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: Hierarchical Brain-Inspired Deep Learning for Autonomous Decision-Making in Complex Dynamic Environments
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: Development of a Web Workf low Tool That Facilitates and Optimizes the Process of Preparing Scientific Articles
items: Optimizing Traffic Signal Control Using Machine Learning and Environmental Data
items: E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
items: International Trade Demand Forecasting Model Based on Transformer-XGBoost-LightGBM
items: Optimal Policy Strategy for Pandemic Outbreak Control: A Deep Reinforcement Approach
items: Beyond Binary: Adaptive Frameworks for Autonomous AI Governance
items: Quantifying AI Autonomy: A Multidimensional Framework for Agentic AI Governance and Risk Assessment
items: Artificial Intelligence in Research: Benefits, Challenges, and Strategies for Implementing AI Tools in Research in Afghanistan
items: Addressing Small and Imbalanced Medical Image Datasets Using Generative Models
items: Synthetic Data in AI: Performance Gains versus Hallucination Risk
items: The Effectiveness of Using Fuzzy Neural Networks in Predicting the Sales Volume of Perishable Products
items: Are Peruvian Tertiary Students Using Artificial Intelligence in Their Academic Activities?
items: Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
items: AI-Driven Adaptive VM Placement Using Performance-to-Power Ratio for Sustainable Data Center Management
items: A Systematic Investigation of the Current State of Security in Electronic Medical Images: 2019–2024
items: Effective Denoising of Epileptic High-Frequency Oscillations (HFOs) in Scalp EEG Using a Temporal Generative Adversarial Network (TimeGAN)
items: How an Author Character-based Generative AI Chatbot Can Arouse Interest in Reading: The Perspective of an Online Service?
items: Development of a Hyperparameter-Optimized Decision Support System for Cardiovascular Disease Prediction
items: Communication-Efficient Federated Learning: A Systematic Review of Model Compression and Aggregation Techniques
items: Enhancing Trust and Transparency in Education Using Blockchain: A Hyperledger Fabric-Based Framework
items: A Danish Chatbot for Kaj Munk: Experiences in Developing a Chatbot for the Study of Kaj Munk’s Ordet
items: An Analytical Framework for Addressing Imbalance in Fake Review Detection Using Augmented Text and Swarm Optimized Classifier
items: Evolution of Generative Adversarial Networks (GANs) in Medicine: A Systematic Review of Architectures, Applications, and Implementation Challenges
items: IDS-IoT: Intrusion Detection System for the Internet of Things Using Enhanced Long-Short Term Memory
items: Smart Farming: Crop Recommendation Using Machine Learning with Challenges and Future Ideas
items: DDoS Attack Detection With and Without SMOTEENN Dataset Balancing Strategy: Deep Learning Approaches
items: Integrating Pattern Recognition and CNN-Based Models for Improved Bean Disease Detection and Agricultural Yield Enhancement

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: decisionmaking
  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

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

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

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

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

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

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

Quantifying AI Autonomy: A Multidimensional Framework for Agentic AI Governance and Risk Assessment
type: item
description: 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 analys…
subject: Research Article
  topic: section
subject: agentic AI
  topic: keywords
subject: AI autonomy
  topic: keywords
subject: cognitive autonomy
  topic: keywords
subject: ethical governance
  topic: keywords
subject: operational flexibility
  topic: keywords
subject: risk assessment
  topic: keywords
creator: Gabriel Silva-Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
doi: 10.47852/bonviewAIA52026694

Artificial Intelligence in Research: Benefits, Challenges, and Strategies for Implementing AI Tools in Research in Afghanistan
type: item
description: 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 …
subject: Research Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: benefits
  topic: keywords
subject: challenges
  topic: keywords
subject: AI tools in research
  topic: keywords
subject: implementing AI
  topic: keywords
subject: research infrastructure
  topic: keywords
subject: ChatGPT
  topic: keywords
creator: Wahidullah Zgham
creator: Massoud Danishmal
creator: Mohammad Hamed Haidary
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-12
publication date: 2025-10-12
starting page: 83
ending page: 91
doi: 10.47852/bonviewAIA52024855

