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.
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
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.
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.
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.
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.
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.
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.
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.
Public Discussion of DeepSeek Large Language Model on Twitter: A Mixed-Methods Sentiment and Topic Modeling
Artificial intelligence (AI) has moved from research laboratories into everyday tools used by millions worldwide. In recent years, advances in natural language AI systems have sparked extensive public exploration and discussion. This study investigates overall public sentiment and key discussion topics related to the DeepSeek large language model (LLM) on Twitter (now rebranded as X) and examines sentiment differences across discussion topics during various DeepSeek-related events. After data collection, Python was used to perform preliminary cleaning and screening of English-language tweets. The Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis tool was applied to classify tweet sentiment. Based on the VADER labels, the dataset was stratified to obtain a high-quality sample of 5000 tweets while preserving the original sentiment distribution. To further explore discussion themes, latent Dirichlet allocation combined with coherence score evaluation was employed for topic modeling. Topic-level sentiment analysis was then conducted across different DeepSeek-related events to assess public attitudes toward each discussion topic. Results indicate that overall public sentiment toward DeepSeek LLM is predominantly positive. Topic modeling identified 10 optimal discussion topics, covering areas such as technical performance, economic impact, political and cultural context, and international competition. The findings also reveal significant differences in sentiment distribution across topics, demonstrating the practical value of combining sentiment analysis and topic modeling for business intelligence and AI product optimization. Received: 29 August 2025 | Revised: 26 December 2026 | Accepted: 11 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/bwandowando/tweets-and-reaction-on-deepseek-models and https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification. Author Contribution Statement Wei Chien Ng: Conceptualization, Methodology, Resources, Writing – original draft, Supervision, Project administration, Funding acquisition. Shiwen Chen: Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft. Quan Kai Ang: Writing – review & editing, Visualization. Yu Qing Soong: Validation, Writing – review & editing.
A New Method for Designing Intelligent User Interfaces Through Image Analysis to Facilitate Human Interaction
Traditional graphical user interfaces (GUIs) for human-computer interaction (HCI) often suffer from design complexity, cognitive overload, poor accessibility, long development times, and limited adaptability, typically requiring technical expertise for modifications. This study aims to democratize GUI design by introducing a novel framework called Intuitive Image-based User Interface (IIUI). The primary objective is to enable users to intuitively design interactive interfaces directly from digital images without requiring advanced programming knowledge. The IIUI system leverages state-of-the-art image recognition and computer vision algorithms to automatically detect and assign interactive widgets within user-defined regions of digital images. The system also facilitates dynamic interaction assignment through a streamlined user interface. The effectiveness of the widget assignment process was evaluated through experimental validation. Experimental validation of the IIUI system demonstrated a high widget assignment accuracy exceeding 99.8%. The IIUI approach significantly reduces the technical barrier to GUI creation, offering a faster and more inclusive design process compared to traditional methods. Practical implementations across diverse domains such as assistive technologies, industrial shape detection, and robotic control highlight the adaptability of the system. The IIUI framework represents a transformative step toward accessible and intelligent GUI design. By integrating AI-driven image recognition with user-centered design principles, it provides a highly intuitive method for interface creation. The high performance and versatility of the system suggest strong potential for widespread adoption across various industrial and social contexts. Received: 26 August 2025 | Revised: 29 December 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 sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Naji Guedri: Conceptualization, Software, Validation, Formal analysis, Writing – original draft, Writing – review & editing. Abdessalam Guedri: Investigation, Resources, Writing – review & editing. Boulbaba Guedri: Methodology, Data curation. Rached Gharbi: Supervision, Project administration.
