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. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
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.
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.
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.
A Short Review on Computer Vision: Visualizing the World Through Machine
Computer vision is an important part of artificial intelligence. It helps machines interpret and make decisions based on visual data. As machines take on more responsibility in decision-making, vision is a key way for them to understand their surroundings. The ability for machines to see and understand the world through visual input raises the question of whether they can truly understand complex situations based on the objects and interactions around them. This paper explores the main concepts and algorithms behind computer vision, beginning with its early development. It discusses foundational techniques and how they have changed over time, including innovations that are shaping the field. The paper also looks at the limitations of these foundational concepts and how they have impacted the growth of current technologies. It includes a critical examination of present-day technologies, pointing out their challenges and shortcomings despite many improvements. Finally, the paper covers the various applications of machine vision in different fields and the promising future for further advancements in computer vision technologies. Received: 19 August 2025 | Revised: 7 January 2026 | Accepted: 28 January 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 Mohammad Mehedi Hassan: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing – original draft, Visualization, Project administration. Stephen Karungaru: Validation, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Rezaul Bashar: Validation, Writing – review & editing.
Performance Analysis of YOLOv11-m and Related Architectures in Pediatric X-ray Fracture Detection
Wrist fractures are one of the most frequent fractures in children that should be diagnosed properly and immediately to avoid any further complications. Regular radiographic analysis is time-consuming, relies on qualified radiologists, and is subject to human error. There has been a lot of buzz about the potential of deep learning technologies to automate medical image analysis in recent times. The algorithms in the YOLO series are sophisticated and refined, representing some of the best work in the field. The GRAZPEDWRI-DX pediatric wrist dataset is used in this study to address the variation in the YOLOv11 detection model’s inference time, recall, precision, and mean average precision (mAP). A medium-scaled variant of YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10 models were compared with YOLOv11-m. According to the experimental results, YOLOv11-m achieves the highest mAP@50–95 (0.569) for fracture detection, whereas YOLOv11-x attains the highest mAP@50–95 (0.424) across all category classes. An ablation study of YOLOv11 architectural elements was done to determine how the model optimized for medical imaging through the attention mechanism. On the other hand, the performance in fracture detection and inference time for YOLOv11 is superior to that of previous YOLO models, while it has lower computational complexity. Additionally, a performance comparison among recent YOLO-based models and the RT-DETR and Faster R-CNN models reveals variations in detection precision, recall, and accuracy, providing insight into the strengths and trade-offs of each approach. YOLOv11 could therefore make recurrent contributions and be of great value to clinical decision-making. Received: 18 August 2025 | Revised: 2 February 2026 | Accepted: 25 March 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 at https://doi.org/10.1038/s41597-022-01328-z, reference number [15]. Author Contribution Statement Muhanad Abdul Elah Alkhalisy: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Qusay Shihab Hamad: Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Ali Retha Hasoon Khayeat: Validation, Investigation, Writing – review & editing. Shahrel Azmin Suandi: Writing – review & editing, Supervision.
Comprehensive Review of Artificial Intelligence and Edge Computing for Precision Weed Control
Targeted weed management is an important element of precision agriculture, and accurate weed identification is a foundation for precision weed control. Over the past decade, convolutional neural networks have demonstrated high accuracy and generalization in recognizing weeds in agricultural environments. Edge computing, via edge devices, is one secure method to effectively deploy artificial intelligence algorithms (AI) for weed control on agricultural platforms. This study presents a bibliometric and systematic review of AI algorithms and edge-based systems for precision weed control from 2015 to 2025. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted in Scopus and Web of Science, resulting in the inclusion of 43 documents. Results show a significant surge in publications on AI-based weed control systems deployed on edge computing resources since 2019. The analysis reveals that RGB cameras are the preferred data acquisition method, while object detection models, specifically the YOLO family, are widely adopted for AI deployment. Pretraining or training optimizations are preferred over post-training model optimization to maximize inference and improve detection accuracy, while NVIDIA Jetson series edge devices are commonly used for deploying AI algorithms. Precision chemical control approaches dominate, but laser weeding technology is emerging as an alternative. Despite technological advances, challenges hinder commercial deployment, including reliance on manual data annotation and model vulnerability to environmental variability. Future research should focus on semi-supervised learning, synthetic data generation, and multimodal vision systems to improve robustness and reduce annotation dependence. Unified evaluation protocols are also needed to benchmark system performance. Received: 22 December 2025 | Revised: 24 February 2026 | Accepted: 23 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 Adeayo Adewumi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Dharmendra Saraswat: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
An Enhanced Traffic Matrix Prediction in Software-Defined Networks Using Bi-LSTM with Attention Mechanism
Software-defined networks (SDNs) play a vital role in network traffic management, traffic estimation, traffic engineering, and routing. The effective management of SDNs relies on traffic matrix prediction (TMP). The traffic matrix (TM) provides a comprehensive view of each origin–destination flow. An SDN paradigm offers various techniques to obtain additional information about the TM. This information is useful for managing network operations, such as performance diagnostics, traffic engineering, and network design. The TMs are critical for network operation and management. However, TMP remains a challenging task due to the dynamic and complex nature of network traffic patterns. To address this challenge, a Bi-LSTM model with attention mechanism is proposed for better prediction. The proposed model captures both forward and backward temporal dependencies in historical traffic data that enable the model to learn short-term and long-term traffic correlations, while the attention mechanism focuses on critical timesteps that help the model learn the traffic behavior and traffic patterns more effectively. The proposed model is experimented with real-world topologies such as Abilene, GÉANT, Nobel-Germany, and Germany50. The TMs of these topologies are in the flattened form with different granularities and horizons. The performance of the proposed model is evaluated using metrics such as mean squared error, mean absolute error, coefficient of determination, and average inference time. The experimental results show that the Bi-LSTM model with attention mechanism has outperformed the Bi-LSTM with Adam model in terms of accuracy with better TMP in all considered real-world topologies. Received: 20 July 2025 | Revised: 9 January 2026 | Accepted: 2 February 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 Prabu. U.: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization. Prabhala Harichandana: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization. Manepalli Baby Kavyasri: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization. Uppala Bhargavi: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization. Geetha. V.: Formal analysis, Writing – review & editing, Supervision, Project administration.
