Intelligent Data Engineering and Analytics

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This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applicatio...

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Beschreibung

This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 45 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines.
Highlights the latest research in the field of intelligent computing Gathers the outcomes of FICTA 2020, held at NIT Surathkal, India Offers a valuable resource for researchers and practitioners in academia and industry
Autorentext
Suresh Chandra Satapathy is a Professor , School of Computer Engg, KIIT Deemed to be University, Bhubaneswar, India. His research interest includes machine learning, data mining, swarm intelligence studies and their applications to engineering. He has more than 140 publications to his credit in various reputed international journals and conference Proceedings. He has edited many volumes from Springer AISC, LNEE, SIST etc. He is a senior member of IEEE and Life Member of Computer society of India. Prof. (Dr.) Yu-Dong Zhang received his Ph.D. degree from Southeast University, China, in 2010. From 2010 to 2013, he worked as post-doc and then as a research scientist at Columbia University, USA. He served as Professor at Nanjing Normal University from 2013 to 2017, and is currently a Full Professor at the University of Leicester, UK. His research interests include deep learning in communication and signal processing, and medical image processing. 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 150 quality publications in various international journals and conference proceedings. He has edited more than 25 volumes of conference proceedings with Springer Nature (AISC, SIST, LNEE, LNNS Series). He is associate editor of IJSE and IJRSDA.and presently EiC of IJNCR journal under IGI Global.Dr. Ritanjali Majhi is an Associate Professor at the School of Management, National Institute of Technology Karnataka, Surathkal, India. She is an expert on green marketing, big data analysis, consumer decision-making, time series prediction, AI applications in management (marketing effectiveness) and marketing analytics. She has more than 15 years of research experience, including in projects funded by the Indian government. She has published about 100 research papers in various peer-reviewed international journals and at conferences.
Inhalt
Classification of dry/wet snow using Sentinel-2 high spatial resolution optical data.- Potential of Robust Face Recognition from real-time CCTV video stream for Biometric Attendance using Convolutional Neural Network.- ATM Theft Investigation using Convolutional Neural Network.- "Classification and Prediction of Rice Crop Diseases using CNN & PNN".- SAGRU : A Stacked Autoencoder based Gated Recurrent Unit Approach to Intrusion Detection.- COMPARISON OF KNN AND SVM ALGORITHMS TO DETECT CLINICAL MASTITIS IN COWS USING INTERNET OF ANIMAL HEALTH THINGS.- Two-way face scrutinizing system for elimination of proxy attendances using deep learning.- Ontology driven Sentiment Analysis in Indian Healthcare Sector.- Segmentation of Nuclei in Microscopy Images across Varied Experimental Systems.- Transitional and parallel approach of PSO and SGO for solving optimization problems.