Artificial Intelligence and Applications
Artificial intelligence is a discipline of science and technology for making intelligent machines to simulate human abilities in learning, perception, thinking, decision, behavior and interaction. The technology is widely used in various areas in society, including sensing data analysis and understanding, big data analytics, security surveillance, management and planning, education, medical care, robotics, unmanned driving, and so on. Keeping AI's impact on society beneficial motivates research in many areas, from economics and law to technical topics. Artificial Intelligence and Applications (AIA) is a peer-reviewed journal publishing original contributions to the theory, methods and applications of artificial intelligence. AIA aims to enhance the development and application of artificial intelligence by bringing scientists in the field together. Research articles and reviews on all scientific or application topics in AI are welcome. AIA is sponsored by Hunan Normal University (HNNU), a comprehensive university with strong expertise in science, engineering, and interdisciplinary research. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
Erratum to: Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System
During the production process, incorrect text was inadvertently introduced into the Conclusion section of the published article [Toward a Causal PM2.5 Concentration Forecasting by Inferencing from Local Vehicle Tracking with a Low-Cost End-to-End Sensor System]. The correct Conclusion should read as follows: This study has successfully demonstrated the feasibility of our novel approach to estimate PM2.5 concentrations using traffic density and readily available hardware, hinting at the possibility of PM2.5 measurement based solely on the amounts of motorbikes and cars. The implementation of a streamlined vehicle counting algorithm based on Yolov7 ensures robust performance in various conditions. For estimation, it is recommended to use the Cubist mode over the mathematical method due to Cubist’s superior performance. By synergizing hardware and algorithmic advancements, we have opened avenues for further research into the determinants of urban air quality. Future work can focus on refining the system design, incorporating additional environmental factors, and exploring more sophisticated machine learning models to further enhance prediction accuracy. The Publisher apologizes to the authors and readers for this error. Reference [1] Le, C. D., Pham, H. V., Tran, T. G., Le, A. D., Pham, A.-D., Vo, D. T., . . . , & Han, H.-D. (2026). Toward a causal PM2.5 concentration forecasting by inferencing from local vehicle tracking with a low-cost end-to-end sensor system. Artificial Intelligence and Applications, 4(2), 187–196. https://doi.org/10.47852/bonviewAIA62024212
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
CAPS: Compositional Attack Path Scoring for LLM Deployment Stacks
Evaluating the security posture of large language model (LLM) deployment stacks is a critical challenge in modern AI security. Traditional vulnerability management frameworks—such as the Common Vulnerability Scoring System (CVSS) and component-level checklists—assume that software components can be evaluated in isolation. In real-world agentic and retrieval-augmented generation (RAG)-based LLM ecosystems, this assumption is systematically violated: attackers exploit complex topologies, chaining seemingly low–risk vulnerabilities (e.g., indirect prompt injection) with downstream tools (e.g., SQL execution) to achieve catastrophic compromises. Applying independent scoring methods to deeply integrated stacks therefore yields inflated risk assessments, misaligned mitigation priorities, and a failure to capture compositional attack paths. We propose Compositional Attack Path Scoring (CAPS), a framework engineered to quantify end-to-end multi-hop risks in LLM architectures. CAPS integrates three capabilities: (i) directed graph topological modeling, which maps the deployment stack from attacker entry points to high-value assets; (ii) dynamic mitigation attenuation, which calculates the “Effective Exploitability” of nodes based on deployed guardrails; and (iii) a compositional path engine that scores risk via an explicit exponential decay factor reflecting the friction of traversing trust boundaries. CAPS also provides an automated return on investment engine to rank mitigations by systemic risk reduction. Empirical evaluation on standardized architectures (RAG Chatbots, Autonomous Coding Agents, and Enterprise Model Routers) shows that CAPS improves risk calibration: against the Autonomous Agent benchmark, it computes a realistic critical path score of 51.3, correcting the naive 85.0 overestimation of component-level CVSS scoring. CAPS establishes a rigorous benchmark for quantitative vulnerability management in complex, agentic LLM environments. Received: 1 June 2026 | Revised: 15 July 2026 | Accepted: 31 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the CAPS repository [GitHub] at https://github.com/vinhqdang/CAPS-Compositional-Attack-Path-Scoring-for-LLM-Deployment-Stacks. Author Contribution Statement Quang-Vinh Dang: Conceptualization, Methodology, Software, Writing – original draft. Hoang-Viet Vu: Software, Validation, Visualization. Ngoc-Son-An Nguyen: Investigation, Data curation. Minh Ngoc Dinh: Formal analysis, Investigation. Dat Le: Writing – review & editing, Supervision, Project administration.
Generative AI in Management Research After ChatGPT: A Bibliometric Map of Themes, Influence, and Governance
Generative artificial intelligence (GenAI), particularly since the introduction of ChatGPT, has not only led to a quantitative increase in publications in management and business research but also created a qualitative transformation in research themes and collaborative structures. However, the current literature is insufficient to comprehensively present the epistemic and thematic consequences of this transformation in the field of management. This study aims to fill this gap by examining 316 articles indexed in the Social Sciences Citation Index database within the Web of Science Core Collection and published between 2023 and 2025 using bibliometric analysis. The analyses show that normative and governance-oriented themes such as ethics, artificial intelligence (AI) literacy, and agentic AI have become increasingly visible in the management literature in the post-ChatGPT era; rather than displacing performance- and productivity-centered approaches, these normative themes emerged alongside them within an increasingly diversified keyword and thematic structure. The findings reveal that GenAI research in management has evolved from being merely a technological innovation to a research axis that reshapes discussions on organizational responsibility, human–machine interaction, and governance. In this respect, the study goes beyond mapping the current state of the literature and offers theoretical and methodological implications on how GenAI should be positioned in management research. Received: 18 May 2026 | Revised: 24 June 2026 | Accepted: 15 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Irem Tanyıldızı Baydili: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Burak Tasci: Software, Writing–original draft, Writing – review & editing, Visualization. Sengul Dogan: Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization. Turker Tuncer: Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization.
