Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Applied Artificial Intelligence and Data Science
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Applied Artificial Intelligence and Data Science

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 September 2025 | Viewed by 8146

Special Issue Editors

Dr. Tymoteusz I. Miller

E-Mail Website
Guest Editor
Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
Interests: machine learning; artificial intelligence; data science; synthetic data
Prof. Dr. Yoonsik Choe

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Interests: statistical pattern recognition; dimensionality reduction in deep learning; sparse signal representation; big data analysis; statistical image signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of Applied Artificial Intelligence (AI) and Data Science is at the forefront of technological advancement, driving innovation and transforming industries across the globe. This Special Issue aims to explore the latest developments, practical applications, and theoretical advancements in AI and Data Science. We invite original research articles, reviews, and case studies that address real-world challenges and showcase cutting-edge solutions in various domains such as Biology and Life Sciences, Chemistry and Materials Science, Environmental and Earth Sciences, Physical Sciences, and Engineering. Topics of interest include but are not limited to machine learning, natural language processing, big data analytics, predictive modeling, and AI ethics. This Special Issue seeks to provide a comprehensive platform for researchers and practitioners to share their insights and contribute to this dynamic field's growing body of knowledge.

Dr. Tymoteusz I. Miller
Prof. Dr. Yoonsik Choe
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

  • artificial intelligence
  • machine learning
  • data science
  • big data analytics
  • predictive modeling
  • natural language processing
  • AI ethics
  • applied AI
  • data-driven solutions
  • innovative technologies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 5165 KiB  
Article
YOLOv5-Based Electric Scooter Crackdown Platform
by Seung-Hyun Lee, Sung-Hyun Oh and Jeong-Gon Kim
Appl. Sci. 2025, 15(6), 3112; https://doi.org/10.3390/app15063112 - 13 Mar 2025
Viewed by 417
Abstract
As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You [...] Read more.
As the use of personal mobility (PM) devices continues to rise, regulatory violations have become more frequent, highlighting the need for technological solutions to ensure efficient enforcement. This study addresses these challenges by proposing an AI-based enforcement platform. The system integrates the You Only Look Once version 5 (YOLOv5) object detection model, a deep-learning-based framework, with Global Positioning System (GPS) location data, Raspberry Pi 5, and Amazon Web Services (AWS) for data processing and web-based implementation. The YOLOv5 model was deployed in two configurations: one for detecting electric scooter usage and another for identifying legal violations. The system utilized AWS Relational Database Service (RDS), Simple Storage Service (S3), and Elastic Compute Cloud (EC2) to store violation records and host web applications. The detection performance was evaluated using mean average precision (mAP) metrics. The electric scooter detection model achieved mAP50 and mAP50-95 scores of 99.5 and 99.457, respectively. Meanwhile, the legal violation detection model attained mAP50 and mAP50-95 scores of 99.5 and 81.813, indicating relatively lower accuracy for fine-grained violation detection. This study presents a practical technological platform for monitoring regulatory compliance and automating fine enforcement for shared electric scooters. Future improvements in object detection accuracy and real-time processing capabilities are expected to enhance the system’s overall reliability. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence and Data Science)
►▼ Show Figures

Figure 1

18 pages, 606 KiB  
Article
Is Open Source the Future of AI? A Data-Driven Approach
by Domen Vake, Bogdan Šinik, Jernej Vičič and Aleksandar Tošić
Appl. Sci. 2025, 15(5), 2790; https://doi.org/10.3390/app15052790 - 5 Mar 2025
Cited by 1 | Viewed by 1025
Abstract
Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means [...] Read more.
Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means to enhance trust and transparency. Yet, open-sourcing comes with its own challenges, such as risks of illicit applications, limited financial incentives, and intellectual property concerns. Positioned between these extremes are hybrid approaches—including partially open models and licensing restrictions—that aim to balance openness with control. In this paper, we adopt a data-driven approach to examine the open-source development of LLMs. By analyzing contributions in model improvements, modifications, and methodologies, we assess how community efforts impact model performance. Our findings indicate that the open-source community can significantly enhance models, demonstrating that community-driven modifications can yield efficiency gains without compromising performance. Moreover, our analysis reveals distinct trends in community growth and highlights which architectures benefit disproportionately from open-source engagement. These insights provide an empirical foundation to inform balanced discussions among industry experts and policymakers on the future direction of AI development. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence and Data Science)
►▼ Show Figures

