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Applied and Innovative Computational Intelligence Systems: 4th Edition
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Applied and Innovative Computational Intelligence Systems: 4th Edition

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2057

Special Issue Editors

Prof. Dr. Pedro J. S. Cardoso

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Guest Editor
NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Instituto Superior de Engenharia (ISE), Universidade do Algarve, 8005-139 Faro, Portugal
Interests: operational research; machine learning; data science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João M. F. Rodrigues

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Guest Editor
NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Instituto Superior de Engenharia (ISE), Universidade do Algarve, 8005-139 Faro, Portugal
Interests: computer vision; human–computer interaction; human–machine cooperation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cristina Portales

E-Mail Website
Guest Editor
Institute of Robotics and Information and Communication Technologies (IRTIC), Universitat de València, Valencia, Spain
Interests: data visualization; image processing; augmented reality; digital cultural heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on ‘Applied and Innovative Computational Intelligence Systems: 4th Edition’ provides a place where Computational Intelligence (CI) researchers and practitioners can publish their theoretical and experimental outcomes in a journal with an Impact Factor of of 2.5 and CiteScore of 5.5 in 2024. Supported in huge pillars (such as Neural Networks, Fuzzy Systems or Evolutionary Computation), CI practitioners seek an intelligent system that is characterized by computational adaptability, fault tolerance and high performance in the form of adaptive platforms that enable or facilitate intelligent behavior in complex and dynamic environments, developing technology that enables machines to think, behave or act more humanely.

In this context, this Special Issue intends to explore CI and complementary application and theory fields including, but not restricted to, Artificial Intelligence in general, Machine Learning, Deep Learning, Computer Vision, Augmented Reality, Human–Computer Interaction, Smart Spaces, Smart Cities, Ubiquitous Intelligence, Data Analysis and Science, Time-Series, Internet of Things/Everything, Fault Detection, Affective Computing, Natural Language Processing, Privacy and Ethics, Operational Research, Evolutionary Computation, Fuzzy Logic, Robotics, etc.

Accepted papers will build a comprehensive collection of research and development trends on contemporary applied and innovative computational intelligence systems that will serve as a convenient reference for other CI experts as well as newly arrived practitioners, introducing them to the field’s trends. Following the journal’s policy, there is no limit on the documents’ length and full experimental details should be provided, allowing other researchers to reproduce results. Furthermore, electronic files and software can be deposited as supplementary electronic material, allowing full reproducibility and future analysis, which increases the authors’ and works’ visibility.

Looking forward to working with you,

Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. João M. F. Rodrigues
Prof. Dr. Cristina Portales
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
  • machine learning
  • deep learning
  • computer vision
  • augmented reality
  • human–computer interaction
  • smart spaces
  • smart cities
  • ubiquitous intelligence
  • data analysis and science
  • time-series
  • internet of things/everything
  • fault detection
  • affective computing
  • natural language processing
  • privacy and ethics
  • operational research
  • evolutionary computation
  • robotics

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

Published Papers (4 papers)

