Data Intelligence and Cognitive Informatics (eBook, PDF)

Proceedings of ICDICI 2022

• Format: PDF

Produktbeschreibung

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 6–7, 2022. This book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems.

Produktdetails

• Verlag: Springer Nature Singapore
• Erscheinungstermin: 2. Dezember 2022
• Englisch
• ISBN-13: 9789811960048
• Artikelnr.: 66798017

Autorenporträt

Jeena Jacob is working as Professor in Computer Science and Engineering department at GITAM University, Bangalore, India. She actively participates on the development of the research field by conducting international conferences, workshops and seminars. She has published many articles in referred journals. She has guest edited an issue for International Journal of Mobile Learning and Organization. Her research interests include mobile learning and computing. Selvanayaki Kolandapalayam Shanmugam holds a Bachelor’s degree in Mathematics and Masters in Computer Applications from Bharathiar University, and her Master of Philosophy in Computer Science from Bharathidasan University, and a Ph.D. in Computer Science from Anna University. She takes various positions as Teaching Faculty, Research Advisor and Project -Coordinator in the field of academics from 2002 in various reputed institutions. She has related to the IT industry for more than five years by taking her prestigious role as Business Analyst Consultant. Her primary research interests are in the application of computing and information technologies to problems which impacts societal benefits. Ivan Izonin graduated from Lviv Polytechnic National University in 2011 (M.Sc. in Computer Sciences) and Ivan Franko National University of Lviv in 2012 (M.Sc. in Economic Cybernetics). He got his Ph.D. in Artificial Intelligence in 2016. He is currently working as Assistant at Publishing Information Technologies Department, Lviv Polytechnic National University, Lviv, Ukraine. He has published more than 80 publications, including eight patents for inventions and one tutorial. His major research interests are computational intelligence; high-speed neural-like systems; non-iterative machine learning algorithms. Dr. Izonin is participating in the developing two international Erazmus+ projects – MASTIS and DocHub. He is Technical Committee Member of several international conferences.

Inhaltsangabe

Modelling Muller Lyer Illusion Using Information Geometry.- Building up a Categorical Sentiment Dictionary for Tourism Destination Policy Evaluation.- Statistical Analysis of Stress Prediction from Speech Signatures.- DermoCare.AI: A Skin Lesion Detection System using Deep Learning Concepts.- Analysis of Phishing Base Problems using Random Forest Features Selection Techniques and Machine Learning Classifiers.- Cost Prediction for Online Home-Based Application Services by Using Linear Regression Techniques.- Convolutional neural network-based intrusion detection system and predict the DDoS attack.- BERT Transformer based Fake News Detection in Twitter Social Media.- The Facial Expression Recognition Using Deep Neural Network.- New IoT Based Portable Microscopic Somatic Cell Count Analysis.

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Modelling Muller Lyer Illusion Using Information Geometry.- Building up a Categorical Sentiment Dictionary for Tourism Destination Policy Evaluation.- Statistical Analysis of Stress Prediction from Speech Signatures.- DermoCare.AI: A Skin Lesion Detection System using Deep Learning Concepts.- Analysis of Phishing Base Problems using Random Forest Features Selection Techniques and Machine Learning Classifiers.- Cost Prediction for Online Home-Based Application Services by Using Linear Regression Techniques.- Convolutional neural network-based intrusion detection system and predict the DDoS attack.- BERT Transformer based Fake News Detection in Twitter Social Media.- The Facial Expression Recognition Using Deep Neural Network.- New IoT Based Portable Microscopic Somatic Cell Count Analysis.