Artificial Intelligence and Applications
type: channel
description: 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.
publisher: Bon View Publishing Pte Ltd.
language: en
publication name: Artificial Intelligence and Applications
issn: 2811-0854
items: The Nexus of Human and Machine: Exploring Perception as a Spur in AI-Powered Employee Training
items: Modeling the Double-Edged Effects of AI Companions on Chinese Generation Z: A Dual-Pathway Analysis of Risk and Protection
items: Treatment Regimen Segmentation from Handwritten Medical Prescriptions Using Advanced Neural Network
items: A Short Review on Computer Vision: Visualizing the World Through Machine
items: An Enhanced Traffic Matrix Prediction in Software-Defined Networks Using Bi-LSTM with Attention Mechanism
items: Adaptive Footwear Stiffness Driven by Biomechanical Signals for Improving High-Intensity Metabolic-Neuromuscular Performance: A Randomized Controlled Trial
items: Hierarchical Brain-Inspired Deep Learning for Autonomous Decision-Making in Complex Dynamic Environments
items: Adaptive Mechanisms of Foot Function and Control Induced by Dance Training
items: Deep Reinforcement Learning-Enabled IoT Framework for Real-Time and Personalized Diabetes Diagnosis Using Wearable Sensors
items: Heuristic-Guided Selective Random Forests: Immune-Inspired Heuristic Ensembles for Robust Intrusion Detection in IoT Networks
items: A New Domain-Independent Approach for Classification of Bacteria, Fungus, and Virus-Infected Fruit and Leaf Images
items: Blockchain Platforms for Developing Smart Contracts and Their Computational Performance Evaluation: A Systematic Literature Review
items: A Neurological Assessment in COVID-19 Using Adaptive and Machine Learning Technique Under EEG Signals
items: Generative AI in Physics Education: A PRISMA-Guided Systematic Review of Empirical Studies
items: Python-Programmed Embedded ECU for Drowsiness-Responsive Dynamic Braking in Electric Vehicles
items: Public Discussion of DeepSeek Large Language Model on Twitter: A Mixed-Methods Sentiment and Topic Modeling
items: A New Method for Designing Intelligent User Interfaces Through Image Analysis to Facilitate Human Interaction
items: Risk Assessment of Large Language Model Implementation in the Electric Power Sector of Ukraine
items: Development of a Web Workf low Tool That Facilitates and Optimizes the Process of Preparing Scientific Articles
items: System for the Generation and Georeferenced Visualization of Agricultural Crop Datasets Using Imagro Hybrid Platform
items: A Multi-class Obesity Risk Prediction Using Machine Learning and Explainable Artificial Intelligence
items: Optimizing Traffic Signal Control Using Machine Learning and Environmental Data
items: Curved Inference: A Guide to Geometric Interpretability
items: A Novel STCA-LightGBM-SVR Hybrid Model for Port Cargo Throughput Prediction
items: E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
items: Literature Review of Methods for Detecting Non-Technical Electricity Losses in Distribution Grids
items: International Trade Demand Forecasting Model Based on Transformer-XGBoost-LightGBM
items: Machine Learning-Based Theme Classification for Video Content Analysis: A Bilingual Approach on the StoryBox
items: Optimal Policy Strategy for Pandemic Outbreak Control: A Deep Reinforcement Approach
items: Beyond Binary: Adaptive Frameworks for Autonomous AI Governance

The Nexus of Human and Machine: Exploring Perception as a Spur in AI-Powered Employee Training
type: item
description: 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 c…
subject: Research Article
  topic: section
subject: artificial intelligence (AI)
  topic: keywords
subject: AI-powered training
  topic: keywords
subject: employee perception
  topic: keywords
subject: learning effectiveness
  topic: keywords
subject: organizational support
  topic: keywords
creator: Chanchal Molla
creator: Khaled Islam
creator: Md. Razib Hossain
creator: Pronoy Kumar Paul
creator: Md. Najibul Kabir
creator: Abu Naser Mohammad Saif
creator: Rehnuma Mostafa
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-24
publication date: 2026-02-24
doi: 10.47852/bonviewAIA62027189

