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.
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
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.
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.
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.
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/
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
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.
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.
Machine Learning-Based Theme Classification for Video Content Analysis: A Bilingual Approach on the StoryBox
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 Hüseyin Parmaksız: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Önder Öztürk: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Osman Akarsu: Conceptualization, Validation, Investigation, Resources, Writing – review & editing, Supervision, Project administration.
Optimal Policy Strategy for Pandemic Outbreak Control: A Deep Reinforcement Approach
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 pandemic data at https://ourworldindata.org/coronavirus, and Wikipedia at https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic. Author Contribution Statement Raphael Ibraimoh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Mohammed Saraee: Validation, Writing – review & editing, Supervision, Project administration. Kaveh Kiani: Writing – review & editing, Supervision, Project administration. Danial Saraee: Writing – review & editing.
Beyond Binary: Adaptive Frameworks for Autonomous AI Governance
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 created or analyzed in this study. Author Contribution Statement Gabriel Silva-Atencio: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
Quantifying AI Autonomy: A Multidimensional Framework for Agentic AI Governance and Risk Assessment
The objective is to develop models that can quantify agency to enhance risk assessment and management in the context of increasingly autonomous artificial intelligence (AI). The Agency Spectrum Framework (ASF) uses a unique, multifaceted approach to measure the cognitive autonomy (CA), operational flexibility (OF), and ethical weight (EW) of AI, which uses a logarithmic scale to assess the morality of AI. CA refers to the AI ability to think strategically and adapt to new situations, OF measures the AI ability to create tools and adapt to new environments, and EW uses a logarithmic scale to evaluate the moral implications. The ASF takes a distinctive, multidimensional approach to evaluating AI. This value is significantly higher than the requirements set by the National Institute of Standards and Technology (NIST) (AUC = 0.96 vs. 0.67). The probability of emergent behavior increases by a factor of 4.8 (95% CI: 4.2-5.4, p < 0.001) when AI exhibits more realistic behavior at AS ≥ 7 due to a significant threshold effects. 92.4% of the experts surveyed agreed in their response, according to the results of the Delphi method. As a result of the deployment, sector-specific constraints and adaptive regulatory triggers were established. These tools successfully addressed 84% of the issues and repaired shortcomings in the European Union (EU) AI Act and the NIST Risk Management Framework. The research explains how technical skills affect ethics and proposes a mathematical framework for evidence-based AI governance that balances innovation and resource management. Received: 3 July 2025 | Revised: 4 November 2025 | Accepted: 12 December 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gabriel Silva Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Development of Financial Strategies Using Machine Learning for Right-Tail Value at Risk Estimation
In this study, we propose a novel methodology for strategic financial planning based on the assessment of right-tail value-at-risk (VaR) for interest rates under volatile market conditions. The core of the approach lies in the integration of three model types: classical (GJR-GARCH), machine learning (XGBoost), and deep learning (long short-term memory, LSTM). Each component targets a distinct dimension of risk: analytical structure, nonlinear dependencies, and complex temporal patterns, respectively. The results showed that the LSTM model delivered the highest forecasting accuracy under structural instability, achieving the lowest residual volatility (σ̂ ≈ 0.0137) and a high level of explained variance (R² ≈ 0.69). An ensemble model (weighted 40% toward LSTM and 30% toward XGBoost) demonstrated superior reliability in risk estimation according to formal backtesting, along with the lowest average exceedance in VaR breaches. The practical value of the proposed approach is its ability to operate effectively under data scarcity and elevated volatility, enabling adaptive management of debt exposure. An elevated VaR level serves as a signal to restrict borrowing, while a low VaR opens opportunities for credit expansion. This model can be integrated into risk management systems of banking institutions and corporate finance departments as an effective tool for identifying, assessing, and mitigating right-tail risks during periods of economic instability. Received: 2 July 2025 | Revised: 3 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 and scripts that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/ m9.figshare.30939428. Author Contribution Statement Vitaliy Makohon: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Oleksandra Mandych: Conceptualization, Validation, Resources, Writing – review & editing, Visualization, Supervision. Tetiana Staverska: Methodology, Resources, Data curation, Writing – review & editing, Project administration. Oleksandr Horokh: Investigation, Resources, Writing – review & editing, Visualization. Denys Zabolotny: Conceptualization, Software, Validation, Investigation, Writing – original draft, Visualization.