Uncertainty-Aware and Bias-Calibrated Vision Transformer–Graph Neural Network for Reliable Skin Cancer Detection
Deep learning models for skin cancer diagnosis have improved, but most cannot quantify predictive uncertainty, are insensitive to dataset bias, and are unreliable under distributional shifts. Previous experiments using hybrid Vision Transformer–Graph Neural Networks (ViT–GNNs) with adaptive attention showed strong classification performance, but they were deterministic and made over–confident, biased predictions. We propose a hybrid ViT–GNN model with adaptive uncertainty and bias attention to improve skin cancer detection reliability and clinical acceptability. A supplementary prediction head estimates epistemic and aleatoric uncertainty using Monte Carlo dropout and deep ensembles, extending the Region-Adaptive Attention ViT–GNN architecture. A fairness calibration module uses group-wise temperature scaling and regularization to reduce performance disparities across skin tones and imaging situations. An out-of-distribution (OOD) detection system identifies anomalies and unfamiliar inputs to avoid overconfidence in wrong predictions. A clinical risk stratification layer classifies predictions as low, medium, or high risk based on confidence and uncertainty. The framework was evaluated on ISIC 2020, HAM10000, and PH2 datasets and cross-domain validated on unseen dermoscopic sources. The Expected Calibration Error, Brier score, fairness gap, and OOD detection metrics assess reliability, calibration, fairness, and safety beyond accuracy metrics. The empirical results suggest that the proposed model improves calibration and fairness without lowering competitive diagnostic accuracy, creating a reliable, interpretable, and ready-to-deploy skin cancer screening system. Received: 14 May 2026 | Revised: 15 July 2026 | Accepted: 24 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/nour12347653/skin-disease-detection-dataset-ham10000-isic. Author Contribution Statement Aswani Dogga: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sivasubramanian R.: Software, Investigation, Resources, Supervision. Shanthi S.: Project administration.
An Integrated Advanced Pattern Mining Framework for Strategic Retail Decision Intelligence
Retail businesses increasingly rely on data-driven intelligence to support strategic decision-making; however, existing approaches often focus on isolated pattern mining or predictive models, limiting their practical impact. This paper proposes an Integrated Advanced Pattern Mining Framework that unifies frequent, temporal, and utility-driven patterns extracted from large-scale transactional data. The mined patterns are consolidated and ranked using an Integrated Pattern Score, enabling the identification of actionable and business-relevant knowledge. To operationalize these insights, a hybrid recommendation mechanism combining interpretable rule-based reasoning and convolutional neural network–based predictive learning is introduced. Experiments conducted on a real-world retail dataset comprising one year of transactional records from a Bangladeshi supermarket demonstrate that the proposed hybrid approach achieves an F1-score of 0.84. Under offline evaluation, the framework achieved the highest projected revenue contribution (10.6%) compared with the classical association rule mining baseline (7.5%), while also outperforming standalone recommendation models. The results confirm that integrating advanced pattern mining with hybrid learning models provides an effective and scalable solution for strategic retail decision intelligence. Received: 30 April 2026 | Revised: 15 June 2026 | Accepted: 2 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in SupershopBd1-Retail-Market-Dataset at https://github.com/Sohel-Rana-2/SupershopBd1-Retail-Market-Dataset. Author Contribution Statement Sohel Rana: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Md. Nazrul Islam Mondal: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision. Md. Rabiul Islam: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision.
AI-Based Teacher Chatbots Fostering Self-Regulating Learning: Saudi Universities’ Perspectives
Chatbots have secured public attention and researchers’ interest in the education field to improve instruction and learning. The current study investigates the Saudi students’ perspectives on fostering self-regulating learning utilizing artificial intelligence (AI)- based teacher chatbots. Data were collected from 393 undergraduate and postgraduate students enrolled in the summer semester at Ha’il University. The study found that the students had a positive perception of the AI-based teacher chatbot’s role in fostering their self-regulating learning (motivation, strategy use, and behavioral change), utilizing their learning processes, with a focus on fostering behavioral change; allocation of effort and time for learning, seeking help behavior and peer learning, and actively setting their learning goals. The findings indicate a generally positive attitude toward AI chatbots as tools for enhancing learning experiences. Accordingly, the study suggested that AI-based teacher chatbots are viable for enhancing self-regulating learning consolidation among Saudi university students. It has the potential to foster independent learning habits, personalizing learning and improving academic achievement. Received: 29 September 2025 | Revised: 25 February 2026 | Accepted: 10 June 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Yousef Almoslamni: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Post-Quantum Blockchain and AI-Based Security Framework for Smart Healthcare Systems
The security of smart healthcare systems has become an important research topic as patient records are progressively digitalized, and new cyber threats continue to emerge. Traditional cryptographic mechanisms may become less reliable in the presence of quantum computing capabilities, which motivates the need for quantum-resistant security solutions. A simulation-based human-in-the-loop reference architecture for smart healthcare systems with post-quantum cryptographic mechanisms, blockchain-enabled auditability, and artificial intelligence (AI)-enabled threat detection to enhance security, privacy, and real-time monitoring is proposed. Our proposed framework integrates post-quantum digital signatures, quantum-resistant key establishment mechanisms with tamper-evident blockchain logging, and AI-based anomaly detection to facilitate secure and traceable healthcare data processing. Simulation experiments were performed using synthetic healthcare cybersecurity scenarios involving electronic health record access behavior and Internet of Medical Things traffic (e.g., unauthorized access attempts, anomalous data exfiltration, SQL injection, or distributed denial-of-service (DDoS)-like flooding attacks). The proposed AI-based detection model achieved an accuracy of 92.1%, a precision of 91.4%, a recall of 93.2%, an F1-score of 92.3%, and a false-positive rate (FPR) of 5.1%, with a detection-cycle latency of 200 ms. Scalability was evaluated using up to 10,000 simulated healthcare endpoints, with stable detection performance and blockchain-logging behavior observed in the simulation environment. The results indicate that the suggested framework can offer a scalable and privacy-aware security architecture for next-generation smart healthcare systems. The framework must be treated as a simulation-based reference that needs to be validated against real healthcare datasets and deployment scenarios. Received: 18 March 2026 | Revised: 26 May 2026 | Accepted: 22 June 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Ahmad H. Alenezi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – revie
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0
A Reproducible Machine Learning Framework for Detecting High-Risk Password Behavior in Healthcare Staff
This study develops a reproducible machine learning framework to detect self-reported high-risk password behavior among healthcare staff, using structured survey data from a private hospital in Phuket. After deterministic cleaning and duplicate removal, 362 unique responses were retained from 416 initial records. The target variable was defined as a binary risk proxy indicating endorsement of at least two insecure password practices or unsafe security beliefs. Four supervised models, support vector machine (SVM), Random Forest, gradient-boosted trees, and logistic regression, including a TF-IDF-based baseline, were evaluated using stratified five-fold cross–validation, alongside rule-based and majority-class baselines. Macro-F1 was used as the primary metric to address class imbalance, with weighted F1, accuracy, and area under the receiver operating characteristic curve reported as complementary measures. Random Forest achieved the highest macro-F1 (0.678 ± 0.091) and weighted F1 (0.795 ± 0.058), indicating the best overall balance between minority and majority-class prediction. However, paired statistical tests showed that its advantage over SVM and logistic regression was not statistically significant ( p > 0.05), although it significantly outperformed weaker baselines ( p < 0.05; Cohen’s d = 1.25–2.63). Feature–group ablation showed that knowledge and perception variables contributed the largest gains in performance, whereas demographic and contextual variables had limited impact. Receiver operating characteristic analysis indicated moderate discriminative ability (area under the curve up to 0.707), and confusion matrices showed higher specificity than sensitivity. The findings demonstrate that a leak–free, statistically validated, and interpretable machine learning framework can support meaningful but cautious risk stratification from healthcare survey data. Received: 19 April 2026 | Revised: 16 June 2026 | Accepted: 24 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Github at https://github.com/pjarupunphol/Datasets/blob/main/dataset.zip. Author Contribution Statement Pita Jarupunphol: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration. Siwatchaya Suksai: Software, Validation, Investigation, Resources, Data curation, Visualization.
