Deep learning applications . Volume 2 / / M. Arif Wani, Taghi M. Khoshgoftaar, Vasile Palade, editors

• Record Nr.: UNINA9910484944303321
• Pubbl/distr/stampa: Gateway East, Singapore : , : Springer, , [2021]; ©2021
• ISBN: 981-15-6759-X
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (XII, 300 p. 128 illus., 108 illus. in color.)
• Collana: Advances in Intelligent Systems and Computing, , 2194-5357 ; ; 1232
• Disciplina: 006.31
• Soggetti: Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Deep Learning Based Recommender Systems -- A Comprehensive Set of Novel Residual Blocks for Deep Learning Architectures for Diagnosis of Retinal Diseases from Optical Coherence Tomography Images -- Three-Stream Convolutional Neural Network for Human Fall Detection -- Diagnosis of Bearing Faults in Electrical Machines using Long Short-Term Memory -- Automatic Solar Panel Detection from High Resolution Orthoimagery Using Deep Learning Segmentation Networks -- Training Deep Learning Sequence Models to Understand Driver Behavior -- Exploiting Spatio-temporal Correlation in RF Data using Deep Learning -- Human Target Detection and Localization with Radars Using Deep Learning -- Thresholding Strategies for Deep Learning with Highly Imbalanced Big Data -- Vehicular Localisation at High and Low Estimation Rates during GNSS Outages: A Deep Learning Approach -- Multi-Adversarial Variational Autoencoder Nets for Simultaneous Image Generation and Classification -- Non-convex Optimization using Parameter Continuation Methods for Deep Neural Networks.
• Sommario/riassunto: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures ; and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.