Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough

Latest Trends in AI
Herausgegeben:Gunjan, Vinit Kumar; Zurada, Jacek M.; Raman, Balasubramanian; Gangadharan, G. R.

• Broschiertes Buch

Produktbeschreibung

This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.

Produktdetails

• Studies in Computational Intelligence 885
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-38447-0
• 1st ed. 2020
• Seitenzahl: 256
• Erscheinungstermin: 5. Februar 2021
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 394g
• ISBN-13: 9783030384470
• ISBN-10: 3030384470
• Artikelnr.: 60875333

Autorenporträt

Vinit Kumar Gunjan is an Associate Professor at the Department of Computer Science & Engineering at CMR Institute of Technology, Hyderabad (Affiliated to Jawaharlal Nehru Technological University, Hyderabad). He has published research papers in IEEE, Elsevier & Springer conferences, authored several books and edited volumes of Springer series, most of which are indexed in the SCOPUS database. In 2016, he received the prestigious Early Career Research Award from the Science Engineering Research Board, Department of Science & Technology, Government of India. He was a senior member of IEEE, an active volunteer in the IEEE Hyderabad section, and was the treasurer, secretary & chairman of the IEEE Young Professionals Affinity Group & IEEE Computer Society. He has been involved in organizing several technical & non-technical workshops, seminars & conferences, where he had the honour of working with top IEEE leaders. He was received the best IEEE Young Professional award in 2017 from the IEEE Hyderabad Section.  Dr. Jacek Zurada (SM'85, F'96, LF'14) is a Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, Kentucky, USA, where he served as the Department Chair and a Distinguished University Scholar. He was a Professor at Princeton University, Northeastern University, Auburn University, the National University of Singapore, Nanyang Technological University in Singapore, Chinese University of Hong Kong, the University of Chile, Santiago, Toyohashi University of Technology, Japan, the University of Stellenbosch, South Africa, the Marie Curie University, Paris, France, and was a Postdoctoral Fellow at the Swiss Federal Institute of Technology, Zurich.   Balasubramanian Raman has been an Associate Professor at the Department of Computer Science and Engineering at Indian Institute of Technology Roorkee since 2013. He obtained his M.Sc. degree in Mathematics from Madras Christian College (University of Madras) in 1996 and Ph.D. from the Indian Institute of Technology Madras in 2001. He was a Postdoctoral Fellow at the University of Missouri Columbia, USA, in 2001-2002 and a Postdoctoral Associate at Rutgers, the State University of New Jersey, USA, in 2002-2003. He joined the Department of Mathematics at the Indian Institute of Technology Roorkee as a Lecturer in 2004 and became an Assistant Professor in 2006 and Associate Professor in 2012. He was a Visiting Professor and a member of the Computer Vision and Sensing Systems Laboratory at the Department of Electrical and Computer Engineering at the University of Windsor, Canada, in May-August 2009. He has published more than 190 papers in leading journals and conferences. His area of research includes vision geometry, digital watermarking using mathematical transformations, image fusion, biometrics and secure image transmission over wireless channels, content-basedimage retrieval and hyperspectral imaging.   G.R. Gangadharan is an Associate Professor at the National Institute of Technology (NIT), Tiruchirappalli. His research interests are mainly located at the interface between technological and business perspectives. He holds a Ph.D. degree in Information and Communication Technology from the University of Trento, Italy, and the European University Association. He is a senior member of the IEEE and ACM.

Inhaltsangabe

Face Recognition using Raspberry PI.- Features Extraction for Network Intrusion Detection using Genetic Algorithm(GA).- Chemical Sensing through Cogno-Monitoring System for Air Quality Evaluation.- 3 DOF Autonomous Control Analysis of an Quadcopter Using Artificial Neural Network.- Cognitive Demand Forecasting with Novel Features using Word2Vec and Session of the Day.- A Curvelet Transformer Based Computationally Efficient Speech Enhancement for Kalman Filter.- Dexterous Trashbot.- Automated Question Generation and Answer Verification using Visual Data.- Comprehensive Survey on Deep Learning Approaches in Predictive Business Process Monitoring.- Machine Learning Based Risk-Adaptive Access Control System to Identify Genuineness of the Requester.- An Approach to End to End Anonymity.- PHT and KELM Based Face Recognition.- Link Failure Detection in MANET: A Survey.- Review of Low Power Techniques for Neural Recording Applications.- Machine Learning Techniques for Thyroid Disease Diagnosis:A Systematic Review.- Heuristic Approach to Evaluate the Performance of Optimization Algorithms in VLSI Floor Planning for ASIC design.- Enhancement in Teaching Quality Methodology by Predicting Attendance using Machine Learning Technique.- Improvement in Extended Object tracking with the Vision-Based Algorithm.