This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Tabella dei contenuti
Use of Artificial Neural Network for Abnormality Detection in Medical Images.- Deep Learning Techniques for Crime Hotspot Detection.- Optimization Techniques for Machine Learning.- A Package Including Pre-Processing, Feature Extraction, Feature Reduction, and Classification for MRI Classification.- Predictive Analysis of Lake Water Quality Using an Evolutionary Algorithm.- A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swam Optimization.- A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure using Fuzzy C-Means and Cohort Intelligence.- Development of Algorithm for Spatial Modelling of Climate Data for Agriculture Management for the Semi-Arid Area of Maharashtra in India.- A Survey on Human Group Activity Recognition by Analyzing Person Action from Video Sequences using Machine Learning Techniques.- Artificial Intelligence in Journalism: A Boon or Bane?.- A Perspective of Artificial Intelligence in Public Relations: The Way Forward.- Roulette Wheel Selection Based Computational Intelligence Technique to Design an Efficient Transmission Policy for Energy Harvesting Sensors.
Circa l’autore
Anand J. Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore; an M.S. in AI from the University of Regina, Canada; and Bachelor of Engineering from Shivaji University, India. He worked as a Research Fellow on a cross-border supply-chain disruption project at Odette School of Business, University of Windsor, Canada. Currently, he is the Head and an Associate Professor at the Symbiosis Institute of Technology, Pune, India. His research interests include optimization algorithms, multiobjective optimization, multiagent systems, complex systems, swarm optimization, game theory, and self-organizing systems. He is the founder and Chairman of the OAT Research Lab. Anand has published over 40 research papers in peer-reviewed journals and conferences as well as two books.
Suresh Chandra Satapathy is a Professor at the School of Computer Engineering, KIIT, Odisha, India. Previously, he was a Professor and the Head of the Department of CSE at ANITS, AP, India. He received his Ph.D. in CSE from JNTU, Hyderabad, and M.Tech. in CSE from the NIT, Odisha. He has more than 27 years of teaching and research experience. His research interests include machine learning, data mining, swarm intelligence and applications. He has published more than 98 papers in respected journals and conferences and has edited numerous volumes for Springer AISC and LNCS. In addition to serving on the editorial board of several journals, he is a senior member of the IEEE and a life member of the Computer Society of India, where he is the National Chairman of Division-V (Education and Research).
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