Addressing Small and Imbalanced Medical Image Datasets Using Generative Models
type: item
description: 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 findi…
subject: Research Article
  topic: section
subject: medical image augmentation
  topic: keywords
subject: generative models
  topic: keywords
subject: Progressive Growing Generative Adversarial Networks (PGGANs)
  topic: keywords
subject: Denoising Diffusion Probabilistic Models (DDPMs)
  topic: keywords
subject: synthetic data integration
  topic: keywords
creator: Iman Khazrak
creator: Shakhnoza Takhirova
creator: Mostafa M. Rezaee
creator: Mehrdad Yadollahi
creator: Robert C. Green II
creator: Shuteng Niu
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
doi: 10.47852/bonviewAIA52026661

Synthetic Data in AI: Performance Gains versus Hallucination Risk
type: item
description: 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, …
subject: Research Article
  topic: section
subject: synthetic data
  topic: keywords
subject: AI hallucinations
  topic: keywords
subject: model robustness
  topic: keywords
subject: domain-specific risks
  topic: keywords
subject: FAITH metrics
  topic: keywords
subject: responsible AI
  topic: keywords
creator: Gabriel Silva-Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-12-23
publication date: 2025-12-23
doi: 10.47852/bonviewAIA52026620

The Effectiveness of Using Fuzzy Neural Networks in Predicting the Sales Volume of Perishable Products
type: item
description: 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 & …
subject: Research Article
  topic: section
subject: sales volume
  topic: keywords
subject: demand
  topic: keywords
subject: forecasting
  topic: keywords
subject: dynamics series
  topic: keywords
subject: neural network
  topic: keywords
subject: neuro-fuzzy network
  topic: keywords
subject: average error
  topic: keywords
creator: Nataliya Mutovkina
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
doi: 10.47852/bonviewAIA52026341

Are Peruvian Tertiary Students Using Artificial Intelligence in Their Academic Activities?
type: item
description: 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. …
subject: Research Article
  topic: section
subject: generative artificial intelligence
  topic: keywords
subject: technology acceptance
  topic: keywords
subject: higher education
  topic: keywords
subject: academic activities
  topic: keywords
subject: technology acceptance model (TAM)
  topic: keywords
subject: information and communication technologies (ICT)
  topic: keywords
creator: Mildher Ugaz-Rivero
creator: Yulissa Navarro-Castillo
creator: Janet Corzo-Zavaleta
creator: Alberto Urueña
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-19
publication date: 2025-09-19
doi: 10.47852/bonviewAIA52026465

Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
type: item
description: 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. Hail…
subject: Review
  topic: section
subject: deep learning
  topic: keywords
subject: missing modalities
  topic: keywords
subject: image synthesis
  topic: keywords
subject: knowledge transfer
  topic: keywords
subject: latent space
  topic: keywords
subject: medical image analysis
  topic: keywords
creator: Jinzhao Qian
creator: Ankita Joshi
creator: Hailong Li
creator: Nehal A. Parikh
creator: Jonanthan R. Dillman
creator: Lili He
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
doi: 10.47852/bonviewAIA52026392

AI-Driven Adaptive VM Placement Using Performance-to-Power Ratio for Sustainable Data Center Management
type: item
description: 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 –…
subject: Research Article
  topic: section
subject: cloud computing
  topic: keywords
subject: VM placement
  topic: keywords
subject: performance-to-power ratio
  topic: keywords
subject: thermal risk
  topic: keywords
subject: Q-Learning
  topic: keywords
subject: thermal management
  topic: keywords
subject: resource optimization
  topic: keywords
creator: Abdelhadi Amahrouch
creator: Youssef Saadi
creator: Said El Kafhali
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.47852/bonviewAIA52026353