Modeling the Double-Edged Effects of AI Companions on Chinese Generation Z: A Dual-Pathway Analysis of Risk and Protection
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI companions
  topic: keywords
subject: affective computing
  topic: keywords
subject: Gen Z
  topic: keywords
subject: adaptive pacing
  topic: keywords
subject: digital well-being
  topic: keywords
creator: Haiyang Li
creator: Jing Liu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-10
publication date: 2026-02-10
doi: 10.47852/bonviewAIA62027719

Treatment Regimen Segmentation from Handwritten Medical Prescriptions Using Advanced Neural Network
type: item
description: 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.: Co…
subject: Research Article
  topic: section
subject: handwritten medical prescription
  topic: keywords
subject: semantic block segmentation
  topic: keywords
subject: deep learning
  topic: keywords
subject: U-Net
  topic: keywords
subject: attention mechanism
  topic: keywords
creator: Rekha G. R.
creator: Siddesha S.
creator: V. N. Manjunath Aradhya
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-25
publication date: 2026-01-25
doi: 10.47852/bonviewAIA62027648

A Short Review on Computer Vision: Visualizing the World Through Machine
type: item
description: 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.
subject: Review
  topic: section
subject: computer vision
  topic: keywords
subject: deep learning
  topic: keywords
subject: machine learning
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: AI applications
  topic: keywords
creator: Mohammad Mehedi Hassan
creator: Stephen Karungaru
creator: Rezaul Bashar
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-03-10
publication date: 2026-03-10
doi: 10.47852/

An Enhanced Traffic Matrix Prediction in Software-Defined Networks Using Bi-LSTM with Attention Mechanism
type: item
description: 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 …
subject: Research Article
  topic: section
subject: software-defined networks
  topic: keywords
subject: traffic matrix prediction
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: attention mechanism
  topic: keywords
creator: Prabu. U.
creator: Prabhala Harichandana
creator: Manepalli Baby Kavyasri
creator: Uppala Bhargavi
creator: Geetha. V.
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-15
publication date: 2026-02-15
doi: 10.47852/bonviewAIA62026867

Adaptive Footwear Stiffness Driven by Biomechanical Signals for Improving High-Intensity Metabolic-Neuromuscular Performance: A Randomized Controlled Trial
type: item
description: 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 sup…
subject: Research Article
  topic: section
subject: lower-limb biomechanics
  topic: keywords
subject: adaptive footwear stiffness
  topic: keywords
subject: machine learning
  topic: keywords
subject: metabolic-neuromuscular coupling task
  topic: keywords
subject: basketball
  topic: keywords
creator: Yining Xu
creator: Yang Song
creator: Dong Sun
creator: Zhiyi Zheng
creator: Mingwei Sun
creator: Wenlong Li
creator: Jiachao Cai
creator: Xuanzhen Cen
creator: Zixiang Gao
creator: Liangliang Xiang
creator: Monèm Jemni
creator: Yaodong Gu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-03-10
publication date: 2026-03-10
doi: 10.47852/bonviewAIA62028408

Hierarchical Brain-Inspired Deep Learning for Autonomous Decision-Making in Complex Dynamic Environments
type: item
description: 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 202…
subject: Research Article
  topic: section
subject: brain-inspired computing
  topic: keywords
subject: hierarchical deep learning (HDL)
  topic: keywords
subject: cognitive architecture
  topic: keywords
subject: deep reinforcement learning (DRL)
  topic: keywords
subject: decision-making
  topic: keywords
subject: complex environments
  topic: keywords
creator: Dhaya Ramakrishnan
creator: Kanthavel Radhakrishnan
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-12
publication date: 2026-01-12
doi: 10.47852/bonviewAIA62026154

Adaptive Mechanisms of Foot Function and Control Induced by Dance Training
type: item
description: 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 Ji…
subject: Research Article
  topic: section
subject: dance training
  topic: keywords
subject: neuro-biomechanical adaptation
  topic: keywords
subject: feedforward control
  topic: keywords
subject: foot biomechanics
  topic: keywords
subject: non-negative matrix factorization
  topic: keywords
creator: Xiangli Gao
creator: Datao Xu
creator: Minjun Liang
creator: Zanni Zhang
creator: Tianle Jie
creator: Huiyu Zhou
creator: Jingyi Ye
creator: Zsolt Radak
creator: Yaodong Gu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
doi: 10.47852/bonviewAIA62028273

