Computers

26 pages, 613 KB

Open AccessArticle

AutoQALLMs: Automating Web Application Testing Using Large Language Models (LLMs) and Selenium

by Sindhupriya Mallipeddi, Muhammad Yaqoob, Javed Ali Khan, Tahir Mehmood, Alexios Mylonas and Nikolaos Pitropakis

Computers 2025, 14(11), 501; https://doi.org/10.3390/computers14110501 - 18 Nov 2025

Abstract

Modern web applications change frequently in response to user and market needs, making their testing challenging. Manual testing and automation methods often struggle to keep up with these changes. We propose an automated testing framework, AutoQALLMs, that utilises various LLMs (Large Language Models), [...] Read more.

Modern web applications change frequently in response to user and market needs, making their testing challenging. Manual testing and automation methods often struggle to keep up with these changes. We propose an automated testing framework, AutoQALLMs, that utilises various LLMs (Large Language Models), including GPT-4, Claude, and Grok, alongside Selenium WebDriver, BeautifulSoup, and regular expressions. This framework enables one-click testing, where users provide a URL as input and receive test results as output, thus eliminating the need for human intervention. It extracts HTML (Hypertext Markup Language) elements from the webpage and utilises the LLMs API to generate Selenium-based test scripts. Regular expressions enhance the clarity and maintainability of these scripts. The scripts are executed automatically, and the results, such as pass/fail status and error details, are displayed to the tester. This streamlined input–output process forms the core foundation of the AutoQALLMs framework. We evaluated the framework on 30 websites. The results show that the system drastically reduces the time needed to create test cases, achieves broad test coverage (96%) with Claude 4.5 LLM, which is competitive with manual scripts (98%), and allows for rapid regeneration of tests in response to changes in webpage structure. Software testing expert feedback confirmed that the proposed AutoQALLMs method for automated web application testing enables faster regression testing, reduces manual effort, and maintains reliable test execution. However, some limitations remain in handling complex page changes and validation. Although Claude 4.5 achieved slightly higher test coverage in the comparative evaluation of the proposed experiment, GPT-4 was selected as the default model for AutoQALLMs due to its cost-efficiency, reproducibility, and stable script generation across diverse websites. Future improvements may focus on increasing accuracy, adding self-healing techniques, and expanding to more complex testing scenarios. Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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17 pages, 3941 KB

Open AccessArticle

Deep Learning-Based Citrus Canker and Huanglongbing Disease Detection Using Leaf Images

by Maryjose Devora-Guadarrama, Benjamín Luna-Benoso, Antonio Alarcón-Paredes, Jose Cruz Martínez-Perales and Úrsula Samantha Morales-Rodríguez

Computers 2025, 14(11), 500; https://doi.org/10.3390/computers14110500 - 17 Nov 2025

Abstract

Early detection of plant diseases is key to ensuring food production, reducing economic losses, minimizing the use of agrochemicals, and maintaining the sustainability of the agricultural sector. Citrus plants, an important source of vitamin C, fiber, and antioxidants, are among the world’s most [...] Read more.

Early detection of plant diseases is key to ensuring food production, reducing economic losses, minimizing the use of agrochemicals, and maintaining the sustainability of the agricultural sector. Citrus plants, an important source of vitamin C, fiber, and antioxidants, are among the world’s most significant fruit crops but face threats such as canker and Huanglongbing (HLB), incurable diseases that require management strategies to mitigate their impact. Manual diagnosis, although common, I s imprecise, slow, and costly; therefore, efficient alternatives are emerging to identify diseases from early stages using Artificial Intelligence techniques. This study evaluated four deep learning models, specifically convolutional neural networks. In this study, we evaluated four convolutional neural network models (DenseNet121, ResNet50, EfficientNetB0, and MobileNetV2) to detect canker and HLB in citrus leaf images. We applied preprocessing and data-augmentation techniques; transfer learning via selective fine-tuning; stratified k-fold cross-validation; regularization methods such as dropout and weight decay; and hyperparameter-optimization techniques. The models were evaluated by the loss value and by metrics derived from the confusion matrix, including accuracy, recall, and F1-score. The best-performing model was EfficientNetB0, which achieved an average accuracy of 99.88% and the lowest loss value of 0.0058 using cross-entropy as the loss function. Since EfficientNetB0 is a lightweight model, the results show that lightweight models can achieve favorable performance compared to robust models, models that can be useful for disease detection in the agricultural sector using portable devices or drones for field monitoring. The high accuracy obtained is mainly because only two diseases were considered; consequently, it is possible that these results do not hold in a database that includes a larger number of diseases. Full article