A Systematic Investigation of the Current State of Security in Electronic Medical Images: 2019–2024
type: item
description: 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.
subject: Review
  topic: section
subject: privacy
  topic: keywords
subject: security
  topic: keywords
subject: e-health data
  topic: keywords
subject: electronic medical images
  topic: keywords
subject: steganography
  topic: keywords
subject: cryptography
  topic: keywords
subject: computational complexity
  topic: keywords
creator: Divya Sharma
creator: Chander Prabha
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.47852/bonviewAIA52026322

Effective Denoising of Epileptic High-Frequency Oscillations (HFOs) in Scalp EEG Using a Temporal Generative Adversarial Network (TimeGAN)
type: item
description: 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 achiev…
subject: Research Article
  topic: section
subject: epilepsy
  topic: keywords
subject: EOG
  topic: keywords
subject: EMG
  topic: keywords
subject: HFOs
  topic: keywords
subject: denoising
  topic: keywords
subject: temporal GAN
  topic: keywords
creator: Sahbi Chaibi
creator: Wadhah Ayadi
creator: Abdennaceur Kachouri
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
doi: 10.47852/bonviewAIA52025860

How an Author Character-based Generative AI Chatbot Can Arouse Interest in Reading: The Perspective of an Online Service?
type: item
description: 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 …
subject: Research Article
  topic: section
subject: user experience (UX)
  topic: keywords
subject: Generative AI (Gen AI)
  topic: keywords
subject: AI chatbot
  topic: keywords
subject: service design
  topic: keywords
creator: Eunwoo Choi
creator: Gahyeon Kim
creator: Gyeongmi Kim
creator: Dabin Choe
creator: Soonkyu Jang
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-02
publication date: 2025-09-02
doi: 10.47852/bonviewAIA52025459

Development of a Hyperparameter-Optimized Decision Support System for Cardiovascular Disease Prediction
type: item
description: 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 Contr…
subject: Research Article
  topic: section
subject: decision support system
  topic: keywords
subject: cardiovascular disease
  topic: keywords
subject: physiological data
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: parametric values
  topic: keywords
creator: Uzma Nawaz
creator: Mufti Anees-ur-Rahaman
creator: Hafiz Muhammad Ubaidullah
creator: Chaudhry Muhammad Ali Nawaz
creator: Zubair Saeed
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-22
publication date: 2025-09-22
doi: 10.47852/bonviewAIA52025361

Communication-Efficient Federated Learning: A Systematic Review of Model Compression and Aggregation Techniques
type: item
description: Federated Learning (FL) has emerged as a transformative paradigm for decentralized machine learning, enabling collaborative model training across distributed devices while preserving data privacy. However, high communication overhead during model updates remains a major obstacle to real-world deployment. This paper presents a systematic literature review aimed at identifying and evaluating communication-efficient techniques in FL, with a focus on model compression and aggregation methods. Using a PRISMA-based framework, we reviewed 65 peer-reviewed studies published between 2018 and 2024 from IEEE Xplore, ACM Digital Library, and Scopus. The analysis reveals key contrasts between research contexts: 92% of industry-led studies emphasize reproducibility and deployment readiness, compared to 61% of academic publications. Among the most effective methods, quantization techniques offer 40–70% bandwidth savings, while advanced aggregation strategies like FedProx and SCAFFOLD enhance performance under data heterogeneity. Despite these advances, fundamental trade-offs remain between compression ratio, convergence speed, and model accuracy. Additionally, reproducibility is hindered by inconsistent benchmarks and limited open-source tools. Gaps in energy-efficient protocols, sustainability metrics, and cross-domain adaptability also constrain deployment in edge and IoT environments. This review provides a foundation for developing scalable, reproducible, efficient and communication-aware FL systems for practical applications. Received: 27 May 2025 | Revised: 8 August 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 Femi Temitope Johnson: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data cu…
subject: Review
  topic: section
subject: federated learning
  topic: keywords
subject: communication efficiency
  topic: keywords
subject: model compression
  topic: keywords
subject: gradient aggregation
  topic: keywords
subject: edge computing
  topic: keywords
creator: Femi Temitope Johnson
creator: Elugbadebo Oladapo
creator: Olukumoro Olugbenga
creator: Alomaja Victor
creator: Akande Adenike
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.47852/bonviewAIA52026275