Deep Reinforcement Learning-Enabled IoT Framework for Real-Time and Personalized Diabetes Diagnosis Using Wearable Sensors
type: item
description: 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 ope…
subject: Research Article
  topic: section
subject: diabetes diagnosis
  topic: keywords
subject: attention mechanism
  topic: keywords
subject: Dueling DQN
  topic: keywords
subject: personalized health care
  topic: keywords
creator: M. Alamelu
creator: Meera Alphy
creator: Finney Daniel Shadrach
creator: Jayaraj Velusamy
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
doi: 10.47852/bonviewAIA62026306

Heuristic-Guided Selective Random Forests: Immune-Inspired Heuristic Ensembles for Robust Intrusion Detection in IoT Networks
type: item
description: 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 analysi…
subject: Research Article
  topic: section
subject: Internet of Things
  topic: keywords
subject: intrusion detection
  topic: keywords
subject: ensemble learning
  topic: keywords
subject: random forest
  topic: keywords
subject: decision tree
  topic: keywords
subject: negative selection algorithm
  topic: keywords
creator: Saad AL Azzam
creator: Ghassan AL Dharhani
creator: Raenu AL Kolandaisamy
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-15
publication date: 2026-01-15
doi: 10.47852/bonviewAIA62027781

A New Domain-Independent Approach for Classification of Bacteria, Fungus, and Virus-Infected Fruit and Leaf Images
type: item
description: 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 …
subject: Research Article
  topic: section
subject: connected component labeling
  topic: keywords
subject: quality measures
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: fruit/leaf disease classification
  topic: keywords
creator: Poornima Basatti Hanuma Gowda
creator: Basavanna Mahadevappa
creator: Shivakumara Palaiahnakote
creator: Muhammad Hammad Saleem
creator: Niranjan Mallappa Hanumanthu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-13
publication date: 2026-01-13
doi: 10.47852/bonviewAIA62027747

Blockchain Platforms for Developing Smart Contracts and Their Computational Performance Evaluation: A Systematic Literature Review
type: item
description: 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 & edit…
subject: Review
  topic: section
subject: smart contract
  topic: keywords
subject: Ethereum
  topic: keywords
subject: Polkadot
  topic: keywords
subject: Solana
  topic: keywords
subject: Tron
  topic: keywords
subject: Binance Smart Chain (BSC)
  topic: keywords
creator: Alock Gupta
creator: Kamlesh Lakhwani
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-28
publication date: 2025-12-28
doi: 10.47852/bonviewAIA52026923

A Neurological Assessment in COVID-19 Using Adaptive and Machine Learning Technique Under EEG Signals
type: item
description: 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 Satya…
subject: Research Article
  topic: section
subject: electroencephalogram
  topic: keywords
subject: adaptive learning
  topic: keywords
subject: COVID-19
  topic: keywords
subject: machine learning
  topic: keywords
subject: neurological assessment
  topic: keywords
creator: Satyanarayana Murthy K.
creator: Korada Suribabu
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-27
publication date: 2025-12-27
doi: 10.47852/bonviewAIA52027645

Generative AI in Physics Education: A PRISMA-Guided Systematic Review of Empirical Studies
type: item
description: 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: Concept…
subject: Review
  topic: section
subject: generative artificial intelligence
  topic: keywords
subject: large language models
  topic: keywords
subject: ΑΙ in education
  topic: keywords
subject: physics education
  topic: keywords
subject: systematic review
  topic: keywords
creator: Maria Moundridou
creator: Nikolaos Moutis
creator: Konstantinos Charalampopoulos
creator: Nikolaos Matzakos
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
doi: 10.47852/bonviewAIA62027183

Python-Programmed Embedded ECU for Drowsiness-Responsive Dynamic Braking in Electric Vehicles
type: item
description: 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 …
subject: Research Article
  topic: section
subject: electric vehicles
  topic: keywords
subject: driver drowsiness detection
  topic: keywords
subject: fuzzy braking
  topic: keywords
subject: Raspberry Pi Pico
  topic: keywords
subject: Micro-Python
  topic: keywords
subject: embedded ECU
  topic: keywords
creator: Kalaivanan Kumaran
creator: Muhammad Nadzmi Razlan
creator: Ahmad Safwan Abd Shukor
creator: Mohamad Tarmizi Abu Seman
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
doi: 10.47852/bonviewAIA62027474