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Research

23 pages, 2222 KB  
Article
Fine-Tuning Generative AI with Domain Question Banks: Evaluating Multi-Type Question Generation and Grading
by Chien-Hung Lai, You-Jen Chen and Ze-Ping Chen
Appl. Sci. 2025, 15(24), 13050; https://doi.org/10.3390/app152413050 - 11 Dec 2025
Abstract
This study examines the effectiveness of a fine-tuned generative AI system—trained with a domain question bank—for question generation and automated grading in programming education, and evaluates its instructional usability. Methodologically, we constructed an annotated question bank covering nine item types and, under a [...] Read more.
This study examines the effectiveness of a fine-tuned generative AI system—trained with a domain question bank—for question generation and automated grading in programming education, and evaluates its instructional usability. Methodologically, we constructed an annotated question bank covering nine item types and, under a controlled environment, compared pre- and post-fine-tuning performance on question-type recognition and answer grading using Accuracy, Macro Precision, Macro Recall, and Macro F1. We also collected student questionnaires and open-ended feedback to analyze subjective user experience. Results indicate that the accuracy of question-type recognition improved from 0.6477 to 0.8409, while grading accuracy increased from 0.9474 to 0.9605. Students’ subjective perceptions aligned with these quantitative trends, reporting higher ratings for grading accuracy and question generation quality; overall interactive experience was moderately high, though system speed still requires improvement. These findings provide course-aligned empirical evidence that fine-tuning with domain data can jointly enhance the effectiveness and usability of both automatic question generation and automated grading. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 4th Edition)
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26 pages, 702 KB  
Article
CTQRS-Based Reinforcement Learning Framework for Reliable Bug Report Generation Using Open-Source Large Language Models
by Geunseok Yang
Appl. Sci. 2025, 15(23), 12545; https://doi.org/10.3390/app152312545 - 26 Nov 2025
Viewed by 266
Abstract
The advancement of Large Language Models (LLMs) has opened new possibilities for automating bug report generation in software engineering. However, a fundamental limitation remains: the generated reports often fail to maintain both consistent structure and reliable semantic quality. To address this issue, this [...] Read more.
The advancement of Large Language Models (LLMs) has opened new possibilities for automating bug report generation in software engineering. However, a fundamental limitation remains: the generated reports often fail to maintain both consistent structure and reliable semantic quality. To address this issue, this study proposes a Reinforcement Learning (RL) framework that integrates the CTQRS (Completeness, Traceability, Quality, Reproducibility, Specificity) metric as a reward signal. The proposed method aims to enhance both the structural completeness and semantic coherence of generated reports, enabling the automatic creation of reliable bug reports based on open-source LLMs. The training process consists of three stages: Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and Refinement. In the SFT stage, the model learns the formal structure of bug reports, reducing the loss from 1.9 to 1.3 and achieving initial CTQRS and SBERT scores of 0.46 and 0.68, respectively. In the RL stage, a multi-reward function centered on CTQRS is combined with the Proximal Policy Optimization (PPO) algorithm, increasing the reward value from 0.42 to 0.63 with stable convergence confirmed through the Exponential Moving Average (EMA). During this process, the CTQRS and SBERT scores improved to 0.72 and 0.84, demonstrating that the model simultaneously enhanced structural completeness and semantic consistency. In the final Refinement stage, the outcomes of SFT and RL are integrated, and a critic-based fine-grained feedback adjustment strategy is applied to stabilize the final outputs. The refined reports maintained a reward value of approximately 0.65, achieving peak CTQRS and SBERT scores of 0.76 and 0.85, respectively. Throughout the entire training process, the stability of the reward gradients was preserved, and the adjustments to length rewards and repetition penalties effectively prevented excessive verbosity. Experimental results show that the proposed CTQRS-based reinforcement learning framework improves the structural completeness, contextual accuracy, and evaluation stability of bug reports, thereby quantitatively enhancing the reliability of LLM-based (v.Qwen2.5-7B-Instruct) software quality assurance documentation. Future work will focus on further improving formal precision and evaluation consistency by fine-tuning the number of critic iterations (critic_iters) and adjusting detailed reward weights. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 4th Edition)
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31 pages, 2192 KB  
Article
AgentReport: A Multi-Agent LLM Approach for Automated and Reproducible Bug Report Generation
by Seojin Choi and Geunseok Yang
Appl. Sci. 2025, 15(22), 11931; https://doi.org/10.3390/app152211931 - 10 Nov 2025
Viewed by 990
Abstract
Bug reports in open-source projects are often incomplete or low in quality, which reduces maintenance efficiency. To address this issue, we propose AgentReport, a multi-agent pipeline based on large language models (LLMs). AgentReport integrates QLoRA-4bit lightweight fine-tuning, CTQRS (Completeness, Traceability, Quantifiability, Reproducibility, Specificity) [...] Read more.
Bug reports in open-source projects are often incomplete or low in quality, which reduces maintenance efficiency. To address this issue, we propose AgentReport, a multi-agent pipeline based on large language models (LLMs). AgentReport integrates QLoRA-4bit lightweight fine-tuning, CTQRS (Completeness, Traceability, Quantifiability, Reproducibility, Specificity) structured prompting, Chain-of-Thought reasoning, and one-shot exemplar within seven modules: Data, Prompt, Fine-tuning, Generation, Evaluation, Reporting, and Controller. Using 3966 summary–report pairs from Bugzilla, AgentReport achieved 80.5% in CTQRS, 84.6% in ROUGE-1 Recall, 56.8% in ROUGE-1 F1, and 86.4% in Sentence-BERT (SBERT). Compared with the baseline (77.0% CTQRS, 61.0% ROUGE-1 Recall, 85.0% SBERT), AgentReport improved CTQRS by 3.5 percentage points, Recall by 23.6 points, and SBERT by 1.4 points. The inclusion of F1 complemented Recall-only evaluation, offering a balanced framework that covers structural completeness (CTQRS), lexical coverage and precision (ROUGE-1 Recall/F1), and semantic consistency (SBERT). This modular design enables consistent experimentation and flexible scaling, providing practical evidence that multi-agent LLM pipelines can generate higher-quality bug reports for software maintenance. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 4th Edition)
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14 pages, 5144 KB  
Article
Dual-Module Architecture for Robust Image Forgery Segmentation and Classification Toward Cyber Fraud Investigation
by Donghwan Kim and Hansoo Kim
Appl. Sci. 2025, 15(21), 11817; https://doi.org/10.3390/app152111817 - 6 Nov 2025
Viewed by 397
Abstract
This study presents a dual-module architecture for image forgery detection in the context of cyber fraud investigation, designed to provide interpretable and court-admissible forensic evidence. The forgery segmentation module built on an encoder–decoder structure segments forged regions at the pixel level to produce [...] Read more.
This study presents a dual-module architecture for image forgery detection in the context of cyber fraud investigation, designed to provide interpretable and court-admissible forensic evidence. The forgery segmentation module built on an encoder–decoder structure segments forged regions at the pixel level to produce a binary mask. The forgery classification module with two-stream structure integrates contextual and noise-residual cues from the raw image and the binary mask to determine the designated forgery method. The segmentation module achieves an F1-Score of 0.875 and an IoU of 0.78, while the classification module reaches an F1-Score of 0.94. The combined system attains an end-to-end F1-Score of 0.855 and AUC of 0.91, demonstrating reliable detection performance and enhanced explainability. These results highlight the framework’s potential for forensic image analysis and its practical applicability to real-world cyber fraud investigations. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems: 4th Edition)
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