Machine Learning Approaches for Urban Computing
Verlag | Springer |
Auflage | 2022 |
Seiten | 208 |
Format | 15,5 x 1,2 x 23,5 cm |
Gewicht | 341 g |
Artikeltyp | Englisches Buch |
Reihe | Studies in Computational Intelligence 968 |
EAN | 9789811609374 |
Bestell-Nr | 81160937DA |
This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.
Inhaltsverzeichnis:
Urbanization: Pattern, Effects and Modelling.- Extraction of Information from Hyperspectral Imaging using Deep Learning.- Vehicle Detection and count in the captured Stream Video using Machine Learning.- Dimensionality Reduction and Classification in Hyperspectral Images using Deep Learning.- Machine learning and deep learning algorithms in the diagnosis of chronic diseases.- Security Enhancement of Contact less Tachometer Based Cyber Physical System.- Optimization of Loss Function on Human Faces Using Generative Adversarial Networks.