Ethical Frameworks in AI Decision-Making: Comprehensive Analysis of Management and Human Resource Applications in Contemporary Organizations
This literature review examines emerging ethical frameworks for artificial intelligence (AI) decision-making in management and human resource (HR) contexts within contemporary organizations. Through a systematic review of academic literature published between 2021 and 2025, the study synthesizes the theoretical foundations, implementation approaches, and practical challenges associated with the adoption of ethical AI in organizational settings. The analysis focuses on how organizations conceptualize and operationalize ethical principles in AI-driven decision-making. The review shows that most ethical AI frameworks are grounded in core principles such as transparency, fairness, accountability, and respect for human dignity. However, organizations face difficulties in translating these normative principles into operational practices. The findings identify three main barriers to implementation: algorithmic bias, data privacy concerns, and substantial financial costs, often estimated to range from $50,000 to $500,000 for implementation and monitoring. Based on the analysis of 14 academic sources, the study indicates that while organizations often prioritize regulatory compliance when developing ethical AI policies, effective implementation requires broader organizational and cultural transformation. The review integrates theoretical perspectives with examples from industry practices, including emerging metrics used to evaluate ethical AI implementation. The findings suggest that existing ethical AI frameworks must move beyond traditional compliance-oriented approaches as new forms of human–AI interaction reshape organizational decision-making. This is particularly relevant for HR processes such as recruitment algorithms, performance evaluation systems, and workforce management platforms. The study highlights the need for adaptive ethical AI frameworks capable of addressing technological change, evolving regulations, and societal expectations while maintaining organizational efficiency. Received: 13 July 2025 | Revised: 16 January 2026 | Accepted: 1 April 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 Kirill Toropov: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization. Irina Manakhova: Conceptualization, Validation, Resources, Writing – review & editing, Supervision, Project administration.
Transformer Attention-Driven Concept Extraction for Efficient Smishing Detection
Short Message Service (SMS) phishing (smishing) is a form of phishing attack that uses mobile messaging as its delivery medium. Conventional detection methods for smishing attacks fall short at identifying such messages because they resemble spam. The conventional methods rely on static rules or shallow linguistic features. In this study, we propose a transformer-based attention-driven framework for detecting smishing. We develop a concept-level representation to improve both the accuracy and the explainability of our model. The approach extracts and sorts the message signatures uncovered in smishing attacks into three conceptual categories—textual, structural, and behavioral—each of which encompasses a different aspect of how smishing attacks attempt to accomplish. We utilize pretrained Bidirectional Encoder Representations from Transformers (BERT) to construct conceptual representations from the SMS messages. By leveraging BERT’s attention weights on the smishing concept categories, informative tokens and patterns that distinguish smishing from benign messages were identified. The final classification is performed using both a fully connected neural network layer and three classical machine learning baseline models, trained on the same features. The result demonstrates that our model achieves performance of (F1-score: 98.71%, accuracy: 99.32%) outperforming the baseline models. Ablation studies further confirmed that each concept category makes a meaningful contribution to the classification performance, with behavioral concept features having the highest impact. This work highlights the potential of attention-driven concept modeling for robust and explainable smishing detection. Received: 16 December 2025 | Revised: 3 February 2026 | Accepted: 25 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 the UCI Machine Learning Repository at https://doi.org/10.1145/2034691.2034742, reference number [27]. Author Contribution Statement Zahriya Lawal Hassan: Conceptualization, Methodology, Resources, Writing – original draft, Visualization. Nor Fazlida Mohd Sani: Validation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Muhammad Daniel Hafiz Abdullah: Validation, Writing – review & editing, Supervision, Project administration. Norwati Mustapha: Writing – review & editing, Supervision, Project administration.
