Muhammad Izhar^1,*, Khadija Parwez², Saman Iftikhar³, Adeel Ahmad⁴, Shaikhan Bawazeer³, Saima Abdullah⁴

Journal on Artificial Intelligence, Vol.7, pp. 55-83, 2025, DOI:10.32604/jai.2025.062479 - 25 April 2025

Abstract The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a… More >

Qingpeng Wei^1,#, Wenai Song^1,#, Lizhen Fu¹, Yi Lei², Qing Wang^2,*

Journal on Artificial Intelligence, Vol.7, pp. 39-53, 2025, DOI:10.32604/jai.2025.063188 - 23 April 2025

Abstract Pneumoconiosis, a prevalent occupational lung disease characterized by fibrosis and impaired lung function, necessitates early and accurate diagnosis to prevent further progression and ensure timely clinical intervention. This study investigates the potential application of the Vision Transformer (ViT) deep learning model for automated pneumoconiosis classification using digital radiography (DR) images. We utilized digital X-ray images from 934 suspected pneumoconiosis patients. A U-Net model was applied for lung segmentation, followed by Canny edge detection to divide the lungs into six anatomical regions. The segmented images were augmented and used to train the ViT model. Model component… More >

Zheng Zhang, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.7, pp. 17-37, 2025, DOI:10.32604/jai.2025.059607 - 19 March 2025

Abstract This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. Addressing challenges such as the complexity of medical terminology, the difficulty of constructing medical knowledge graphs, and the scarcity of medical data, the method retrieves structured knowledge from clinical cases via external knowledge graphs. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model’s understanding and reasoning capabilities for the task. We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results More >

Guodong Yuan^1,*, Jin Xie²

Journal on Artificial Intelligence, Vol.7, pp. 1-16, 2025, DOI:10.32604/jai.2025.061437 - 14 March 2025

Abstract This paper addresses the problem of access efficiency in multi-robot systems to the monitoring area. A distributed algorithm for multi-robot continuous monitoring, based on the uncertainty of target points, is used to minimize the uncertainty and instantaneous idle time of all target points in the task domain, while maintaining a certain access frequency to the entire task domain at regular time intervals. During monitoring, the robot uses shared information to evaluate the cumulative uncertainty and idle time of the target points, and combines the update list collected from adjacent target points with a utility function More >

Afam Okonkwo^*, Pius Onobhayedo, Charles Igah

Journal on Artificial Intelligence, Vol.6, pp. 379-401, 2024, DOI:10.32604/jai.2024.058649 - 31 December 2024

Abstract Artificial Intelligence, in general, and particularly Natural language Processing (NLP) has made unprecedented progress recently in many areas of life, automating and enabling a lot of activities such as speech recognition, language translations, search engines, and text-generations, among others. Software engineering and Software Development Life Cycle (SDLC) is also not left out. Indeed, one of the most critical starting points of SDLC is the requirement engineering stage which, traditionally, has been dominated by business analysts. Unfortunately, these analysts have always done the job not just in a monotonous way, but also in an error-prone, tedious,… More >

Peng Xia¹, Wu Zeng^2,*, Yin Ni¹, Ye Jin³

Journal on Artificial Intelligence, Vol.6, pp. 361-377, 2024, DOI:10.32604/jai.2024.059114 - 13 December 2024

Abstract Stress is defined as a subjective reflection of an internal psychological state of tension or arousal, manifesting as an interpretive, emotional, and defensive coping process within the body. Prolonged and sustained stress can significantly increase the risk of psychological and physiological disorders. Heart rate variability (HRV) is a key biomarker for assessing autonomic cardiac function, typically increasing during relaxation and decreasing under stress. Although measuring stress through physiological parameters like HRV is a common approach, achieving ultra-high accuracy based on HRV measurements remains a challenging task. In this study, the role of HRV features as… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Arkan Kh Shakr Sabonchi^*

Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

Haojie Yang, Xiang Wen, Peng Geng^*

Journal on Artificial Intelligence, Vol.6, pp. 283-300, 2024, DOI:10.32604/jai.2024.056303 - 18 October 2024

Abstract To enhance the rationality of the layout of electric vehicle charging stations, meet the actual needs of users, and optimise the service range and coverage efficiency of charging stations, this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms. By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius, the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are… More >

Abdulrahman M. Abdulghani^*

Journal on Artificial Intelligence, Vol.6, pp. 241-259, 2024, DOI:10.32604/jai.2024.056259 - 16 October 2024

Abstract Cloud computing has rapidly evolved into a critical technology, seamlessly integrating into various aspects of daily life. As user demand for cloud services continues to surge, the need for efficient virtualization and resource management becomes paramount. At the core of this efficiency lies task scheduling, a complex process that determines how tasks are allocated and executed across cloud resources. While extensive research has been conducted in the area of task scheduling, optimizing multiple objectives simultaneously remains a significant challenge due to the NP (Non-deterministic Polynomial) Complete nature of the problem. This study aims to address… More >