease detection, etc. The editors go on to discuss soft computing in communication systems, looking at graphs, design processes, and mapping techniques, as well as the integration of IoT devices, drone technology, etc. The volume also details how soft computing methodologies can assist in tackling the obstacles associated with signal processing, network optimization, quality of service and beyond.

Several chapters discuss the use of soft computing techniques in data compression, handling of large-scaled heterogenous databases, visualization techniques, etc. Applications of soft computing in image processing are also discussed and cover human face recognition, casualty detection, traffic sign recognition, and predicting soil features using satellite imagery. Soft computing techniques in natural language processing consider text-to-speech signal conversion, NLP and speech recognition, speech emotion recognition and more.

This volume will help to facilitate the amalgamation of theoretical principles and practical applications, bringing forth possible solutions to complex problems in various domains. The book is a welcome resource for researchers, students, professionals, and even for individuals looking for knowledge on soft computing.

Applied Soft Computing Techniques: Theoretical Principles and Practical Applications will help to facilitate the amalgamation of theoretical principles and practical applications, bringing forth possible solutions to complex problems in various domains. The book is a welcome resource for researchers, students, professionals, and even for individuals looking for knowledge on soft computing.

Samarjeet Borah, PhD is Professor and Head in the Department of Computer Applications at SMIT, Sikkim Manipal University, India. He has carried out research projects, published research papers in journals and conferences, and organized workshops and conferences. He is Editor-in-Chief of the book series Research Notes on Computing and Communication Sciences. Dr. Borah has served as a keynote as well as invited speaker at several international conferences. His areas of research are data mining, information security, data science, and machine learning.

Ratna Raja Kumar Jambi, PhD is Principal at the Genba Sopanrao Moze College of Engineering, Balewadi, Pune, India. He has 17 years of teaching and research work. He has patents at both national and international levels and has published papers and book chapters on artificial intelligence and machine learning. He has organized national and international conferences. He received an Innovative Leader Award from the World Education Summit & Awards in 2019 in New Delhi.

Sharifah Sakinah Syed Ahmad, PhD is currently working as Associate Professor in the Department of Intelligent Computing & Analytics (ICA), Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM). Her current research work is on the granular fuzzy rule-based system, evolutionary method, modeling, and data science. Dr. Ahmad has been a member of numerous program committees at national and international conferences in intelligent systems and soft computing, where she is involved in organizing and reviewing activities. She is an editorial board member of the IEEE Transactions on Systems, Man, and Cybernetics: Systems and a member of the Association for Computing Machinery (ACM). She has co-authored numerous journal publications, conference articles, and book chapters. She has also received several grants from various funding agencies, including the Ministry of Education, Malaysia, and the Ministry of Science, Technology, and Innovation, Malaysia. Dr. Ahmad has 15 years of experience in teaching. She was also involved in conducting several workshops related to improving data science and modeling.

Mahendra Prabhakar Deore, PhD is an Assistant Professor of Computer Engineering at MKSSS’s Cummins College of Engineering for Women, Pune, India. With over 16 years of dedicated teaching experience at undergraduate level, Dr. Deore has authored and published more than 20 SCI- and SCOPUS-indexed research papers. His primary areas of interest encompass big data, security, computer networks, and machine learning.