Hybrid Computational Intelligence
Challenges and Applications
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies,...
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Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development.
Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.
Bibliographische Angaben
- Herausgegeben:Bhattacharyya, Siddhartha; Snasel, Vaclav; Gupta, Deepak; Khanna, Ashish
- Verlag: Academic Press
- EAN: 9780128186992
Autoren-Porträt
Siddhartha Bhattacharyya is currently the principal of Rajnagar Mahavidyalaya, Birbhum, India. He was a professor at CHRIST (Deemed to be University), Bangalore, India, and also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He has served VSB Technical University of Ostrava, Czech Republic as a Senior Research Scientist. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He has co-authored six books, co-edited 75 books, and has more than 300 research publications in international journals and conference proceedings to his credit.Vaclav Snasel's research and development experience includes over 25 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, multidimensional data indexing, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, data mining and applications to various real-world problems. He has authored/co-authored several refereed journal/conference papers and book chapters. In 2003 he became a visiting scientist in the Institute of Computer Science, Academy of Sciences of the Czech Republic. Since 2003 he has been vice-dean for Research and Science at Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech Republic. He has been a full professor since 2006. Before turning into a full time academic, he was working with industrial companies where he was involved in different industrial research and development projects for nearly 8 years. He received Ph.D. degree in
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Algebra and Geometry from Masaryk University, Brno, Czech Republic and a Master of Science degree from Palacky University, Olomouc, Czech Republic.
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Inhaltsverzeichnis zu „Hybrid Computational Intelligence “
1. Application and Techniques of Opinion Mining 2. Influence of Big Data in Smart Tourism 3. Deep learning and its applications for Content-Based Video Retrieval 4. A Computationally Intelligent agent for detecting fake news using Generative Adversarial networks (GANs) 5. Detection of Primary Glaucoma using ANN with the help of Back Propagation algo in Bio-medical Image Processing 6. Hybrid Computational Intelligence with Applications to BioMedical Image and HealthCare 7. Application of Hybrid Computational Intelligence in Healthcare 8. Utility System for premature plant disease detection using machine learning 9. Artificial Intelligence-Based Computational Fluid Dynamics (CFD) Approaches 10. Real-Time Video Segmentation using a Vague Adaptive Threshold
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