Public Discussion of DeepSeek Large Language Model on Twitter: A Mixed-Methods Sentiment and Topic Modeling
type: item
description: 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/bwandowa…
subject: Research Article
  topic: section
subject: DeepSeek
  topic: keywords
subject: Twitter
  topic: keywords
subject: large language model
  topic: keywords
subject: natural language processing
  topic: keywords
subject: sentiment analysis
  topic: keywords
creator: Wei Chien Ng
creator: Shiwen Chen
creator: Quan Kai Ang
creator: Yu Qing Soong
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
doi: 10.47852/bonviewAIA62027447

A New Method for Designing Intelligent User Interfaces Through Image Analysis to Facilitate Human Interaction
type: item
description: 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 …
subject: Research Article
  topic: section
subject: computer vision
  topic: keywords
subject: graphical user interfaces (GUIs)
  topic: keywords
subject: human-computer interaction (HCI)
  topic: keywords
creator: Naji Guedri
creator: Abdessalam Guedri
creator: Boulbaba Guedri
creator: Rached Gharbi
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
doi: 10.47852/bonviewAIA62027400

Risk Assessment of Large Language Model Implementation in the Electric Power Sector of Ukraine
type: item
description: 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.…
subject: Research Article
  topic: section
subject: electric power sector
  topic: keywords
subject: large language model (LLM)
  topic: keywords
subject: generative AI
  topic: keywords
subject: risk assessment
  topic: keywords
subject: accountability
  topic: keywords
creator: Hryhoriy Kravtsov
creator: Oleksandr Kravchuk
creator: Artem Taranowski
creator: Dmytro Sinko
creator: Victor Samoylov
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-03
publication date: 2026-02-03
doi: 10.47852/bonviewAIA62027195

Development of a Web Workf low Tool That Facilitates and Optimizes the Process of Preparing Scientific Articles
type: item
description: 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 Ava…
subject: Research Article
  topic: section
subject: scientific writing
  topic: keywords
subject: artificial intelligence (AI)
  topic: keywords
subject: web tools
  topic: keywords
subject: automation
  topic: keywords
subject: academic publishing
  topic: keywords
subject: usability
  topic: keywords
subject: AI-assisted writing
  topic: keywords
creator: Lucrecia Llerena
creator: Nancy Rodríguez
creator: Kelvin Estrada
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-29
publication date: 2025-12-29
doi: 10.47852/bonviewAIA52027141

System for the Generation and Georeferenced Visualization of Agricultural Crop Datasets Using Imagro Hybrid Platform
type: item
description: 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-…
subject: Research Article
  topic: section
subject: georeferencing
  topic: keywords
subject: machine learning
  topic: keywords
subject: MobileNet V1
  topic: keywords
subject: precision agriculture
  topic: keywords
subject: TensorFlow.js
  topic: keywords
creator: Lucrecia Llerena
creator: Nancy Rodríguez
creator: Kevin Ponce
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-20
publication date: 2026-02-20
doi: 10.47852/bonviewAIA62027140

A Multi-class Obesity Risk Prediction Using Machine Learning and Explainable Artificial Intelligence
type: item
description: 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, Visualiza…
subject: Research Article
  topic: section
subject: obesity risk prediction
  topic: keywords
subject: ensemble feature selection
  topic: keywords
subject: hyperparameter tuning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: explainable AI (XAI)
  topic: keywords
creator: Tarequl Hasan Sakib
creator: Mahfuzulhoq Chowdhury
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-11
publication date: 2026-02-11
doi: 10.47852/bonviewAIA62027134

Optimizing Traffic Signal Control Using Machine Learning and Environmental Data
type: item
description: 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.…
subject: Research Article
  topic: section
subject: machine learning
  topic: keywords
subject: forecasting
  topic: keywords
subject: environmental data
  topic: keywords
subject: urbanization
  topic: keywords
subject: traffic
  topic: keywords
subject: air pollution
  topic: keywords
creator: Edidiong E. Akpan
creator: Oluwatobi Akinlade
creator: Oluwaseyi O. Alabi
creator: Oluwasesan A. David
creator: Oluwaseyi F. Afe
creator: Sunday Adeola Ajagbe
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-11-01
publication date: 2025-11-01
starting page: 48
ending page: 56
doi: 10.47852/bonviewAIA52024199

