2 variations prediction; convolutional neural networks; transfer learning; ensemble learning; bike sharing; rebalancing problem; inventory level; machine learning; random forest; imbalanced data; plant disease; classification; MobileNet V2; attention; electricity market; real-time market; day-ahead market; locational marginal pricing; long short-term memory (LSTM); multivariate time series forecasting; smart cities; artificial intelligence; machine learning; digital transformation; computational sustainability; logic programming; the laws of thermodynamics; entropy; equity; asset management; pavement decision-making; resource allocation"> As time goes by, the concept of a "smart city" encompasses an increasing number of aspects. Essentially, when we think of a modern city, we would like it to offer a vibrant and safe living environment for its community of residents: healthy indoor living spaces, green outdoor spaces for leisure and sports, flowing traffic, efficient distribution of goods, various forms of safe transportation for people,  public lighting and surveillance systems, emergency management, information and communication services, etc. A smart city offers much more: it represents a space that favors the development of people and their potential, in a sustainable and resilient manner, by means of the intelligent integration of ICT tools and technologies. The following reprint collects research and innovative approaches to smart city issues. The contributions cover a wide range of topics, from car and bike-sharing models to ways of improving quality of life through digitalization. Readers will get an insight into effective solutions offered by researchers and practitioners working in artificial intelligence, the Internet of Things, data analytics, etc. The synergy of these technologies is illustrated herein, providing a new, challenging view on the smart cities of today and of the future.

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Reprint

Algorithms for Smart Cities

Edited by
September 2024
208 pages
  • ISBN978-3-7258-2009-2 (Hardback)
  • ISBN978-3-7258-2010-8 (PDF)

This is a Reprint of the Special Issue Algorithms for Smart Cities that was published in

Computer Science & Mathematics
Summary

As time goes by, the concept of a "smart city" encompasses an increasing number of aspects. Essentially, when we think of a modern city, we would like it to offer a vibrant and safe living environment for its community of residents: healthy indoor living spaces, green outdoor spaces for leisure and sports, flowing traffic, efficient distribution of goods, various forms of safe transportation for people,  public lighting and surveillance systems, emergency management, information and communication services, etc. A smart city offers much more: it represents a space that favors the development of people and their potential, in a sustainable and resilient manner, by means of the intelligent integration of ICT tools and technologies. The following reprint collects research and innovative approaches to smart city issues. The contributions cover a wide range of topics, from car and bike-sharing models to ways of improving quality of life through digitalization. Readers will get an insight into effective solutions offered by researchers and practitioners working in artificial intelligence, the Internet of Things, data analytics, etc. The synergy of these technologies is illustrated herein, providing a new, challenging view on the smart cities of today and of the future.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
carsharing; station location modeling; genetic algorithm; fix stations and free stations; deep learning; intelligent monitoring; person re-identification; smart streetlight; YOLOv3; multi-scale training; anchor clustering; label smoothing; mixup; IOU; GIOU; fine-grained classification of automobile; smart city; emergency management; monitoring; social distancing; neural network; internet of things; wireless sensors network; near field communication technology; computer vision; energy consumption reduction; HVAC systems; CO2 variations prediction; convolutional neural networks; transfer learning; ensemble learning; bike sharing; rebalancing problem; inventory level; machine learning; random forest; imbalanced data; plant disease; classification; MobileNet V2; attention; electricity market; real-time market; day-ahead market; locational marginal pricing; long short-term memory (LSTM); multivariate time series forecasting; smart cities; artificial intelligence; machine learning; digital transformation; computational sustainability; logic programming; the laws of thermodynamics; entropy; equity; asset management; pavement decision-making; resource allocation

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