Examining the Factors Affecting Vocational and Technical College Students' Acceptance of Automated Written Corrective Feedback: A TAM-Based Quasi-Experimental Approach
The quasi-experimental study investigates the acceptance and effects of the Automated Written Corrective Feedback (AWCF) system iWrite among Chinese vocational and technical college students, applying the Technology Acceptance Model (TAM). The study included 112 students, who were categorized into an experimental group (n = 54) that utilized iWrite for writing feedback and revision and a control group (n = 58) that received traditional teacher-led feedback. Writing proficiency was measured by content, language use, and structure-based pre- and post-tests. Compared with the control group, the experimental group had statistically significant improvements in total writing scores. The results of the TAM-based survey showed a generally positive attitude toward iWrite, with Perceived Usefulness (PU) as the highest mean score. Struct → PU → AT → BI, with Perceived Ease of Use (PEOU)’s direct effect on BI no ural equation modeling confirmed critical pathways of the TA nsignificant, suggesting that PU, rather than PEOU, M: PEOU predominantly influences acceptance in this educational setting. This study supports the pedagogical efficacy of the iWrite system and contributes methodologically by developing and validating a modified TAM framework specifically designed for vocational and technical college students in China. The results provide a technical framework to upgrade AWCF systems to better meet the practical learning needs of this particular user group. Thus, the study advocates the judicious integration of AWCF systems into vocational and technical college English syllabuses, particularly in cases where the educational benefits are clear and aligned with the practical learning objectives of students. Received: 9 February 2026 | Revised: 25 May 2026 | Accepted: 18 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Figshare at https://figshare.com/s/78ad5d90995db17b3cde. Author Contribution Statement Xiaomin Deng: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Supyan Hussin: Validation, Writing – review & editing, Supervision, Project administration. Nur Ainil Sulaiman: Writing – review & editing, Supervision.
Automated Written Corrective Feedback (AWCF)
vocational and technical college students
Using Machine Learning to Enhance the EEG Screening Review by Pre-Screening the EEG
Artificial intelligence (AI) is increasingly being used to assist physicians with their medical diagnosis and patient care and improve communication between doctors and their patients. This paper demonstrates how machine learning (ML), a subset of AI, can pre-screen electroencephalograms (EEGs) to assess the overall quality of the EEG and identify artifacts or abnormalities. We developed the “Brain Panel,” an automated ML tool that supplements clinical neurophysiologists’ quality review screening. The Brain Panel generates results prior to visual inspection or quantitative EEG (QEEG) analysis, highlighting potential technical issues or clinical concerns through novel metrics and interpretation methods. We subjected 100 Brain Panel reports and 100 corresponding physician reports from the same EEGs to independent AI evaluations using Grok and Claude. Results show that the Brain Panel provides a sensitive, neurologically informed method to estimate artifacts, assess EEG quality, detect drowsiness, and indicate the likelihood of brain abnormalities. By identifying optimal combinations of Brain Panel metrics, we created detection algorithms that achieved sensitivities of 89–95% for clinically relevant findings. This approach demonstrates that using AI to analyze and interpret automated Brain Panel reports produces a system with clear clinical value for detecting technical and clinical issues in EEGs, enhancing neurologist productivity without replacing expert judgment. Received: 3 July 2025 | Revised: 25 March 2026 | Accepted: 19 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Online Data Resource https://www.dropbox.com/scl/fo/9lvvnoui19c16oehliarf/AJumW34SIPIwtjmNkRPumCU?rlkey=sw8lxip2dpnlo57eb5ag0w1c3&e=1&dl=0. Author Contribution Statement Thomas Collura: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Agostino Rosace: Methodology, Software, Formal analysis. Robert Turner: Conceptualization, Validation, Investigation, Resources, Supervision. David Ims: Conceptualization, Validation, Investigation, Resources. Bill Brubaker: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Beat-Level Echocardiographic Clip Analysis with Geometry-Video Fusion and Cross–Attention for Accurate Ejection Fraction Estimation and Heart Failure Stratification
Left ventricular ejection fraction (LVEF) is a crucial indicator of cardiac function in the diagnosis and management of heart failure. Recent deep learning approaches have shown promising results for automated LVEF estimation from echocardiographic videos; most studies rely on spatiotemporal information, and few studies investigate the contribution of anatomical descriptors. In this study, we propose a comprehensive beat-level framework for EF estimation and heart failure classification using echocardiographic videos. Representative cardiac beat clips were extracted using tracing-derived annotations. Three architectures were investigated: (a) EchoEF-Net, a spatiotemporal Convolution Neural Network (CNN), which is a video-only model; (b) GeoFusionEF-Net, a multimodal deep learning feature-level fusion of geometry and video features for enhanced robustness and calibration; and (c) CrossAttnFusionEF-Net, which employs a staged cross-attention multimodal architecture to integrate the video and geometry interactions adaptively. Experiments were conducted on the publicly available EchoNet-Dynamic dataset (10,030 videos). On the official test set, the proposed GeoFusionEF-Net model effectively captures interactions between the video and geometry modalities, significantly reducing mean absolute error from 4.61 (EchoEF-Net) and 1.99 (CrossAttnFusionEF-Net) to 0.648, while improving root mean squared error and Pearson correlation to 1.413 and 0.99, respectively. The estimated EF predictions were further utilized to automate heart failure stratification into reduced (HFrEF), mid-range (HFmrEF), and preserved (HFpEF) EF categories. The meta-classifier of all three EF estimators achieved 0.97 accuracy and 0.99 Area Under the Curve (AUC). The proposed framework, combining beat-level motion analysis and geometric features with Shapley Additive Explanations- and Gradient-Weighted Class Activation Mapping-based explainability, enables quantification of ejection fraction and clinically reliable heart failure classification, thereby scaling echocardiographic decision-making. Received: 2 April 2026 | Revised: 15 June 2026 | Accepted: 24 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the EchoNet-Dynamic repository at https://stanfordaimi.azurewebsites.net/datasets/834e1cd1-92f7-4268-9daa-d359198b310a. Author Contribution Statement Chaithra C. S.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Siddesha S.: Conceptualization, Validation, Formal analysis, Investigation, Writing – review & editing, Supervision, Project administration. Vinay Kumar N.: Validation, Formal analysis, Writing – review & editing. V. N. Manjunath Aradhya: Validation, Formal analysis, Writing – review & editing.