(This article belongs to the Section AI-Driven Innovations)

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25 pages, 7447 KB

Open AccessArticle

Machine Learning Models for Subsurface Pressure Prediction: A Data Mining Approach

by Muhammad Raiees Amjad, Rohan Benjamin Varghese and Tehmina Amjad

Computers 2025, 14(11), 499; https://doi.org/10.3390/computers14110499 - 17 Nov 2025

Abstract

Precise pore pressure prediction is highly essential for safe and effective drilling; however, the nonlinear and heterogeneous nature of the subsurface strata makes it extremely challenging. Conventional physics-based methods are not capable of handling this nonlinearity and variation. Recently, machine learning (ML) methods [...] Read more.

Precise pore pressure prediction is highly essential for safe and effective drilling; however, the nonlinear and heterogeneous nature of the subsurface strata makes it extremely challenging. Conventional physics-based methods are not capable of handling this nonlinearity and variation. Recently, machine learning (ML) methods have been deployed by researchers to enhance prediction performance. These methods are often highly domain-specific and produce good results for the data they are trained for but struggle to generalize to unseen data. This study introduces a Hybrid Meta-Ensemble (HME), a meta model framework, as a novel data mining approach that applies ML methods and ensemble learning on well log data for pore pressure prediction. This proposed study first trains five baseline models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Feedforward Neural Network (DFNN), Random Forest (RF), and Extreme Gradient Boost (XGBoost) to capture sequential and nonlinear relationships for pore pressure prediction. The stacked predictions are further improved through a meta learner that adaptively reweighs them according to subsurface heterogeneity, effectively strengthening the ability of ensembles to generalize across diverse geological settings. The experimentation is performed on well log data from four wells located in the Potwar Basin which is one of Pakistan’s principal oil- and gas-producing regions. The proposed Hybrid Meta-Ensemble (HME) has achieved an R² value of 0.93, outperforming the individual base models. Using the HME approach, the model effectively captures rock heterogeneity by learning optimal nonlinear interactions among the base models, leading to more accurate pressure predictions. Results show that integrating deep learning with robust meta learning substantially improves the accuracy of pore pressure prediction. Full article

(This article belongs to the Special Issue Recent Advances in Data Mining: Methods, Trends, and Emerging Applications)

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22 pages, 1862 KB

Open AccessArticle

An Embedded Convolutional Neural Network Model for Potato Plant Disease Classification

by Laila Hammam, Hany Ayad Bastawrous, Hani Ghali and Gamal A. Ebrahim

Computers 2025, 14(11), 498; https://doi.org/10.3390/computers14110498 - 16 Nov 2025

Abstract

Globally, potatoes are one of the major crops that significantly contribute to food security; hence, the field of machine learning has opened the gate for many advances in plant disease detection. For real-time agricultural applications, it has been found that real-time data processing [...] Read more.

Globally, potatoes are one of the major crops that significantly contribute to food security; hence, the field of machine learning has opened the gate for many advances in plant disease detection. For real-time agricultural applications, it has been found that real-time data processing is challenging; this is due to the limitations and constraints imposed by hardware platforms. However, such challenges can be handled by deploying simple and optimized AI models serving the need of accurate data classification while taking into consideration hardware resource limitations. Hence, the purpose of this study is to implement a customized and optimized convolutional neural network model for deployment on hardware platforms to classify both potato early blight and potato late blight diseases. Lastly, a thorough comparison between both embedded and PC simulation implementations was conducted for the three models: the implemented CNN model, VGG16, and ResNet50. Raspberry Pi3 was chosen for the embedded implementation in the intermediate stage and NVIDIA Jetson Nano was chosen for the final stage. The suggested model significantly outperformed both the VGG16 and ResNet50 CNNs, as evidenced by the inference time, number of FLOPs, and CPU data usage, with an accuracy of 95% on predicting unseen data. Full article