Lightweight AI for Cultural Pattern Recognition: Safeguarding South Asian Regional Embroidery Heritage
The most important element of cultural heritage is the embroidery used in the ancient clothes. Embroidery used in the ancient clothes represents centuries of creative tradition, regional uniqueness, and the passing down of skills from one generation to the next. These elaborate textile practices, which have their roots in manual craftsmanship and oral instruction, are increasingly threatened by industrial production, globalization, and the slow decline of traditional artisans. By developing an automated identification structure based on MobileNetV2, an efficient yet highly effective convolutional neural network (CNN), this work presents an AI approach to support the preservation of regional embroidery. The model used in the paper was trained very carefully with a dataset of 5,200 high-resolution images that included a range of regional embroidery styles using pretrained ImageNet weights. With an identification accuracy of 97.3%, the suggested MobileNetV2 outperformed traditional CNN architectures like VGG16 and ResNet by over 2.3%. The results demonstrate how small AI models can make a significant contribution to the preservation of cultural heritage by providing a scalable method for storing and assessing textile arts for museums, scholars, and craftspeople. This methodology exhibits promise in fields such as digital archives, fashion technology, and cultural data analytics for heritage conservation. Received: 17 August 2025 | Revised: 15 December 2025 | Accepted: 3 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/ask1999/embroiderydataset. Author Contribution Statement Amir Sohail Khan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Junjie Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Risk Assessment of Large Language Model Implementation in the Electric Power Sector of Ukraine
This study explored the key aspects and risks associated with the implementation of large language models (LLMs) in the electric power sector of Ukraine. We propose a unique taxonomy of risks, along with a hierarchical structure that enables their assessment using the analytic hierarchy process (AHP) developed by T. Saaty. The LLM lifecycle is described with a focus on both human and technological factors (from knowledge selection and training to operational deployment). The study addresses critical concerns related to confabulations, sensitive information leakage, compliance with personal data protection regulations, and the safeguarding of trade secrets. The paper highlights the importance of employing tools for hallucination detection, sentiment analysis, and legal compliance monitoring. A separate section presents an in-depth analysis of LLMs’ readiness to accurately digitize graphical content—such as schematics, diagrams, and technical drawings, which are common for documentation in the energy sector worldwide. A series of experiments using the state-of-the-art generative AI systems revealed significant limitations in interpreting complex diagrams, logical structures, and semantic relationships between elements. The findings demonstrate both the potential and the critical limitations of LLMs in energy-related applications, particularly in processing graphical content, making decisions based on synthetic data, and managing risks associated with model training, operation, and upgrades. Received: 13 August 2025 | Revised: 1 December 2025 | Accepted: 15 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 Github at https://github.com/oleksandrkravchukatpimee/LLM-risks-evaluation/blob/3443a610d9b2f9a9f8db52b280f2f4fb247525c1/AHP.xlsx and https://gist.github.com/taranowskiatpimee/174973d140a84da2b5c3b365a34f949c. Author Contribution Statement Hryhoriy Kravtsov: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Supervision. Oleksandr Kravchuk: Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing. Artem Taranowski: Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing. Dmytro Sinko: Methodology, Investigation, Writing – original draft. Victor Samoylov: Conceptualization, Supervision.
Development of a Web Workf low Tool That Facilitates and Optimizes the Process of Preparing Scientific Articles
Writing scientific articles is a complex process for novice researchers, who often face difficulties related to content structuring, the proper use of academic language, and reference management. This research presents the development and validation of a web tool called RedactFlow, designed to support the preparation of scientific manuscripts through functionalities that integrate user-centered design principles and artificial intelligence (AI) techniques. The methodology used was non-experimental with a cross-sectional design, and included a systematic literature review to provide theoretically support for the design, as well as the use of the ISO 9241-210:2019 standard for the construction of user-experience-oriented interfaces. The system was developed using an adapted version of the agile SCRUM methodology, and incorporates modules for guided content generation, grammatical correction, real-time suggestions, reference management, and export to standardized academic formats. The validation of the system was carried out through functional, performance, and usability tests, including comparisons between tool-generated texts and expert-revised versions. The results showed significant improvements in the coherence, clarity, and formal structure of the texts, as well as a satisfactory user experience, reflected in an average score of 76.75 on the System Usability Scale (SUS). In addition, the system demonstrated stable behavior under high-demand conditions, maintaining optimal response times. Overall, the findings support the conclusion that RedactFlow is an innovative and effective proposal to support scientific writing, especially in its initial stages, by providing a structured workflow that contributes to strengthening academic writing skills in both training and research contexts. Received: 10 August 2025 | Revised: 5 December 2025 | Accepted: 18 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 Lucrecia Llerena: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Nancy Rodríguez: Software, Validation, Investigation, Supervision, Project administration. Kelvin Estrada: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