Artificial Intelligence in Research: Benefits, Challenges, and Strategies for Implementing AI Tools in Research in Afghanistan
This study aims to evaluate the benefits and challenges of integrating artificial intelligence (AI) into research within Afghanistan and to propose practical strategies for the effective adoption of these technologies to advance and enrich the research landscape. The primary objective is to explore the advantages and drawbacks of using AI tools in research from the perspective of researchers, assess the prevalence and extent of their usage, and outline actionable strategies for their optimal application to enhance the quality, efficiency, and impact of research. An online survey was conducted among 190 university professors and other researchers across the country, then the data was analyzed with SPSS (Statistical Package for the Social Sciences) and MS Excel software tools. The survey results showed that 62.6% of participants use AI tools in their research, while the rest do not. The main reasons for non-use were concerns about data privacy, limited familiarity with these tools among researchers, and the high cost of the tools. The analysis indicates that 36% of the researchers use ChatGPT, a general AI tool, in their research. It also shows that researchers aged 41 years or older use AI tools less in their research compared to younger researchers. High-speed data analysis, time saving, easy access to multiple information sources, and facilitating collaboration between researchers are the key benefits of AI tools in research. However, they also present challenges such as a high learning curve, concerns about data accuracy, poor integration with existing research systems, and overreliance on technology. Strategies that more than 50% of respondents identified as effective include: organizing seminars for researchers on AI, publishing in national languages, conducting public awareness campaigns, adding AI and machine learning (ML) to the curricula of some related fields, and encouraging scholars to conduct research and use AI in their work. Received: 20 November 2024 | Revised: 28 May 2025 | Accepted: 26 August 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 Figshare at https://doi.org/10.6084/m9.figshare.28892885. Author Contribution Statement Wahidullah Zgham: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Massoud Danishmal: Software, Validation, Resources. Mohammad Hamed Haidary: Software, Validation, Resources.
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models
Progress in accurate medical image classification is often hampered by concerns surrounding data privacy and scarcity of data for certain medical diseases, leading to sparsity and unbalanced datasets. To address these challenges, this study uses generative models, namely, Denoising Diffusion Probabilistic Models (DDPMs) and Progressive Growing Generative Adversarial Networks (PGGANs), for dataset improvement. In this article, we propose a framework for understanding how the resultant synthetic images generated by DDPM and PGGANs affect four different models’ performance: a specially crafted Convolutional Neural Network, an untrained VGG16, a pretrained VGG16, and a pretrained ResNet50. For modeling practical constraints in real applications, experiments applied Random Sampling and Greedy K Sampling to obtain small unbalanced datasets. Synthetic image quality was also measured by applying Fréchet Inception Distance (FID), and their impact was further explored by comparing classification results with their original datasets. Experiments reveal that DDPM consistently produced images of higher realism, backed by lower FID scores, and overtakes PGGANs in augmenting classification outcomes of all investigated models and datasets. Addition of DDPM-generated images to original datasets obtained improvement of about 6% in accuracy and therefore enhanced robustness and reliability of models, specifically when datasets are unbalanced. Although Random Sampling obtained better consistency, Greedy K Sampling obtained higher variability but higher FID scores. Overall, this research identifies the potential of DDPM to effectively augment and balance sparse datasets of medical images and subsequently improve training of models and predictive outcomes. Received: 1 July 2025 | Revised: 3 September 2025 | Accepted: 15 September 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/imankhazrak/DDPM_X-Ray. Author Contribution Statement Iman Khazrak: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Shakhnoza Takhirova: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation,Writing – original draft, Writing – review & editing, Visualization. Mostafa M. Rezaee: Validation, Investigation, Resources, Writing – review & editing. Mehrdad Yadollahi: Investigation, Resources, Writing – review & editing. Robert C. Green II: Methodology, Writing – review & editing, Supervision. Shuteng Niu: Methodology, Writing – review & editing, Supervision.
