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: 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: 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: 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: 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: 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: 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: Integrating Pattern Recognition and CNN-Based Models for Improved Bean Disease Detection and Agricultural Yield Enhancement
items: Leveraging Artificial Immune Systems for Mental Health Research: Anomaly Detection in EEG Data
items: Incorporating Fuzzy ARTMAP Network with Spiking Neural Networks
items: Estimation of Physical Characteristics of Noisy Peach Leaves Using a Unified Algorithm
items: Review of the Performance of CNN Models on Handwriting Recognition
items: Deepfake in Digital Business: Systematic Review of Strategies
items: Predictive Artificial Intelligence Models in the Early Identification of Diabetic Retinopathy
items: Artificial Intelligence as a Distributed Actor: Rethinking Organizational Theory Through Sociotechnical Networks
items: Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model
items: Training of the Dynamic Systems Control: A Neural Network or a Learning Algorithm
items: CAD System Utilizing UNet and Hough Transform for Automated Measurement of Fetal Head Circumference and Age in 2D Ultrasound Images
items: Detection of Rice and Corn Plant Leaf Disease Using Invariants of Deep Learning Models and Edge Perspective
items: Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture
items: The Legal Status of Artificial Intelligence: The Need to Form a Legal Personality and Regulate Copyright
items: A Framework for Adaptive Recommendation in Online Environments

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: Oluseyi 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
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

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
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

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: Research Article
  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

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

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/

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
  topic: keywords
subject: intrusion detection system
  topic: keywords
subject: LSTM
  topic: keywords
subject: deep learning
  topic: keywords
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
  topic: keywords
subject: crop recommendation
  topic: keywords
subject: big data
  topic: keywords
subject: yield prediction
  topic: keywords
subject: sustainable agriculture
  topic: keywords
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

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

Leveraging Artificial Immune Systems for Mental Health Research: Anomaly Detection in EEG Data
type: item
description: Mental Health is a physical, mental, and social state affecting 970 million people in the world. Artificial Intelligence and deep learning techniques classifying ElectroEncephaloGraphy (EEG) data have emerged as a promising technology for the detection of mental health disorders. In this context, one underexplored area is the application of Artificial Immune Systems, which is a technique inspired by the human immune system that has been useful in many computational tasks, including anomaly detection. This paper aims to bridge the gap by leveraging Artificial Immune Systems for Mental Health through anomaly detection in EEG Data: a novel Negative Selection Clonal for Anomaly Detection (NSCAD) algorithm is presented and applied on a dataset of 945 samples with individuals diagnosed with disorders and a control group of healthy participants. Efficacy of NSCAD on anomaly detection was assessed using precision, recall, F1-score, and accuracy metrics. Results are promising, with a precision of 0.92, a recall of 0.83, an F1-score of 0.88, and an accuracy of 0.78. A comparative analysis between the evaluation metrics and anomaly detection of NSCAD vs other methods is finally reported together with a critical analysis of the limitations. Received: 18 October 2024 | Revised: 7 April 2025 | Accepted: 24 April 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 Shivani Bhandari: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Neil Buckley: Conceptualization, Methodology, Formal analysis, Resources, Writing – original draft, Visualization, Supervision, Project administration. Emanuele Lindo Secco: Resources, Writing – review & editing, Visualization.
subject: Research Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: artificial immune systems
  topic: keywords
subject: mental health
  topic: keywords
subject: electroencephalography
  topic: keywords
creator: Shivani Bhandari
creator: Neil Buckley
creator: Emanuele Lindo Secco
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-05-19
publication date: 2025-05-19
doi: 10.47852/bonviewAIA52024579

