Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities

Designing for Sustainability
1. Auflage 2022
ISBN: 978-3-030-84458-5
Verlag: Springer International Publishing

Buch, Englisch, 235 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 535 g

Reihe: Springer Optimization and Its Applications

This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities.

Special features include:

• New research on the design of city elements and smart systems with respect to new technologies and scientific thinking
• Discussions on the theoretical background that lead to smart cities for the future
• New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments

The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields.

Zielgruppe

Research

Autoren/Hrsg.

Pardalos, Panos M.

Tsokas, Arsenios

Rassia, Stamatina Th.

Fachgebiete

• Geisteswissenschaften Architektur Ökologische Aspekte in der Architektur
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Cities as Convergent Autopoietic Systems (V. Dobrev).- Digital ‘Vitalism’ and its ‘Epistemic’ Predecessors: ‘Smart’ Neoteric History and Contemporary Approaches (Moraitis).- Unbuildable Cities (Th. Rassia).- Smart Cities as Identities (Tsoniotis).- A Cross-domain Landscape of ICT Services in Smart Cities (Bunhova).- A Novel Data Representation Method for Smart Cities’ Big Data (N. Nagy).- A Pedestrian Level Strategy to Minimize Outdoor Sunlight Exposure (X. Li).- Planning and Management of Charging Facilities for Electric Vehicle Sharing (W. Qi).- A Reactive Architectural Proposal for Fog/edge Computing in the Internet of Things Paradigm with Application in Deep Learning (Belmonte-Fernandez).- Urban Big Data: City Management and Real Estate Markets (Saiz).- Social Media-based Intelligence for Disaster Response and Management in Smart Cities (Khatoon).

Über Autor(innen)

Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. Professor Pardalos is also an affiliated faculty member of the computer and information science department, the Hellenic Studies Center, and the biomedical engineering program. Additionally, he serves as the director of the Center for Applied Optimization. Professor Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, ecommerce, data mining, biomedical applications, and massive computing. Panos Pardalos is a prolific author who lectures all over the world. He is the recipient of a multitude of fellowships and awards, the most recent of which is the Humboldt Research Award (2018).

Stamatina Th. Rassia is an architect engineer holding a diploma in Architecture Engineering from the National Technical University of Athens (NTUA) in Greece. She has an MPhil in Environmental Design in Architecture and a PhD in Architecture from the University of Cambridge in the United Kingdom. Dr. Rassia is an expert on topics of sustainability and public health promotion by architectural design.

Arsenios Tsokas holds a BSc in Mathematics from the Aristotle University of Thessaloniki in Greece and a PhD in Industrial & Systems Engineering from the University of Florida in USA. He has a diverse background including data analysis, optimization, and machine learning.