Curved Inference: A Guide to Geometric Interpretability
type: item
description: 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.
subject: Research Article
  topic: section
subject: large language models
  topic: keywords
subject: interpretability
  topic: keywords
subject: geometric interpretability
  topic: keywords
subject: curved inference
  topic: keywords
creator: Rob Manson
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0
date: 2026-02-05
publication date: 2026-02-05
doi: 10.47852/bonviewAIA62027102

A Novel STCA-LightGBM-SVR Hybrid Model for Port Cargo Throughput Prediction
type: item
description: 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.
subject: Research Article
  topic: section
subject: lightGBM-SVR ensemble learning
  topic: keywords
subject: multimodal data fusion
  topic: keywords
subject: gray correlation analysis
  topic: keywords
subject: port throughput prediction
  topic: keywords
subject: spatial-temporal attention mechanism
  topic: keywords
creator: Tianwen Zhao
creator: Hanyu Xu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-02-18
publication date: 2026-02-18
doi: 10.47852/bonviweAIA62027055

E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
type: item
description: 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 h…
subject: Research Article
  topic: section
subject: generative AI
  topic: keywords
subject: IDP
  topic: keywords
subject: automation agent
  topic: keywords
subject: E2E automation
  topic: keywords
subject: corporate expense processing
  topic: keywords
creator: Cheonsu Jeong
creator: Seongmin Sim
creator: Hyoyoung Cho
creator: Sungsu Kim
creator: Byounggwan Shin
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
doi: 10.47852/bonviewAIA52026307

Literature Review of Methods for Detecting Non-Technical Electricity Losses in Distribution Grids
type: item
description: 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 …
subject: Review
  topic: section
subject: non-technical losses
  topic: keywords
subject: smart grid
  topic: keywords
subject: data analysis
  topic: keywords
subject: electricity theft
  topic: keywords
subject: machine learning
  topic: keywords
creator: Irbek D. Morgoev
creator: Roman V. Klyuev
creator: Angelika D. Morgoeva
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-26
publication date: 2026-01-26
doi: 10.47852/bonviewAIA62026686

International Trade Demand Forecasting Model Based on Transformer-XGBoost-LightGBM
type: item
description: 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 avoid…
subject: Research Article
  topic: section
subject: international trade forecasting
  topic: keywords
subject: hybrid models
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
subject: supply chain management
  topic: keywords
creator: Wenhao Wang
creator: Zhitao Yang
creator: Tianwen Zhao
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-16
publication date: 2025-12-16
doi: 10.47852/bonviewAIA52027024

Machine Learning-Based Theme Classification for Video Content Analysis: A Bilingual Approach on the StoryBox
type: item
description: This research introduces an advanced hybrid machine learning framework for the automatic thematic classification of video content in a bilingual (Turkish–English) setting, with a particular focus on the YouTube StoryBox dataset (172 videos). The proposed pipeline integrates sentence-level embeddings from Sentence-BERT, multilingual zero-shot classification with XLM-RoBERTa, classic clustering algorithms (HDBSCAN, k-means, and spectral clustering), dimensionality reduction via UMAP, and large language model (LLM) based theme labeling with Flan-T5. The StoryBox collection is thematically rich and highly heterogeneous, covering entrepreneurship, education, technology, and industry. As a result, the video embeddings occupy a continuous semantic manifold rather than form compact, well-separated clusters. This leads to weak hard-clustering scores in the original embedding space (negative silhouette values and 100% noise assignments for HDBSCAN), which we interpret as an evidence of intrinsically fuzzy, overlapping themes rather than a failure of the algorithms. Nevertheless, the bilingual use of multilingual transformer models yields a 23% improvement in F1-score over a monolingual baseline for theme consistency. LLM-assisted inspection of UMAP-projected clusters further reveals that “education and development” emerges as the dominant macro-theme, accounting for 54.1% of the corpus. We position the framework as a proof-of-concept for real-world video platforms and discuss its implications for scalable content organization, recommendation systems, and decision support in global, bilingual media environments. Received: 25 July 2025 | Revised: 5 December 2025 | Accepted: 5 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/onder-ozturk/youtube-video-analyzer. Author Contribution Statement H…
subject: Research Article
  topic: section
subject: video content analysis
  topic: keywords
subject: bilingual video classification
  topic: keywords
subject: multilingual transformer models
  topic: keywords
subject: theme classification
  topic: keywords
subject: natural language processing
  topic: keywords
subject: LLMs
  topic: keywords
subject: clustering algorithms
  topic: keywords
creator: Hüseyin Parmaksız
creator: Önder Öztürk
creator: Osman Akarsu
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-20
publication date: 2026-01-20
doi: 10.47852/