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32 pages, 1254 KB

Open AccessReview

Arabic Natural Language Processing (NLP): A Comprehensive Review of Challenges, Techniques, and Emerging Trends

by Abdulaziz M. Alayba

Computers 2025, 14(11), 497; https://doi.org/10.3390/computers14110497 - 15 Nov 2025

Abstract

Arabic natural language processing (NLP) has garnered significant attention in recent years due to the growing demand for automated text and Arabic-based intelligent systems, in addition to digital transformation in the Arab world. However, the unique linguistic characteristics of Arabic, including its rich [...] Read more.

Arabic natural language processing (NLP) has garnered significant attention in recent years due to the growing demand for automated text and Arabic-based intelligent systems, in addition to digital transformation in the Arab world. However, the unique linguistic characteristics of Arabic, including its rich morphology, diverse dialects, and complex syntax, pose significant challenges to NLP researchers. This paper provides a comprehensive review of the main linguistic challenges inherent in Arabic NLP, such as morphological complexity, diacritics and orthography issues, ambiguity, and dataset limitations. Furthermore, it surveys the major computational techniques employed in tokenisation and normalisation, named entity recognition, part-of-speech tagging, sentiment analysis, text classification, summarisation, question answering, and machine translation. In addition, it discusses the rapid rise of large language models and their transformative impact on Arabic NLP. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))

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18 pages, 3397 KB

Open AccessArticle

Recognizing Cattle Behaviours by Spatio-Temporal Reasoning Between Key Body Parts and Environmental Context

by Fangzheng Qi, Zhenjie Hou, En Lin, Xing Li, Jiuzhen Liang and Wenguang Zhang

Computers 2025, 14(11), 496; https://doi.org/10.3390/computers14110496 - 13 Nov 2025

Abstract

The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involving subtle interactions between cattle and environmental objects in group farming [...] Read more.

The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involving subtle interactions between cattle and environmental objects in group farming scenarios. To address this limitation, we propose a novel spatio-temporal feature extraction network that explicitly models the associative relationships between key body parts of cattle and environmental factors, thereby enabling precise behaviour recognition. Specifically, the proposed approach first employs a spatio-temporal perception network to extract discriminative motion features of key body parts. Subsequently, a spatio-temporal relation integration module with metric learning is introduced to adaptively quantify the association strength between cattle features and environmental elements. Finally, a spatio-temporal enhancement network is utilised to further optimise the learned interaction representations. Experimental results on a public cattle behaviour dataset demonstrate that our method achieves a state-of-the-art mean average precision (mAP) of 87.19%, outperforming the advanced SlowFast model by 6.01 percentage points. Ablation studies further confirm the synergistic effectiveness of each module, particularly in recognising behaviours that rely on environmental interactions, such as drinking and grooming. This study provides a practical and reliable solution for intelligent cattle behaviour monitoring and highlights the significance of relational reasoning in understanding animal behaviours within complex environments. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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24 pages, 3558 KB

Open AccessArticle

GrowMore: Adaptive Tablet-Based Intervention for Education and Cognitive Rehabilitation in Children with Mild-to-Moderate Intellectual Disabilities

by Abdullah, Nida Hafeez, Kinza Sardar, Fatima Uroosa, Zulaikha Fatima, Rolando Quintero Téllez and José Luis Oropeza Rodríguez

Computers 2025, 14(11), 495; https://doi.org/10.3390/computers14110495 - 13 Nov 2025

Abstract

Providing equitable, high-quality education to all children, including those with intellectual disabilities (ID), remains a critical global challenge. Traditional learning environments often fail to address the unique cognitive needs of children with mild and moderate ID. In response, this study explores the potential [...] Read more.

