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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 2680

Special Issue Editors


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Guest Editor
Department of Management and Quantitative Studies, Parthenope University, Naples, Italy
Interests: data science; optimization; security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (1 paper)

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Research

11 pages, 4291 KiB  
Article
Evaluation of Urban Traffic Accidents Based on Pedestrian Landing Injury Risks
by Liangliang Shi, Ming Liu, Yu Liu, Qingjiang Zhao, Kuo Cheng, Honghao Zhang and Amir M. Fathollahi-Fard
Appl. Sci. 2022, 12(12), 6040; https://doi.org/10.3390/app12126040 - 14 Jun 2022
Cited by 3 | Viewed by 1512
Abstract
In comparison with vehicle-to-pedestrian collision, pedestrian-to-ground contact usually results in more unpredictable injuries (e.g., intracranial, neck, and abdominal injuries). Although there are many studies for different applications of such methods, this paper conducts an in-depth analysis of urban traffic pedestrian accidents. The effects [...] Read more.
In comparison with vehicle-to-pedestrian collision, pedestrian-to-ground contact usually results in more unpredictable injuries (e.g., intracranial, neck, and abdominal injuries). Although there are many studies for different applications of such methods, this paper conducts an in-depth analysis of urban traffic pedestrian accidents. The effects of pedestrian rotation angle (PRA) and pedestrian facing orientation (PFO) on head and neck injury risk in a ground contact are investigated by the finite element numerical models and different probabilistic analyses. It goes without saying that this study provides a theoretical basis for the prediction and protection study of pedestrian ground contact injury risk. In our experiments, 24 pedestrian-to-ground simulations are carried out by the THUMS v4.0.2 model considering eight PRAs (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, 360°) and three PFOs (x+, x−, y+). Each test was simulated with loading the average linear and rotational velocities that obtained from real-world pedestrian accidents at the pedestrian’s center of gravity. The results show that both PRAs and PFOs have significant impacts on head and neck injuries. Head HIC value caused by PRA 0–135° is much higher than that caused by PRA 180–315°. Neck injury risk caused by PRA 180° is the greatest one in comparison with other PRAs. The PRAs 90° and 270° usually induce a relatively lower neck injury risk. For PFO, the risk of head and neck injury was lower than PFOy+ and PFOx+ or PFOx−, which means PFOy+ was a safer landing orientation for both head and neck. The potential risk of head and neck injuries caused by the ground contact was strongly associated with the symmetry/asymmetric features of human anatomy. Full article
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