Nicholas Sellke^1,2,*, Erin Jesse^1,2, Justin M. Dubin³, Tomislav D. Medved^1,2, Neha S. Basti⁴, Janvi Ramchandra², Robert E. Brannigan⁴, Joshua A. Halpern⁴, Nannan Thirumavalavan^1,2

Canadian Journal of Urology, Vol.32, No.2, pp. 89-94, 2025, DOI:10.32604/cju.2025.065004 - 30 April 2025

Abstract Introduction: Direct-to-consumer (DTC) telemedicine has emerged as an option for transgender patients seeking gender affirming hormone therapy (GAHT). We aimed to characterize the healthcare services provided by DTC telemedicine companies offering GAHT and to compare their costs to a tertiary care center. Methods: We identified DTC telemedicine platforms offering GAHT via internet searches and extracted information from their websites related to evaluation, treatment, monitoring, and cost. Cost of the DTC GAHT was compared to cost for comparable services at a tertiary care center. Results: Six DTC companies were identified. All platforms utilized an informed consent model… More >

Peter T. Scardino

Canadian Journal of Urology, Vol.32, No.2, pp. 73-80, 2025, DOI:10.32604/cju.2025.064714 - 30 April 2025

Abstract This article has no abstract. More >

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 >

Huaruo Chen^1,2, Wanru Song¹, Jian Xie¹, Huadi Wang³, Feifei Zheng³, Ya Wen^4,*

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 379-400, 2025, DOI:10.32604/ijmhp.2025.060370 - 31 March 2025

Abstract Objectives: Teachers are facing unprecedented new challenges leading them to face an increasing number of tasks that are not part of their job, as well as having to cope with the additional skills acquisition that comes with non-traditional forms of teaching and learning, and increased work pressure leading to an increase in the rate of teachers leaving the profession. Therefore, this study aims to explore the mechanism of the career calling on job burnout through career adaptability and work engagement. Methods: This study conducted a cross-sectional survey of 465 primary and secondary school teachers (PSST)… More >

Smita Kapse¹, Ganesh Yenurkar^1,*, Vincent Omollo Nyangaresi^2,3,*, Gunjan Balpande¹, Shravani Kale¹, Manthan Jadhav¹, Sahil Lawankar¹, Vikrant Jaunjale¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 935-976, 2025, DOI:10.32604/cmc.2025.061981 - 26 March 2025

Abstract In today’s fast-paced world, many elderly individuals struggle to adhere to their medication schedules, especially those with memory-related conditions like Alzheimer’s disease, leading to serious health risks, hospitalizations, and increased healthcare costs. Traditional reminder systems often fail due to a lack of personalization and real-time intervention. To address this critical challenge, we introduce MediServe, an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized, secure, and adaptive solution. MediServe features a smart medication box equipped with biometric authentication, such as fingerprint recognition, ensuring authorized access to prescribed medication while… More >