Providing equitable, high-quality education to all children, including those with intellectual disabilities (ID), remains a critical global challenge. Traditional learning environments often fail to address the unique cognitive needs of children with mild and moderate ID. In response, this study explores the potential of tablet-based game applications to enhance educational outcomes through an interactive, engaging, and accessible digital platform. The proposed solution, GrowMore, is a tablet-based educational game specifically designed for children aged 8 to 12 with mild intellectual disabilities. The application integrates adaptive learning strategies, vibrant visuals, and interactive feedback mechanisms to foster improvements in object recognition, color identification, and counting skills. Additionally, the system supports cognitive rehabilitation by enhancing attention, working memory, and problem-solving abilities, which caregivers reported transferring to daily functional tasks. The system’s usability was rigorously evaluated using quality standards, focusing on effectiveness, efficiency, and user satisfaction. Experimental results demonstrate that approximately 88% of participants were able to correctly identify learning elements after engaging with the application, with notable improvements in attention span and learning retention. Informal interviews with parents further validated the positive cognitive, behavioral, and rehabilitative impact of the application. These findings underscore the value of digital game-based learning tools in special education and highlight the need for continued development of inclusive educational technologies. Full article

(This article belongs to the Special Issue Advances in Game-Based Learning, Gamification in Education and Serious Games)

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20 pages, 1628 KB

Open AccessArticle

eXplainable AI Framework for Automated Lesson Plan Generation and Alignment with Bloom’s Taxonomy

by Deborah Olaniyan, Julius Olaniyan, Ibidun C. Obagbuwa and Anthony K. Tsetse

Computers 2025, 14(11), 494; https://doi.org/10.3390/computers14110494 - 13 Nov 2025

Abstract

This paper presents an Explainable Artificial Intelligence (XAI) framework for the automated generation of lesson plans aligned with Bloom’s Taxonomy. The proposed system addresses the dual challenges of accurate cognitive classification and pedagogical transparency by integrating a multi-task transformer-based classifier with a taxonomy-conditioned [...] Read more.

This paper presents an Explainable Artificial Intelligence (XAI) framework for the automated generation of lesson plans aligned with Bloom’s Taxonomy. The proposed system addresses the dual challenges of accurate cognitive classification and pedagogical transparency by integrating a multi-task transformer-based classifier with a taxonomy-conditioned content generation module. Drawing from a locally curated dataset of 3000 annotated lesson objectives, the model predicts both cognitive process levels and knowledge dimensions using attention-enhanced representations, while offering token-level explanations via SHAP to support interpretability. A GPT-based generator leverages these predictions to produce instructional activities and assessments tailored to the taxonomy level, enabling educators to scaffold learning effectively. Empirical evaluations demonstrate strong classification performance (F1-score of 91.8%), high pedagogical alignment in generated content (mean expert rating: 4.43/5), and robust user trust in the system’s explanatory outputs. The framework is designed with a feedback loop for continuous fine-tuning and incorporates an educator-facing interface conceptually developed for practical deployment. This study advances the integration of trustworthy AI into curriculum design by promoting instructional quality and human-in-the-loop explainability within a theoretically grounded implementation. Full article

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19 pages, 1791 KB

Open AccessArticle

Document Encoding Effects on Large Language Model Response Time and Consistency

by Dianeliz Ortiz Martes and Nezamoddin N. Kachouie

Computers 2025, 14(11), 493; https://doi.org/10.3390/computers14110493 - 13 Nov 2025

Abstract

Large language models (LLMs) such as GPT-4 are increasingly integrated into research, industry, and enterprise workflows, yet little is known about how input file formats shape their outputs. While prior work has shown that formats can influence response time, the effects on readability, [...] Read more.

Large language models (LLMs) such as GPT-4 are increasingly integrated into research, industry, and enterprise workflows, yet little is known about how input file formats shape their outputs. While prior work has shown that formats can influence response time, the effects on readability, complexity, and semantic stability remain underexplored. This study systematically evaluates GPT-4’s responses to 100 queries drawn from 50 academic papers, each tested across four formats, TXT, DOCX, PDF, and XML, yielding 400 question–answer pairs. We have assessed two aspects of the responses to the queries: first, efficiency quantified by response time and answer length, and second, linguistic style measured by readability indices, sentence length, word length, and lexical diversity where semantic similarity was considered to control for preservation of semantic context. Results show that readability and semantic content remain stable across formats, with no significant differences in Flesch–Kincaid or Dale–Chall scores, but response time is sensitive to document encoding, with XML consistently outperforming PDF, DOCX, and TXT in the initial experiments conducted in February 2025. Verbosity, rather than input size, emerged as the main driver of latency. However, follow-up replications conducted several months later (October 2025) under the updated Microsoft Copilot Studio (GPT-4) environment showed that these latency differences had largely converged, indicating that backend improvements, particularly in GPT-4o’s document-ingestion and parsing pipelines, have reduced the earlier disparities. These findings suggest that the file format matters and affects how fast the LLMs respond, although its influence may diminish as enterprise-level AI systems continue to evolve. Overall, the content and semantics of the responses are fairly similar and consistent across different file formats, demonstrating that LLMs can handle diverse encodings without compromising response quality. For large-scale applications, adopting structured formats such as XML or semantically tagged HTML can still yield measurable throughput gains in earlier system versions, whereas in more optimized environments, such differences may become minimal. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling (2nd Edition))

