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Feature Review Papers in "Computing and Artificial Intelligence"
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Feature Review Papers in "Computing and Artificial Intelligence"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 3582

Special Issue Editors

Prof. Dr. Paolino Di Felice

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and Economics, University of L'Aquila, Via Giovanni Gronchi n. 18, Pile, 67100 L’Aquila, Italy
Interests: software engineering; model-driven engineering; automatic code generation; quality metrics; metadata repository; reuse of UML artifacts; Internet of Things; Artificial Intelligence of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Javier García Villalba

E-Mail Website
Guest Editor
Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: computer security; cyber security; privacy; information security; cryptography; intrusion detection; malware; trust; anonymity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aleksander Mendyk

E-Mail Website
Guest Editor
Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University-Medical College, Medyczna 9 St, 30-688 Kraków, Poland
Interests: machine learning; artificial intelligence; pharmaceutical technology; biopharmaceutics; clinical trials; statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chilukuri K. Mohan

E-Mail Website
Guest Editor
Deptartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244-4100, USA
Interests: artificial intelligence; evolutionary algorithms data mining; social networks; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Computing and Artificial Intelligence Section of Applied Sciences is running a new innovative series of featured review collections on hot/current topics of significant relevance to researchers in all areas of interest covered by “Computing and Artificial Intelligence” (https://www.mdpi.com/journal/applsci/sections/computing_artificial_intelligence).

The purpose of this Special Issue is to publish a set of high-quality review papers that present the state of the art in relevant sub-areas in the general fields of computing and artificial intelligence and that individuate and promote important directions for future research.

We welcome multidisciplinary research in areas including, but not limited to, the following:

  • Database, information systems, and security;
  • Multiagent systems and pervasive computing;
  • Audio, speech, and music processing;
  • Computer vision, machine learning, and pattern recognition.

Manuscripts focused on the areas of computing and artificial intelligence are all welcome to be submitted.

These featured review papers are expected to be read by a large number of researchers while being highly influential. In addition, all papers presented in this Special Issue will be published in a printed edition, which will be widely promoted within the scientific community.

Prof. Dr. Paolino Di Felice
Prof. Dr. Luis Javier García Villalba
Prof. Dr. Aleksander Mendyk
Prof. Dr. Chilukuri K. Mohan
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

  • database, information systems, and security
  • multiagent systems and pervasive computing
  • audio, speech, and music processing
  • computer vision, machine learning, and pattern recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Review

29 pages, 549 KiB  
Review
Generative Models in Medical Visual Question Answering: A Survey
by Wenjie Dong, Shuhao Shen, Yuqiang Han, Tao Tan, Jian Wu and Hongxia Xu
Appl. Sci. 2025, 15(6), 2983; https://doi.org/10.3390/app15062983 - 10 Mar 2025
Viewed by 1473
Abstract
Medical Visual Question Answering (MedVQA) is a crucial intersection of artificial intelligence and healthcare. It enables systems to interpret medical images—such as X-rays, MRIs, and pathology slides—and respond to clinical queries. Early approaches primarily relied on discriminative models, which select answers from predefined [...] Read more.
Medical Visual Question Answering (MedVQA) is a crucial intersection of artificial intelligence and healthcare. It enables systems to interpret medical images—such as X-rays, MRIs, and pathology slides—and respond to clinical queries. Early approaches primarily relied on discriminative models, which select answers from predefined candidates. However, these methods struggle to effectively address open-ended, domain-specific, or complex queries. Recent advancements have shifted the focus toward generative models, leveraging autoregressive decoders, large language models (LLMs), and multimodal large language models (MLLMs) to generate more nuanced and free-form answers. This review comprehensively examines the paradigm shift from discriminative to generative systems, examining generative MedVQA works on their model architectures and training process, summarizing evaluation benchmarks and metrics, highlighting key advances and techniques that propels the development of generative MedVQA, such as concept alignment, instruction tuning, and parameter-efficient fine-tuning (PEFT), alongside strategies for data augmentation and automated dataset creation. Finally, we propose future directions to enhance clinical reasoning and intepretability, build robust evaluation benchmarks and metrics, and employ scalable training strategies and deployment solutions. By analyzing the strengths and limitations of existing generative MedVQA approaches, we aim to provide valuable insights for researchers and practitioners working in this domain. Full article
(This article belongs to the Special Issue Feature Review Papers in "Computing and Artificial Intelligence")
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24 pages, 399 KiB  
Review
Intelligent Monitoring Systems for Electric Vehicle Charging
by Jaime A. Martins and João M. F. Rodrigues
Appl. Sci. 2025, 15(5), 2741; https://doi.org/10.3390/app15052741 - 4 Mar 2025
Cited by 1 | Viewed by 1589
Abstract
The growing adoption of electric vehicles (EVs) presents new challenges for managing parking infrastructure, particularly concerning charging station utilization and user behavior patterns. This review examines the current state-of-the-art in intelligent monitoring systems for EV charging stations in parking facilities. We specifically focus [...] Read more.
The growing adoption of electric vehicles (EVs) presents new challenges for managing parking infrastructure, particularly concerning charging station utilization and user behavior patterns. This review examines the current state-of-the-art in intelligent monitoring systems for EV charging stations in parking facilities. We specifically focus on two key inefficiencies: vehicles occupying charging spots beyond the optimal fast-charging range (80% state-of-charge) and remaining connected even after reaching full capacity (100%). We analyze the theoretical and practical foundations of these systems, summarizing existing research on intelligent monitoring architectures and commercial implementations. Building on this analysis, we also propose a novel monitoring framework that integrates Internet of things (IoT) sensors, edge computing, and cloud services to enable real-time monitoring, predictive maintenance, and adaptive control. This framework addresses both the technical aspects of monitoring systems and the behavioral factors influencing charging station management. Based on a comparative analysis and simulation studies, we propose performance benchmarks and outline critical research directions requiring further experimental validation. The proposed architecture aims to offer a scalable, adaptable, and secure solution for optimizing EV charging infrastructure utilization while addressing key research gaps in the field. Full article
(This article belongs to the Special Issue Feature Review Papers in "Computing and Artificial Intelligence")
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