Privacy Budgets That Work: Client-Level Differential Privacy with Robust Aggregation for IIoT Intrusion Detection
Federated learning (FL) is promising for intrusion detection for Industrial Internet of Things (IIoT) without the necessity of centralizing raw telemetry, but there exist two stumbling blocks: (i) limiting what can be inferred about the clients (sites/devices) from their updates and (ii) providing reliability under heterogeneous, non-independent and identically distributed (non-IID) data with faulty or Byzantine members. We outline a systems design that intertwines client-level differential privacy (DP) with robust aggregation and experiment on the Edge-IIoTset workload specification and deterministic synthetic experiments with the goal of generating fully reproducible figures. The server applies per-client L2 clipping, adds calibrated Gaussian noise, and tracks privacy with Re´nyi DP (RDP) under subsampling; aggregation uses coordinate-wis → enforcing the target budget. Sweeping privacy budgets 0.5 reduces macro-F1 0.93 → 0.78 (−19.2%), AUROC 0.96 𝜀 ∈ {0.5 e median, → , 1 0.86 (−10.4%), and worst-client F1 0.89 , 2, 5 β , -trimmed mean, or Krum, with an auditable stop-on- 10} yields clear privacy–utility frontiers: tightening from → 0.70 (−21.3%). Under 10% 𝜀 controller 𝜀 = 10 corrupted clients at 𝜀 = 2, coordinate-wise median improves macro-F1 over mean by +11.1% and worst-client F1 by +20.3%. Time per round scales with the number of selected clients m = qK: 0.60 → 2.60 s as K = 10 → 200 at q = 0.1. The recipe exposes deployable knobs (𝜀, C, q, aggregator), auditable privacy via stop-on-𝜀, and tail-aware reporting—charting a practical path to regulator-aligned, privacy-preserving FL for IIoT intrusion detection. Received: 11 December 2025 | Revised: 22 January 2026 | Accepted: 24 March 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/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot, reference number [26]. Author Contribution Statement Mahavir Teraiya: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Madhu Shukla: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration.
PV-CLIP: Synergizing Geometric Heuristics and Zero-Shot Foundation Models for Efficient Fall Recognition
Falls are a major cause of death associated with injuries among the elderly and hence require efficient automated surveillance mechanisms. In a preliminary study, we presented a hybrid framework, which is Faster R-CNN and YOLOv10, at the ICDSAIA 2025. Nevertheless, conventional detectors tend to detect real falls as an ordinary activity of daily living (ADLs), and thus, false positives are high. This extended study has proposed a system of cascaded pipelines, PV-CLIP, which introduces a hybridization of geometric and semantic verification to the traditional bounding-box detecting system. PV-CLIP involves three steps: (1) YOLOv11-Pose, which is an algorithm that detects the human keypoints and measures the geometric properties such as the aspect-ratio collapse; (2) DeepSORT tracking, which is an algorithm that estimates the vertical velocity and removes the standing horizontal position; and (3) CLIP-based zero-shot semantic verification, which is an algorithm that judges high-risk frames with respect to the consistency with fall-related language indicators. The ablation experiments prove that the individual contribution of each pipeline stage itself is significant and results in 98.3% accuracy on the 50-video test set. The accuracy of the YOLOv11-Pose large variant increased to 100%, which proved that the result of the combination of the kinematic and vision-language reasoning is effective to reduce false alarms. Moreover, external validation carried out on the UR Fall Detection Dataset (70 sequences) demonstrated an accuracy of 95.24% indicating that fall detection is effective in practice-based healthcare systems. Received: 27 November 2025 | Revised: 16 March 2026 | Accepted: 27 March 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/payutch/fall-video-dataset, and in UR Fall Detection Dataset at https://fenix.ur.edu.pl/mkepski/ds/uf.html. Author Contribution Statement Benedict Onochie Ibe: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Dagogo Godwin Orifama: Methodology, Investigation, Writing – review & editing. Gbubemi Erics: Investigation, Resources, Writing – review & editing. Dan Ifeanyi Ali: Validation, Formal analysis, Writing – review & editing. Ikechukwu Nwagbo Enumah: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision. Dominic Ogbuagu: Conceptualization, Writing – review & editing, Supervision, Project administration.
Adaptive Footwear Stiffness Driven by Biomechanical Signals for Improving High-Intensity Metabolic-Neuromuscular Performance: A Randomized Controlled Trial
Background: Basketball shooting performance deteriorates under fatigue, often due to compromised biomechanics and suboptimal footwear stiffness. Footwear with dynamically adjustable longitudinal bending stiffness (LBS) could counteract fatigue effects by maintaining optimal support throughout play. Objective: The objective of this study is to evaluate whether an adaptive LBS basketball shoe—controlled by a machine learning algorithm (logistic regression with support vector machine–recursive feature elimination) using biomechanical signals—can improve high-intensity shooting performance under fatigue compared to the fixed-stiffness footwear. Methods: A total of 60 participants were randomly assigned to high-stiffness (HS), low-stiffness (LS), or self-adaptive stiffness (SS) shoe groups. All completed a 2-min high-intensity shooting and rebounding drill designed to induce fatigue, while real-time kinematic data of the lower limbs and physiological data (heart rate, muscle oxygen saturation) guided SS stiffness adjustments. Results: The SS group achieved significantly more successful shots than the HS and LS groups (mean difference +1.662 vs HS, p < 0.01; +2.753 vs LS, p < 0.001) and higher shooting accuracy (+0.093 vs HS, p < 0.01; +0.117 vs LS, p < 0.001). Under fatigue, SS footwear preserved favorable lower-limb joint kinematics (e.g., maximum hip rotation angle +0.234 rad vs HS, p = 0.014) without increasing cardiovascular or metabolic demands (no significant differences in heart rate or SmO2 ). Conclusion: Adaptive footwear stiffness integrating biomechanical sensing and machine learning improved basketball shooting performance and mitigated fatigue-induced biomechanical degradation, highlighting its potential for enhancing sports performance. Received: 25 November 2025 | Revised: 26 January 2026 | Accepted: 25 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 the supplementary files of this article. Author Contribution Statement Yining Xu: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Yang Song: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Dong Sun: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Zhiyi Zheng: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Mingwei Sun: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Wenlong Li: Methodology, Software, Writing – review & editing. Jiachao Cai: Methodology, Software, Writing – review & editing. Xuanzhen Cen: Methodology, Software, Writing – review & editing. Zixiang Gao: Methodology, Software, Writing – review & editing. Liangliang Xiang: Methodology, Software, Writing – review & editing. Monèm Jemni: Methodology, Software, Writing – review & editing. Yaodong Gu: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Hierarchical Brain-Inspired Deep Learning for Autonomous Decision-Making in Complex Dynamic Environments
The intersection of artificial intelligence and neuroscience has resulted in the development of brain-inspired computational frameworks that simulate the human brain’s hierarchical decision-making and learning. In this work, we propose a Hierarchical Brain-Inspired Reinforcement Learning (HBRL) architecture that combines the benefits of Deep Reinforcement Learning (DRL) with a biologically inspired cognitive hierarchy. The proposed architecture functions by simulating cortical–subcortical processing of information in which a high-level Policy-Gradient manager conducts abstract and long-term planning, and the low-level Deep Q-Network (DQN) agents complete real-time short-term actions. The proposed architecture’s multilayer structure includes temporal abstraction, modular learning, and the ability to refine policies to optimize experience, which makes it appropriate for dynamic and uncertain environments. We applied HBRL in three common scenarios: GridWorld, autonomous vehicle navigation, and smart-city infrastructure control to evaluate the proposed system design. Overall, we found that HBRL had a 15%–20% higher rate of completing tasks, 1.4–2.4 times faster learning efficiency, along with 70–100 points higher cumulative reward when high-level and low-level HBRL agents were compared to baseline approaches (e.g., DQN, Proximal Policy Optimization, and Soft Actor-Critic). A statistical analysis using two-tailed t-tests also assessed the significance of improvements (p < 0.01) among all tested environments. The hierarchical decomposition of tasks serves both to promote convergence and improve agents’ generalization capacity in unseen conditions. In its entirety, the proposed HBRL framework provides a scalable and cognitive-inspired learning paradigm for developing intelligent autonomous systems that exhibit human-like adaptability and efficient decision-making capabilities in complex, nonstationary real-world environments. Received: 14 May 2025 | Revised: 16 October 2025 | Accepted: 19 December 2025 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 Kanthavel Radhakrishnan: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Dhaya Ramakrishnan: Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration.
Adaptive Mechanisms of Foot Function and Control Induced by Dance Training
This study explored the adaptive remodeling of the foot’s neuro-biomechanical system induced by dance training. A multimodal function-and-control framework was established, integrating kinematic and kinetic analyses, electromyographic synergy modeling, and finite element simulation to reveal adaptive changes from multiple perspectives. The results showed that Latin dancers exhibited greater ankle and metatarsophalangeal flexion angles and joint moments but lower angular velocities during landing, indicating a more efficient energy absorption and release mechanism. Their muscular synergy patterns were activated earlier and in a more compact manner. Finite element analysis revealed higher stress concentrations in the first and second metatarsals and phalanges, suggesting adaptive changes in load transmission. These findings demonstrate that dance training can induce a feedforward-driven, multilevel coupling mechanism that enhances coordination among neural control, mechanical output, and structural remodeling, thereby improving impact absorption and postural stability. This study reveals the hierarchical plasticity of the neuro-biomechanical system during complex skill learning and provides new perspectives for the development of precision training, injury prevention, and rehabilitation strategies. Received: 17 November 2025 | Revised: 4 February 2026 | Accepted: 2 March 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://doi.org/10.6084/m9.figshare.31333936. Author Contribution Statement Xiangli Gao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Datao Xu: Methodology, Resources. Minjun Liang: Methodology, Resources. Zanni Zhang: Investigation, Data curation. Tianle Jie: Investigation, Data curation. Huiyu Zhou: Methodology. Jingyi Ye: Methodology. Zsolt Radak: Supervision. Yaodong Gu: Supervision, Project administration, Funding acquisition.
Deep Reinforcement Learning-Enabled IoT Framework for Real-Time and Personalized Diabetes Diagnosis Using Wearable Sensors
The increasing number of diabetes patients worldwide reveals an immediate necessity for technical diagnostic solutions that offer smart operations and full data protection capabilities. This research presents FL-Hybrid, which unites the CNN-LSTM-Attention network structure together with Dueling Deep Q-Network (DQN) to perform individual diabetes screening using wearable sensor information through federated learning. The PhysioNet BIG IDEAs dataset with CGM and physiological sensor signals feeds into the model that combines spatiotemporal transformers with context-driven decision functions. The system protects user privacy through its deployment on edge devices, combined with federated learning training that eliminates the need to send sensitive information to centralized servers. The CNN-LSTM backbone finds deep physiological patterns, and the attention mechanism focuses on crucial diagnostic indicators such as post-meal glucose spikes and sleep-related heart rate variability decreases. The Dueling DQN agent develops optimum diagnostic actions through integration of clinical accuracy with efficiency and prediction reliability measures. Experimental tests conducted on multiple models show that FL-Hybrid outperforms all other systems by reaching a 97.85% accuracy rate with 97.31% precision, 97.96% recall, and 98.41% AUC-ROC. These results exceed those of CNNs and RNNs as well as centralized DRL models. The model displays 95.34% accuracy when noise occurs while also demonstrating exceptional user adaptation ability at a 95.62% adaptation rate. The proposed system provides organizations with a scalable, privacy-focused solution for continuous diabetes monitoring, which represents an advanced mobile health technology. Received: 30 May 2025 | Revised: 3 December 2025 | Accepted: 28 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 PhysioNet at https://doi.org/10.13026/zthx-5212, reference number [29]. Author Contribution Statement M. Alamelu: Conceptualization, Methodology, Software, Validation, Writing – review & editing, Visualization. Meera Alphy: Software, Validation, Formal analysis, Data curation, Writing – original draft. Finney Daniel Shadrach: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Visualization, Supervision. Jayaraj Velusamy: Data curation, Writing – review & editing, Project administration.
Perceptions of Potentials and Risks of Artificial Intelligence Across Diverse Levels of AI-literacy
Perceptions of the potential benefits and risks of artificial intelligence (AI) vary widely among people with different levels of knowledge about the technology. By better understanding these variations, we can identify and address challenges in the adoption and responsible use of AI for all. This study employs a qualitative approach to explore these differences through criterion sampling. A total of 15 participants who self-identified as belonging to one of three AI-literacy categories (Limited, Intermediate, and Expert) were interviewed. The audio-recorded and transcribed interviews were thematically analyzed, identifying differences in understanding among different literacy groups regarding AI’s impact on employment, creativity, bias, trust, data privacy, and the need for regulation and awareness. The study reveals that varying levels of AI-literacy shape perceptions of bias, privacy, creativity, job security, and trust. A key finding was that participants with limited AI-literacy did not link AI to data and therefore did not associate concerns like data privacy with AI, and also did not believe that AI can be biased, as they viewed bias as a human quality rather than something a machine could possess. These findings underline the need for initiatives that strengthen AI-literacy across different AI user groups to enable informed and equitable engagement with AI technologies, while also highlighting the need for education, transparency, and policy measures that build trust and accountability. In addition, how diverse users understand and interact with AI has important implications for developers, educators, and policymakers seeking to advance explainability and inclusive literacy. Received: 23 February 2025 | Revised: 1 August 2025 | Accepted: 13 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 Zeba Khadhijah: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualisation, Project administration. Bhupesh Kumar Mishra: Conceptualisation, Methodology, Software, Validation, Resources, Writing – review & editing, Visualisation, Supervision.
Heuristic-Guided Selective Random Forests: Immune-Inspired Heuristic Ensembles for Robust Intrusion Detection in IoT Networks
The increasing adoption of the Internet of Things (IoT) has brought considerable benefits to all aspects of daily life, but the IoT remains vulnerable to a wide range of security threats that compromise device continuity and can lead to significant disruption and losses. Traditional intrusion detection systems (IDSs) often rely on machine learning models, which perform relatively well but struggle when faced with unusual traffic patterns. To address these challenges, this study presents a heuristic-based ensemble method. The proposed method combines decision tree detectors with a biologically inspired filtering mechanism and mimics the behavior of the immune system by retaining only trees that achieve a detection accuracy greater than a specified threshold. This method reduces the memory required to store weak decision trees that can affect the final decision, thus improving classification performance. Experimental results demonstrate an overall accuracy of 93.4%, outperforming baseline algorithms. The research presents a framework that balances detection performance with computational efficiency, a critical requirement for IoT networks with limited resources. Received: 28 September 2025 | Revised: 4 December 2025 | Accepted: 15 December 2025 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 UNSW-NB15 dataset at https://doi.org/10.1109/MilCIS.2015.7348942. Author Contribution Statement Saad AL Azzam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Ghassan ALDharhani: Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Raenu AL Kolandaisamy: Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration.
A New Domain-Independent Approach for Classification of Bacteria, Fungus, and Virus-Infected Fruit and Leaf Images
Early and reliable detection of bacterial, fungal, and viral infections in fruits and leaves is essential for improving crop productivity, preventing disease spread, and supporting food security. Most existing approaches are domain-specific and struggle to generalize across diverse plant organs or varying image qualities. To address this challenge, we propose a novel domain-independent classification framework that integrates quality-metric features—Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM)—with an adapted lightweight Convolutional Neural Network (CNN). This is the first approach that explores quality measures as features for addressing challenges of classification of fruits and leaves infected by virus, fungus, and bacteria. The method first performs connected-component analysis on K-means clusters generated from R, G, B, and Gray channels to isolate disease-relevant regions and extract quality-based features. These features are fused with visual features extracted from the RGB images using a multimodal CNN architecture. Extensive experiments conducted on the proposed fruit–leaf dataset and four external benchmark datasets demonstrate that the model achieves high accuracy, strong robustness to blur, noise, rotation, and scaling, and superior generalization performance compared with state-of-the-art methods. Cross-domain evaluations further confirm that the proposed method is domain-independent and reliable for the classification of fruits and leaves infected by bacteria, fungi, and viruses. Received: 24 September 2025 | Revised: 2 December 2025 | Accepted: 25 December 2025 Conflicts of Interest Palaiahnakote Shivakumara is the Editor-in-Chief for Artificial Intelligence and Applications, and he is not involved in the editorial review or the decision to publish this article. 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 Poornima Basatti Hanuma Gowda: Conceptualization, Software, Data curation, Writing – original draft, Visualization. Basavanna Mahadevappa: Formal analysis, Investigation, Supervision, Project administration. Shivakumara Palaiahnakote: Methodology, Writing – review & editing. Muhammad Hammad Saleem: Validation. Niranjan Mallappa Hanumanthu: Resources.
The Integration of Chatbots and AI-Powered Virtual Assistants into Customer Service Frameworks of the Banking Sector
The integration of artificial intelligence (AI) into the financial system, especially in the banking sector, has become one of the most important directions of technological progress. The aim of the article is to reveal the specifics of the application of AI in banking services, emphasizing the role of chatbots and virtual assistants in the customer-centric services and risk management system. The article presents the theoretical foundations and main directions of application of AI in the banking system. Four main directions are analyzed: customer-centric solutions, process optimization, banking services market management, and improvement of regulatory mechanisms. The experience of international banks (Bank of America, HSBC, DBS, Armenians banks, and others) indicates that the use of AI contributes to reducing operating costs and accelerating and personalizing customer service. However, the integration of AI also raises challenges related to data privacy, cybersecurity, legislative regulations, and the transformation of professional skills. Special attention is paid to the field of credit scoring, where machine learning methods allow for a more accurate assessment of borrower behavior and reduce financial risks. The relevance of the research is due to the fact that AI is no longer an additional technology for banks but a necessary tool for maintaining competitiveness and sustainable development. Received: 21 September 2025 | Revised: 5 January 2026 | Accepted: 10 March 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 PES at https://doi.org/10.24874/PES06.02.023, reference number [31], in ASPUR at https://doi.org/10.61552/JAI.2024.01.004, reference number [32], in International Accountancy Training Centre at https://doi.org/10.59503/29538009-2024.2.14-121, reference number [33]. Author Contribution Statement Suren H. Parsyan: Conceptualization, Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Frida F. Baharyan: Conceptualization, Investigation, Resources, Data curation, Writing – original draft. Gayane A. Avagyan: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing – original draft, Writing – review & editing. Sergo A. Episkoposian: Methodology, Validation, Visualization. Vardan S. Aleksanyan: Software, Investigation, Supervision. Ararat Kostanian: Writing – review & editing, Visualization. Lilik M. Beglaryan: Methodology, Visualization, Writing – review & editing.
Blockchain Platforms for Developing Smart Contracts and Their Computational Performance Evaluation: A Systematic Literature Review
An independent, trusted third party or governing body is no longer necessary to conduct secure financial transactions because of blockchain technology. The topic of smart contracts and their ability to facilitate additional computational progress has risen to the forefront of academic and industry conversations in response to the dizzying rate of growth in blockchain technology. The scholarly work takes into account the material that has been assessed by experts and aims to explain the fundamental idea and provide a comprehensive computational analysis of relevant literature. Such an approach contributes to the advancement of decentralized applications (dApps) by providing technical insights into their development frameworks. The initial section presents a brief overview of smart contracts, including their conceptual foundations, system architecture, and application domains. Furthermore, in a detailed review of existing platforms for developing smart contracts, it was found by comparison that the Tron and CoreDAO blockchains offer the most computationally efficient platforms to enhance the quality-of-services (QoS) in decentralized environments. These low-cost transaction models support the creation of resource-efficient smart contracts. In addition, this study includes a simulation work that considers the blockchain transactions as a dataset to train an artificial intelligence model that would support the computational prediction of the success and failure of the transactions. Received: 25 July 2025 | Revised: 23 October 2025 | Accepted: 5 December 2025 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 Alock Gupta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Kamlesh Lakhwani: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Supervision.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
A Neurological Assessment in COVID-19 Using Adaptive and Machine Learning Technique Under EEG Signals
The COVID-19 pandemic has emerged as a profound threat to brain integrity, requiring advanced, multi-phase neurological assessment protocols. Electroencephalogram (EEG) metrics encapsulate real-time brain dynamics. However, classic machine learning techniques rely on fixed training sets, thereby limiting their responsiveness to evolving electrophysiological signatures. We report a responsive EEG-recognition pipeline that is capable of continuous, bedside surveillance of neurological compromise in SARS-CoV-2-infected individuals by coupling persistent EEG streaming, on-the-fly feature extraction, and incremental model augmentation. Central to our architecture is a multi-tier preprocessing chain that harmonizes Mel-frequency cepstral coefficients and wavelet-transformed time–frequency distributions, thus packing spectral and temporal context into a compact feature space. Adaptive random forest (ARF) is then employed. Unlike static ensembles, ARF inserts, prunes, and refines decision trees as new epochs arrive, thereby calibrating to the neurophysiological uniqueness of each patient within seconds. Formal evaluation against publicly available EEG archives confirms that the adaptive pipeline exceeds static counterparts on all critical metrics—accuracy, precision, sensitivity, and F1—by statistically validated margins, as substantiated via McNemar’s equivalence test and validated at 95% confidence. Collectively, these findings affirm that the described adaptive EEG framework delivers a robust, expandable, and clinically actionable infrastructure for real-time neuro-monitoring in COVID-19. Received: 12 September 2025 | Revised: 20 November 2025 | Accepted: 12 December 2025 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/marcjuniornkengue/covid500hz. Author Contribution Statement Satyanarayana Murthy K.: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Visualization, Project administration. Korada Suribabu: Investigation, Writing – review & editing, Supervision.
Generative AI in Physics Education: A PRISMA-Guided Systematic Review of Empirical Studies
Generative artificial intelligence (GenAI) tools are redefining education, particularly in science disciplines such as physics. Even though these systems are widely used, there are still only a few studies that clearly summarize their use in teaching and their impact. In this PRISMA-guided systematic review, we analyzed 21 empirical studies (from 2023 to 2025) on the role of GenAI in physics education. We combined bibliometric mapping with content-based refinement and classified the studies by educational level, tools used, research methodology, and pedagogical role. Three main functions were identified: problem solver (57% of studies), student’s tutor (38%), and teacher’s assistant (33%). Problem-solving roles demonstrated variable accuracy, ranging from 100% to 0% across task types. Tutoring roles, though few, showed the strongest evidence of positive learning outcomes. Assistant roles mainly supported efficiency in grading and content generation, with less direct evidence of learning outcomes. The review contributes by providing this physics-specific PRISMA synthesis of GenAI in education and by identifying the roles GenAI tools assume in this context. This research is limited by the small, diverse set of included studies, which focus mainly on university contexts, and by its reliance on Scopus-indexed literature. Our findings suggest that GenAI tools can potentially improve conceptual understanding, formative assessment, and instructional efficiency. However, their effective integration requires human supervision, critical evaluation, and strategies to address issues of accuracy, equity, and multimodality. Received: 12 August 2025 | Revised: 29 December 2025 | Accepted: 28 January 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 Maria Moundridou: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision. Nikolaos Moutis: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration. Konstantinos Charalampopoulos: Investigation, Writing – review & editing. Nikolaos Matzakos: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Intelligent Technique for Neuromuscular Disorders Prediction Based on Stockwell Transform
Electromyography (EMG) signals, which reflect the electrical activity of skeletal muscles, serve as a fundamental tool in diagnosing neuromuscular disorders. Yet, due to their inherently nonstationary characteristics, extracting meaningful features from these signals remains a persistent challenge in the field. To address this, the present study introduces a hybrid methodological framework. Initially, EMG signals are transformed into time–frequency representations using the Stockwell transform (ST), effectively converting complex signal data into image-like formats amenable to advanced analysis. Subsequently, the framework leverages the power of five pretrained deep convolutional neural networks (CNNs)—namely, VGG16, ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2—as automated feature extractors. This approach reduces dependence on traditional handcrafted features and exploits the capacity of CNNs to uncover intricate signal patterns. The investigation utilizes the publicly available EMGLAB database, encompassing EMG data from individuals diagnosed with amyotrophic lateral sclerosis, myopathy, and healthy controls. The signals are segmented and transformed into Stockwell-based images prior to feature extraction. For classification, features derived from the CNNs are evaluated using conventional machine learning algorithms, including support vector machine (SVM), random forest, k-nearest neighbor, and naïve Bayes. Empirical results reveal that the combination of VGG16 and SVM produces the most accurate classification, with achieved accuracies ranging between 97.0% and 98.5% across five distinct classification tasks. These findings underscore the efficacy and potential of the ST combined with CNN-based strategies for robust and accurate classification of neuromuscular disorders, establishing a valuable benchmark for future EMG research. Received: 31 August 2025 | Revised: 7 January 2026 | Accepted: 27 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 dataset N2001 at http://www.emglab.net. Author Contribution Statement Nahla F. Abdel-Maboud: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing – original draft, Visualization. Marco Alfonse: Validation, Formal analysis, Investigation, Writing – review & editing, Visualization. Silvia Stoyanova Parusheva: Validation, Writing – review & editing, Visualization, Supervision. Abdel-Badeeh M. Salem: Validation, Writing – review & editing, Visualization, Supervision, Project administration.