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20 pages, 4147 KB

Open AccessArticle

An Augmented Reality Mobile App for Recognizing and Visualizing Museum Exhibits

by Madina Ipalakova, Zhiger Bolatov, Yevgeniya Daineko, Dana Tsoy, Damir Khojayev and Ekaterina Reznikova

Computers 2025, 14(11), 492; https://doi.org/10.3390/computers14110492 - 13 Nov 2025

Abstract

Augmented reality (AR) offers a novel way to enrich museum visits by deepening engagement and enhancing learning. This study presents the development of a mobile application for the Abylkhan Kasteyev State Museum of Arts (Almaty, Kazakhstan), designed to recognize and visualize exhibits through [...] Read more.

Augmented reality (AR) offers a novel way to enrich museum visits by deepening engagement and enhancing learning. This study presents the development of a mobile application for the Abylkhan Kasteyev State Museum of Arts (Almaty, Kazakhstan), designed to recognize and visualize exhibits through AR. Using computer vision and machine learning, the application identifies artifacts via a smartphone camera and overlays interactive 3D models in an augmented environment. The system architecture integrates Flutter plugins for AR rendering, YOLOv8 for exhibit recognition, and a cloud database for dynamic content updates. This combination enables an immersive educational experience, allowing visitors to interact with digital reconstructions and multimedia resources linked to the exhibits. Pilot testing in the museum demonstrated recognition accuracy above 97% and received positive feedback on usability and engagement. These results highlight the potential of AR-based mobile applications to increase accessibility to cultural heritage and enhance visitor interaction. Future work will focus on enlarging the exhibit database, refining performance, and incorporating additional interactive features such as multi-user collaboration, remote access, and gamified experiences. Full article

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19 pages, 913 KB

Open AccessArticle

Decision-Making Model for Risk Assessment in Cloud Computing Using the Enhanced Hierarchical Holographic Modeling

by Auday Qusay Sabri and Halina Binti Mohamed Dahlan

Computers 2025, 14(11), 491; https://doi.org/10.3390/computers14110491 - 13 Nov 2025

Abstract

Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered [...] Read more.

Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered decision-making using an Enhanced Hierarchical Holographic Modeling (EHHM) approach for cloud computing security risk assessment. Two methods were used, the Entropy Weight Method (EWM) and Criteria Importance Through Intercriteria Correlation (CRITIC), to provide a multi-factor decision-making risk assessment framework across the different security domains that exist with cloud computing. Additionally, fuzzy set theory provided the respective levels of complexity dispersion and ambiguities, thus facilitating an accurate and objective participation for a cloud risk assessment across asymmetric information. The trapezoidal membership function measures the correlation, rank, and scores, and was applied to each corresponding cloud risk security domain. The novelty of this re-search is represented by enhancing HHM with an expanded security-transfer domain that encompasses the client side, integrating dual-objective weighting (EWM + CRITIC), and the use of fuzzy logic to quantify asymmetric uncertainty in judgments unique to this study. Informed, data-related, multidimensional cloud risk assessment is not reported in previous studies using HHM. The different Integrated Weight measures allowed for accurate risk judgments. The risk assessment across the calculated cloud computing security domains resulted in a total score of 0.074233, thus supporting the proposed model in identifying and prioritizing risk assessment. Furthermore, the scores of the cloud computing dimensions highlight EHHM as a suitable framework to support and assist corporate decision-making in cloud computing security activity and informed risk awareness with innovative activity amongst a turbulent and dynamic cloud computing environment with corporate operational risk. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Mining)

