This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25 - 26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Evolution in Computational Intelligence. The conference papers included herein presents both theoretical as well as practical aspects of different areas like ANN and genetic algorithms, human-computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, multi-agent systems, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines.

Autorentext

Vikrant Bhateja is Associate Professor, Department of ECE in SRMGPC, Lucknow. His areas of research include digital image and video processing, computer vision, medical imaging, machine learning, pattern analysis, and recognition. He has around 160 quality publications in various international journals and conference proceedings. He is a associate editor of IJSE and IJACI. He has edited more than 30 volumes of conference proceedings with Springer Nature and is presently EiC of IGI Global: IJNCR journal. Dr. Jinshan Tang is currently a professor in the College of Computing at Michigan Technological University. He received his Ph.D. degree from Beijing University of Posts and Telecommunications and postdoctoral training at Harvard Medical School and the National Institute of Health. His research covers wide areas related to image processing and imaging technologies. His specific research interests include machine learning, biomedical image analysis and biomedical imaging, biometrics, computer vision, and image understanding. He has obtained more than three million US dollars grants as a PI or Co-PI. He has published more than 110 refereed journals and conference papers. He has also served as a committee member at various international conferences. He is a senior member of IEEE and a co-chair of the Technical Committee on Information Assurance and Intelligent Multimedia-Mobile Communications, IEEE SMC Society. He serves/served as a editors or guest editors of more than 10 journals. Suresh Chandra Satapathy is Ph. D in Computer Science, currently working as Professor and at KIIT (Deemed to be University), Bhubaneshwar, Odisha, India. He held the position of the National Chairman Div-V (Educational and Research) of Computer Society of India and is also a senior member of IEEE. He has been instrumental in organizing more than 20 International Conferences in India as Organizing Chair and edited more than 30 book volumes from Springer LNCS, AISC, LNEE, and SIST Series as Corresponding Editor. He is quite active in research in the areas of swarm intelligence, machine learning, data mining. He has developed a new optimization algorithm known as social group optimization (SGO) published in Springer Journal. He has delivered a number of Keynote address and Tutorials in his areas of expertise in various events in India. He has more than 100 publications in reputed journals and conference proceedings. He is in Editorial Board of IGI Global, Inderscience, Growing Science journals and also Guest Editor for Arabian Journal of Science and Engineering published by Springer. Peter Peer is a full professor of computer science at the University of Ljubljana, Slovenia, where he heads the Computer Vision Laboratory, coordinates the double degree study program with the Kyungpook National University, South Korea, and serves as a vice-dean for economic affairs. He received his doctoral degree in computer science from the University of Ljubljana in 2003. Within his post-doctorate, he was an invited researcher at CEIT, San Sebastian, Spain. His research interests focus on biometrics and computer vision. He participated in several national and EU-funded R&D projects and published more than 100 research papers in leading international peer reviewed journals and conferences. He is co-organizer of the Unconstrained Ear Recognition Challenge and Sclera Segmentation Benchmarking Competition. He serves as Associated Editor of IEEE Access and IET Biometrics. He is a member of the EAB, IAPR, and IEEE. Dr. Ranjita Das is currently serving as Head and Assistant Professor, Department of Computer Science and Engineering, National Institute of Technology Mizoram. She has joined the National Institute of Technology Mizoram in the year 2011. She did her PhD. from NIT Mizoram, M. Tech from Tezpur University, and B. Tech. from NIT Agartala. She has over 10 years of teaching experience. Her research was in the areas of pattern recognition, information retrieval, computational biology, and machine learning. She has published 20 journal and international conference papers in various journals with SCI impact factors, SCOPUS index, and also in conference proceedings of Springer, IEEE, etc. She has two ongoing sponsored projects funded by DBT and SERB. Under her supervision, presently ten research scholars are doing research work. She was recipient of best paper awards in the conferences IEEE-INDICON-2017, ICACCP-2019, IC4E-2020.

Inhalt

A Comprehensive Study of Page-rank algorithm.- Live Emotion Verifier for Chat Applications using Emotional Intelligence.- Text to Speech conversion of Handwritten Kannada Words using various Machine Learning Models.- An Improved approach for automated essay scoring with LSTM and Word Embedding.- RoMaPla: Using t-test for Evaluating Robustness of Marathi Plagiarism Checker (MaPla).- To Analyse the impact of water scarcity in developing countries using Machine Learning.- Deep Learning Algorithms for Object Detection- A Study.- A Novel Multiblock Region Based Arnold Transformation for Image Watermarking combined with DWT-PSO Technique.- Automated Evaluation of SQL Queries: Eval_SQL.- Unsupervised Feature Selection Approaches for Medical Dataset using Soft Computing Techniques.