System for the Generation and Georeferenced Visualization of Agricultural Crop Datasets Using Imagro Hybrid Platform
The agriculture sector in Ecuador faces significant challenges in its digital transformation process, primarily due to the lack of accessible tools for the capture and management of georeferenced crop data. In this context, this study proposes Imagro, a hybrid platform comprised of a mobile application and a web application, designed to capture, store, visualize, and classify crop images with integrated spatial metadata. This solution incorporates lightweight machine learning models, specifically MobileNet V1 with TensorFlow.js, which enables real-time automatic crop classification from web browsers, without the need for specialized infrastructure. The research employs a hybrid methodological approach that combines documentary techniques, such as systematic literature reviews and comparative analysis of tools. with experimental techniques including evolutionary prototyping, functional tests, and usability assessment. The inductive method was used to analyze user experience through direct observation and the application of the System Usability Scale (SUS) questionnaire, and analytical methods were used to study the system architecture, the performance of the model, and its main functionalities. The dataset used was built from open repositories and expert contributions, and the Imagro platform has been evaluated by users in agricultural and academic environments. The results show an SUS score of 78.2 and a 98% success rate in task execution, confirming the potential of Imagro as an accessible and functional digital tool to strengthen precision agriculture, boost applied research, and support agricultural education in rural contexts with limited connectivity. Received: 10 August 2025 | Revised: 31 December 2025 | Accepted: 4 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 GitHub at https://github.com/Darling-P11/imagro_web. Author Contribution Statement Lucrecia Llerena: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Nancy Rodríguez: Software, Supervision, Project administration, Validation, Investigation. Kevin Ponce: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Software, Supervision, Project administration, Validation, Investigation.
A Multi-class Obesity Risk Prediction Using Machine Learning and Explainable Artificial Intelligence
Because of the intricate relationships between dietary practices, physical characteristics, and lifestyle, weight-related health issues have grown to be a global concern. The shortcomings of current machine learning (ML) techniques include inefficient feature selection, imbalanced datasets, a binary classification focus, decreased accuracy, and inadequate hyperparameter tweaking. This paper uses a clinically validated dataset of 1,638 patients from Bangladeshi healthcare facilities to provide a complete framework for ML-based multiclass obesity risk prediction in order to fill these gaps. The proposed approach combines 5-fold cross-validation with GridSearchCV for systematic hyperparameter tuning and ensemble feature selection. The Synthetic Minority Over-sampling Technique was used just on the training set to address class imbalance and guarantee balanced learning across the seven weight categories. The proposed XGBoost outperformed the other ML algorithms that were assessed for obesity risk prediction due to their high accuracy score of 95.4%. According to the results, the proposed method outperformed previous works by at least 5.18% in accuracy increase and 10.30% in F1-score gain. Furthermore, explainable Artificial Intelligence methods based on SHAP offer insights into the decision-making process through feature contributions. Received: 8 August 2025 | Revised: 25 December 2025 | Accepted: 30 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 GitHub at https://github.com/pymche/Machine-LearningObesity-Classification. Author Contribution Statement Tarequl Hasan Sakib: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Optimizing Traffic Signal Control Using Machine Learning and Environmental Data
Traffic signal control is a critical component of urban transportation management, and optimizing its performance can significantly reduce congestion, decrease travel times, and improve air quality. This study proposes a novel approach to optimizing traffic signal control using machine learning and environmental data. This work focuses on the interplay between smart city infrastructure and environmental data to provide a novel method for traffic pattern prediction. Mitigating traffic congestion is a pressing concern in urbanized societies and emerging smart cities. This study explores leveraging publicly available air pollution data as an environmental indicator to enhance urban mobility and predict traffic patterns. Taking into account factors including vehicle emissions, weather patterns, and topographical features, the study will look at possible connections between air pollution and traffic congestion. The goal of this project is to develop a prediction model that uses real-time air quality data for traffic forecasting by utilizing big data analytics and machine learning approaches. According to our research, the K-nearest neighbors (KNN) model performs better than any other regression model examined. According to experimental findings, the KNN model considerably lowers the error rate in traffic congestion prediction by over 30%. Received: 29 August 2024 | Revised: 1 July 2025 | Accepted: 17 October 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 Edidiong E. Akpan: Conceptualization, Supervision, Project administration. Oluwatobi Akinlade: Data curation, Writing – original draft. Oluwaseyi O. Alabi: Methodology, Formal analysis, Resources, Data curation, Visualization. Oluwasesan A. David: Software, Validation. Oluwaseyi F. Afe: Software, Investigation. Sunday Adeola Ajagbe: Conceptualization, Resources, Writing – review & editing.