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19 pages, 4107 KB

Open AccessArticle

Structured Prompting and Collaborative Multi-Agent Knowledge Distillation for Traffic Video Interpretation and Risk Inference

by Yunxiang Yang, Ningning Xu and Jidong J. Yang

Computers 2025, 14(11), 490; https://doi.org/10.3390/computers14110490 - 9 Nov 2025

Abstract

Comprehensive highway scene understanding and robust traffic risk inference are vital for advancing Intelligent Transportation Systems (ITS) and autonomous driving. Traditional approaches often struggle with scalability and generalization, particularly under the complex and dynamic conditions of real-world environments. To address these challenges, we [...] Read more.

Comprehensive highway scene understanding and robust traffic risk inference are vital for advancing Intelligent Transportation Systems (ITS) and autonomous driving. Traditional approaches often struggle with scalability and generalization, particularly under the complex and dynamic conditions of real-world environments. To address these challenges, we introduce a novel structured prompting and multi-agent collaborative knowledge distillation framework that enables automatic generation of high-quality traffic scene annotations and contextual risk assessments. Our framework orchestrates two large vision–language models (VLMs): GPT-4o and o3-mini, using a structured Chain-of-Thought (CoT) strategy to produce rich, multiperspective outputs. These outputs serve as knowledge-enriched pseudo-annotations for supervised fine-tuning of a much smaller student VLM. The resulting compact 3B-scale model, named VISTA (Vision for Intelligent Scene and Traffic Analysis), is capable of understanding low-resolution traffic videos and generating semantically faithful, risk-aware captions. Despite its significantly reduced parameter count, VISTA achieves strong performance across established captioning metrics (BLEU-4, METEOR, ROUGE-L, and CIDEr) when benchmarked against its teacher models. This demonstrates that effective knowledge distillation and structured role-aware supervision can empower lightweight VLMs to capture complex reasoning capabilities. The compact architecture of VISTA facilitates efficient deployment on edge devices, enabling real-time risk monitoring without requiring extensive infrastructure upgrades. Full article

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15 pages, 3130 KB

Open AccessArticle

Preliminary Study on Image-Finding Generation and Classification of Lung Nodules in Chest CT Images Using Vision–Language Models

by Maiko Nagao, Atsushi Teramoto, Kaito Urata, Kazuyoshi Imaizumi, Masashi Kondo and Hiroshi Fujita

Computers 2025, 14(11), 489; https://doi.org/10.3390/computers14110489 - 9 Nov 2025

Abstract

In the diagnosis of lung cancer, imaging findings of lung nodules are essential for benign and malignant classifications. Although numerous studies have investigated the classification of lung nodules, no method has been proposed for obtaining detailed imaging findings. This study aimed to develop [...] Read more.

In the diagnosis of lung cancer, imaging findings of lung nodules are essential for benign and malignant classifications. Although numerous studies have investigated the classification of lung nodules, no method has been proposed for obtaining detailed imaging findings. This study aimed to develop a novel method for generating image findings and classifying benign and malignant nodules in chest computed tomography (CT) images using vision–language models. In this study, we collected chest CT images of 77 patients diagnosed with either benign or malignant tumors at Fujita Health University Hospital. For these images, we cropped the regions of interest around the nodules, and a pulmonologist provided the corresponding image findings. We used vision–language models for image captioning to generate image findings. The findings generated by these two models were grammatically correct, with no deviations in notation, as expected from the image findings. Moreover, the descriptions of benign and malignant characteristics were accurately obtained. The bootstrapping language–image pretraining (BLIP) base model achieved an accuracy of 79.2% in classifying nodules, and the bilingual evaluation understudy-4 score for agreement with physician findings was 0.561. These results suggest that the proposed method may be effective for classifying and generating lung nodule findings. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))

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28 pages, 1195 KB

Open AccessArticle

A Multifaceted Deepfake Prevention Framework Integrating Blockchain, Post-Quantum Cryptography, Hybrid Watermarking, Human Oversight, and Policy Governance

by Mohammad Alkhatib

Computers 2025, 14(11), 488; https://doi.org/10.3390/computers14110488 - 8 Nov 2025

Abstract

Deepfake technology, driven by advances in artificial intelligence (AI) and deep learning (DL), has become one of the foremost threats to digital trust and the authenticity of information. Despite the rapid development of deepfake detection methods, the dynamic evolution of generative models continues [...] Read more.

