Artificial Intelligence and Applications
Artificial intelligence is a discipline of science and technology for making intelligent machines to simulate human abilities in learning, perception, thinking, decision, behavior and interaction. The technology is widely used in various areas in society, including sensing data analysis and understanding, big data analytics, security surveillance, management and planning, education, medical care, robotics, unmanned driving, and so on. Keeping AI's impact on society beneficial motivates research in many areas, from economics and law to technical topics. Artificial Intelligence and Applications (AIA) is a peer-reviewed journal publishing original contributions to the theory, methods and applications of artificial intelligence. AIA aims to enhance the development and application of artificial intelligence by bringing scientists in the field together. Research articles and reviews on all scientific or application topics in AI are welcome. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing
This paper presents a case study of end-to-end (E2E) automation of corporate financial expense processing by combining generative AI (GenAI) and intelligent document processing (IDP) technologies with automation agents and shows the automation of intelligent tasks in a modern digital transformation environment. Although conventional RPA is effective in automating repetitive, rule-based, and simple tasks, it has limitations in handling unstructured data, responding to exceptions, and making complex decisions. In this study, we designed and implemented a four-step integration process, including automatic recognition of proofs such as receipts through OCR/IDP, item classification based on policy database, intelligent judgment support for exceptional situations through GenAI (LLMs), and human final decision and system learning (human-in-the-loop) through automation agents. As a result of the application to Company S, a large Korean company, quantitative effects such as reducing the processing time of branch receipt expenses by more than 80%, reducing error rates, and improving compliance rates were confirmed, as well as qualitative effects such as improving work accuracy and consistency, increasing employee satisfaction, and supporting data-based decision-making. In addition, the system learns from human judgment and continuously improves its ability to automatically handle exceptions, creating a virtuous cycle. This study empirically demonstrates that the organic combination of GenAI, IDP, and an automation agent overcomes the limitations of existing automation and is effective in realizing E2E automation of complex corporate tasks. In addition, it suggests the possibility of expansion to various business areas such as accounting, human resources, and purchasing in the future, as well as the development direction of AI-based hyperautomation. Received: 30 May 2025 | Revised: 30 July 2025 | Accepted: 22 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Cheonsu Jeong: Conceptualization, Supervision, Writing – original draft. Seongmin Sim: Software, Writing – review & editing. Hyoyoung Cho: Software, Writing – review & editing. Sungsu Kim: Software, Writing – review & editing. Byounggwan Shin: Software, Writing – review & editing.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in Research: Benefits, Challenges, and Strategies for Implementing AI Tools in Research in Afghanistan
This study aims to evaluate the benefits and challenges of integrating artificial intelligence (AI) into research within Afghanistan and to propose practical strategies for the effective adoption of these technologies to advance and enrich the research landscape. The primary objective is to explore the advantages and drawbacks of using AI tools in research from the perspective of researchers, assess the prevalence and extent of their usage, and outline actionable strategies for their optimal application to enhance the quality, efficiency, and impact of research. An online survey was conducted among 190 university professors and other researchers across the country, then the data was analyzed with SPSS (Statistical Package for the Social Sciences) and MS Excel software tools. The survey results showed that 62.6% of participants use AI tools in their research, while the rest do not. The main reasons for non-use were concerns about data privacy, limited familiarity with these tools among researchers, and the high cost of the tools. The analysis indicates that 36% of the researchers use ChatGPT, a general AI tool, in their research. It also shows that researchers aged 41 years or older use AI tools less in their research compared to younger researchers. High-speed data analysis, time saving, easy access to multiple information sources, and facilitating collaboration between researchers are the key benefits of AI tools in research. However, they also present challenges such as a high learning curve, concerns about data accuracy, poor integration with existing research systems, and overreliance on technology. Strategies that more than 50% of respondents identified as effective include: organizing seminars for researchers on AI, publishing in national languages, conducting public awareness campaigns, adding AI and machine learning (ML) to the curricula of some related fields, and encouraging scholars to conduct research and use AI in their work. Received: 20 November 2024 | Revised: 28 May 2024 | Accepted: 26 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.28892885. Author Contribution Statement Wahidullah Zgham: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Massoud Danishmal: Software, Validation, Resources. Mohammad Hamed Haidary: Software, Validation, Resources.
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models
Progress in accurate medical image classification is often hampered by concerns surrounding data privacy and scarcity of data for certain medical diseases, leading to sparsity and unbalanced datasets. To address these challenges, this study uses generative models, namely, Denoising Diffusion Probabilistic Models (DDPMs) and Progressive Growing Generative Adversarial Networks (PGGANs), for dataset improvement. In this article, we propose a framework for understanding how the resultant synthetic images generated by DDPM and PGGANs affect four different models’ performance: a specially crafted Convolutional Neural Network, an untrained VGG16, a pretrained VGG16, and a pretrained ResNet50. For modeling practical constraints in real applications, experiments applied Random Sampling and Greedy K Sampling to obtain small unbalanced datasets. Synthetic image quality was also measured by applying Fréchet Inception Distance (FID), and their impact was further explored by comparing classification results with their original datasets. Experiments reveal that DDPM consistently produced images of higher realism, backed by lower FID scores, and overtakes PGGANs in augmenting classification outcomes of all investigated models and datasets. Addition of DDPM-generated images to original datasets obtained improvement of about 6% in accuracy and therefore enhanced robustness and reliability of models, specifically when datasets are unbalanced. Although Random Sampling obtained better consistency, Greedy K Sampling obtained higher variability but higher FID scores. Overall, this research identifies the potential of DDPM to effectively augment and balance sparse datasets of medical images and subsequently improve training of models and predictive outcomes. Received: 1 July 2025 | Revised: 3 September 2025 | Accepted: 15 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/imankhazrak/DDPM_X-Ray. Author Contribution Statement Iman Khazrak: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Shakhnoza Takhirova: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation,Writing – original draft, Writing – review & editing, Visualization. Mostafa M. Rezaee: Validation, Investigation, Resources, Writing – review & editing. Mehrdad Yadollahi: Investigation, Resources, Writing – review & editing. Robert C. Green II: Methodology, Writing – review & editing, Supervision. Shuteng Niu: Methodology, Writing – review & editing, Supervision.
Progressive Growing Generative Adversarial Networks (PGGANs)
Denoising Diffusion Probabilistic Models (DDPMs)
The Effectiveness of Using Fuzzy Neural Networks in Predicting the Sales Volume of Perishable Products
This article discusses methods for predicting the sales volume of perishable products to identify the best method that provides the most accurate forecast value of the indicator under study. The initial data for forecasting are the volumes of daily perishable products that we sell. For a trading company, the volume of products purchased for retail sale must match the volume of demand. Therefore, the presence of product residues at the end of the trading day is unacceptable. That is especially true for products with a short shelf life (chilled meat, dairy products, fruits, vegetables, etc.). We must write off the products as losses if the sale period has expired. Therefore, identifying the most accurate forecasting method in this area is a significant task. Solving this problem will reduce losses from trading activities. For comparison, the following three methods were used: alignment of the obtained dynamics series according to the Fourier series, forecasting using a two-layer neural network, and forecasting using a neuro-fuzzy network. We determined the accuracy of each method by calculating the average approximation error. The prediction method using a neuro-fuzzy network showed higher efficiency. The discrepancies between the actual and theoretical values of the implementation volume were minimal. Therefore, the developed neuro-fuzzy model can be used to predict the sales volume of perishable products. We are further testing the neuro-fuzzy model on new sales data. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 14 September 2025 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Nataliya Mutovkina: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0
Are Peruvian Tertiary Students Using Artificial Intelligence in Their Academic Activities?
The widespread emergence of generative artificial intelligence (GAI) has prompted growing interest in understanding the factors that drive its adoption in academic contexts. This study explores the behavioral intention of Peruvian university students to use GAI tools in their educational activities by applying the technology acceptance model (TAM) through partial least squares structural equation modeling (PLS-SEM). A total of 350 valid responses were collected from students at the Universidad Nacional Agraria La Molina. The model demonstrated strong reliability and validity, with key constructs such as perceived usefulness and attitude toward use explaining 12% and 9% of the variance in intention, respectively. Notably, behavioral intention significantly predicted actual use, accounting for 58.8% of the variance. To address concerns of common method bias, statistical controls were implemented. The results underscore the importance of designing user-friendly and pedagogically relevant GAI tools because ease of use was found to strongly influence perceived usefulness and attitudes. This study contributes to the literature by validating a TAM-based model in the Latin American higher education context and identifying actionable variables that institutions can leverage to foster ethical and effective adoption of GAI. It also highlights students’ current reliance on tools such as ChatGPT® and ChatPDF® for information retrieval and summarization. These findings support the development of informed policies and training initiatives to guide the responsible integration of AI in academic environments. Received: 13 June 2025 | Revised: 8 August 2025 | Accepted: 4 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Mildher Ugaz-Rivero: Validation, Data curation. Yulissa Navarro-Castillo: Methodology, Formal analysis, Visualization. Janet Corzo-Zavaleta: Investigation, Writing – original draft. Alberto Urueña: Conceptualization, Writing – review & editing, Supervision.
information and communication technologies (ICT)
Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
Missing modalities pose a significant challenge on multi-modal studies by disrupting the comprehensive analysis of diverse data sources. Deep learning addresses this issue by employing algorithms that can effectively infer and integrate the absent information, thereby ensuring robustness and accuracy of the models while increasing the study’s statistical power. This study aims to provide a systematic literature review on deep learning solutions for missing imaging modalities in multi-modal medical data analysis. Articles on PubMed, IEEE explore digital library, and Scopus were searched in the range from January 2013 to May 2025. This systematic search and review identified 234 articles. Adhering to the specified search criteria, 61 published studies were eligible. Among these, 47% employed image synthesis methods, 20% applied knowledge transfer methods, and 33% used latent feature space-based methods. The paper explores the research gaps and challenges associated within each of these categories. Additionally, this review paper illuminates the popular public datasets for multi-modal studies with missing modalities. Furthermore, it presents evaluation metrics and their key attributes. The review concludes with its limitations and a detailed discussion of current challenges and future directions in this domain. Received: 6 June 2025 | Revised: 17 September 2025 | Accepted: 24 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Jinzhao Qian: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Ankita Joshi: Conceptualization, Methodology, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hailong Li: Writing – review & editing, Project administration. Nehal A. Parikh: Writing – review & editing. Jonanthan R. Dillman: Writing – review & editing. Lili He: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
How an Author Character-based Generative AI Chatbot Can Arouse Interest in Reading: The Perspective of an Online Service?
In modern society, the MZ generation (Millennials and Generation Z) reads fewer paper books than in the past, as smartphone usage has become prevalent. This generation is accustomed to watching videos that present summarized information on online platforms. Consequently, they typically acquire information about books through these summaries available online. This study aims to propose the use of artificial intelligence (AI) human characters based on Generative AI to stimulate reading interest among the MZ generation. The recent advancements in Generative AI technology allow for experiences that simulate communication with real people. In addition, various services communicate with users through AI human characters that resemble real individuals. This research investigates whether providing a chatbot, based on AI human characters, with actual conversations from the author of a book can serve as a positive service for users. To explore this concept, the study developed an AI character based chatbot representing Kim, a renowned Korean novelist, as a Generative AI. A prototype was created to facilitate discussions about the book’s content with users. The research employed the System Usability Scale (SUS) test on subjects from the MZ generation using the prototype app service. The SUS test yielded a score of 79.5 points, indicating that it is usable. Furthermore, the normality of the SUS test results for each subject was assessed, confirming that the results are normally distributed, which enhances the reliability of the findings. This research aspires to contribute positively to the reading culture through digital devices and online services utilizing Generative AI. Received: 20 February 2025 | Revised: 14 July 2025 | Accepted: 29 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Eunwoo Choi: Writing – original draft, Writing – review & editing, Investigation, Supervision. Gahyeon Kim: Visualization, Investigation. Gyeongmi Kim: Visualization, Investigation, Writing – review & editing. Dabin Choe: Visualization, Investigation, Writing – review & editing. Soonkyu Jang: Conceptualization, Methodology, Validation, Formal analysis, Project administration.
Development of a Hyperparameter-Optimized Decision Support System for Cardiovascular Disease Prediction
Cardiovascular disease (CVD) remains one of the leading health dilemmas all over the world, and it is among the leading causes of morbidity and mortality. To solve this problem, more than traditional diagnostic methods are needed; smart decision support systems (DSS) are required to help clinicians recognize the condition at an early stage and provide advice on treatment regimens. We develop an improved DSS with multiple machine learning (ML) techniques in cardiovascular risk prediction. Our framework is also focused on clinical applications, as compared to many other prior models that are seen as only theoretically viable. To determine its adequacy, eight ML and deep learning (DL) models were trained and optimized on a clinical set founded on feature selection and hyper-parameter tuning schemes. Among these, the XGBoost classifier exceeded by far the others in terms of accuracy, interpretability, and speed of computation and its operation, and as such would be the best candidate to deploy. Another characteristic of our system is that the Shapley Additive Explanations (SHAP) analysis is applied, which facilitates increasing the confidence of the results by clearly indicating how they may be compiled for clinicians. The benefits of the proposed DSS lie not only in supporting accurate diagnosis but also in translating to real-time reports and recommendations, which are both actionable and supportive of patient management. Additionally, its architecture is scalable and can fit in a variety of healthcare systems and help address the issue of early intervention to reduce the burden of CVD as a whole. Received: 5 February 2025 | Revised: 18 August 2025 | Accepted: 30 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the CardiovascularDataset repository at https://github.com/zubi00/CardioviscularDataset. Author Contribution Statement Uzma Nawaz: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mufti Anees-ur-Rahaman: Methodology, Software, Writing – review & editing. Hafiz Muhammad Ubaidullah: Software validation, Formal analysis, Writing – review & editing. Chaudhry Muhammad Ali Nawaz: Visualization, Investigation, Resources. Zubair Saeed: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Enhancing Trust and Transparency in Education Using Blockchain: A Hyperledger Fabric-Based Framework
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 Contribution Statement Arvind Panwar: Conceptualization, Software, Writing – original draft. Urvashi Sugandh: Methodology, Validation. Achin Jain: Formal analysis, Investigation, Writing – review & editing. Vanita Jain: Supervision. Arun Kumar Dubey: Resources, Data curation. Saheli Majumdar: Visualization. Saurav Mallik: Project administration, Funding acquisition.
IDS-IoT: Intrusion Detection System for the Internet of Things Using Enhanced Long-Short Term Memory
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.
Smart Farming: Crop Recommendation Using Machine Learning with Challenges and Future Ideas
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/doi/full/10.36227/techrxiv.23504496, reference number [41]. Author Contribution Statement Devendra Dahiphale: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Pratik Shinde: Validation, Investigation, Writing – review & editing. Koninika Patil: Writing – review & editing. Vijay Dahiphale: Validation, Writing – review & editing, Visualization, Project administration.
Integrating Pattern Recognition and CNN-Based Models for Improved Bean Disease Detection and Agricultural Yield Enhancement
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, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Joseph P. Telemala: Software, Validation, Formal analysis, Writing – review & editing.
Leveraging Artificial Immune Systems for Mental Health Research: Anomaly Detection in EEG Data
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.
Incorporating Fuzzy ARTMAP Network with Spiking Neural Networks
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.
Review of the Performance of CNN Models on Handwriting Recognition
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 no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Ekush (Bengali) at https://doi.org/10.1007/978-981-13-9187-3_14; in the Devanagari handwritten character Database at https://archive.ics.uci.edu/dataset/389; in Kuzushiji-49 at https://github.com/rois-codh/kmnist; in the Telugu handwritten character database at https://www.pseudoaj.com/2016/05/pseudoajdataset0-telugu-handwritten.html; in EMNIST at https://www.nist.gov/itl/products-and-services/emnist-dataset; in the Arabic handwritten character dataset (AHCD) at https://github.com/AmrHendy/Arabic-Handwritten-Images-Recognition; in the Tamil handwritten word database at https://doi.org/10.1109/ICDAR.2013.162; in the English, Hindi, and Bengali handwritten word dataset at http://dx.doi.org/10.1109/ICFHR.2012.238; and in the Arabic handwritten word dataset at http://www.ifnenit.com/. Author Contribution Statement Sabyasachi Mazumder: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sayan Neogy: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sahana Das: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision. Kaushik Roy: Conceptualization, Methodology, Formal analysis, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Umapada Pal: Conceptualization, Writing – review & editing, Supervision, Project administration.
Deepfake in Digital Business: Systematic Review of Strategies
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 no conflicts of interest in this work. Data Availability Statement Data sharing does not apply to this article as no new data were created or analyzed in this study. Author Contribution Statement Gabriel Silva-Atencio: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Predictive Artificial Intelligence Models in the Early Identification of Diabetic Retinopathy
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, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Maziar Lalezar: Writing - review & editing, Visualization, Supervision, Project administration.
Artificial Intelligence as a Distributed Actor: Rethinking Organizational Theory Through Sociotechnical Networks
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.
Enhancing DNS-over-HTTPS Traffic Classification in Heterogeneous Networks Through Latent Space Analysis with a Tabular-Variational Autoencoder and Self-Attention Classifier Model
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, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Poornima Athikatte Sampigerayappa: Validation, Writing – review and editing, Supervision, Project administration.
Training of the Dynamic Systems Control: A Neural Network or a Learning Algorithm
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.
CAD System Utilizing UNet and Hough Transform for Automated Measurement of Fetal Head Circumference and Age in 2D Ultrasound Images
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: Conceptualization, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Ahmed Abed Mohammed: Methodology, Validation, Formal analysis, Writing – review & editing. Zhang Bo: Software. Qiu Maidi: Software. Mustafa M. Abd Zaid: Validation, Project administration. Putra Sumari: Visualization, Supervision, Project administration.
Detection of Rice and Corn Plant Leaf Disease Using Invariants of Deep Learning Models and Edge Perspective
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.
Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture
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 this study are openly available in PlantVillage Dataset at https://www.kaggle.com/datasets/emmarex/plantdisease, reference number [33]. Author Contribution Statement Ritesh Janga: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Rushit Dave: Conceptualization, Validation, Writing – review & editing, Supervision.
The Legal Status of Artificial Intelligence: The Need to Form a Legal Personality and Regulate Copyright
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 – original draft, Writing – review & editing, Visualization, Funding acquisition.
A Framework for Adaptive Recommendation in Online Environments
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: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, and Project administration. Vítor Filipe: Conceptualization, Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, and Project administration.
An Effective ResNet Model for Respiratory Disease Detection: A Case Study on COVID-19 Chest X-ray Images
The COVID-19 pandemic has placed an extraordinary burden on healthcare systems worldwide and underscored the urgent need for faster, more reliable diagnostic methods for respiratory diseases. In this study, we explored how artificial intelligence, particularly deep convolutional neural networks (CNNs), can assist in diagnosing COVID-19 using chest X-ray images. Although new CNN architectures continue to emerge, residual neural network (ResNet) models (ResNet50, ResNet101, and ResNet152) remain popular because of their balance of accuracy and robustness. We compared their performance with other established models such as VGG (VGG11 and VGG16) and AlexNet to examine the trade-offs between architectural complexity and practical effectiveness. Among these, ResNet101 achieved the best results, with an accuracy of 93%, a precision of 91.27%, and an AUC–ROC of 97.41%, making it the leading model in our study. Despite promising outcomes, we also highlighted current limitations in deep learning for medical imaging, such as small sample sizes and limited clinical validation. Our findings aim to support the future development of reliable AI-assisted tools in medical diagnostics and highlight the need for more comprehensive validation and ethical considerations. Received: 22 November 2024 | Revised: 11 April 2025 | Accepted: 5 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/donjon00/covid19-detection?select=Data. Author Contribution Statement Bushra Hassan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Kaveh Kiani: Conceptualization, Methodology, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Taha Mansouri: Conceptualization, Methodology, Investigation, Resources, Supervision.
Preterm Birth Prediction Using an Explainable Machine Learning Approach
Pregnancy and childbirth are critical periods, with complications like preterm birth presenting serious risks to both mother and baby. Preterm birth is defined as delivery before 37 weeks of gestation. It poses serious health risks to newborns, including respiratory complications, developmental delays, and long-term disabilities. Early prediction of preterm birth can enable timely interventions to reduce these risks and improve maternal-fetal health outcomes. This paper proposes a machine learning-based framework for preterm birth prediction using a blended approach. This approach combines multiple classifiers into blended model to enhance the prediction accuracy. Feature selection techniques such as variance threshold, Pearson’s correlation, and mutual information are applied exclusively to the training set to enhance model performance. The proposed blended model consistently outperforms the standalone base models. It achieves the highest accuracy of 74.61%, precision of 71.83%, recall of 70.94%, F1 score of 71.38%, and an area under the curve (AUC) of 84.56% during 5-fold cross-validation. For holdout testing, it also maintains a superior performance with an accuracy of 73.82% and AUC of 83.91%. Additionally, explainable artificial intelligence techniques are applied to interpret the model’s predictions. It identifies that abnormal amniotic fluid, prenatal care, and household income have a substantial impact on predicting both preterm and normal birth outcomes. The proposed approach shows strong potential for real-world healthcare applications to offer clinicians valuable insights for early preterm birth intervention. Received: 10 October 2024 | Revised: 9 December 2024 | Accepted: 18 February 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 Midwifery at https://doi.org/10.1016/j.midw.2020.102670, reference number [40]. Author Contribution Statement Ahmad Hassan: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Saba Nawaz: Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Project administration. Sidra Tahira: Validation, Investigation, Data curation, Writing – review & editing, Visualization, Project administration. Arslan Ahmed: Investigation, Data curation, Writing – review & editing, Supervision, Project administration.
Design and Simulation Research of Supine Rehabilitation Robot
Many existing rehabilitation robots are designed with limited functional adaptability. To better assist patients with lower limb deficiencies in rehabilitation, this paper presents a new supine rehabilitation robot designed specifically for the motor rehabilitation needs of the human lower limb. To assess the feasibility of the proposed rehabilitation robot, a coupling model of the supine rehabilitation robot and human musculoskeletal system was constructed. The gait model and the human-robot coupling model were visualized and biomechanically analyzed using OpenSim. This simulation analysis compared changes in muscle activity during gait and coupled motion. The simulation results demonstrate that coupled exercise more effectively stimulates the hip and knee joint-related muscle groups. Additionally, the training targets a broader range of muscle types, with significant improvements observed in the lumbariliac muscle group, broad fascia tensor, adductor muscle group, and suture muscles, showing enhancement rates of 131%, 200%, 571%, and 310%, respectively. These findings confirm that the recumbent rehabilitation robot designed in this study is capable of facilitating effective rehabilitation for the human body and lower limbs. Received: 13 November 2024 | Revised: 5 March 2025 | Accepted: 11 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 Yifan Zhao: Conceptualization, Methodology, Software, Formal analysis. Quanyu Wu: Resources, Data curation, Writing – original draft, Funding acquisition. Jiaqi Fan: Writing – review & editing, Visualization. Shuyan Xiao: Software, Supervision. Yongxing Wang: Project administration. Xiaojie Liu: Validation, Investigation. Lingjiao Pan: Methodology, Formal analysis.
EN-IRAM: Enhance Iris Authentication Model with Soft-Vote Ensemble Learner and Gradual Feature Selector
Authentication systems have facilitated the effective safeguarding of sensitive data and information from unauthorized access. To establish a more robust security architecture for user authentication and verification across various systems, concerted efforts have been focused on integrating one or more biometric characteristics with encryption techniques or machine learning frameworks. Despite the excellent results achieved by these methodologies, a persistent dilemma persists in ascertaining the necessary quantity of features essential to furnish systems with sufficient security safeguards. This paper investigates the prospective benefits of utilizing the gradual feature space reduction algorithm and Vote Ensemble Learner in relation to iris features, along with their impacts on the effectiveness of developing reliable and efficient user verification systems. The assessment outcomes generated by the proposed enhanced iris authentication model across a diverse array of datasets demonstrate accuracy, precision, and F1-score ranges of 0.77 to 1.00. The developed model achieves an average score of 0.01%, 0%, and 0.03% for EER, FAR, and FRR respectively with an accuracy of 99.99%. Received: 19 July 2024 | Revised: 12 December 2024 | Accepted: 28 February 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 dataset, Iris of eye dataset at https://www.kaggle.com/datasets/mohmedmokhtar/iris-of-eye-dataset. Author Contribution Statement Folu Ajayi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Adewale Aromolaran: Methodology, Formal analysis, Investigation, Resources, Data curation, Supervision, Project administration. Alomaja Victor: Validation, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Al-Amin Usman: Software, Formal analysis, Resources, Visualization, Supervision, Project administration. Femi Johnson: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
The Impact of AI on Creativity: Enhancing Human Potential or Challenging Creative Expression
The purpose of this study is to investigate how artificial intelligence (AI) can revolutionize creativity, aiming to determine whether AI enhances human creative potential or challenges established modes of creative expression. This study uses qualitative, quantitative, and theoretical methods, including mathematical frameworks and practical trials with computer simulations. By exploring the theoretical foundations of AI–creativity interactions, it analyzes the advantages and limitations of AI in creative domains such as art, music, and science. Computational creativity is examined using experiments that define metrics for evaluating AI systems, integrating emotional intelligence, generative theory, and creativity theory. This study leverages large language models such as BingAI, HuggingChat, BERT (Gemini), and GPT to assess creative tasks such as narrative collaboration, problem-solving, and writing. A mathematical approach is introduced to evaluate AI’s creative intelligence, revealing its potential to rival human creativity in cost-efficient applications. The findings clarify the intricate relationship between AI and creativity, emphasizing collaborative creation and highlighting AI’s dual role as both a catalyst and a disruptor. This study underscores the importance of individualism in human creativity and provides insights into evolving AI-driven creative processes. By systematizing literature reviews and experimental validations, this study advances understanding of AI’s impacts and encourages further research into human–AI synergy. Received: 26 October 2024 | Revised: 13 June 2025 | Accepted: 4 July 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 Zenodo at https://doi.org/10.5281/zenodo.15786656. Author Contribution Statement Luka Baklaga: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.