Enhancing Trust and Transparency in Education Using Blockchain: A Hyperledger Fabric-Based Framework
type: item
description: Blockchain is now considered a disruptive force in education and is providing stronger approaches for trust, transparency, and security in education processes. Present work focuses on the current status of blockchain-based educational applications for the verification of credentials, management of academic records, and empowerment of learners. Our Hyperledger Fabric-based implementation offers a substantial performance improvement compared to conventional approaches. For instance, the blockchain system achieved 250 transactions per second on average and always maintained a smart contract execution time within 160 ms, having a success ratio for credential verification that was higher than 98% in all configurations. Moreover, the time required for data synchronization was approximately 40% lower than that of the centralized framework. This validation empirically demonstrates the possibility and efficacy of using blockchain for academic infrastructures. In addition, this study rationalizes recent progress, discusses challenges such as scalability and privacy, and advocates for regulatory harmony. In providing such a broad discussion of current research while outlining areas of the field requiring further exploration, we argue that this study adds to emerging literature addressing the incorporation of blockchain technologies into the educational landscape in a manner that is secure, ethical, and scalable. We end with a prospective view of the role of blockchain technology in education transformation and future research. This study has both theoretical and practical implications, providing a solid background for the development of blockchain-enabled educational systems. Received: 27 May 2025 | Revised: 7 July 2025 | Accepted: 4 August 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 Co…
subject: Research Article
  topic: section
subject: blockchain
  topic: keywords
subject: education
  topic: keywords
subject: Hyperledger Fabric
  topic: keywords
subject: smart contracts
  topic: keywords
subject: credential verification
  topic: keywords
subject: educational framework
  topic: keywords
subject: decentralization
  topic: keywords
creator: Arvind Panwar
creator: Urvashi Sugandh
creator: Achin Jain
creator: Vanita Jain
creator: Arun Kumar Kumar Dubey
creator: Saheli Majumdar
creator: Saurav Mallik
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-08-15
publication date: 2025-08-15
doi: 10.47852/bonviewAIA52026268

A Danish Chatbot for Kaj Munk: Experiences in Developing a Chatbot for the Study of Kaj Munk’s Ordet
type: item
description: This study presents a pilot project developing a chatbot that is capable of conversing regarding Kaj Munk’s drama Ordet (The Word) within a Danish cultural framework. Building on the argument for a dedicated Danish language foundation model, the project explores both linguistic and cultural challenges in generating accurate and idiomatic Danish responses. Three systems based on LLaMA2 (with and without Danish fine-tuning) and LLaMA3 were compared through factual and interpretive dialogs with the play. By fine-tuning a Danish translation of the OASST2 dataset and applying retrieval-augmented generation (RAG) to the digital Kaj Munk archive, this study evaluates model performance based on correctness, fluency, and cultural adequacy. Results show significant improvements with RAG and fine-tuning yet persistent traces of English language structure and bias. Beyond linguistic accuracy, the analysis highlights the need for models trained on culturally grounded corpora that reflect Danish literary traditions shaped by authors such as Grundtvig and Munk. The findings illustrate both the potential and limitations of large language models as tools for literary interpretation and for sustaining national cultural identity in AI-mediated dialogs. Received: 14 January 2025 | Revised: 3 September 2025 | Accepted: 22 October 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 http:doi.org/10.6084/m9.figshare.30250096. Author Contribution Statement David Jakobsen: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. Simon K. Pacis: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. Sara F. Jalk: Validation, Formal analys…
subject: Online First Articles
  topic: section
subject: Kaj Munk
  topic: keywords
subject: LLM
  topic: keywords
subject: LLaMA2
  topic: keywords
subject: LLaMA3
  topic: keywords
subject: Danish
  topic: keywords
creator: David Jakobsen
creator: Simon K. Pacis
creator: Sara F. Jalk
creator: Peter Øhrstrøm
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-11-13
publication date: 2025-11-13
doi: 10.47852/bonviewAIA52025212

An Analytical Framework for Addressing Imbalance in Fake Review Detection Using Augmented Text and Swarm Optimized Classifier
type: item
description: The rapid spread of online fake reviews threatens the consumers’ trust, business reputation, and confidence in e-commerce platforms. Detecting fake reviews is challenging because distinguishing them from real reviews is difficult for humans. Researchers have employed various machine learning models to address fake review detection. However, publicly available datasets often suffer from severe class imbalance, with significantly fewer fake reviews than real ones. Previous research has struggled with this data imbalance problem, yielding biased results and thereby failing to detect fraud reviews efficiently. Traditionally, oversampling or undersampling techniques have been used to handle imbalance, which results in information loss and/or overfitting. To address this data imbalance problem, GlOBiL, a novel framework that uses GPT-2 with GloVe embedding and an optimized Bi-LSTM classifier, has been proposed. To optimize Bi-LSTM, a novel staggered particle swarm optimization (SPSO) algorithm has also been proposed. GlOBiL operates in three phases: Phase I generates contextually similar, synthetic fraudulent reviews via GPT-2 augmentation. Phase II processes this augmented dataset using GloVe embedding. Further, the Bi-LSTM classifier is optimized using the proposed SPSO. Finally, Phase III trains the optimized classifier and identifies the reviews as fake and real. Four benchmark review datasets—YelpZIP, YelpNYC, YelpCHI hotel, and YelpCHI restaurant—were used for experimentation. The results show that the proposed GlOBiL outperformed baseline methods and nine published approaches. Average accuracy increased to 95.37%, 96.49%, 97.67%, and 97.13%, respectively. Consequently, GlOBiL aids consumers and businesses in detecting fake reviews, enhancing trust, and supporting informed decision-making on e-commerce platforms. Received: 22 May 2025 | Revised: 12 August 2025 | Accepted: 22 October 2025 Conflicts of Interest The authors declare that they have no conflicts of intere…
subject: Research Article
  topic: section
subject: fake review detection
  topic: keywords
subject: machine learning analytics
  topic: keywords
subject: swarm optimization
  topic: keywords
subject: text data augmentation
  topic: keywords
subject: neural network training
  topic: keywords
creator: Richa Gupta
creator: Indu Kashyap
creator: Vinita Jindal
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
doi: 10.47852/bonviewAIA52026221

Evolution of Generative Adversarial Networks (GANs) in Medicine: A Systematic Review of Architectures, Applications, and Implementation Challenges
type: item
description: Generative Adversarial Networks (GANs) have gained increasing attention in healthcare as a promising approach to addressing data scarcity, offering synthetic alternatives that support research while mitigating privacy risks. This review examines the landscape of GAN-based synthetic data generation in healthcare, with applications spanning medical imaging, electronic health records, genomics, and multimodal datasets. A systematic search guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework identified 81 peer-reviewed studies published between 2014 and 2025, ensuring comprehensive coverage of methodological and translational developments. The review maps the diversity of GAN architectures employed, synthesizes evidence on evaluation strategies, and outlines ethical, privacy, and regulatory considerations that influence adoption. Results indicate that GANs often achieve strong fidelity and downstream utility, with emerging fairness-aware models addressing demographic bias. However, inconsistent validation practices, limited clinical integration, and unresolved ethical and governance challenges continue to hinder translation into real-world settings. Overall, the review consolidates methodological trends, barriers, and future directions, highlighting the potential of GANs to serve as viable tools to overcome data scarcity in healthcare research and practice. Received: 22 May 2025 | Revised: 27 August 2025 | Accepted: 12 November 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 Wan Aezwani Wan Abu Bakar: Conceptualization, Methodology, Formal analysis, Resources, Visualization, Supervision, Funding acquisition. Nur Laila Najwa Josdi: Software, Data curation, Writing – original draft, Writing – review & editing, Fundin…
subject: Research Article
  topic: section
subject: data fidelity
  topic: keywords
subject: electronic health records (EHRs)
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subject: generative adversarial networks (GANs)
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subject: genomics
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subject: medical imaging
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subject: synthetic data
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creator: Wan Aezwani Wan Abu Bakar
creator: Nur Laila Najwa Josdi
creator: Mustafa Man
creator: Evizal Abdul Kadir
creator: Bishwajeet Kumar Pandey
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-11-17
publication date: 2025-11-17
doi: 10.47852/bonviewAIA52026216

IDS-IoT: Intrusion Detection System for the Internet of Things Using Enhanced Long-Short Term Memory
type: item
description: Network security and intrusion detection have become significant challenges with the emergent inclusion of Internet of Things (IoT) devices across several domains. In this article, we proposed an enhanced long-short term memory (E-LSTM) method for detecting intrusions in IoT-based datasets for the designing of more resilient and competent intrusion detection systems (IDSs) in the dynamic domain of IoT environments, as well as for the thoughtful selection of models appropriate for various dataset characteristics. Four distinct datasets were used in this study: KDD-Cup’99, NSL-KDD, UNSW-NB15, and CICIoT2023. The aim was to estimate and compare the performance across datasets. We provide subtle insights into model behaviors and their capacity to adjust to the particulars of each dataset through rigorous analysis. The proposed enhanced LSTM approach revealed significant differences in precision, recall, accuracy, and F1-score compared to other approaches like AdaBoost, DNN, RNN, and Logistic Regression. It was discovered that, for every dataset, the accuracy rate exceeded 95%. Received: 22 December 2024 | Revised: 15 July 2025 | Accepted: 14 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 KDD-Cup’99 dataset at http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, NSL-KDD dataset at http://www.unb.ca/cic/datasets/nsl.html, UNSW-NB15 dataset at http://doi.org/10.1109/MilCIS.2015.7348942, and CICIoT2023 dataset at https://doi.org/10.3390/s23135941. Author Contribution Statement Gaurav Meena: Conceptualization, Software, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration. Ajay Indian: Methodology, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
subject: Research Article
  topic: section
subject: security
  topic: keywords
subject: IoT
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subject: intrusion detection system
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subject: LSTM
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subject: deep learning
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creator: Gaurav Meena
creator: Ajay Indian
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-02
publication date: 2025-10-02
doi: 10.47852/AIAbonview52025066

Smart Farming: Crop Recommendation Using Machine Learning with Challenges and Future Ideas
type: item
description: This paper addresses the critical challenge of optimizing crop selection in agriculture to enhance food production sustainably. The problem is framed as a multi-class classification task where the goal is to recommend the most suitable crop based on a set of environmental and soil features. While traditional methods rely on time-consuming and labor-intensive expert knowledge, this work proposes a data-driven approach using machine learning. The novelty of our investigation lies in the comprehensive comparative analysis of seven machine learning algorithms and the development of a highly accurate neural network model. We utilize a publicly available dataset from Kaggle, which has been preprocessed to ensure data quality. We provide a detailed account of our feature engineering and hyperparameter tuning processes. Our proposed neural network model, with a specific architecture of 30–20–10 neurons, achieves a validation accuracy of 97.73%. This work also discusses the challenges of deploying such models, including real-world data variability and the need for model interpretability. We demonstrate that our approach, particularly the neural network model, provides a robust, scalable, and adaptable solution for crop recommendation, outperforming other models (in holistic view) like Random Forest which achieved a slightly higher accuracy of 99.5% on this specific dataset but with less generalization potential. The findings of this study can empower farmers to make informed decisions, ultimately leading to improved crop yields, enhanced soil fertility, and greater profitability. Received: 22 May 2025 | Revised: 11 July 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 Kaggle at https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset/data and in TechRxiv at https://www.techrxiv.org/…
subject: Research Article
  topic: section
subject: smart farming
  topic: keywords
subject: precision agriculture
  topic: keywords
subject: machine learning
  topic: keywords
subject: deep learning
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subject: crop recommendation
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subject: big data
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subject: yield prediction
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subject: sustainable agriculture
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creator: Devendra Dahiphale
creator: Pratik Shinde
creator: Koninika Patil
creator: Vijay Dahiphale
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-06
publication date: 2025-09-06
doi: 10.47852/bonviewAIA52026214

DDoS Attack Detection With and Without SMOTEENN Dataset Balancing Strategy: Deep Learning Approaches
type: item
description: DDoS attacks flood the target systems with bulky, abnormal traffic, rendering them unavailable to benign users, and could lead to the crash of servers or computing devices, a loss of money, and a loss of productivity in time-dependent sectors like banks, airlines, online shopping, and more. Many researchers have sought to address DDoS attack issues using various techniques despite these attacks continuing to rise. To address these concerns, the study employed PRISMA guidelines to excavate open issues from recent and pertinent research articles to provide viable solutions and employed diverse deep-learning models. Each model was fine-tuned and trained with and without the SMOTEENN dataset balancing strategy using diverse train-test-validation splits. When looking at the models’ efficacy, it was evident that all models achieved remarkable accuracy rates on the test dataset following the application of the SMOTEENN dataset balancing strategy. Among others, the combination of the MLP model with SMOTEENN scored the top accuracy of 98.90%. The SMOTEENN technique reduces the FNR and FPR for models like DNN and BiLSTM. While slightly increasing FPR, it lowers FNR for the majority of models, including MLP, LSTM, DCNN, CNN-LSTM, and DNN-AE. Despite significantly improving the accuracy, the SMOTEENN technique did not reduce the model’s train-test computational times. The review findings reveal that applying relevant feature selection strategies could reduce model computational time. The study demonstrates the workflow for DDoS attack detection, classification, and mitigation using the proposed model. Received: 19 May 2025 | Revised: 10 September 2025 | Accepted: 11 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 Andualem Girma: Conceptualization, Methodology,…
subject: Research Article
  topic: section
subject: deep learning techniques
  topic: keywords
subject: DDoS attack detection
  topic: keywords
subject: dataset balancing strategy
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subject: SMOTEENN
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subject: network traffic analysis
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subject: DDoS attacktypes
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subject: CICDDoS2019
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creator: Andualem Girma
creator: Kibreab Adane
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0
date: 2026-01-13
publication date: 2026-01-13
doi: 10.47852/bonviewAIA62026187

Integrating Pattern Recognition and CNN-Based Models for Improved Bean Disease Detection and Agricultural Yield Enhancement
type: item
description: Early detection of plant diseases is crucial for minimizing crop losses and improving agricultural productivity. However, current manual detection methods are time-consuming and inaccurate. Although advanced techniques such as pattern recognition (PR) and image processing have demonstrated potential in automating disease identification, there is still a need for more accurate and efficient methods that can handle various image qualities such as complex backgrounds and complex leaf structures. This paper introduces a method that uses PR and convolutional neural network (CNN) models to detect and classify diseases in infected bean leaves based on shape features. Using contour detection, a computer vision-based PR method, we can effectively detect and distinguish different shapes in images. Two datasets were prepared: original bean leaf images subjected to background removal and enhanced images subjected to background removal, binary thresholding, and contour detection. These preprocessing steps reduced noise, improved color differentiation, and enhanced shape detection for more precise disease identification. Experimental results show that the models trained on the enhanced images showed up to an 8.16% improvement in accuracy, precision, and sensitivity compared to those trained on the original images. This study highlights the potential of integrating image processing techniques with CNN models for more accurate and efficient plant disease detection. Received: 18 September 2024 | Revised: 9 June 2025 | Accepted: 11 July 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 AI-Lab-Makerere at https://github.com/AI-Lab-Makerere/ibean/. Author Contribution Statement Farian S. Ishengoma: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Wr…
subject: Research Article
  topic: section
subject: beans
  topic: keywords
subject: machine learning
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: pattern recognition
  topic: keywords
subject: contour detection
  topic: keywords
creator: Farian S. Ishengoma
creator: Joseph P. Telemala
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-07-24
publication date: 2025-07-24
starting page: 385
ending page: 391
doi: 10.47852/bonviewAIA52024376