Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

Jiyang Xu¹, Qi Wang^1,*, Xin Xiong², Weidong Min^1,3, Jiang Luo⁴, Di Gai¹, Qing Han^1,3

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025

Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >

Mohammed Elgarhy¹, Amal S. Hassan², Najwan Alsadat³, Oluwafemi Samson Balogun⁴, Ahmed W. Shawki⁵, Ibrahim E. Ragab^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2547-2583, 2025, DOI:10.32604/cmes.2025.058362 - 03 March 2025

Abstract Accurately modeling heavy-tailed data is critical across applied sciences, particularly in finance, medicine, and actuarial analysis. This work presents the heavy-tailed power XLindley distribution (HTPXLD), a unique heavy-tailed distribution. Adding one more parameter to the power XLindley distribution improves this new distribution, especially when modeling leptokurtic lifetime data. The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness. Its statistical features, like the quantile function, moments, extropy measures, incomplete moments, stochastic ordering, and stress-strength parameters, are explored. We further investigate its use in actuarial science through the computation of pertinent metrics,… More >

Abdelilah Makaoui¹, El Bachir Lahmer^1,*, Jaouad Benhamou^1,2, Mohammed Amine Moussaoui¹, Ahmed Mezrhab¹

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 207-230, 2025, DOI:10.32604/fhmt.2024.060166 - 26 February 2025

Abstract This study focuses on numerically investigating thermal behavior within a differentially heated cavity filled with nanofluid with and without obstacles. Numerical comparison with previous studies proves the consistency and efficacy of the lattice Boltzmann method associated with a single relaxation time and its possibility of studying the nanofluid and heat transfer with high accuracy. Key parameters, including nanoparticle type and concentration, Rayleigh number, fluid basis, and obstacle position and dimension, were examined to identify optimal conditions for enhancing heat transfer quality. Principal findings indicated that increasing the Rayleigh number boosts buoyancy forces and alters vortex More > Graphic Abstract

Thai-Viet Dang^1,*, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025

Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

Amjad Khan¹, Rahim Khan^1,*, Fahad Alturise^2,*, Tamim Alkhalifah³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3161-3178, 2025, DOI:10.32604/cmc.2025.060372 - 17 February 2025

Abstract The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the… More >

Ruoxin Li^1,*, Shaoxiong Wu¹, Fengping Deng¹, Zhongli Tian¹, Hua Cai¹, Xiang Li¹, Xu Xu¹, Qi Liu^2,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

Wenkai Zhang^1,2, Hao Zhang^1,2, Xianming Liu¹, Xiaoyu Guo^1,2, Xinzhe Wang¹, Shuiwang Li^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025

Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

Lihua Xie¹, Ni Tang¹, Qing Chen^1,*, Jun Li^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3381-3397, 2025, DOI:10.32604/cmc.2024.058950 - 17 February 2025

Abstract In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands; (2) overlooking the syntactic dependencies between Chinese… More >

Gang Xu^1,2,6, Xinyu Fan¹, Xiu-Bo Chen², Xin Liu⁴, Zongpeng Li⁵, Yanhui Mao^6,7, Kejia Zhang^3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2293-2309, 2025, DOI:10.32604/cmc.2024.057882 - 17 February 2025

Abstract Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure,… More >