Curved Inference: A Guide to Geometric Interpretability
This paper introduces a unified framework for analyzing the internal geometry of inference in transformer-based language models. Building on a series of prior studies, we present a consolidated introduction to “Curved Inference”: a methodology that measures how token representations evolve in the residual stream as geometric trajectories. Using metrics such as curvature, salience, and semantic surface area, we show that residual trajectories reflect meaningful semantic structure, and are empirically associated with emotional and moral concern, covert intent in sleeper agents, and computational self-modeling dynamics. We consolidate these findings into a reproducible, falsifiable pipeline, supported by formal mathematical definitions and open-source tools. This geometric approach shifts the focus of interpretability from static attribution to dynamic, modelnative inference analysis. The results provide evidence that residual stream geometry is not only measurable, but also structurally related to complex behaviors in the models we study. We invite researchers to replicate, extend, or falsify these claims and test the boundaries of curved inference as a new paradigm for model understanding. Received: 6 August 2025 | Revised: 12 January 2026 | Accepted: 21 January 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 GitHub at https://github.com/robman/FRESH-model/tree/main/ benchmarks/curved-inference. Author Contribution Statement Rob Manson: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0
A Novel STCA-LightGBM-SVR Hybrid Model for Port Cargo Throughput Prediction
Accurate port cargo throughput prediction is critical for global supply chain optimization yet remains challenging due to complex spatiotemporal dependencies and heterogeneous data integration. This study proposes a novel STCA-LightGBM-SVR (spatiotemporal crossattention mechanism) hybrid model integrating spatiotemporal cross-attention mechanisms with a dual-stage ensemble learning framework—specifically, a developed LightGBM-SVR ensemble where LightGBM handles feature selection followed by SVR for nonlinear fitting—to address these limitations. The parallel-designed attention module dynamically captures seasonal patterns (peak attention weight 0.18 ± 0.03) and inter-port correlations (attention coefficients 0.12–0.81), while the dual-stage ensemble combines LightGBM’s feature selection (F1 score = 0.89) with SVR’s nonlinear fitting capability (15.2% R² improvement). Experimental results on multi-port datasets (2010–2023) demonstrate superior performance over state-of-the-art algorithms such as Boosted DeepVAR and AutoGluon-Timeseries, with 4.82 million tons RMSE (15% reduction), 21.3% MAE decrease in long-term predictions, and 23.4% error reduction for extreme events, showcasing robust spatiotemporal modeling and practical applicability in port operations. Received: 3 August 2025 | Revised: 16 October 2025 | Accepted: 4 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 Tianwen Zhao: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Hanyu Xu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization.
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
This paper presents a case study of end-to-end (E2E) automation of corporate financial expense processing by combining generative AI (GenAI) and intelligent document processing (IDP) technologies with automation agents and shows the automation of intelligent tasks in a modern digital transformation environment. Although conventional RPA is effective in automating repetitive, rule-based, and simple tasks, it has limitations in handling unstructured data, responding to exceptions, and making complex decisions. In this study, we designed and implemented a four-step integration process, including automatic recognition of proofs such as receipts through OCR/IDP, item classification based on policy database, intelligent judgment support for exceptional situations through GenAI (LLMs), and human final decision and system learning (human-in-the-loop) through automation agents. As a result of the application to Company S, a large Korean company, quantitative effects such as reducing the processing time of branch receipt expenses by more than 80%, reducing error rates, and improving compliance rates were confirmed, as well as qualitative effects such as improving work accuracy and consistency, increasing employee satisfaction, and supporting data-based decision-making. In addition, the system learns from human judgment and continuously improves its ability to automatically handle exceptions, creating a virtuous cycle. This study empirically demonstrates that the organic combination of GenAI, IDP, and an automation agent overcomes the limitations of existing automation and is effective in realizing E2E automation of complex corporate tasks. In addition, it suggests the possibility of expansion to various business areas such as accounting, human resources, and purchasing in the future, as well as the development direction of AI-based hyperautomation. Received: 30 May 2025 | Revised: 30 July 2025 | Accepted: 22 September 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 Cheonsu Jeong: Conceptualization, Supervision, Writing – original draft. Seongmin Sim: Software, Writing – review & editing. Hyoyoung Cho: Software, Writing – review & editing. Sungsu Kim: Software, Writing – review & editing. Byounggwan Shin: Software, Writing – review & editing.
Literature Review of Methods for Detecting Non-Technical Electricity Losses in Distribution Grids
The widespread deployment of advanced metering infrastructure (AMI) and smart grid technologies has significantly expanded the availability of high-resolution electricity consumption data in distribution networks. This creates new opportunities for addressing non-technical losses (NTL). This paper presents a systematic literature review of data-driven methods for NTL detection based on an analysis of 79 relevant scientific publications from leading databases (IEEE Xplore, Springer, MDPI, and ScienceDirect) indexed in Scopus/Scimago over the period 2020–2025. The reviewed methods are categorized according to a novel triad of criteria: the analytical paradigm, the input data structure, and the required level of grid digitalization. A comparative analysis reveals distinct performance profiles: supervised classification methods, particularly hybrid and ensemble models, enable high-accuracy detection, with many studies reporting F1-scores exceeding 0.95 on benchmark datasets, yet they require extensive labeled data from AMI systems. Unsupervised clustering techniques offer a practical alternative for grids with partial or no AMI by analyzing aggregated consumption patterns. Forecasting-based (regression) methods facilitate continuous consumption monitoring and anomaly detection via deviation analysis, while scenario-modeling techniques provide strategic tools for evaluating the potential impact of NTL reduction measures. This review critically examines the causes of NTL, feature engineering strategies, software tools, and key challenges such as data imbalance, model interpretability, and real-world deployment constraints. The synthesis provides a utility-centric framework for method selection, offering practical recommendations tailored to different infrastructure profiles. The proposed systematization bridges methodological gaps and supports the development of context-aware, data-driven solutions for modern and evolving power systems. Received: 3 July 2025 | Revised: 8 January 2026 | Accepted: 14 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 Irbek D. Morgoev: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Roman V. Klyuev: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Angelika D. Morgoeva: Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration.
International Trade Demand Forecasting Model Based on Transformer-XGBoost-LightGBM
The global economic situation is complex and constantly changing. Under such a macroenvironment, it is extremely difficult to accurately predict the demand for international trade. This study presents a hybrid prediction model relying on the Transformer-XGBoost-LightGBM framework, aiming to significantly optimize the accuracy and stability of the prediction. This model innovatively integrates the Transformer’s ability to identify temporal correlations and nonlinear patterns over long time spans, as well as the advantages of XGBoost and LightGBM in dealing with structured features. Moreover, the model achieves the integration effect of dynamic weights through SHAP values. From the experimental results, when this model is used for short-term prediction, its weighted mean absolute percentage error (WMAPE) is 9.2%, which is 23.5% higher than that of the LSTM model and 15.8% higher than that of the model using only the Transformer. When encountering extremely severe shock events like the COVID-19 pandemic, this model demonstrated strong stability, with WMAPE remaining below 19.8% all the time, which was 38.7% stronger than that of the traditional VAR model. This model demonstrates the key trade-driving factors (exchange rate and tariff) and the main trade links between countries (the weight of the China–US channel is 0.41) through the SHAP value and the attention mechanism, thereby endowing the model with a certain degree of interpretability. In the actual deployment of enterprise supply chains, this model has optimized the inventory turnover rate by 22%, reduced the out-of-stock rate by 18%, and brought about outstanding economic benefits. The net present value over three years reached 12.7 million US dollars, and the return on investment was 310%. This study provides a high-performance, interpretable, and stable advanced tool for international trade forecasting. It has significant theoretical and operational significance for global supply chain operation and risk avoidance. Received: 1 August 2025 | Revised: 31 October 2025 | Accepted: 13 November 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 GitHub at https://github.com/Tianwen-Zhao/AIA25.10.30. Author Contribution Statement Wenhao Wang: Conceptualization, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Supervision. Zhitao Yang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Tianwen Zhao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.