Incorporating Fuzzy ARTMAP Network with Spiking Neural Networks
type: item
description: In this paper, we have incorporated the Fuzzy ARTMAP classification module into the Spiking Neural Networks (SNNs). This new network can have multiple hidden layers, a supervised corrected layer, and an ARTMAP module. The objective of the supervised layer is to correct the spikes given by the Spike-timing-dependent plasticity (STDP) layer in a supervised manner. Then the resulting corrected spikes are fed into an ARTMAP network. When an output spiking train code is similar to one of the stored ART codes. It modifies that code to incorporate the new input into that code category. This combination required a new training algorithm. The hidden layers are trained using the popular STDP. This unsupervised learning is followed by a supervised layer where new supervised weight update formulas are presented. This new combination is compared to other spiking algorithms using datasets from the UCI repository. It is shown that SNN_ARTMAP provides better recognition results than other similar spiking supervised learning algorithms. Received: 14 April 2025 | Revised: 4 June 2025 | Accepted: 22 August 2025 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 the UC Irvine Machine Learning Repository at https://archive.ics.uci.edu/. Author Contribution Statement Issam Dagher: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
subject: Research Article
  topic: section
subject: SNN
  topic: keywords
subject: ARTMAP
  topic: keywords
subject: STDP
  topic: keywords
subject: supervised learning
  topic: keywords
creator: Issam Dagher
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-10-13
publication date: 2025-10-13
doi: 10.47852/bonviewAIA52025930

Estimation of Physical Characteristics of Noisy Peach Leaves Using a Unified Algorithm
type: item
description: Peach production and breeding benefit from digital agriculture and image processing technology. However, there is little knowledge about the optimal image denoising and processing algorithms for estimating the physical attributes of peach leaves from images. The objective of this study was to evaluate different image denoising and processing techniques to obtain optimal methods and establish a unified approach for estimating the physical characteristics of healthy peach leaves, including length, width, perimeter, and area. Twenty denoising filters were evaluated, and the bilateral filter was determined as the optimal filter for peach leaf image processing with Gaussian and Poisson noise. Twenty-four color space models, including twenty vegetation indices, were evaluated, among which the CIELAB (L*a*b*) color space performed best in the segmentation task using K-means clustering. Seven segmentation algorithms were evaluated and K-means clustering was found to be the optimal method based on a custom metric function. The unified algorithm including optimal denoising, color space selection, and segmentation techniques successfully estimated the physical characteristics of peach tree leaves. The results show that a unified approach is reliable and accurate for estimating the physical characteristics of peach leaves from images. These techniques are crucial for efficient orchard management and other digitalization-based applications in peach production and breeding. Received: 14 April 2025 | Revised: 15 September 2025 | Accepted: 28 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 the PlantVillage Dataset on Kaggle at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset. Author Contribution Statement Chunxian Chen: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation…
subject: Research Article
  topic: section
subject: bilateral filter
  topic: keywords
subject: CIELAB color space
  topic: keywords
subject: Gaussian noise
  topic: keywords
subject: K-means clustering
  topic: keywords
subject: peach leaves
  topic: keywords
subject: physical characteristics
  topic: keywords
subject: Poisson noise
  topic: keywords
creator: Chunxian Chen
creator: Haixin Wang
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-21
publication date: 2025-10-21
doi: 10.47852/bonviewAIA52025928

Review of the Performance of CNN Models on Handwriting Recognition
type: item
description: Handwritten text recognition continues to be a dynamic area of research, with practical uses such as processing bank checks, digitizing historical manuscripts, enabling handwriting-based user interfaces, and other OCR-related applications. Despite its potential, the task remains difficult because of a wide variety of handwriting styles, stroke patterns, and visual structures found across different writing systems. This research provides a comprehensive comparative study of deep convolutional neural network (CNN) architectures for handwritten character and word recognition of world popular scripts such as Roman (English), Devanagari, Bengali, Tamil, Telugu, Hiragana, and Arabic. Most recent and popular works on CNNs are considered. We conduct comprehensive benchmarking of widely used CNN architectures—such as VGG, ResNet, and Inception—on handwritten datasets spanning multiple scripts. Our experimental findings yield important information regarding comparative performances under diverse conditions, in addition to insights regarding the impact due to architectural extensions, i.e., attention mechanisms and regularization schemes, to recognition performance. Ensemble schemes, i.e., majority voting and stacking to obtain additional boost in performance, yield measurable increments in prediction faithfulness. Our investigation encompasses all training, validation, and testing stages and discovers key patterns such as overfitting tendencies, specifically for scripts with high visual complexity. These observations emphasize careful model selection and provide practical suggestions regarding designing robust, script-aware CNNs for multilingual handwritten text recognition. Received: 30 April 2024 | Revised: 26 March 2025 | Accepted: 25 June 2025 Conflicts of Interest Umapada Pal is an Advisory Board Member for Artificial Intelligence and Applications, and he was not involved in the editorial review or the decision to publish this article. The authors declare that they have …
subject: Review
  topic: section
subject: CNN
  topic: keywords
subject: comparative study
  topic: keywords
subject: HCR
  topic: keywords
subject: word recognition
  topic: keywords
creator: Sabyasachi Mazumder
creator: Sayan Neogy
creator: Sahana Das
creator: Kaushik Roy
creator: Umapada Pal
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-09-08
publication date: 2025-09-08
doi: 10.47852/bonviewAIA52023297

Deepfake in Digital Business: Systematic Review of Strategies
type: item
description: This research provides an extensive examination of the adoption of deepfake technology throughout digital business ecosystems, analyzing 186 cross-sector deployments from 2017 to 2024 using a mixed-methods methodology that combines qualitative case studies with quantitative risk-benefit indicators. The study reveals notable differences in sectoral adoption results. For example, marketing had a 28% return on investment from tailored synthetic material, even if trust fell by 22% when the content was revealed. On the other hand, finance saw a 62% drop in synthetic fraud, although it cost $4.2 million to fix false positives. A geographic study shows that adoption rates are different in different parts of the world. For example, Asia-Pacific has a 63% acceptance rate under utilitarian-driven governance (2.9/5 regulation score), whereas the EU has a more conservative 38% acceptance rate under the General Data Protection Regulation (4.2/5 score). Technical benchmarks reveal significant detection weaknesses, with accuracy decreasing from 97% in controlled environments to 68.2% in practical applications, compounded by an 83% accessibility deficit for small and medium-sized enterprises (SMEs) requiring 8.7 TFLOPS equipment. The study enhances the field by presenting a Composite Risk-Benefit Index for sectoral risk assessment, suggesting a tiered governance framework in accordance with IEEE standards, and setting empirically validated detection thresholds. Key results show that effective adoption needs cross-functional governance (odds ratio = 4.2), dynamic transparency standards (73% consent uptake), and fair access solutions to close the gaps across SMEs. These ideas provide practical direction for companies navigating the equilibrium between innovation and responsibility in an age characterized by the widespread dissemination of synthetic media. Received: 19 February 2025 | Revised: 19 May 2025 | Accepted: 22 August 2025 Conflicts of Interest The author declares that he has…
subject: Research Article
  topic: section
subject: cross-sector adoption
  topic: keywords
subject: deepfake technology
  topic: keywords
subject: digital business models
  topic: keywords
subject: digital governance
  topic: keywords
subject: ethical AI
  topic: keywords
subject: risk-benefit analysis
  topic: keywords
creator: Gabriel Silva-Atencio
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-10-18
publication date: 2025-10-18
doi: 10.47852/bonviewAIA52025448

Predictive Artificial Intelligence Models in the Early Identification of Diabetic Retinopathy
type: item
description: This study aims to evaluate the precision of a mathematical system using Artificial Intelligence (AI) in forecasting retinal anomalies linked to Diabetic Retinopathy (DR). The study adopted a quantitative, descriptive, and exploratory approach. A standard sample of 1684 ocular fundus images was analyzed. These images were divided into two groups: Class 0 for healthy eyes and Class 1 for eyes with DR. A finite population model was used to determine the sample size, which came from a publicly available database. Experts in the field validated the results obtained to guarantee the accuracy of the findings. The study used the Vision AI solution to train and test 3,752 publicly available medical images. During the training phase, an independent set of 1,684 medical images that had not been included in the training sample was selected. The sample was then classified into two groups: (1) Class 0 for healthy eyes; and (2) Class 1 for eyes with DR. To evaluate the model’s performance, a statistical analysis was conducted using key metrics such as accuracy, sensitivity, specificity, F1-score, and confusion matrix. The AI-based model demonstrated an accuracy exceeding 90%, with statistically significant findings supporting the study’s hypothesis. The findings highlight the model’s ability to detect and predict DR in real time, improving the accuracy of disease detection. Received: 28 February 2025 | Revised: 26 June 2025 | Accepted: 26 August 2025 Conflicts of Interest Data sharing does not apply to this article as no new data were created or analyzed in this study. Data Availability Statement The authors declare that they have no conflict of interest to this work. 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. Edwin Acuña-Acuña: Conceptualization, Methodol…
subject: Research Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: diabetic retinopathy
  topic: keywords
subject: early detection
  topic: keywords
subject: predictive model
  topic: keywords
subject: retinal abnormalities
  topic: keywords
creator: Gabriel Silva-Atencio
creator: Edwin Acuña-Acuña
creator: Maziar Lalezary
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
doi: 10.47852/bonviewAIA52025555

Artificial Intelligence as a Distributed Actor: Rethinking Organizational Theory Through Sociotechnical Networks
type: item
description: The rapid integration of Artificial Intelligence (AI) into organizational contexts is profoundly changing the fundamental assumptions in contemporary management and organizational theory. In response to these changes, this article introduces the Distributed Sociotechnical Agency Model (DSAM), a novel conceptual framework that synthesizes insights from Actor-Network Theory (ANT), sociomateriality, and principles of ethical governance. The DSAM framework conceptualizes AI as a distributed actor operating within complex networks and highlights three core dimensions: distributed agency, relational dynamics between human and non-human actors, and the imperative of ethical accountability. By using an integrated approach combining extensive literature review and analysis of real-life case studies, including a practical example involving an AI-driven university monitoring system, this study demonstrates how DSAM can facilitate more inclusive, user-friendly, flexible, responsive, and ethically aligned approaches to AI integration in organizations. To provide clarity and practical understanding, this article includes a detailed flowchart and a comparative table as well. Furthermore, recent academic studies and evolving policy developments are included in the discussion to strengthen the global relevance of the model. The article concludes by offering strategic recommendations for organizational governance and identifying promising directions for future empirical research. Received: 27 February 2025 | Revised: 14 July 2025 | Accepted: 22 August 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 Hasibe Aysan: Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review and editing, Visualization, Supervision, Project administration.
subject: Research Article
  topic: section
subject: distributed agency
  topic: keywords
subject: actor-network theory
  topic: keywords
subject: sociomateriality
  topic: keywords
subject: AI governance
  topic: keywords
subject: organizational dynamics
  topic: keywords
creator: Hasibe Aysan
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
doi: 10.47852/bonviewAIA52025546

Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model
type: item
description: Cybersecurity threats and attacks are increasing day by day, bringing real focus on Domain Name System (DNS)–based data exfiltration—a stealth technique used by attackers to steal sensitive information from compromised networks. DNS query exchange is the initial part of any data exchange in the Internet and is the most neglected in traditional monitoring systems. These enable attackers to create covert channels to carry out various advanced persistent threats and unauthorized exfiltration attempts. In this research study, we present a novel detection approach of these DNS patterns through low-dimensional latent representations extracted via a Tabular-Variational AutoEncoder (Tab-VAE), specifically tailored for DNS-over-HTTPS (DoH) traffic. The latent space obtained by the Tab-VAE is subsequently fed into a multi-head self-attention classifier to perform a multi-class classification. We evaluated our experiments using the BCCC-CIC-Bell-DNS-2024 dataset, which provides a realistic snapshot of DoH traffic patterns. Notably, the proposed model demonstrated robust generalization across varying batch sizes and achieved competitive performance metrics with an improved accuracy of 80% and precision score of 75% for a batch size of 128. These findings highlight the potential of advanced machine learning architectures in reinforcing cybersecurity posture. By integrating such techniques, organizations can improve the detection of covert DNS-based attacks and better protect sensitive assets against emerging threats. Received: 28 February 2025 | Revised: 4 July 2025 | Accepted: 22 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest in this work. Data Availability Statement The data supporting the findings of this study are openly available in Behaviour-Centric Cybersecurity Center (BCCC) at https://www.yorku.ca/research/bccc/ucs-technical/cybersecurity-datasets-cds/. Author Contribution Statement Ravi Veerabhadrappa: Conceptualization, Met…
subject: Research Article
  topic: section
subject: DNS over HTTPS
  topic: keywords
subject: Tab-VAE
  topic: keywords
subject: self-attention classifier
  topic: keywords
subject: heterogeneous networks
  topic: keywords
creator: Ravi Veerabhadrappa
creator: Poornima Athikatte Sampigerayappa
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-08-13
publication date: 2025-08-13
doi: 10.47852/bonviewAIA52025552

Training of the Dynamic Systems Control: A Neural Network or a Learning Algorithm
type: item
description: The dynamic system control problem under conditions of a priori uncertainty regarding the parameters of the controlled object is considered. The properties of controllers typically used in control systems are studied. Among them are a neural network, a proportional–integral–derivative (PID) controller, and a learning algorithm. The sign-changing input signal is considered in dynamic systems using the minimum time criterion. A dynamic system is represented by the first-order differential equations system, which allows using the state space method in the analysis. A feature of the research is the study of the quality of the system tuning under conditions of parametric uncertainty and the presence of homogeneous non-Gaussian noise in the phase coordinate measurement channels. The system’s reaction results for the studied approaches for the proposed mathematical model are compared. The learning algorithm showed an improvement over conventional methods by at least 40% in the evaluated indicators, in which the influence of interference is leveled by introducing a unique function of the “hysteresis” type. The modeling results are given in support of the conclusions made. Received: 17 February 2025 | Revised: 26 May 2025 | Accepted: 26 June 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://drive.google.com/drive/u/0/folders/1gqzZ6Tla1RDaJAl86rp7yHHbs6Ggj_wp. Author Contribution Statement Dmytro Kucherov: Conceptualization, Methodology, Formal analysis, Investigation, Supervision, Project administration, Funding acquisition. Natalia Khalimon: Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ihnat Myroshnychenko: Software, Validation. Valerii Tkachenko: Validation.
subject: Research Article
  topic: section
subject: neural network
  topic: keywords
subject: PID controller
  topic: keywords
subject: learning algorithm
  topic: keywords
creator: Dmytro Kucherov
creator: Ihnat Myroshnychenko
creator: Natalia Khalimon
creator: Valerii Tkachenko
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-07-15
publication date: 2025-07-15
doi: 10.47852/bonviewAIA52025435

