Recent Progress in Machine Learning and Computational Intelligence in Smart Cities
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 3105
Special Issue Editors
Interests: data science; optimization; security
Special Issues, Collections and Topics in MDPI journals
Interests: operations research; simulation modeling; optimization; NP-hard problems; liner shipping scheduling; evolutionary computation; mathematical programming; hybrid algorithms; metaheuristics; transportation engineering; GPS data processing
Special Issues, Collections and Topics in MDPI journals
Interests: supply chain management; healthcare systems; sustainable logistics and production management; optimization algorithms; heuristics; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of wearable devices, Internet of Things, Internet of vehicles tends to stimulate deep transformations in smart cities, not only at the technological level but also at the societal and economic level. Data is generated at a rate of petabytes per day. Given this amount of data, intelligent processing is needed. Also, because of the advances in high performance computing, large data sets can now be used for training machine learning algorithms. Specifically, deep learning paradigms enable sophisticated transformation of data into usable, operational knowledge.
New services can be offered to citizen, firms, and public administrators. For instance, intelligent systems will provide services such as Smart transportation and parking, Smart homes, Intelligent Surveillance Systems, Smart Grids, Weather monitoring, Healthcare and E-Learning.
Hence, there is a demand to further explore the abundant applications of soft computing methods, including deep learning, fuzzy logic, evolutionary methods, and various data mining techniques. This Special Issue invites qualitative and quantitative research on the usage of machine learning techniques to process data in smart environments.
Prof. Dr. Ugo Fiore
Dr. Maxim A. Dulebenets
Dr. Amir M. Fathollahi-Fard
Guest Editors
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Keywords
- AI-based and green-based supply chains in smart cities
- smart built environment (city, building, transportation, and construction)
- novel AI-based data analytics in smart cities
- data-driven innovations for planning and management in the built environment
- soft computing methods for smart cities
- meta-heuristic algorithms in smart cities
- computational intelligence for sustainable smart cities
- novel or improved nature-inspired optimization algorithms in smart cities
- generative Adversarial Learning in smart cities
- intelligent transportation systems
- advanced machine learning and deep networks for smart cities
- trend analysis with big data and artificial intelligence for smart cities
- societal and economic impact of intelligent data analytics in smart cities
- information management and data analytics in the built environment
- explainable machine learning in smart cities
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