Deepfake technology, driven by advances in artificial intelligence (AI) and deep learning (DL), has become one of the foremost threats to digital trust and the authenticity of information. Despite the rapid development of deepfake detection methods, the dynamic evolution of generative models continues to outpace current mitigation efforts. This highlights the pressing need for more effective and proactive deepfake prevention strategy. This study introduces a comprehensive and multifaceted deepfake prevention framework that leverages both technical and non-technical countermeasures and involves collaboration among key stakeholders in a unified structure. The proposed framework has four modules: trusted content assurance, detection and monitoring, awareness and human-in-the-loop verification, and policy, governance, and regulation. The framework uses a combination of hybrid watermarking and embedding techniques, as well as cryptographic digital signature algorithms (DSAs) and blockchain technologies, to make sure that the media is authentic, traceable, and cannot be denied. Comparative experiments were conducted in this research using both classical and post-quantum DSAs to evaluate their efficiency, resource consumption, and gas costs in blockchain operations. The results revealed that the Falcon-512 algorithm outperformed other post-quantum algorithms while consuming fewer resources and lowering gas costs, making it a preferable option for real-time, quantum-resilient deepfake prevention. The framework also employed AI-based detection models and human oversight to enhance detection accuracy and robustness. Overall, this research offers a novel, multifaceted, and governance-aware strategy for deepfake prevention. The proposed approach significantly contributes to mitigating deepfake threats and offers a practical foundation for secure and transparent digital media ecosystems. Full article

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18 pages, 735 KB

Open AccessArticle

Artificial Intelligence in Stock Market Investment Through the RSI Indicator

by Alberto Agudelo-Aguirre, Néstor Duque-Méndez and Alejandro Galvis-Flórez

Computers 2025, 14(11), 487; https://doi.org/10.3390/computers14110487 - 7 Nov 2025

Abstract

Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within [...] Read more.

Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within acceptable risk levels, in accordance with their investment profile. The purpose of this research is to develop a model with a high predictive capacity for equity asset returns through the application of artificial intelligence techniques that integrate genetic algorithms and neural networks. The methodology is framed within a technical analysis-based investment approach, using the Relative Strength Index as the main indicator. The results show that more than 58% of the predictions generated with the proposed methodology outperformed the results obtained through the traditional technical analysis approach. These findings suggest that the incorporation of genetic algorithms and neural networks constitutes an effective alternative for optimizing investment strategies in equity assets, by providing superior returns and more accurate predictions in most of the analyzed cases. Full article

(This article belongs to the Section AI-Driven Innovations)

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51 pages, 4543 KB

Open AccessArticle

Ripple Evolution Optimizer: A Novel Nature-Inspired Metaheuristic

by Hussam N. Fakhouri, Hasan Rashaideh, Riyad Alrousan, Faten Hamad and Zaid Khrisat

Computers 2025, 14(11), 486; https://doi.org/10.3390/computers14110486 - 7 Nov 2025

Abstract

This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a [...] Read more.

This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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32 pages, 3419 KB

Open AccessArticle

NLP Models for Military Terminology Analysis and Detection of Information Operations on Social Media

by Bayangali Abdygalym, Madina Sambetbayeva, Aigerim Yerimbetova, Anargul Nekessova, Nurbolat Tasbolatuly, Nurzhigit Smailov and Aksaule Nazymkhan

Computers 2025, 14(11), 485; https://doi.org/10.3390/computers14110485 - 6 Nov 2025

Abstract

This paper presents Multi_mil, a multilingual annotated corpus designed for the analysis of information operations in military discourse. The corpus consists of 1000 texts collected from social media and news platforms in Russian, Kazakh, and English, covering military and geopolitical narratives. A multi-level [...] Read more.