CAD System Utilizing UNet and Hough Transform for Automated Measurement of Fetal Head Circumference and Age in 2D Ultrasound Images
type: item
description: Two-dimensional (2D) medical ultrasound is a widely used imaging modality for the anatomical and functional assessment of fetal development due to its low cost, availability, real-time capability, and the absence of radiation hazards. Head circumference (HC) is an essential biometric to measure fetal growth. However, the low signal-to-noise ratio in ultrasound imaging can make it difficult for clinicians to identify the fetal plane correctly. Additionally, manually measuring HC can be expensive, involving accurately placing three minor and major parameter points from the ultrasound machine. To address these issues, research has been conducted to develop an automated system for measuring HC. This study presents a computer-aided diagnosis (CAD) system for the automatic measurement of fetal HC and fetal age using hybrid feature extraction. Using Convolutional Neural Networks (CNNs), self-supervised learning (SSL), vision transformers (ViTs), UNet deep learning model for segmentation, and Hough transform to measure performance, this study achieved higher performance compared to previous studies with a Dice similarity coefficient (DSC) of 97.23 ± 2.78, an average distance factor (ADF) of 2.8 ± 2.93 mm, a Jaccard Index of 88.57 ± 3.79, and an accuracy of 97.2%. After that, we enhance UNet using an attention mechanism that achieved a Dice coefficient of 98.5 ± 2.5, an ADF of 2.4 ± 2.8 mm, and an accuracy of 98.1%. This system provides a more cost-effective and accurate measurement of HC, aiding clinicians in assessing fetal development. Received: 14 February 2025 | Revised: 16 May 2025 | Accepted: 29 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 GitHub at https://github.com/pranjalrai-iitd/Fetal-head-segmentation-and-circumference-measurement-from-ultrasound-images. Author Contribution Statement Hamzah Jaber: Conce…
subject: Research Article
  topic: section
subject: fetal ultrasound
  topic: keywords
subject: segmentation
  topic: keywords
subject: head circumference
  topic: keywords
subject: UNet
  topic: keywords
subject: Hough transform
  topic: keywords
creator: Hamzah Jaber
creator: Ahmed Abed Mohammed
creator: Bo Zhang
creator: Maidi Qiu
creator: Mustafa M. Abd Zaid
creator: Putra Sumari
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-08-20
publication date: 2025-08-20
doi: 10.47852/bonviewAIA52025416

Detection of Rice and Corn Plant Leaf Disease Using Invariants of Deep Learning Models and Edge Perspective
type: item
description: Rice and corn hold significant importance due to their daily consumption worldwide. Naked-eye observations are not accurate. Therefore, we need an autonomous system that can accurately detect and classify diseases in both plants. We trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy. We added a new dense layer to the known DCNN-based deep learning models and achieved improved accuracy. The best results were observed when our invariants of the InceptionV3, ResNet152, and MobileNetV2 deep learning models were used on corn plant leaves (98.09%, 98.51%, and 89.73%, respectively). These models also performed well on rice plant leaves (98.51%, 93.59%, and 98.57%, respectively). Because InceptionV3 performed well for both plants, we implemented it in NVIDIA Jetson Nano as an end device for the detection and classification of diseases from both plant leaves. Received: 4 January 2025 | Revised: 26 May 2025 | Accepted: 13 June 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/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset. Author Contribution Statement Zubair Saeed: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – original draft, Project administration. Uzma Nawaz: Validation, Formal analysis, Writing – review & editing. Ali Raza: Conceptualization, Validation, Formal analysis, Writing – review & editing, Visualization. Kamran Javed: Validation, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision.
subject: Research Article
  topic: section
subject: deep learning
  topic: keywords
subject: an invariant of Inception
  topic: keywords
subject: InceptionV3
  topic: keywords
subject: ResNet152
  topic: keywords
subject: MobileNetV2
  topic: keywords
subject: NVIDIA Jetson Nano
  topic: keywords
subject: plant disease
  topic: keywords
creator: Zubair Saeed
creator: Uzma Nawaz
creator: Ali Raza
creator: Kamran Javed
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-07-16
publication date: 2025-07-16
doi: 10.47852/bonviewAIA52025155

Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture
type: item
description: The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aiming to develop a scalable, efficient, and secure solution for crop disease detection tailored to the environmental and operational conditions of Minnesota farms. By maintaining sensitive farm data locally and enabling collaborative model updates, our proposed framework seeks to achieve high accuracy in crop disease classification without compromising data privacy. We outline a methodology involving data collection from Minnesota farms, application of local deep learning algorithms, transfer learning, and a central aggregation server for model refinement, aiming to achieve improved accuracy in disease detection, good generalization across agricultural scenarios, lower costs in communication and training time, and earlier identification and intervention against diseases in future implementations. We outline a methodology and anticipated outcomes, setting the stage for empirical validation in subsequent studies. This work comes in a context where more and more demand for data-driven interpretations in agriculture has to be weighed with concerns about privacy from farms that are hesitant to share their operational data. This will be important to provide a secure and efficient disease detection method that can finally revolutionize smart farming systems and solve local agricultural problems with data confidentiality. In doing so, this paper bridges the gap between advanced machine learning techniques and the practical, privacy-sensitive needs of farmers in Minnesota and beyond, leveraging the benefits of federated learning. Received: 25 December 2024 | Revised: 18 April 2025 | Accepted: 8 May 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…
subject: Review
  topic: section
subject: federated learning
  topic: keywords
subject: smart farming
  topic: keywords
subject: disease detection
  topic: keywords
subject: privacy preservation
  topic: keywords
subject: transfer learning
  topic: keywords
subject: network pruning
  topic: keywords
creator: Ritesh Janga
creator: Rushit Dave
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-06-03
publication date: 2025-06-03
doi: 10.47852/bonviewAIA52025089

The Legal Status of Artificial Intelligence: The Need to Form a Legal Personality and Regulate Copyright
type: item
description: The article addresses the issues of legal regulation of artificial intelligence (AI) in the context of its rapid development and penetration into various spheres of life. The introduction raises the problem of uncertainty in the legal environment regarding content created using AI, with a particular focus on copyright issues and the possibility of legislative recognition of AI as a subject of law. Market statistics analysis shows the growth of the global AI market and underscores the importance of developing legislation governing authorship and intellectual rights to prevent potential legal disputes and protect personal data. Special attention is given to the differences in approaches to the legal status of AI worldwide, including the USA, the UK, the European Union, China, and Russia, as well as initiatives by international organizations such as UNESCO and the World Intellectual Property Organization. The research methodology is based on comparative legal analysis, examination of regulatory acts, and expert evaluation, which allowed for identifying common features and significant differences in AI regulation across various jurisdictions. The article also explores key ethical issues related to the use of AI, including personal data protection and preventing data leaks. The research aims to propose possible solutions and adaptations of legislation considering the rapid development of AI technologies. It will be useful not only for lawyers and intellectual property specialists but also for a wide range of readers interested in modern technologies and their legal aspects. Received: 22 July 2024 | Revised: 25 October 2024 | Accepted: 11 February 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 Anna Pokrovskaya: Conceptualization, Formal analysis, Writing – origina…
subject: Research Article
  topic: section
subject: AI
  topic: keywords
subject: intellectual property rights
  topic: keywords
subject: ethical issues
  topic: keywords
subject: global AI regulation
  topic: keywords
subject: AI-related legal disputes
  topic: keywords
creator: Anna Pokrovskaya
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
date: 2025-02-24
publication date: 2025-02-24
starting page: 179
ending page: 191
doi: 10.47852/bonviewAIA52023901

A Framework for Adaptive Recommendation in Online Environments
type: item
description: Recent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users' dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic. Received: 15 December 2024 | Revised: 12 June 2025 | Accepted: 30 June 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 X-Wines Research Project at https://sites.google.com/farroupilha.ifrs.edu.br/xwines. Author Contribution Statement Rogério Xavier de Azambuja: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, and Project administration. A. Jorge Morais:…
subject: Research Article
  topic: section
subject: recommender systems
  topic: keywords
subject: dynamic taste profile
  topic: keywords
subject: deep neural networks
  topic: keywords
subject: transformers
  topic: keywords
subject: attention model
  topic: keywords
creator: Rogério Xavier de Azambuja
creator: A. Jorge Morais
creator: Vítor Filipe
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
date: 2025-07-16
publication date: 2025-07-16
doi: 10.47852/bonviewAIA52025020