Progressive Growing Generative Adversarial Networks (PGGANs)
Denoising Diffusion Probabilistic Models (DDPMs)
Synthetic Data in AI: Performance Gains versus Hallucination Risk
The incorporation of synthetic data into AI training pipelines poses a basic paradox: although it improves model resilience and mitigates data shortages, it also increases the likelihood of hallucinations. Using a mixed-methods approach, this research rigorously analyzes this trade-off and shows that synthetic data increase hallucination rates by a factor of 4.7 while improving perturbation resistance by 23%. Spatially improbable artifacts in computer vision (17% increase), factual mistakes in natural language processing (22% of outputs), and clinically dangerous errors in healthcare (3.7% of incidences) are examples of domain-specific manifestations. The Synthetic Data Fidelity Theorem, which extends the traditional bias-variance decomposition to explicitly encompass synthetic artifact propagation, is presented to fill the theoretical gap in the knowledge of these effects. Additionally, with a prediction accuracy of R2 = 0.89, the FAITH metric system (Factuality, Alignment, Integrity Tracking for Hallucinations) is designed for real-time risk management. According to causal analysis, 23.4% of synthetic-data-induced hallucinations are caused by reward hacking and feature entanglement. Evidence suggests that hybrid data regimes (≤60% synthetic content) minimize mistakes by 41% without compromising performance, which defies the notion of universal application. To guarantee responsible deployment in crucial AI systems, the results call for a paradigm change toward domain-specific governance, backed by evidence-based recommendations for architectural choices, validation procedures, and policy frameworks. Received: 27 June 2025 | Revised: 9 October 2025 | Accepted: 20 October 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gabriel Silva-Atencio: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
The Effectiveness of Using Fuzzy Neural Networks in Predicting the Sales Volume of Perishable Products
This article discusses methods for predicting the sales volume of perishable products to identify the best method that provides the most accurate forecast value of the indicator under study. The initial data for forecasting are the volumes of daily perishable products that we sell. For a trading company, the volume of products purchased for retail sale must match the volume of demand. Therefore, the presence of product residues at the end of the trading day is unacceptable. That is especially true for products with a short shelf life (chilled meat, dairy products, fruits, vegetables, etc.). We must write off the products as losses if the sale period has expired. Therefore, identifying the most accurate forecasting method in this area is a significant task. Solving this problem will reduce losses from trading activities. For comparison, the following three methods were used: alignment of the obtained dynamics series according to the Fourier series, forecasting using a two-layer neural network, and forecasting using a neuro-fuzzy network. We determined the accuracy of each method by calculating the average approximation error. The prediction method using a neuro-fuzzy network showed higher efficiency. The discrepancies between the actual and theoretical values of the implementation volume were minimal. Therefore, the developed neuro-fuzzy model can be used to predict the sales volume of perishable products. We are further testing the neuro-fuzzy model on new sales data. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 14 September 2025 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Nataliya Mutovkina: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Are Peruvian Tertiary Students Using Artificial Intelligence in Their Academic Activities?