Optimal Policy Strategy for Pandemic Outbreak Control: A Deep Reinforcement Approach
type: item
description: Since the global spread of COVID-19 pandemic in January 2020, residents in the United Kingdom (UK) have altered their daily routines due to the transmissibility of the virus. Sanitisation, quarantine, contact tracing, mass testing, and vaccination are implemented, affecting virus control, quality of life, resources, and economic development. From January 2020 to January 2021, data from repositories from the Office for National Statistics, NHS England, and the WHO provided statistics on confirmed cases, recoveries, and mortality. Wikipedia and Our World In Data provided the UK lockdown and travel restriction timelines. Deep reinforcement learning, a Dueling Q-learning algorithm, and a well-defined reward function determined the optimal lockdown and travel restriction timings. Initially, our agent (model) suggested strict lockdown and travel restrictions. By mid-March, advisories decreased significantly. In late March, key public health initiatives were introduced. Over the initial three months, the recommendations of our agent had gained support, which proposed slightly smaller lockdown measures than the public health policy but stricter travel restrictions. Our agent advised lockdown and travel limitations, generally suggesting measures before public health authorities or the government approved them. Our agent recommended implementing policies in late January, while authorities delayed until late March. Furthermore, our agent (model) advised against postponing UK policy implementation. Received: 15 July 2025 | Revised: 4 November 2025 | Accepted: 27 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 Johns Hopkins COVID-19 Data Repository at https://github.com/CSSEGISandData/COVID-19, World Health Organization at https://www.who.int/emergencies/diseases/novel-coronavirus-2019, Our World In Data COVID-19 pande…
subject: Research Article
  topic: section
subject: COVID-19
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: discrete action space
  topic: keywords
subject: dueling Q-network
  topic: keywords
subject: lockdown
  topic: keywords
subject: travel restrictions
  topic: keywords
creator: Raphael Ibraimoh
creator: Mohammed Saraee
creator: Kaveh Kiani
creator: Danial Saraee
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.47852/bonviewAIA52026822

Beyond Binary: Adaptive Frameworks for Autonomous AI Governance
type: item
description: Since independent AI agents are becoming more and more common in important areas, governance models that go beyond traditional autonomy-control theories are needed. This study introduces and tests two new ideas for context-aware AI governance: the Adaptive Containment Framework (ACF) and the Weighted Autonomy Acceptance Index (WAAI). The ACF is a dynamic governance model that allows real-time autonomy calibration through ethical sensors and multi-stakeholder validation. The WAAI is a psychometrically robust metric (α = 0.89, CR = 0.91) for quantifying sector-specific autonomy thresholds. The study uses a sequential exploratory mixed-methods approach that includes Delphi studies with 15 subject experts, sector-stratified polls, and computer models to arrive at three key conclusions: (1) acceptance of autonomy is influenced by decision reversibility and harm potential, which explains 68% of cross-domain variation; (2) system explainability shows diminishing returns beyond an 82.3% comprehensibility threshold (χ²(3) = 24.71, p < 0.001), ending long-running XAI debates; and (3) uncertainty avoidance is the most important cultural factor explaining 41.3% of cross-national variation in acceptance of autonomy (Sobel’s z = 3.28, p < 0.001). The ACF performs better than static frameworks in many ways. It lowers bias events by 41% while keeping 92% of autonomy’s efficiency benefits and going above and beyond static frameworks in operating freedom by 119%. The way modern society think about dynamic equilibrium government has changed since these changes were made. They give politicians authority rules that are specific to a sector and developers ways to carry out projects that are sensitive to different cultures. Received: 12 July 2025 | Revised: 10 November 2025 | Accepted: 27 November 2025 Conflict of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were cr…
subject: Research Article
  topic: section
subject: adaptive governance
  topic: keywords
subject: AI ethics
  topic: keywords
subject: autonomy thresholds
  topic: keywords
subject: explainable AI
  topic: keywords
subject: human-AI interaction
  topic: keywords
creator: Gabriel Silva-Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-12-26
publication date: 2025-12-26
doi: 10.47852/bonviewAIA52026790