Transformer-Based Approaches in Paraphrasing Texts
In a variety of natural language processing tasks, such as text generation, classification, sentiment analysis, and question answering, large language models like GPT have recently shown impressive capabilities. However, maintaining coherence and preserving semantic integrity make it difficult to generate high-quality paraphrases, especially for lengthy textual inputs. By utilizing three well-known and extensively used LLM architectures, including T5, Pretraining with Extracted Gap-sentences for Abstractive Summarization, and Bidirectional and Auto-Regressive Transformer, this study explores this problem by putting forth a strong multi-model framework for automatic paraphrasing. The main goal is to evaluate how well they can produce coherent and semantically correct paraphrases at the sentence and paragraph levels without requiring text segmentation. To assess output quality, the experimental setup uses common evaluation metrics, such as Recall-Oriented Understudy for Gisting Evaluation and Bilingual Evaluation Understudy scores. According to empirical findings, T5 consistently produces better results, especially in terms of semantic fidelity and linguistic fluency, even though all three models demonstrate strong paraphrasing abilities. These results highlight T5’s efficacy in complex paraphrasing tasks and provide insightful information for future research in data augmentation, summarization, and automatic content rewriting. Received: 20 August 2025 | Revised: 6 March 2026 | Accepted: 1 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/cherradii/Paraphrasage/blob/main/datasets_paraphrases.csv.zip. Author Contribution Statement Mohamed Cherradi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hajar El Mahajer: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Cyber Threats in E-healthcare and an Autonomous Cybersecurity Agent: A Comprehensive Review
In the future, an AI-driven security agent could be designed to face cyberattacks for small-scale E-healthcare systems. With the evolution of the Internet, E-healthcare information is made available within seconds on any electronic device, irrespective of the user’s geographical location. Since E-healthcare information can be accessed flexibly by individuals on the Internet, as such these are prone to cyberattacks by hackers and even the critical healthcare infrastructure. This study is a critical narrative review of the threats faced by the E-healthcare sector while proposing autonomous AI-driven security agents for improving cyber resilience. The major attacks faced by the E-healthcare systems are ransomware, phishing, data breaches, malware, Distributed Denial of Service, Structured Query Language injection, and Man-in-the-Middle, together with their influence on privacy, confidentiality, authenticity, and patient safety. In this article, a seven-stage cybersecurity response framework consisting of identification, protection, detection, response, recovery, procurement, and security audit is introduced. This architecture is capable of sensing, analyzing, responding to, and learning from the changing attack patterns to ensure security from future attacks. This article also highlights the significance of AI-assisted security mechanisms, staff awareness, and privacy-preserving practices in enhancing defenses of the electronic healthcare sector. In the future, the proposed framework could be implemented and validated as eventually cybersecurity needs to be adaptive to cyberattacks. Received: 6 March 2026 | Revised: 25 June 2026 | Accepted: 2 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Divya Sharma: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing – original draft, Visualization. Chander Prabha: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – review & editing, Supervision, Project administration. Amna Bamaqa: Software, Investigation, Resources, Data curation. Wedad O. Alahamade: Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Mohammad Zubair Khan: Validation, Supervision, Project administration.
Fine-Tuning IndoBERTa for Indonesian Digital News Sentiment Classification
The rapid growth of Indonesian digital news content necessitates automated sentiment analysis systems capable of handling formal journalistic discourse, which differs substantially from the social media text used to train most existing sentiment classifiers. This study investigates domain adaptation for Indonesian news sentiment analysis by fine-tuning IndoBERTa, a RoBERTa-based model for Indonesian language processing. Using a structured data mining workflow inspired by the Cross-Industry Standard Process for Data Mining, we collected and manually annotated 1300 news articles from two major Indonesian news portals (Kompas and Detik) into positive, negative, and neutral categories. Zero-shot evaluation using a social-media-trained sentiment model yielded 14% accuracy, with over 89% of samples predicted as neutral, indicating a strong domain-induced classification bias. After fine-tuning, IndoBERTa achieved 98% accuracy, a Cohen’s kappa score of 0.98, and balanced F1-scores across all sentiment classes. While keyword-guided data collection and class balancing likely inflate absolute performance, the results clearly demonstrate the effectiveness of domain-specific adaptation for Indonesian news sentiment classification. The fine-tuned model is deployed in a real-time analysis pipeline and publicly released to support further research in Indonesian Natural Language Processing (NLP). Received: 31 August 2025 | Revised: 9 February 2026 | Accepted: 7 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Author Contribution Statement Desi Masdin Dama: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Tati Mardiana: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision. Riki Supriyadi: Conceptualization, Methodology, Validation, Supervision. Zico Pratama Putra: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision. Dicky Octaviano: Validation, Investigation, Writing – review & editing. Achmad Bayhaqy: Software, Resources, Data curation.
Automated Sentiment Intelligence for Educational Quality Assurance: A Topic–Sentiment Framework for Thai Student Feedback with Deep Model Instantiations
Student comments provide direct evidence about teaching, curriculum delivery, and learning conditions, but reviewing a large volume of free-text responses by hand is difficult to sustain. Automated analysis is therefore attractive, although Thai feedback presents particular challenges. Thai text does not consistently mark word boundaries, and student comments often include abbreviated wording, informal language, and expressions whose sentiment depends heavily on context. This paper proposes an Automated Sentiment Intelligence (ASI) framework for Thai educational feedback that (1) structures raw institutional comments into topic and sentiment signals, (2) defines a reproducible workflow for data cleansing, annotation, and dual-task modeling, and (3) supports deployment-oriented reporting through standard classification metrics and confusion-matrix-based error analysis. Using 4145 feedback records collected from a vocational education information system and refining them to 2873 entries after preprocessing, ASI organizes feedback into four topic domains (instructor, curriculum, facility, and other) and three sentiment classes (positive, neutral, negative). The framework is instantiated with two representative deep natural language processing architectures: a word-level Bidirectional Long Short-Term Memory (BiLSTM) pipeline (newmm tokenization + Thai Word2Vec) and a transformer-based XLM‐R fine-tuning pipeline (SentencePiece subword tokenization). Experimental results show strong topic classification performance for both instantiations, with higher overall topic accuracy for XLM‐R (0.922) compared to BiLSTM (0.882). For sentiment analysis, XLM‐R achieves a higher overall accuracy (0.80) than BiLSTM (0.729), with notably improved performance on the neutral and negative classes. The proposed ASI–XLM-R framework provides a scalable, reproducible approach to transforming Thai student feedback into actionable intelligence to support institutional decision-making. Received: 1 March 2026 | Revised: 24 June 2026 | Accepted: 26 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in in GitHub at https://github.com/nasith/thai-student-feedback-topic-sentiment. Author Contribution Statement Tanagrit Chansaeng: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Nasith Laosen: Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Boonmee Nissaidee: Validation, Investigation, Resources, Data curation, Writing – review & editing, Project administration. Pita Jarupunphol: Conceptualization, Methodology, Software, Validation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration.
