Advanced Machine Intelligence and Signal Processing (eBook, PDF)

Redaktion: Gupta, Deepak; Agarwal, Sonali; Prasad, Mukesh; Sambyo, Koj

• Format: PDF

Produktbeschreibung

This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 876
• Erscheinungstermin: 25. Juni 2022
• Englisch
• ISBN-13: 9789811908408
• Artikelnr.: 64232366

Autorenporträt

Dr. Deepak Gupta is Assistant Professor at the Department of Computer Science and Engineering of National Institute of Technology, Arunachal Pradesh. He received the Ph.D. degree in Computer Science and Engineering from the Jawaharlal Nehru University, New Delhi, India. His research interests include support vector machines, ELM, RVFL, KRR, and other machine learning techniques. He has published over 40 referred journal and conference papers of international repute. His publications have around 495 citations with an h-index of 12 and i10-index of 16 (Google Scholar, 24/07/2021). He is Recipient of the 2017 SERB-Early Career Research Award in Engineering Sciences which is the prestigious award of India at early career level. He is Senior Member of IEEE and currently Active Member of many scientific societies like IEEE, SMC, CIS, CSI, and many more. He is currently Member of an editorial review board of applied intelligence. He has also served as Reviewer of many scientific journals and various national and international conferences. He is currently Principal Investigator (PI) or Co-PI of 02 major research projects funded by the Science & Engineering Research Board (SERB), Government of India.  Dr. Koj Sambyo received his Ph.D. in Computer Science and Engineering from National Institute of Technology, Arunachal Pradesh, in 2017 and M.Tech. degree in Computer Science and Engineering from Rajiv Gandhi University, Arunachal Pradesh, India, in 2011. Currently, he is working as Assistant Professor in the Department of Computer Science and Engineering in National Institute of Technology, Arunachal Pradesh. His research activities mainly focused on cloud computing and natural language processing. He is Author of numerous international refereed journals and in referred international conferences. Dr. Mukesh Prasad is Senior Lecturer at the School of Computer Science in the Faculty of Engineering and IT at UTS who has made substantial contributions to the fields of machine learning, artificial intelligence, and the Internet of things. Mukesh's research interests include also big data, computer vision, brain computer interface, and evolutionary computation. He is working also in the evolving and increasingly important field of image processing, data analytics, and edge computing, which promise to pave the way for the evolution of new applications and services in the areas of health care, biomedical, agriculture, smart cities, education, marketing, and finance. His research has appeared in numerous prestigious journals, including IEEE/ACM Transactions, and at conferences, and he has written more than 100 research papers. Mukesh started his academic career as Lecturer with UTS in 2017 and became Core Member of the University's world-leading Australian Artificial Intelligence Institute (AAII), which has a vision to develop theoretical foundations and advanced technologies for AI and to drive progress in related areas. His research is backedby industry experience, specifically in Taiwan, where he was Principal Engineer (2016-17) at the Taiwan Semiconductor Manufacturing Company (TSMC). There, he developed new algorithms for image processing and pattern recognition using machine learning techniques. He was also Postdoctoral Researcher leading a big data and computer vision team at National Chiao Tung University, Taiwan (2015). Mukesh received an M.S. degree from the School of Computer and Systems Sciences at the Jawaharlal Nehru University in New Delhi, India (2009), and a Ph.D. from the Department of Computer Science at the National Chiao Tung University in Taiwan (2015). Dr. Sonali Agarwal is working as Associate Professor in the Information Technology Department of Indian Institute of Information Technology (IIIT), Allahabad, India. She received her Ph.D. degree at IIIT, Allahabad, and joined as faculty at IIIT, Allahabad, where she is teaching since October 2009. She holds Bachelor of Engineering (B.E.) degree in Electrical Engineering from Bhilai Institute of Technology, Bhilai, (C.G.) India, and Masters of Engineering (M.E.) degree in Computer Science from Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India. Her main research interests are in the areas of big data, big data mining, complex event processing system, support vector machines, stream analytics, and software engineering. She is having hands-on experience on stream computing and complex processing platforms such as Apache Spark, Apache Flink, and ESPER. She has focused in the last few years on the research issues in data mining application especially in big data, stream computing, and smart cities. She has attended many national and international conferences/workshops, and she has more than 70 research papers in national/ international journals and conferences. She has completed her Master's Thesis work at Liverpool John Moores University (LJMU), Liverpool, UK, during November 1999 to February 2000 under Indo-UK REC Project, a collaboration in between School of Computing & Mathematical Science, LJMU Liverpool, UK, and Motilal Nehru National Institute of Technology, Allahabad. She has also taken part in Indo Swiss Joint Research Program (ISJRP), and full financial support was awarded to carry out joint research work and to gain knowledge regarding the recent research and experimental facility/work at EPFL, Switzerland, from December 2011 to January 2012. She has also visited Thailand and Sri Lanka for attending/organizing international-level conference/workshops. She has also been Member of IEEE, ACM, CSI, and supervising three Ph.D. scholars and several graduate and undergraduate students in big data mining and stream analytics domain.

Inhaltsangabe

Leukocyte Subtyping using Convolutional Neural Networks for Enhanced Disease Prediction.- Comparative analysis of novel approaches to automated COVID-19 detection using radiography images.- OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer.- An Empirical Study on Graph-based Clustering Algorithms using Schizophrenia Genes.- Traffic Rule Violation Detection System: Deep Learning Approach.- A Web Application for Early Prediction of Diabetes Using Artificial Neural Network.- Web based disease prediction system via machine learning approach.