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 >

Khulud Salem Alshudukhi¹, Mamoona Humayun^2,*, Ghadah Naif Alwakid¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1973-1986, 2025, DOI:10.32604/cmc.2025.063077 - 16 April 2025

Abstract The Internet of Things (IoT) and edge computing have substantially contributed to the development and growth of smart cities. It handled time-constrained services and mobile devices to capture the observing environment for surveillance applications. These systems are composed of wireless cameras, digital devices, and tiny sensors to facilitate the operations of crucial healthcare services. Recently, many interactive applications have been proposed, including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT services. Nonetheless, most solutions lack optimizing relaying methods and impose excessive overheads for maintaining devices’ connectivity. Alternatively, data More >

Mourad Benmalek^1,*,#,*, Abdessamed Seddiki^2,#, Kamel-Dine Haouam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1157-1184, 2025, DOI:10.32604/cmes.2025.062841 - 11 April 2025

Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

Hyunwoo Park^1,2, Jaedong Lee^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 827-847, 2025, DOI:10.32604/cmes.2025.061178 - 11 April 2025

Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >

Saleh Albahli^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1095-1128, 2025, DOI:10.32604/cmes.2025.058821 - 11 April 2025

Abstract Predicting hospital readmission and length of stay (LOS) for diabetic patients is critical for improving healthcare quality, optimizing resource utilization, and reducing costs. This study leverages machine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade. A comprehensive preprocessing pipeline, including feature selection, data transformation, and class balancing, was implemented to ensure data quality and enhance model performance. Exploratory analysis revealed key patterns, such as the influence of age and the number of diagnoses on readmission rates, guiding the More >

Josua Käser¹, Thomas Nagy¹, Patrick Stirnemann¹, Thomas Hanne^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 201-217, 2025, DOI:10.32604/cmc.2025.061527 - 26 March 2025

Abstract We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference… More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622 - 28 February 2025

Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

Laith Abualigah^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025

Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >