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Applied Sciences
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Advances in Intelligent Information Systems and AI Applications—2nd Edition
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Advances in Intelligent Information Systems and AI Applications—2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 May 2026 | Viewed by 1326

Special Issue Editors

Prof. Dr. Grigorios Beligiannis

E-Mail Website
Guest Editor
Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
Interests: artificial intelligence; computational intelligence; machine learning; genetic/evolutionary algorithms; decision support theory; intelligent information systems; multi-objective optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios A. Tsirogiannis

E-Mail Website
Guest Editor
Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
Interests: modeling; statistical techniques and machine learning; dynamic systems; big data management; resource optimization; photolithography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, intelligent information systems and AI applications have become very popular, and the combination of intelligent algorithms coming from different areas of artificial intelligence, and especially their hybridization, are being widely applied to effectively and efficiently solve difficult real-world problems in engineering, operations research, finance, the physical sciences, chemistry and material science, biological science and engineering, and environmental and Earth sciences. The application of such intelligent schemes has indicated that intelligent algorithms succeed in solving some very difficult real-world problems in which the application of deterministic algorithms is either not possible or is otherwise extremely time-consuming. This Special Issue will comprise papers focused on experimental and theoretical results regarding advances in intelligent information systems and AI applications in engineering, operations research, and Earth sciences.

Prof. Dr. Grigorios Beligiannis
Dr. Georgios A. Tsirogiannis
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 250 words) can be sent to the Editorial Office for assessment.

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
  • computational intelligence
  • machine learning
  • intelligent information systems
  • hybrid intelligent algorithms

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Related Special Issue

Published Papers (2 papers)

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Research

25 pages, 4783 KB  
Article
Hybrid Physics-Informed Neural Network Correction of the Lotka–Volterra Model Under Noisy Conditions: Sensitivity Analysis of the λ Parameter
by Norbert Annuš and Tibor Kmeť
Appl. Sci. 2025, 15(22), 12316; https://doi.org/10.3390/app152212316 - 20 Nov 2025
Viewed by 529
Abstract
In recent years, hybrid systems combining data-driven and physics-based approaches have gained increasing attention for solving complex real-world problems where deterministic modeling alone is insufficient. Within this framework, Physics-Informed Neural Networks and related hybrid models have been successfully applied across physics, engineering, and [...] Read more.
In recent years, hybrid systems combining data-driven and physics-based approaches have gained increasing attention for solving complex real-world problems where deterministic modeling alone is insufficient. Within this framework, Physics-Informed Neural Networks and related hybrid models have been successfully applied across physics, engineering, and biological systems to improve predictive accuracy under uncertainty. However, their stability and sensitivity to noise and model misspecification remain open questions. This study investigates a hybrid Lotka–Volterra population dynamics model augmented with a neural correction term, aiming to analyze how the strength of the neural contribution, controlled by the coupling parameter λ (0≤λ ≤1), affects model performance under noisy and distorted conditions. Here, λ=0 corresponds to the purely physical Lotka–Volterra system, whereas λ=1 represents the fully neural-corrected model. Three experimental setups were implemented: (1) evaluation on noisy data, (2) analysis of the λ sensitivity and its stabilizing effect, and (3) compensation of parameter distortion through neural adaptation. The results indicate that, in the absence of parameter-induced distortions, relying solely on noisy data, moderate neural correction provides the most accurate and stable model behavior, whereas excessive neural influence may distort the original system dynamics. Conversely, when the underlying Lotka–Volterra model incorporates biased parameters, neural correction with a higher λ effectively compensates for structural inaccuracies, enhancing predictive robustness. Full article
(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications—2nd Edition)
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18 pages, 308 KB  
Article
Comparative Analysis of Self-Labeled Algorithms for Predicting MOOC Dropout: A Case Study
by George Raftopoulos, Georgios Kostopoulos, Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis and Achilles Kameas
Appl. Sci. 2025, 15(22), 12025; https://doi.org/10.3390/app152212025 - 12 Nov 2025
Viewed by 331
Abstract
Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learner dropout. Unlike traditional supervised models that rely solely on [...] Read more.
Massive Open Online Courses (MOOCs) have expanded global access to education but continue to struggle with high attrition rates. This study presents a comparative analysis of self-labeled Semi-Supervised Learning (SSL) algorithms for predicting learner dropout. Unlike traditional supervised models that rely solely on labeled data, self-labeled methods iteratively exploit both labeled and unlabeled instances, alleviating the scarcity of annotations in large-scale educational datasets. Using real-world MOOC data, ten self-labeled algorithms, including self-training, co-training, and tri-training variants, were evaluated across multiple labeled ratios. The experimental results show that ensemble-based methods, such as Co-training Random Forest, Co-Training by Committee, and Relevant Random subspace co-training, achieve predictive accuracy comparable to that fully supervised baselines even with as little as 4% labeled data. Beyond predictive performance, the findings highlight the scalability and cost-effectiveness of self-labeled SSL as a data-driven approach for enhancing learner retention in massive online learning. Full article
(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications—2nd Edition)
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