This paper presents Multi_mil, a multilingual annotated corpus designed for the analysis of information operations in military discourse. The corpus consists of 1000 texts collected from social media and news platforms in Russian, Kazakh, and English, covering military and geopolitical narratives. A multi-level annotation scheme was developed, combining entity categories (e.g., military terms, geographical references, sources) with pragmatic features such as information operation type, emotional tone, author intent, and fake claim indicators. Annotation was performed manually in Label Studio with high inter-annotator agreement (

κ

= 0.82). To demonstrate practical applicability, baseline models and the proposed Onto-IO-BERT architecture were tested, achieving superior performance (macro-F1 = 0.81). The corpus enables the identification of manipulation strategies, rhetorical patterns, and cognitive influence in multilingual contexts. Multi_mil contributes to advancing NLP methods for detecting disinformation, propaganda, and psychological operations. Full article

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35 pages, 3229 KB

Open AccessArticle

Bidirectional Privacy Preservation in Web Services

by Sumit Kumar Paul and D. A. Knox

Computers 2025, 14(11), 484; https://doi.org/10.3390/computers14110484 - 6 Nov 2025

Abstract

In web-based services, users are often required to submit personal data, which may be shared with third parties. Although privacy regulations mandate the disclosure of intended recipients in privacy policies, this does not fully alleviate users’ privacy concerns. The presence of a privacy [...] Read more.

In web-based services, users are often required to submit personal data, which may be shared with third parties. Although privacy regulations mandate the disclosure of intended recipients in privacy policies, this does not fully alleviate users’ privacy concerns. The presence of a privacy policy does not ensure compliance, since users must assess the trustworthiness of all parties involved in data sharing. On the other hand, service providers want to minimize the costs associated with preserving user privacy. Indeed, service providers may have their own privacy preservation requirements, such as hiding the identities of third-party suppliers. We present a novel framework designed to tackle the dual challenges of bidirectional privacy preservation and cost-effectiveness. Our framework safeguards the privacy of service users, providers, and various layers of intermediaries in data-sharing environments, while also reducing the costs incurred by service providers related to data privacy. This combination makes our solution a practical choice for web services. We have implemented our solution and conducted a performance analysis to demonstrate its viability. Additionally, we prove its privacy and security within a Universal Composability (UC) framework. Full article

(This article belongs to the Special Issue Emerging Trends in Network Security and Applied Cryptography)

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22 pages, 2598 KB

Open AccessArticle

trustSense: Measuring Human Oversight Maturity for Trustworthy AI

by Kitty Kioskli, Theofanis Fotis, Eleni Seralidou, Marios Passaris and Nineta Polemi

Computers 2025, 14(11), 483; https://doi.org/10.3390/computers14110483 - 6 Nov 2025

Abstract

The integration of Artificial Intelligence (AI) systems into critical decision-making processes necessitates robust mechanisms to ensure trustworthiness, ethical compliance, and human oversight. This paper introduces trustSense, a novel assessment framework and tool designed to evaluate the maturity of human oversight practices in AI [...] Read more.

The integration of Artificial Intelligence (AI) systems into critical decision-making processes necessitates robust mechanisms to ensure trustworthiness, ethical compliance, and human oversight. This paper introduces trustSense, a novel assessment framework and tool designed to evaluate the maturity of human oversight practices in AI governance. Building upon principles from trustworthy AI, cybersecurity readiness, and privacy-by-design, trustSense employs a structured questionnaire-based approach to capture an organisation’s oversight capabilities across multiple dimensions. The tool supports diverse user roles and provides tailored feedback to guide risk mitigation strategies. Its calculation module synthesises responses to generate maturity scores, enabling organisations to benchmark their practices and identify improvement pathways. The design and implementation of trustSense are grounded in user-centred methodologies, with defined personas, user flows, and a privacy-preserving architecture. Security considerations and data protection are integrated into all stages of development, ensuring compliance with relevant regulations. Validation results demonstrate the tool’s effectiveness in providing actionable insights for enhancing AI oversight maturity. By combining measurement, guidance, and privacy-aware design, trustSense offers a practical solution for organisations seeking to operationalise trust in AI systems. This work contributes to the discourse on governance of trustworthy AI systems by providing a scalable, transparent, and empirically validated human maturity assessment tool. Full article

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