The widespread emergence of generative artificial intelligence (GAI) has prompted growing interest in understanding the factors that drive its adoption in academic contexts. This study explores the behavioral intention of Peruvian university students to use GAI tools in their educational activities by applying the technology acceptance model (TAM) through partial least squares structural equation modeling (PLS-SEM). A total of 350 valid responses were collected from students at the Universidad Nacional Agraria La Molina. The model demonstrated strong reliability and validity, with key constructs such as perceived usefulness and attitude toward use explaining 12% and 9% of the variance in intention, respectively. Notably, behavioral intention significantly predicted actual use, accounting for 58.8% of the variance. To address concerns of common method bias, statistical controls were implemented. The results underscore the importance of designing user-friendly and pedagogically relevant GAI tools because ease of use was found to strongly influence perceived usefulness and attitudes. This study contributes to the literature by validating a TAM-based model in the Latin American higher education context and identifying actionable variables that institutions can leverage to foster ethical and effective adoption of GAI. It also highlights students’ current reliance on tools such as ChatGPT® and ChatPDF® for information retrieval and summarization. These findings support the development of informed policies and training initiatives to guide the responsible integration of AI in academic environments. Received: 13 June 2025 | Revised: 8 August 2025 | Accepted: 4 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 Mildher Ugaz-Rivero: Validation, Data curation. Yulissa Navarro-Castillo: Methodology, Formal analysis, Visualization. Janet Corzo-Zavaleta: Investigation, Writing – original draft. Alberto Urueña: Conceptualization, Writing – review & editing, Supervision.
information and communication technologies (ICT)
Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
Missing modalities pose a significant challenge on multi-modal studies by disrupting the comprehensive analysis of diverse data sources. Deep learning addresses this issue by employing algorithms that can effectively infer and integrate the absent information, thereby ensuring robustness and accuracy of the models while increasing the study’s statistical power. This study aims to provide a systematic literature review on deep learning solutions for missing imaging modalities in multi-modal medical data analysis. Articles on PubMed, IEEE explore digital library, and Scopus were searched in the range from January 2013 to May 2025. This systematic search and review identified 234 articles. Adhering to the specified search criteria, 61 published studies were eligible. Among these, 47% employed image synthesis methods, 20% applied knowledge transfer methods, and 33% used latent feature space-based methods. The paper explores the research gaps and challenges associated within each of these categories. Additionally, this review paper illuminates the popular public datasets for multi-modal studies with missing modalities. Furthermore, it presents evaluation metrics and their key attributes. The review concludes with its limitations and a detailed discussion of current challenges and future directions in this domain. Received: 6 June 2025 | Revised: 17 September 2025 | Accepted: 24 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 Jinzhao Qian: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Ankita Joshi: Conceptualization, Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hailong Li: Writing – review & editing, Project administration. Nehal A. Parikh: Writing – review & editing. Jonanthan R. Dillman: Writing – review & editing. Lili He: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
AI-Driven Adaptive VM Placement Using Performance-to-Power Ratio for Sustainable Data Center Management
Cloud data centers provide essential scalable computing resources but often suffer from inefficient resource allocation, resulting in excessive energy consumption and increased carbon emissions. This paper proposes a Q-learning-driven adaptive virtual machine placement strategy that simultaneously optimizes thermal performance and energy efficiency in heterogeneous data centers. The proposed approach explicitly considers the physical location of servers within racks as well as their processor performance-to-power ratio (PPR). By dynamically adjusting CPU utilization thresholds according to the servers’ rack positions, the method ensures that servers operate near their optimal PPR. The algorithm formally classifies servers into “best gear,” “high preferred gear,” and “low preferred gear” states. A reinforcement learning framework based on Q-Learning learns optimal placement policies to minimize energy consumption, maintain stable service-level agreements (SLAs), and reduce the risk of thermal hotspots. Experimental results show that our approach reduces energy usage by 18.43% compared to particle swarm optimization, 20% compared to genetic algorithms, and 13% compared to predictive thermal-aware cloud optimization. Furthermore, it significantly lowers SLA violations and hotspot occurrences. By improving both thermal and energy management, this work contributes to more sustainable, efficient, and environmentally responsible cloud data center operations. Received: 3 June 2025 | Revised: 5 September 2025 | Accepted: 15 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest in this work. Data Availability Statement The data that support the findings of this study are openly available in the Microsoft Azure 2019 dataset at https://github.com/Azure/AzurePublicDataset, reference number [27]. Author Contribution Statement Abdelhadi Amahrouch: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Youssef Saadi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Supervision, Project administration. Said El Kafhali: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration.