Figure 1

18 pages, 4476 KiB  
Article
Evaluating LLMs for Automated Scoring in Formative Assessments
by Pedro C. Mendonça, Filipe Quintal and Fábio Mendonça
Appl. Sci. 2025, 15(5), 2787; https://doi.org/10.3390/app15052787 - 5 Mar 2025
Viewed by 976
Abstract
The increasing complexity and scale of modern education have revealed the shortcomings of traditional grading methods in providing consistent and scalable assessments. Advancements in artificial intelligence have positioned Large Language Models (LLMs) as robust solutions for automating grading tasks. This study systematically compared [...] Read more.
The increasing complexity and scale of modern education have revealed the shortcomings of traditional grading methods in providing consistent and scalable assessments. Advancements in artificial intelligence have positioned Large Language Models (LLMs) as robust solutions for automating grading tasks. This study systematically compared the grading performance of an open-source LLM (LLaMA 3.2) and a premium LLM (OpenAI GPT-4o) against human evaluators across diverse question types in the context of a computer programming subject. Using detailed rubrics, the study assessed the alignment between LLM-generated and human-assigned grades. Results revealed that while both LLMs align closely with human grading, equivalence testing demonstrated that the premium LLM achieves statistically and practically similar grading patterns, particularly for code-based questions, suggesting its potential as a reliable tool for educational assessments. These findings underscore the ability of LLMs to enhance grading consistency, reduce educator workload, and address scalability challenges in programming-focused assessments. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence and Data Science)
►▼ Show Figures

Figure 1

28 pages, 8055 KiB  
Article
The New Paradigm of Deepfake Detection at the Text Level
by Cosmina-Mihaela Rosca, Adrian Stancu and Emilian Marian Iovanovici
Appl. Sci. 2025, 15(5), 2560; https://doi.org/10.3390/app15052560 - 27 Feb 2025
Cited by 3 | Viewed by 815
Abstract
The world is currently facing the issue of text authenticity in different areas. The implications of generated text can raise concerns about manipulation. When a photo of a celebrity is posted alongside an impactful message, it can generate outrage, hatred, or other manipulative [...] Read more.
The world is currently facing the issue of text authenticity in different areas. The implications of generated text can raise concerns about manipulation. When a photo of a celebrity is posted alongside an impactful message, it can generate outrage, hatred, or other manipulative beliefs. Numerous artificial intelligence tools use different techniques to determine whether a text is artificial intelligence-generated or authentic. However, these tools fail to accurately determine cases in which a text is written by a person who uses patterns specific to artificial intelligence tools. For these reasons, this article presents a new approach to the issue of deepfake texts. The authors propose methods to determine whether a text is associated with a specific person by using specific written patterns. Each person has their own written style, which can be identified in the average number of words, the average length of the words, the ratios of unique words, and the sentiments expressed in the sentences. These features are used to develop a custom-made written-style machine learning model named the custom deepfake text model. The model’s results show an accuracy of 99%, a precision of 97.83%, and a recall of 90%. A second model, the anomaly deepfake text model, determines whether the text is associated with a specific author. For this model, an attempt was made to determine anomalies at the level of textual characteristics that are assumed to be associated with particular patterns of a certain author. The results show an accuracy of 88.9%, a precision of 100%, and a recall of 89.9%. The findings outline the possibility of using the model to determine if a text is associated with a certain author. The paper positions itself as a starting point for identifying deepfakes at the text level. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence and Data Science)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1635 KiB  
Review
AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
by Tymoteusz Miller, Irmina Durlik, Adrianna Łobodzińska, Lech Dorobczyński and Robert Jasionowski
Appl. Sci. 2024, 14(24), 11612; https://doi.org/10.3390/app142411612 - 12 Dec 2024
Cited by 1 | Viewed by 3447
Abstract
This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, [...] Read more.
This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence and Data Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop