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Deep Learning Applications de M. Arif Wani

Deep Learning Applications: Advances in Intelligent Systems and Computing, cartea 1098

Editat de M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh
en Limba Engleză Paperback – 29 feb 2020
This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
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Specificații

ISBN-13: 9789811518157
ISBN-10: 9811518157
Pagini: 178
Ilustrații: X, 178 p. 76 illus., 68 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.27 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seria Advances in Intelligent Systems and Computing

Locul publicării:Singapore, Singapore

Cuprins

Trends in Deep Learning Applications.- Optimization Strategies.- Quasi-Newton Optimization Methods.- Application to Deep Reinforcement Learning.- Medical Image Segmentation using Deep Neural Networks with Pre-trained Encoders.- Enabling Robust and Autonomous Material handling in Logistics through applied Deep Learning Algorithms.- Performance metric.- Dataset creation.- Detecting Work Zones in SHRP2 NDS Videos Using Deep Learning Based Computer Vision.- Deep Learning Framework and Architecture Selection.- Action Recognition in Videos Using Multi-Stream Convolutional Neural Networks.- Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimers disease.

Notă biografică

Prof. M. Arif Wani completed his M.Tech. in Computer Technology at the Indian Institute of Technology, Delhi, and his Ph.D. in Computer Vision at Cardiff University, UK. Currently, he is a Professor at the University of Kashmir, having previously served as a Professor at California State University Bakersfield. His main research interests are in gene expression datasets, face recognition techniques/algorithms, artificial neural networks, and deep architectures. He has published many papers in reputed journals and conferences in these areas. He was honored with the International Technology Institute Award in 2002 by the International Technology Institute, California, USA. He is a member of many academic and professional bodies, e.g., the Indian Society for Technical Education, Computer Society of India, IEEE USA, and Optical Society of America.
Dr. Mehmed Kantardzic received his Ph.D. in Computer Science in 1980, M.S. in Computer Science in 1976, and B.S. in Electrical Engineering in 1972, all from the University of Sarajevo, Bosnia, and Herzegovina. He served as an Assistant, and Associate Professor at the University of Sarajevo, and later as Associate and since 2004 as Full Professor at the University of Louisville. Currently, he is the Director of the Data Mining Lab as well as the Director of CECS Graduate Studies at the CECS Department. His research focuses on data mining & knowledge discovery, machine learning, soft computing, click fraud detection and prevention, concept drift in streaming data, and distributed intelligent systems. Dr. Kantardzic is the author of six books including the textbook: “Data Mining: Concepts, Models, Methods, and Algorithms” (John Wiley, second edition, 2011) which is accepted for data mining courses at more than hundred universities in USA and abroad. He is the author of over 40 peer-reviewed journal publications, 20 book chapters, and over 200 reviewed articles in the proceedings of international conferences. His recent research projects are supported by NSF, KSTC, US Treasury Department, U.S. Army, and NASA. Dr. Kantardzic was selected as Fulbright Specialist in Information Technology in 2012. Dr. Kantardzic has served on the Editorial Boards for several international journals, and he is currently an Associate Editor for WIREs Data Mining and Knowledge Discovery Journal.
Prof. Moamar Sayed-Mouchaweh received his Ph.D. from the University of Reims, France. He was working as Associated Professor in Computer Science, Control, and Signal Processing at the University of Reims, France, in the Research Centre in Sciences and Technology of the Information and the Communication. In December 2008, he obtained the Habilitation to Direct Research (HDR) in Computer science, Control, and Signal Processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines Telecom Lille Douai (France), Department of Computer Science and Automatic Control. He edited and wrote several Springer books and served as a Guest Editor of several special issues of international journals. He also served as IPC Chair and Conference Chair of several international workshops and conferences. He is serving as a member of the Editorial Board of several international journals.

Textul de pe ultima copertă

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Caracteristici

Describes novel ways of using deep learning architectures for real-world applications Discusses new algorithms for deep learning networks Presents the results of using deep learning models for selected applications