A Systematic Investigation of the Current State of Security in Electronic Medical Images: 2019–2024
Currently, there is a need to study the state of development of data-hiding techniques for securing electronic medical images (EMIs) when being transmitted or when stored on third-party storage. EMIs are large-sized and are of varying dimensions. Thus, they are commonly stored in third-party cloud storage. Because EMIs are accessed in real time, data-hiding techniques need to be lightweight and secure to ensure reduced computational time complexity. The research goal of this article is to study the state of research work that has been done to secure EMIs. A total of 147 articles were studied, focusing on e-health data and their security from 2019 to 2024. This paper focuses on the challenges that the conventional techniques face. The results found in this research article have been organized to help future researchers understand the trends in the programming language currently being used, their research goals, the achievement of these goals, the time required for these techniques, and the dataset on which the proposed techniques were evaluated. There is a tabulated study on the commonly used programming language. This research also provides an in-depth study of the currently popular performance metrics used, with a graph-based comparative analysis of their achieved results. Received: 31 May 2025 | Revised: 14 October 2025 | Accepted: 23 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 Divya Sharma: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft. Chander Prabha: Data curation, Writing – review & editing, Visualization, Supervision, Project administration.
Anomaly Detection Utilizing PatchCore for Reimagining Industrial Visual Inspection
In industrial manufacturing, ensuring quality control is critical to maintaining high standards and operational efficiency. Manual defect detection, however, is often time-consuming, error-prone, and costly, thereby driving the need for automated solutions. This paper investigates a technique for industrial anomaly detection (IAD) by utilizing the state-of-the-art PatchCore algorithm in conjunction with the widely recognized MVTec AD dataset. The dataset consists of 5354 high-resolution color images representing diverse objects and textures, including defect-free samples for training and numerous anomalous instances for testing. With over seventy distinct defect types—such as scratches, dents, contamination, and other structural irregularities—the dataset presents substantial challenges for conventional visual inspection (VI) techniques. The approach integrates convolutional neural networks (CNNs) with patch-based feature extraction methods, enabling PatchCore to accurately identify and localize even subtle anomalies within complex industrial imagery. Experimental results demonstrate that PatchCore significantly enhances detection accuracy, reduces false positives, and streamlines the overall inspection process. These improvements have important implications for operational productivity and quality assurance in various industrial sectors, paving the way for more reliable and cost-effective manufacturing practices. Received: 31 May 2025 | Revised: 26 September 2025 | Accepted: 22 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 at https://www.mvtec.com/company/research/datasets/mvtec-ad. Author Contribution Statement Shalini Kumari: Conceptualization, Software, analysis, Resources, Data curation, Writing – original draft. Chander Prabha: Methodology, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
HD-SMART: A Novel Machine Learning Framework for High-Accuracy Cardiovascular Risk Prediction Using Advanced Feature Engineering
This work presents a novel concept known as Heart Disease Systematic Machine learning Analytical Risk prediction Technology (HD-SMART) that is considered a unique perspective in handling cardiac disease and the identification of sophisticated risk markers using superior computational technology. The work offers a rather peculiar approach to harnessing multiple machine learning algorithms in combination with feature selection methods that proved themselves highly accurate in cardiovascular risk estimation. The novelty is that these algorithms, namely, the Random Forest, Support Vector Machines (SVM), and Logistic Regression, are to be adopted in a systematic manner with supplementary feature engineering and with hyperparameter optimization, respectively. Based on the UCI Heart Disease dataset of 303 instances with 14 attributes, the new HD-SMART framework illustrated exceptional predictive capability, with the Random Forest at 97.57%, followed by SVM (95.23%) and Logistic Regression at 94.18%. The methodology achieved unique convergent optimization regarding feature selection with a minimum cost function of 0.0004 at iteration 50, while the Root Mean Square Error convergence was reached within the first four iterations with a value of 0.030. The innovative approach of data preprocessing and feature analysis in the framework pointed out critical patterns in cardiological parameters, such as in chest pain distribution (n ≈ 410 typical angina cases), bimodal blood pressure peaks (130–140 mmHg and 190–200 mmHg), as well as electrocardiogram variabilities (450 normal and 350 ST-T-wave abnormalities). The new proportion of 70–30 train-test split ratio was more optimal for model performance. This work introduces a completely new, computational diagnostic approach that not only outperforms but significantly surpasses conventional methods, and its robust statistical validity is maintained across a number of performance metrics. HD-SMART contributes to the advancement of cardiovascular diagnosis as a new, effective tool for early detection of heart disease for health practitioners based on big data analysis. Received: 30 May 2025 | Revised: 11 November 2025 | Accepted: 8 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Heart Disease data set that supports the findings of this study is openly available at https://doi.org/10.1007/s00521-021-06124-1 and at https://doi.org/10.1109/iciccs48265.2020.9121169, reference number [22, 24]. Author Contribution Statement Gitanjali Gupta: Conceptualization, Methodology, Writing – original draft. Meena Malik: Conceptualization, Software, Validation, Visualization. Ramandeep Sandhu: Formal analysis, Investigation. Chander Prabha: Methodology, Resources, Data curation, Writing – review & editing, Supervision. Aimin Li: Investigation, Resources, Project administration. Saurav Mallik: Writing – review & editing, Visualization, Supervision.
Effective Denoising of Epileptic High-Frequency Oscillations (HFOs) in Scalp EEG Using a Temporal Generative Adversarial Network (TimeGAN)
Over the past two decades, High-Frequency Oscillations (HFOs) have been widely recognized as promising biomarkers for delineating the specific regions of the brain responsible for seizure onset, and for monitoring the spatiotemporal dynamics of epileptogenic activity. While deep learning (DL) techniques have shown promise in processing HFOs in intracranial EEG (iEEG) data, they often struggle to perform effectively on scalp electroencephalography (EEG). This limitation is particularly owing to the inherently lower signal-to-noise ratio of scalp HFOs, their smaller amplitude, and their increased susceptibility to artifact contamination, which together obscure subtle high-frequency phenomena. Despite these restrictions or barriers, clinicians predominantly rely on scalp EEG recordings due to their noninvasive nature, low cost, and recognized safety. To extend the benefits or applicability of DL-based HFO detection to a broader patient population, automated scalp EEG systems incorporating HFO denoising algorithms are highly warranted and necessary. Hence, the present study aimed at proposing, developing, and comparing the effectiveness of five optimized DL-based paradigms for denoising unwanted artifacts from realistic scalp EEG. The target data was created by combining intracranial HFO samples with injected realistic contaminants such as electrooculogram (EOG) and electromyography (EMG), accurately replicating scalp EEG profiles. In this regard, we considered evaluating and comparing the following time-series DL architectures, referred to as: one-dimensional Convolutional Neural Networks (CNN-1D), Long Short-Term Memory (LSTM) networks, Stacked Autoencoders (SAE), one-dimensional Transformer (Transformer-1D), and Generative Adversarial Networks (GAN). This analysis aims to elucidate each model’s denoising performance strengths and weaknesses. The reached quantitative and qualitative experimental results revealed that the proposed current GAN model significantly achieved superior performance compared to other DL approaches, suggesting its effectiveness as a robust solution for denoising scalp HFOs, thereby enhancing noninvasive HFOs detection. Received: 7 April 2025 | Revised: 15 August 2025 | Accepted: 30 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data were available from the corresponding author upon reasonable request. Author Contribution Statement Sahbi Chaibi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Wadhah Ayadi: Conceptualization, Methodology, Validation. Abdennaceur Kachouri: Validation, Supervision.
How an Author Character-based Generative AI Chatbot Can Arouse Interest in Reading: The Perspective of an Online Service?
In modern society, the MZ generation (Millennials and Generation Z) reads fewer paper books than in the past, as smartphone usage has become prevalent. This generation is accustomed to watching videos that present summarized information on online platforms. Consequently, they typically acquire information about books through these summaries available online. This study aims to propose the use of artificial intelligence (AI) human characters based on Generative AI to stimulate reading interest among the MZ generation. The recent advancements in Generative AI technology allow for experiences that simulate communication with real people. In addition, various services communicate with users through AI human characters that resemble real individuals. This research investigates whether providing a chatbot, based on AI human characters, with actual conversations from the author of a book can serve as a positive service for users. To explore this concept, the study developed an AI character based chatbot representing Kim, a renowned Korean novelist, as a Generative AI. A prototype was created to facilitate discussions about the book’s content with users. The research employed the System Usability Scale (SUS) test on subjects from the MZ generation using the prototype app service. The SUS test yielded a score of 79.5 points, indicating that it is usable. Furthermore, the normality of the SUS test results for each subject was assessed, confirming that the results are normally distributed, which enhances the reliability of the findings. This research aspires to contribute positively to the reading culture through digital devices and online services utilizing Generative AI. Received: 20 February 2025 | Revised: 14 July 2025 | Accepted: 29 July 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 Eunwoo Choi: Writing – original draft, Writing – review & editing, Investigation, Supervision. Gahyeon Kim: Visualization, Investigation. Gyeongmi Kim: Visualization, Investigation, Writing – review & editing. Dabin Choe: Visualization, Investigation, Writing – review & editing. Soonkyu Jang: Conceptualization, Methodology, Validation, Formal analysis, Project administration.
Development of a Hyperparameter-Optimized Decision Support System for Cardiovascular Disease Prediction
Cardiovascular disease (CVD) remains one of the leading health dilemmas all over the world, and it is among the leading causes of morbidity and mortality. To solve this problem, more than traditional diagnostic methods are needed; smart decision support systems (DSS) are required to help clinicians recognize the condition at an early stage and provide advice on treatment regimens. We develop an improved DSS with multiple machine learning (ML) techniques in cardiovascular risk prediction. Our framework is also focused on clinical applications, as compared to many other prior models that are seen as only theoretically viable. To determine its adequacy, eight ML and deep learning (DL) models were trained and optimized on a clinical set founded on feature selection and hyper-parameter tuning schemes. Among these, the XGBoost classifier exceeded by far the others in terms of accuracy, interpretability, and speed of computation and its operation, and as such would be the best candidate to deploy. Another characteristic of our system is that the Shapley Additive Explanations (SHAP) analysis is applied, which facilitates increasing the confidence of the results by clearly indicating how they may be compiled for clinicians. The benefits of the proposed DSS lie not only in supporting accurate diagnosis but also in translating to real-time reports and recommendations, which are both actionable and supportive of patient management. Additionally, its architecture is scalable and can fit in a variety of healthcare systems and help address the issue of early intervention to reduce the burden of CVD as a whole. Received: 5 February 2025 | Revised: 18 August 2025 | Accepted: 30 August 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 CardiovascularDataset repository at https://github.com/zubi00/CardioviscularDataset. Author Contribution Statement Uzma Nawaz: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mufti Anees-ur-Rahaman: Methodology, Software, Writing – review & editing. Hafiz Muhammad Ubaidullah: Software validation, Formal analysis, Writing – review & editing. Chaudhry Muhammad Ali Nawaz: Visualization, Investigation, Resources. Zubair Saeed: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.