Python-Programmed Embedded ECU for Drowsiness-Responsive Dynamic Braking in Electric Vehicles
Electric vehicles (EVs) are increasingly central to sustainable mobility, but braking control remains a safety-critical challenge. EVs must balance regenerative braking, which recovers kinetic energy, with dynamic braking, which ensures rapid deceleration in emergency situations. At the same time, driver drowsiness contributes to nearly one-fifth of serious accidents worldwide, underscoring the importance of systems that respond to external conditions and human states. This study presents a Python-programmed electronic control unit (ECU) on a Raspberry Pi Pico, integrating real-time driver drowsiness detection with adaptive braking control. Inputs from ultrasonic sensors, wheel encoders, and a camera-based drowsiness detection module are transmitted via the Message Queuing Telemetry Transport (MQTT) protocol. At the same time, a fuzzy inference engine processes driver condition, vehicle speed, and obstacle distance to generate proportional pulse width modulation (PWM) signals for motor braking. Experimental validation using a laboratory-scale prototype demonstrated distinct braking profiles under three conditions: slightly drowsy states produced proportional speed reductions as early warnings, drowsy states resulted in smooth full stops with consistent deceleration between 0.042 and 0.050 m/s², and emergency braking delivered rapid stops with shorter distances of 0.116 to 0.204 meters. While a 1-second latency was observed in some slightly drowsy runs, the system consistently adapted braking behavior and restored regular operation when drowsiness signals ceased. These findings validate that a Micro-Python-based ECU can reliably integrate behavioral monitoring with adaptive braking, offering a low-cost, scalable solution for future EV safety systems. Received: 30 August 2025 | Revised: 15 December 2025 | Accepted: 3 January 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 Kalaivanan Kumaran: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Muhammad Nadzmi Razlan: Software, Validation, Investigation, Data curation, Writing – review & editing. Ahmad Safwan Abd Shukor: Software, Validation, Investigation, Data curation, Writing – review & editing. Mohamad Tarmizi Abu Seman: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration.
A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI: A Case of Generative AI Chatbot Services
This study proposes a comprehensive framework to analyze and evaluate the ethical and trustworthiness problems of Generative AI. Generative AI such as ChatGPT has innovative potential but simultaneously causes ethical and social issues including bias, harmfulness, copyright infringement, privacy infringement, and hallucinations. Existing artificial intelligence (AI) evaluation methodologies mainly focus on performance and accuracy, limiting their ability to address these complex problems. This study emphasizes the need for new evaluation criteria that are human-centered and consider social impact. This study defines key factors for evaluating the ethics and reliability of Generative AI, including fairness, transparency, accountability, safety, privacy, accuracy, consistency, robustness, explainability, copyright protection, and source traceability, presenting detailed indicators and evaluation methodologies for each factor. AI ethics policies and guidelines of major countries such as South Korea, the United States, the European Union, and China are compared and analyzed to derive implications for each country. The proposed evaluation framework is applicable throughout the AI system life cycle and contributes to effective identification and management of AI ethics and reliability issues by integrating multidisciplinary perspectives with technical evaluation. To demonstrate practical applicability, pilot experiments in a Generative AI chatbot system verify the usefulness of the indicators, laying an academic foundation for responsible AI development. The framework provides practical guidelines for stakeholders such as policymakers, developers, and users, contributing to the positive social impact of AI technology. Received: 29 August 2025 | Revised: 12 January 2026 | Accepted: 17 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 Cheonsu Jeong: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Seunghyun Lee: Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Seonhee Jeong: Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Sungsu Kim: Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization.