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Journals
Future Internet
Special Issues
Challenges in Real-Time Intelligent Systems
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Challenges in Real-Time Intelligent Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2454

Special Issue Editors

Prof. Dr. Pit Pichappan

E-Mail Website
Guest Editor
Digital Information Research Labs, Chennai 600017, India
Interests: natural language processing; real-time mining; intelligent information retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Simon James Fong

E-Mail Website
Guest Editor
Faculty of Science and Technology, University of Macau, Macau 999078, China
Interests: E-commerce; data mining; business intelligence; intelligent agent technology; electronic governance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yao-Liang Chung

E-Mail Website
Guest Editor
Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
Interests: green ICTs; wireless and cellular networks; performance evaluation; deep learning; deep reinforcement learning; intelligent computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The infusion of intelligence in computing with maximum success is a challenge. Massive amounts of multidimensional data reflecting the trajectories of objects are produced by a developing diverse, real-life systems, ranging from mobile to different apps and surveillance systems, from object tracking systems to sensors. Intelligent computing can be used to develop newer algorithms, designs and models that can solve more complicated tasks in dynamic environments. The frenzied emergence of new systems and the surrounding potential have generated excitement about the possibility of intelligent computing to transform research to solve the confronting issues. Real-time intelligent computing has significantly impacted human beings in many respects. A breakthrough in computational intelligence is warranted to progress further in achieving the peak. Solving complexity in real-time intelligence is a way to move forward with an interdisciplinary approach. Recent research in deep learning, machine learning and cloud help achieve multilevel tasks in more dynamic environments. This Special Issue will reflect the new challenges and solutions to attain real-time intelligence and its applications in different domains.

Prof. Dr. Pit Pichappan
Dr. Simon James Fong
Prof. Dr. Yao-Liang Chung
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • artificial intelligence techniques
  • processing intelligent databases
  • software engineering solutions
  • expert systems
  • machine learning
  • intelligent data mining
  • deep learning
  • critical real-time applications

Published Papers (1 paper)

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Research

14 pages, 3418 KiB  
Article
Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
by Pradeep Kumar, Guo-Liang Shih, Bo-Lin Guo, Siva Kumar Nagi, Yibeltal Chanie Manie, Cheng-Kai Yao, Michael Augustine Arockiyadoss and Peng-Chun Peng
Future Internet 2024, 16(2), 50; https://doi.org/10.3390/fi16020050 - 31 Jan 2024
Viewed by 2105
Abstract
Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a [...] Read more.
Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system. Full article
(This article belongs to the Special Issue Challenges in Real-Time Intelligent Systems)
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