A Novel PneumoniaNet Framework Integrating Explainable AI for Pediatric Pneumonia Detection from Chest X-rays
Pneumonia remains one of the leading causes of death among children worldwide, and therefore, it is necessary to use reliable and efficient tools to detect the disease early. In this study, a novel hybrid residual-dense deep learning architecture called PneumoniaNet is presented that detects pediatric pneumonia from chest X-ray images with high accuracy and interpretability. The proposed model is designed on the basis of the MobileNetV2 framework and utilizes fine-tuned deep layers with frozen shallow layers. This design enables learning high-level radiological features while preserving low-level visual information. The architecture has residual-dense connectivity to improve the propagation of gradients, feature reuse, and model generalization. The model was evaluated with a widely used benchmark pediatric chest X-ray dataset, and its results were comparatively analyzed with the performance of a number of state-of-the-art CNNs, such as DenseNet121, InceptionV3, ResNet50, EfficientNetB0, and AlexNet. PneumoniaNet yielded the best results with an accuracy of 93.11%, precision of 92.84%, recall of 93.09%, specificity of 92.43%, and F1-score of 94.29%. In addition, Gradient-weighted Class Activation Mapping visualizations showed focus on clinically relevant regions, improving interpretability and transparency. An ablation study of four PneumoniaNet variants, trained in the same manner, suggested that residual-dense connections along with selective layer freezing provide the most generalizable and effective feature representations. This work is unique because it combines residual-dense connectivity with selective layer freezing in a lightweight MobileNetV2 architecture, enabling effective feature reuse, facilitating gradient flow, and providing interpretable predictions—all within a computationally efficient framework. Received: 29 November 2025 | Revised: 8 April 2026 | Accepted: 21 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kermany’s CXR Images Datasets [Kaggle] at https://www.kaggle.com/datasets/riyadhhalmosawi/kermanys-cxr-images-datasets. Author Contribution Statement Shahriar Siddique Arjon: Conceptualization, Methodology, Software, Investigation, Data curation, Writing – original draft, Writing – review & editing. Tamanna Yasmin: Software, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization. Ankur Kumar Mondol: Software, Formal analysis. Nakib Aman: Supervision, Project administration. Shabbir Mahmood: Validation.
A Multi-Part Attention-Guided Spatial-Temporal GCN Framework for Gait-Based Person Recognition
Gait recognition has appeared as an important biometric modality because of its nonintrusive nature and straightforward implementation, enabling identification without physical contact. In contrast to systems that rely on silhouette information and other visual attributes, skeleton-based approaches retrieve gait data independently of appearance indicators. However, most approaches in the field utilize manually extracted features and adjacency matrices based on joint physical connectivity. This dependence poses a significant challenge for acquiring semantically rich representations of joint interactions and fundamental motion patterns, which are crucial for real-world gait understanding. To focus on these issues, this paper introduces a skeleton-based multi-part attention-guided spatial–temporal graph convolutional network (ST-GCN) gait recognition approach, MAST-GCN, which enhances the modeling of spatial and temporal dependencies in skeletal data through a multi-part attention mechanism. Compared with ST-GCNs, which rely on rigid graph structures and struggle to capture long-range interactions essential for identifying subtle gait differences, our method divides the skeleton into distinct anatomical regions and applies a part-wise attention module. By integrating attention-weighted features through a hierarchical fusion process, the model effectively captures both detailed and broad gait patterns across multiple temporal scales. The framework’s effectiveness has been verified on benchmark datasets such as the CASIA-B and OUMVLP-Pose, achieving rank-1 precisions of 95.8%, 91.8%, and 88.5% under normal walking (NM), carrying bag (BG), and wearing coat (CL) conditions, respectively, on the CASIA-B dataset and 93.0% on the OUMVLP-Pose dataset, showing superior performance. Our approach outperforms state-of-the-art methods, particularly highlighting the benefits of part-based, attention-driven feature extraction for robust, precise gait recognition. Received: 25 November 2025 | Revised: 12 March 2026 | Accepted: 20 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in CASIA-B at http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp and in OU-MVLP-Pose at http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html. Author Contribution Statement Md. Khaliluzzaman: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Kaushik Deb: Validation, Resources, Writing – review & editing, Supervision, Project administration.
Spatial-Temporal Graph Convolutional Networks (ST-GCNs)
A Review for Bridging Clinical and Technical Gaps with Hybrid ML in Schizophrenia Diagnosis
Schizophrenia is a complex psychiatric disorder in which traditional diagnostic methods, relying on subjective clinical assessment, suffer from significant limitations in scalability and early intervention. Machine learning can bring a paradigm shift to objective, data-driven diagnosis, but a critical gap exists between its technical performance and real-world clinical translation. The review systematically analyzes and synthesizes findings from 30 seminal studies on ML applications in schizophrenia diagnosis within the period of 2018–2026, following a structured methodology to evaluate models, datasets, performance, and translational challenges. Our review indicates that, though high accuracies (82–96%) have been reported using conventional and deep learning models in controlled research settings, essential barriers critically restrain their clinical utility: heavy class imbalance, a lack of model interpretability, that is, the “black-box” problem, biased datasets, and high computational costs. These limitations diminish their diagnostic accuracy in the real world and clinician trust. Hybrid ML frameworks are available with integrated explainable AI for transparency, GANs for data augmentation, and federated learning for privacy-preserving collaboration in order to bridge these gaps. The road to equitable precision psychiatry will have to be charted by overcoming socio-technical barriers through interdisciplinary co-design and adherence to emerging global ethical AI standards, for example, IEEE P7000, developing lightweight, accessible tools. This review provides a strategic roadmap to transition ML from a research tool into a clinically viable, equitable, and trustworthy asset for global mental health, with the ultimate aim of reducing misdiagnosis and improving patient outcomes. Received: 22 November 2025 | Revised: 3 April 2026 | Accepted: 25 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Syed Mossabbir Hossain: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft. Nitun Kumar Podder: Conceptualization, Software, Validation, Resources, Writing – review & editing, Supervision, Project administration. Md. Raihanul Haque: Formal analysis, Data curation, Visualization. Poly Akter: Formal analysis, Investigation. Tasfia Rahman Asma: Formal analysis, Investigation.
Efficient Extractive Text Summarization Using BiLSTM, Hypergraph, and Dominating Set Property
Automatic text summarization condenses voluminous text while preserving crucial information. Despite significant advancements in this field, challenges persist in accurately identifying key content, maintaining contextual coherence, and ensuring computational efficiency. The existing approaches struggle to effectively capture global relationships between sentences and minimize redundancy. In this study, we propose an extractive text summarization framework using Bidirectional Long Short-Term Memory with a hypergraph and a dominating set mechanism. A neural encoding module derives context-aware sentence embeddings, and a hypergraph-based representation models higher-order relational dependencies among sentences. A dominating set-based selection strategy is then applied to identify the most informative sentences for summary generation. The proposed model is evaluated using the CNN/Daily Mail dataset and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Empirical evaluation attains ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.4221, 0.3563, and 0.3901, respectively, and demonstrates the proposed model’s capability to effectively capture key content and generate coherent and informative summaries. The proposed framework proves to be an efficient approach to automatic summarization and has the potential to be applied in journalism, healthcare, legal analysis, and digital content management. Received: 12 February 2026 | Revised: 8 June 2026 | Accepted: 23 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail/data1. Author Contribution Statement Pradeepa Sampath: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing – review & editing, Project administration. Shrijaa Venkatasubramanian Subashini: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing – review & editing. Vimal Shanmuganathan: Validation, Formal analysis, Investigation, Resources, Visualization, Supervision. Seifedine Kadry: Validation, Formal analysis, Investigation, Resources, Visualization, Supervision.
Stock Market Movement Forecasting Using Machine Learning and Complex Network Measures
We investigate whether complexity-inspired network descriptors extracted from visibility-graph (VG) representations of price dynamics can improve short-horizon forecasting of the Standard & Poor’s 500 movements. We consider two aligned forecasting tasks at a 14-day horizon: (i) Up/Down directional classification and (ii) regression of the 14-day-ahead standardized forward return. Using daily close prices from 1981 to 2025, we construct sliding window graphs (50/75/100 trading days) and compute a broad set of interpretable spectral, topological, and mesoscopic features, including spectral radius, algebraic/natural connectivity, graph index complexity, clustering/transitivity, efficiencies, assortativity, and path-length statistics. Beyond the standard (“natural”) VG, we introduce a volatility-adaptive quantile VG (VAQ-VG) and two additional geometric/motif descriptors—visibility angle entropy and square-motif density—to form a multi-view feature bank. Features are lagged (1–7 days), standardized, and filtered via mutual information. Six learning algorithms (regularized linear models, random forest, k-nearest neighbors, Gaussian process, and stochastic gradient descent) are evaluated under a purged expanding cross-validation protocol with a 150-day embargo to eliminate sliding window leakage, with hyperparameters tuned by randomized search. Empirically, the directional task shows modest but consistent skill: top models reach the area under the receiver operating characteristic curve ≈ 0.70–0.72 with accuracy ≈ 0.65–0.66, remaining reliably above chance across thresholds. In contrast, point prediction of 14-day return magnitudes remains challenging, with the best R 2 below 0.20 and limited gains from more complex regressors. Overall, VG-derived features provide a compact, interpretable representation that supports leakage-robust directional forecasting, while VAQ-VG yields small, model-dependent shifts that suggest complementary structure across views rather than a decisive single best construction. Received: 31 August 2025 | Revised: 24 February 2026 | Accepted: 10 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/Butman2099/Complex-systems-book and https://github.com/Butman2099/Complex-Network-Paper-for-Artificial-Intelligence-and-Applications-AIA-journal. Author Contribution Statement Andrii Bielinskyi: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Vladimir Soloviev: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration. Andriy Matviychuk: Conceptualization, Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing. Vitalii Bezkorovainyi: Methodology, Software, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization.
The Nexus of Human and Machine: Exploring Perception as a Spur in AI-Powered Employee Training
More phased than before, organizations are relying on artificial intelligence (AI) in an increasing capacity to develop and/or train workers. However, we still do not know what leads to the successful performance of AI-based training platforms. This study examines the following dynamics: content quality, personalization, usability, skill development, employee motivation, and organizational support, which are likely to influence perceived platform effectiveness, with employee perception acting as a moderating variable. Data were collected from 300 participants across multiple companies and analyzed using Partial Least Squares Structural Equation Modeling. The results indicate that employees’ perceptions related to the value of training provided via an AI-enabled training platform are positively influenced by relevant information, engaging learning opportunities, perceived development of skills, and motivational factors. The perceived organizational support has a direct, large effect on platform impact, while perceived usability has a small effect on perceived platform impact. More specifically, the data indicate that employee perceptions associated with the platform influence the platform’s features and the effectiveness of the training. Overall, this study has implications for better design toward impactful AI-enabled training systems and contributes to the developing field of AI and organizational learning. It underlines the significance of user-centered approaches and organizational readiness for maximizing training effectiveness. Received: 12 August 2025 | Revised: 27 November 2025 | Accepted: 9 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.18435557. Author Contribution Statement Chanchal Molla: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Khaled Islam: Conceptualization, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Md. Razib Hossain: Conceptualization, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pronoy Kumar Paul: Conceptualization, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Md. Najibul Kabir: Conceptualization, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Abu Naser Mohammad Saif: Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Rehnuma Mostafa: Methodology, Validation, Resources, Writing – review & editing.
Neural Morphogenesis Architecture for Self-Organizing Robotic Intelligence: A Developmental Control Framework
Recent breakthroughs in robotics and artificial intelligence have permitted more autonomous systems, although most current techniques are limited by inflexible control structures developed from classical automation. Such frameworks stand in stark contrast to biological systems, which develop intelligence via ongoing structural and functional reconfiguration. Neural morphogenesis takes a developmental approach to machine intelligence, seeing robotic cognition as a dynamic and adaptable process. In this paradigm, artificial agents gradually adapt their internal circuitry, behavioral tactics, and physical morphology as they interact with their surroundings. Learning and design therefore occur together rather than sequentially. The approach integrates embodied perception and safety-governed developmental adaptation within a closed-loop control framework. We introduce a three-layer developmental controller that couples online neural plasticity, morphogenetic regulation, and energy-aware policy adaptation under explicit rollback safety constraints. In over 105 simulation cycles involving populations of 50, 100, and 200 agents, neural morphogenetic architectures achieved energy efficiency gains of up to 47%, network modularity increases of around 40%, and reductions in informational entropy between 13 and 15% (p < 0.01) when compared to deep reinforcement learning and model predictive control baselines. These gains were accompanied by improved cooperative behavior, steady performance under stress, and greater resistance to environmental shocks. Preliminary bio-hybrid tests suggest a link between morphological flexibility and integrated information (Φ), emphasizing the impact of physical structure on cognitive capabilities. These findings establish brain morphogenesis as a strong basis for adaptable, resilient, and sustainable intelligent robotic systems. Received: 5 September 2025 | Revised: 16 December 2025 | Accepted: 26 February 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Zenodo at https://zenodo.org/records/18175173, reference number [45]. Author Contribution Statement Edwin Gerardo Acuña Acuña: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Operationalising Consulting Methodology Through Workflow Orchestration: A Design Science Study of CWORT with Governed Agentic AI Support
Consulting engagements play a critical role in organisational decision-making, yet delivery often remains highly manual, fragmented, and dependent on individual practitioner experience. While consulting methodologies provide conceptual guidance, they are rarely operationalised as executable systems, leading to variability in delivery quality, limited traceability between inputs and outputs, and challenges in scaling consistent advisory practices. The growing use of digital and artificial intelligence (AI)-enabled tools in consulting has tended to focus on isolated tasks rather than the orchestration of end-to-end workflows, raising concerns around governance and accountability. This paper introduces CWORT (Consulting Workflow Orchestration Tool), a socio-technical system that operationalises consulting methodology through a digitally governed, workflow-based approach. CWORT represents consulting delivery as a sequence of explicit workflow states, integrating role-based governance, artefact traceability, and constrained agentic reasoning components to support analysis and synthesis activities. AI capabilities are embedded within predefined workflow stages and operate under strict human-in-the-loop control, ensuring that professional judgement and accountability remain central to advisory outcomes. The study is framed within a design science research paradigm and presents the conceptual model, system architecture, and a structured case-based evaluation in a real consulting engagement. The evaluation adopts a within-context comparative field design, demonstrating that workflow orchestration improves transparency, consistency, and traceability of consulting outputs, while enabling substantial reductions in discovery and assessment effort relative to prior approaches. AI-supported components are shown to augment interpretation and insight generation without altering deterministic analytical outcomes. Received: 2 January 2026 | Revised: 7 April 2026 | Accepted: 12 May 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analysed in this study. Author Contribution Statement Oluwaseun Iyiola: Conceptualisation, Methodology, Software, Writing – original draft, Writing – review & editing, Visualisation, Supervision.
Analysis of Organizational Barriers to Collaborative Teaching of Information Literacy in Libraries in Chinese Higher Education Institutions (HEIs)
Practice has proved that the information literacy collaborative teaching model has good teaching results. Mature experience has been accumulated abroad, while relevant practices in China have not been popularized. Existing research mainly focuses on teaching models but lacks the analysis of deeply influencing factors. This study aims to explore the key organizational elements that hinder the collaborative teaching of information literacy in the libraries of higher education institutions in China through interviews. The research finds that the core obstacles include the following three aspects: (1) structural inertia, systematic lack of top-level design and resource guarantee; (2) cognitive inertia, deep-rooted professional bias; and (3) political inertia, inversion of incentive mechanisms. The obstacles to collaborative teaching are deeply rooted in the fabric of the organizational system, and behind them is the conflict of institutional logic. Solving the dilemma requires intervention from four aspects: formulating top-level design, unifying cognition and identity, pilot practice, and technology empowerment, aiming to provide theoretical and practical enlightenment for relevant research. Viewed from the perspective of artificial intelligence (AI) applications in education, these findings explain why the institutionalization of AI-related information literacy teaching depends not only on digital tools but also on organizational conditions that support curriculum integration, cross-departmental coordination, and responsible pedagogical use. Received: 26 December 2025 | Revised: 1 April 2026 | Accepted: 13 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest in this work. Data Availability Statement The data that support the findings of this study are openly available in figshare at https://figshare.com/articles/dataset/Raw_data/32130100. Author Contribution Statement Shanfang Qin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Project administration. Ap-azli Bunawan: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Nor Erlissa Abd Aziz: Conceptualization, Software, Resources, Data curation, Writing – review & editing, Visualization, Supervision.
Modeling the Double-Edged Effects of AI Companions on Chinese Generation Z: A Dual-Pathway Analysis of Risk and Protection
We study Xingye AI (星野·AI皆你所见), a generative emotional companion used by Chinese Gen Z. This research took advantage of a quantitative research approach by employing survey as the data collection method and descriptive statistics as the data analysis method. With survey data from N = 402 active users, we tested a dual-path model: two risk factors (bedtime procrastination and problematic use) and two protection factors (anthropomorphism and perceived empathy). We also used hierarchical regression models. Risks align with higher depression, anxiety, loneliness, and sleep disturbance; protections show the opposite pattern. Adding protections improves model fit across outcomes (ΔR² ≈ 0.06–0.09). Beyond these behavioral results, we turn them into design. The coefficients set parameters for an adaptive pacing mechanism and a lightweight bandit/RL updater that tunes empathy and timing per user. The loop keeps a supportive tone by day and discourages over-engagement after 23:00. This links measurement to implementation and points to well-being-oriented affective computing. Received: 21 September 2025 | Revised: 12 December 2025 | Accepted: 21 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.18279792. Author Contribution Statement Haiyang Li: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Jing Liu: Conceptualization, Writing – review & editing, Supervision.
Treatment Regimen Segmentation from Handwritten Medical Prescriptions Using Advanced Neural Network
Handwritten medical prescriptions are a critical yet under-digitized component of clinical workflows, often serving as a source of ambiguity due to illegible handwriting, overlapping text blocks, and structural inconsistencies. The automatic segmentation of such prescriptions into meaningful textual blocks is vital for downstream tasks like drug recognition and dosage extraction. Traditional methods grounded on connected components or projection profiles often falter under the irregularities of freeform handwriting. To address these limitations, the paper proposes an advanced deep learning architecture—PrescNet—that primarily segment the treatment regimen (medicine and its associated components) as text-blocks using classical U-Net design with spatial–channel attention gates and a lightweight 32 channel projection layer to better capture salient features in prescription images. The model is trained on a custom dataset with pixel-level annotations and evaluated using 10-fold cross-validation with varying data splits. Experimental results demonstrated that the proposed architecture significantly transcend the baseline variants and a few state-of-the-art deep learning models of text-line segmentation achieving an Intersection over Union (IoU) of 87.2%, Dice score of 92.9%, and a minimal Dice loss of 0.071. The results validate its effectiveness in handling complex handwritten layouts, establishing its suitability for real-world clinical applications. Received: 12 September 2025 | Revised: 30 December 2025 | Accepted: 14 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Rekha G. R.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Siddesha S.: Conceptualization, Validation, Formal analysis, Investigation, Writing – review & editing, Supervision, Project administration. V. N. Manjunath Aradhya: Validation, Formal analysis, Investigation, Writing – review & editing, Supervision.
Prediction of Drug–Drug Interactions Based on Artificial Intelligence: A Systematic Literature Review
The comprehensive knowledge about the simultaneous use of multiple drugs to treat a disease is essential for the medical community to determine the best decisions for patient health. The use of various drugs at the same time to treat a disease can result in drug–drug interaction, raising the possibility of serious side effects. This study conducted a systematic literature review that describes the declarative information about drug–drug interactions, including the research papers from 2019 to 2025. The study focused on significant areas that can enhance modern research in drug–drug interactions, which were not included in previous studies. It is composed of artificial intelligence techniques, particularly those based on machine learning and deep learning for predicting drug–drug interactions. The PRISMA-based flow chart concept is used in the literature review stage. After a thorough review of the research papers, 33 studies were chosen. This work presents four research questions that were addressed and answered by the obtained results. The study found that the drug–drug interaction trend increased starting from 2021. It also found that deep learning models and their hybrid frameworks are the most commonly used. It was also observed that most studies used DrugBank and TWOSIDE data repositories. The findings also reveal that the F1-score is the most frequent evaluation measure. It found that there is a bridge to validate the model’s performance on a real-time clinical dataset, thereby assessing its true power. The research findings highlight significant study trends and knowledge gaps in the medical industry. Received: 31 August 2025 | Revised: 9 January 2026 | Accepted: 26 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Faisal Asad ur Rehman: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Abdulkarim Kanaan Jebna: Validation, Investigation, Visualization, Supervision, Project administration. Touqeer Ahmad: Methodology, Resources. Arif Ur Rahman: Data curation, Writing – review & editing, Project administration. Fasee Ullah: Formal analysis, Writing – review & editing, Visualization.
A Multi-Instant Machine Learning Algorithm Model for Infant Fingerprinting Verification
Understanding the challenges associated with infant biometrics, like immature ridge formation, very small fingerprints, and their fast physiological development, this research implements a multi-instance machine learning technique to enhance accuracy. Multiple instances of fingerprints for each infant were recorded, which enhanced the robustness of the system by dealing with inter-class variability. The datasets used 500 pixels-per-inch fingerprints of infants less than 12 months old that were recorded during two different instances. After preprocessing techniques, feature extraction was performed using a convolutional neural network (CNN), and matching scores were computed. The resulting scores were analyzed using a combination of three learning algorithms, CNN, CNN + SVM, and CNN + RF. Metrics showed that CNN + RF model outperformed all others with regard to its performance, having achieved verification accuracy of 81% compared to 75% and 82%, respectively (the previous multi-instance learning technique had achieved highest accuracy of only 69.05%), training accuracy of 98% compared to 58% and 50%, respectively, validation accuracy of 85% compared to 84% and 83%, respectively, and minimum training loss of 0.2 while having considerably high validation loss of 2.5. Although higher computational cost, CNN + RF proved most active for real-world applications in healthcare and identity management. Upcoming research should explore larger datasets to further enhance model precision. Received: 21 August 2025 | Revised: 26 January 2026 | Accepted: 22 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Olatayo Moses Olaniyan: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization, Supervision, Project administration. Kazeem Aderemi Bello: Conceptualization, Methodology, Investigation, Resources, Data Curation, Writing – original draft. Rendani Wilson Maladzhi: Conceptualization, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Johnson Olugbenga Joseph: Conceptualization, Investigation, Resources, Data curation, Writing – original draft. Adebimpe Esan: Methodology, Software, Validation, Formal analysis, Writing – review & editing, Visualization, Supervision, Project administration. Quadri Ademola Mumuni: Conceptualization, Investigation, Resources, Data Curation, Writing – original draft. Bolaji Abigael Omodunbi: Formal analysis, Writing – review & editing. Chinedu Michael Chiejine: Software, Project administration.
WiFi-Based Human Activity Recognition and Fall Detection with Taxonomy, Benchmarks, and Future Directions: A Narrative Review
WiFi-based human activity recognition (HAR) and fall detection have emerged as promising alternatives to vision- and wearable-based systems for modern assisted living and clinical monitoring for the elderly. These approaches exploit variations in Channel State Information, capturing amplitude and phase fluctuations across subcarriers to characterize how human motion perturbs these patterns through multipath propagation. This narrative review synthesizes 26 peer-reviewed studies published between 2017 and 2025, focusing on systems evaluated in real-world, non-laboratory environments. While recent deep learning models demonstrate near-perfect benchmark accuracy on fine-grained datasets, cross-environment assessments consistently reveal a severe “reality gap.” Specifically, several reviewed studies reported accuracy declines exceeding 20% when deployed in previously unseen buildings, while multi-user interference reduced recognition accuracy by 21–46%. Furthermore, dataset availability and reproducibility are critical considerations; only a limited number of reviewed studies utilized publicly available datasets, and none provided a standardized benchmarking guide-line. To address these gaps, this review presents a comprehensive taxonomy of post-2017 architectures, a comparative synthesis of laboratory versus deployment performance, and a critical analysis of key implementation challenges. We conclude by proposing a set of recommended reporting practices and outlining actionable future research. In doing so, the paper outlines key recommendations for developing adaptive, location-independent models, emphasizing the need for domain-transfer techniques, cross-building validation, and expanding open datasets to enhance generalizability. Received: 31 August 2025 | Revised: 13 March 2026 | Accepted: 25 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Kok Chung Chua: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Kai Liang Lew: Resources, Writing – original draft, Writing – review & editing, Project administration. Chean Khim Toa: Conceptualization, Validation, Resources, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Matthew Alexander Paudianto: Investigation, Data Curation, Writing– original draft, Writing – review & editing. Ivan Nathanael: Investigation, Data curation, Writing – original draft, Writing – review & editing. Abel Nathanael Hutapea: Investigation, Data curation, Writing – original draft, Writing – review & editing.
ResProtoNet: A Skeleton-Aware Few-Shot Framework for Yoga Pose Classification
Deep learning models for yoga pose classification traditionally require large, diverse, carefully annotated datasets, which are costly and time-consuming. While open-source yoga datasets are available, collecting and annotating new ones, especially for complex poses, remains a significant challenge. This limitation motivated the use of few-shot learning (FSL) to enable efficient pose classification under limited data conditions. This study proposes ResProtoNet, combining a ResNet-18 feature extractor with a Prototypical Network classifier, evaluated against a supervised baseline using RGB and skeleton-based images. The latter contains joint-based pose encodings extracted via the MediaPipe model. Experiments conducted on five fundamental yoga poses under 1-shot, 3-shot, and 5-shot configurations demonstrated several findings. First, ResProtoNet consistently outperformed the ResNet-18 baseline, achieving 98.2% accuracy in the 3-shot setting, compared to 97.6%. Second, ResNet-18 consistently delivered the strongest baseline performance across both modalities, underscoring its robustness and justifying its role as backbone. A key contribution is a comprehensive stress test comparing RGB and skeleton modalities within an FSL context. Robustness analysis highlighted that skeleton-based input preserved up to 20% higher accuracy under heavy occlusion and reduced accuracy loss by 5.7% under resolution degradation, confirming resilience when visual information degrades. In contrast, RGB retained advantages under light occlusion and higher-resolution inputs, where texture and background remained informative. Overall, applying FSL to a supervised baseline enables reliable pose classification under limited supervision, substantially reducing dependence on large-scale annotated datasets. ResProtoNet demonstrated strong potential for real-world healthcare and exercise monitoring systems, where annotated resources and data quality are constrained. Received: 31 August 2025 | Revised: 8 January 2026 | Accepted: 22 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Yoga Poses Dataset [Kaggle] at https://www.kaggle.com/datasets/niharika41298/yoga-poses-dataset, in EasyFSL (PyTorch library for few-shot learning) [GitHub] at https://github.com/sicara/easy-few-shot-learning, in Yoga-82 Dataset [Kaggle] at https://www.kaggle.com/datasets/akashrayhan/yoga-82, and in Blazepose_skeletons_Yoga_82 Dataset [Kaggle] at https://www.kaggle.com/datasets/rashiniyasp/blazepose-skeletons-yoga-82. Author Contribution Statement Chean Khim Toa: Conceptualization, Methodology, Validation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Kai Liang Lew: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Sin Pei Ton: Conceptualization, Formal analysis, Resources.