Optimization, Machine Learning and Fuzzy Logic : Theory, Algorithms and Applications

Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.

Mzili Toufik holds the position of Assistant Professor in the Department of Computer Science at the Faculty of Science, Chouaib Doukkali University. Recognized as a distinguished researcher, he has made noteworthy contributions to the fields of metaheuristics, optimization, and scheduling problems. His scholarly impact is evident through numerous publications in esteemed Q1 journals. Additionally, Dr. Mzili Toufik has demonstrated his expertise as a peer reviewer for several prestigious journals, showcasing his commitment to advancing the academic discourse in his field.

Adarsh Arya competent professional with nearly 20 years of experience in Research and Development / Teaching chemical engineering graduates and post graduates. • Exercises judgment within generally defined practices in selecting methods & techniques for obtaining teaching solutions; ensuring compliance to quality measures and maintenance requisite documentation as well as records • Skills in managing the administrative activities entailing student management, faculty appraisal/training and upholding of the institution's motto • Proficiency in teaching Oil and Gas Pipeline and core chemical engineering subjects to Chemical Engineering Graduate and post graduate students. • Experience in developing curriculum to accommodate different learning styles & maximize students' comprehension • Received research awards • Exposure in working with T.A.N.A.P. Gas Pipeline Project at Worley Parsons. • Exposure in planning and organizing day-to-day research activities and resolving the procedural problem as appropriate to the timely accomplishment of research objectives • An effective leader with proven skills in leading teams during the project phase, training & guiding team members and enabling knowledge sharing among the team

Dr. Dragan Pamucar is a Professor at the University of Belgrade, Faculty of Organizational Sciences. Dr. Pamucar received a Ph.D. in Applied Mathematics specializing in multi-criteria modeling and soft computing techniques from the University of Defence in Belgrade, Serbia, in 2013 and an MSc degree from the Faculty of Transport and Traffic Engineering in Belgrade in 2009. His research interest is in Computational Intelligence, Multi-criteria decision-making problems, Neuro-fuzzy systems, fuzzy, rough and intuitionistic fuzzy set theory, and neutrosophic theory. Application areas include a wide range of logistics and engineering problems. Dr. Pamucar has five books and over 300 research papers published in SCI indexed International Journals, including Experts Systems with Applications, Applied Soft Computing, Soft Computing, computational Intelligence, Computers and Industrial Engineering, Engineering Applications of Artificial Intelligence, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions of Fuzzy Systems, IEEE Transactions on Transportation Electrification, Information Sciences and research and so on, and many more. According to Scopus and Stanford University, he is among the World top 2% of scientists as of 2021. According to WoS and Clarivate, he is among the top 1% of highly cited researchers.

Momina Shaheen Fellow , Higher Education Academy (UK), is Lecturer in Computing, in University of Roehampton, London, United Kingdom. She is a Ph.D Scholar with 15+ journal publications and 4 conference publications in Computing and cutting edge technologies. She earned her master’s in software engineering from Bahria University Islamabad Campus in 2016. She has more than 6 years of experience in research and academia. She has supervised and initiated a number of projects in her career. She has lead different project in the area of Machine Learning, Data Science, Artificial Intelligence, Internet of things, Cognitive Sciences and Distributed Systems.