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Machine Learning Techniques for Smart City Applications: Trends and Solutions

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Table of Contents

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    Book Overview
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    Chapter 1 Applying Deep Learning to Predict Civic Purpose Development: Within the Smart City Context
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    Chapter 2 Convolution Neural Network Scheme for Detection of Electricity Theft in Smart Grids
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    Chapter 3 Helping Hand: A GMM-Based Real-Time Assistive Device for Disabled Using Hand Gestures
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    Chapter 4 A Review on Hand Gesture and Sign Language Techniques for Hearing Impaired Person
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    Chapter 5 DriveSense: Adaptive System for Driving Behaviour Analysis and Ranking
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    Chapter 6 Classification and Tracking of Vehicles Using Videos Captured by Unmanned Aerial Vehicles
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    Chapter 7 Tracking Everyone and Everything in Smart Cities with an ANN Driven Smart Antenna
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    Chapter 8 Wavelet-Based Saliency and Ensemble Classifier for Pedestrian Detection in Infrared Images
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    Chapter 9 A Survey of Emerging Applications of Machine Learning in the Diagnosis and Management of Sleep Hygiene and Health in the Elderly Population
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    Chapter 10 Smart City Traffic Patterns Prediction Using Machine Learning
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    Chapter 11 Emergency Department Management Using Regression Models
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    Chapter 12 Machine Learning in Wind Energy: Generation to Supply
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    Chapter 13 Multi-class Segmentation of Trash in Coastal Areas Using Encoder-Decoder Architecture
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    Chapter 14 Learning Analytics for Smart Classroom System in a University Campus
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    Chapter 15 Predictive Analytics for Smart Health Monitoring System in a University Campus
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    Chapter 16 SysML-Based Design of Autonomous Multi-robot Cyber-Physical System Using Smart IoT Modules: A Case Study
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    Chapter 17 Vulnerabilities and Ethical Issues in Machine Learning for Smart City Applications
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Title
Machine Learning Techniques for Smart City Applications: Trends and Solutions
Published by
Springer International Publishing, September 2022
DOI 10.1007/978-3-031-08859-9
ISBNs
978-3-03-108858-2, 978-3-03-108859-9
Editors

Hemanth, D. Jude

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