Jitendra Kumar Samriya¹, Virendra Singh², Gourav Bathla³, Meena Malik⁴, Varsha Arya^5,6, Wadee Alhalabi⁷, Brij B. Gupta^8,9,10,11,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >

Liang Mu¹, Yurui Kang¹, Zixu Yan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2495-2512, 2025, DOI:10.32604/cmc.2025.063208 - 03 July 2025

Abstract The accurate prediction and analysis of emergencies in Urban Rail Transit Systems (URTS) are essential for the development of effective early warning and prevention mechanisms. This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data. To address issues related to data structuredness and missing values, we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network (ER-GAIN) for data structuring and imputation. Additionally, to mitigate the impact of imbalanced data on the predictive performance of emergencies, we introduced an Adaptive Boosting Ensemble… More >

Chaoliang Wang^1,*, Qi Fu², Zhaohui Li¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3707-3729, 2025, DOI:10.32604/cmc.2025.062795 - 03 July 2025

Abstract Cluster-based models have numerous application scenarios in vehicular ad-hoc networks (VANETs) and can greatly help improve the communication performance of VANETs. However, the frequent movement of vehicles can often lead to changes in the network topology, thereby reducing cluster stability in urban scenarios. To address this issue, we propose a clustering model based on the density peak clustering (DPC) method and sparrow search algorithm (SSA), named SDPC. First, the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads (CHs). More >

Gozde Karatas Baydogmus^*, Onder Demir

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3393-3406, 2025, DOI:10.32604/cmc.2025.062643 - 03 July 2025

Abstract Detection of Autism Spectrum Disorder (ASD) is a crucial area of research, representing a foundational aspect of psychological studies. The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years. Interdisciplinary efforts have further propelled research into detection methods. Consequently, this study aims to contribute to both the fields of psychology and computer science. Specifically, the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder. This study is structured into two distinct phases: data preprocessing and… More >

Absar Hussain¹, Abdul Aziz¹, Hassan Jamil Syed^2,*, Shoaib Raza¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3105-3124, 2025, DOI:10.32604/cmc.2025.062628 - 03 July 2025

Abstract Kubernetes has become the dominant container orchestration platform, with widespread adoption across industries. However, its default pod-to-pod communication mechanism introduces security vulnerabilities, particularly IP spoofing attacks. Attackers can exploit this weakness to impersonate legitimate pods, enabling unauthorized access, lateral movement, and large-scale Distributed Denial of Service (DDoS) attacks. Existing security mechanisms such as network policies and intrusion detection systems introduce latency and performance overhead, making them less effective in dynamic Kubernetes environments. This research presents PodCA, an eBPF-based security framework designed to detect and prevent IP spoofing in real time while minimizing performance impact. PodCA… More >

Jafar Aminu^1,2,*, Rohaya Latip^1,*, Zurina Mohd Hanafi¹, Shafinah Kamarudin¹, Danlami Gabi²

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2989-3011, 2025, DOI:10.32604/cmc.2025.062451 - 03 July 2025

Abstract As mobile edge computing continues to develop, the demand for resource-intensive applications is steadily increasing, placing a significant strain on edge nodes. These nodes are normally subject to various constraints, for instance, limited processing capability, a few energy sources, and erratic availability being some of the common ones. Correspondingly, these problems require an effective task allocation algorithm to optimize the resources through continued high system performance and dependability in dynamic environments. This paper proposes an improved Particle Swarm Optimization technique, known as IPSO, for multi-objective optimization in edge computing to overcome these issues. To this… More >

Pooja Bidwai^1,2, Shilpa Gite^1,3, Biswajeet Pradhan^4,*, Abdullah Almari⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

Shivani Sood¹, Harjeet Singh^2,*, Surbhi Bhatia Khan^3,4,5,*, Ahlam Almusharraf⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025

Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >

Sunil K. Singh¹, Sudhakar Kumar^1,*, Manraj Singh¹, Savita Gupta², Razaz Waheeb Attar³, Varsha Arya^4,5, Ahmed Alhomoud⁶, Brij B. Gupta^7,8,9

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3927-3941, 2025, DOI:10.32604/cmc.2025.059643 - 03 July 2025

Abstract As quantum computing continues to advance, traditional cryptographic methods are increasingly challenged, particularly when it comes to securing critical systems like Supervisory Control and Data Acquisition (SCADA) systems. These systems are essential for monitoring and controlling industrial operations, making their security paramount. A key threat arises from Shor’s algorithm, a powerful quantum computing tool that can compromise current hash functions, leading to significant concerns about data integrity and confidentiality. To tackle these issues, this article introduces a novel Quantum-Resistant Hash Algorithm (QRHA) known as the Modular Hash Learning Algorithm (MHLA). This algorithm is meticulously crafted… More >

Cesar E. Montelongo Hernandez^1,*, Carol S. North¹, E. Whitney Pollio², David E. Pollio³

International Journal of Mental Health Promotion, Vol.27, No.6, pp. 737-752, 2025, DOI:10.32604/ijmhp.2025.065317 - 30 June 2025

Abstract Background: Disaster mental health outcomes of individuals may be affected by the families they inhabit, with effects rippling through the entire family system. Existing research on the experience of children in disasters has typically been limited to examining single individuals or, at most, family dyads. Research is needed to explore interactions within families as a whole, including interactions among multiple family members, as well as with community entities in a broad systems approach with dynamic analysis of family systems over time. The purpose of this study was to combine quantitative and qualitative data using structured… More >