Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4995-5013, 2025, DOI:10.32604/cmc.2025.068429 - 23 October 2025

Abstract Ensuring the secure transmission of secret messages, particularly through video—one of the most widely used media formats—is a critical challenge in the field of information security. Relying on a single-layered security approach is often insufficient for safeguarding sensitive data. This study proposes a triple-lightweight cryptographic and steganographic model that integrates the Hill Cipher Technique (HCT), Rotation Left Digits (RLD), and Discrete Wavelet Transform (DWT) to embed secret messages within video frames securely. The approach begins with encrypting the secret text using a private key matrix (PK₁) of size 2 × 2 up to 6 × 6… More >

Marwa Ahmim¹, Nour Ouafi¹, Insaf Ullah^2,*, Ahmed Ahmim³, Djalel Chefrour³, Reham Almukhlifi⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5093-5116, 2025, DOI:10.32604/cmc.2025.067641 - 23 October 2025

Abstract The Internet of Healthcare Things (IoHT) marks a significant breakthrough in modern medicine by enabling a new era of healthcare services. IoHT supports real-time, continuous, and personalized monitoring of patients’ health conditions. However, the security of sensitive data exchanged within IoHT remains a major concern, as the widespread connectivity and wireless nature of these systems expose them to various vulnerabilities. Potential threats include unauthorized access, device compromise, data breaches, and data alteration, all of which may compromise the confidentiality and integrity of patient information. In this paper, we provide an in-depth security analysis of LAP-IoHT,… More >

Qiang Peng^1,2, Jia-Yu Yang¹, Xu-Yu Xiang^1,*

Journal on Artificial Intelligence, Vol.7, pp. 437-450, 2025, DOI:10.32604/jai.2025.072332 - 20 October 2025

Abstract Camellia oleifera is one of the four largest oil tree species in the world, and also an important economic crop in China, which has overwhelming economic benefits. However, Camellia oleifera is invaded by various diseases during its growth process, which leads to yield reduction and profit damage. To address this problem and ensure the healthy growth of Camellia oleifera, the purpose of this study is to apply the lightweight network to the identification and detection of camellia oleifolia leaf disease. The attention mechanism was combined for highlighting the local features and improve the attention of the model to the More >

Zhe Liu^1,2,*, Jiahao Shi³, Dania Santina⁴, Yulong Huang¹, Nabil Mlaiki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3531-3555, 2025, DOI:10.32604/cmes.2025.071145 - 30 September 2025

Abstract The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions More >

Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025

Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

Florentin Smarandache¹, Saleh I. Alzahrani², Sulaiman Al Amro³, Ijaz Ahmad⁴, Mubashir Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3715-3735, 2025, DOI:10.32604/cmes.2025.068736 - 30 September 2025

Abstract Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight… More >

Thai-Viet Dang^1,*, Dinh-Manh-Cuong Tran¹, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2651-2680, 2025, DOI:10.32604/cmc.2025.067500 - 23 September 2025

Abstract Precise and robust three-dimensional object detection (3DOD) presents a promising opportunity in the field of mobile robot (MR) navigation. Monocular 3DOD techniques typically involve extending existing two-dimensional object detection (2DOD) frameworks to predict the three-dimensional bounding box (3DBB) of objects captured in 2D RGB images. However, these methods often require multiple images, making them less feasible for various real-time scenarios. To address these challenges, the emergence of agile convolutional neural networks (CNNs) capable of inferring depth from a single image opens a new avenue for investigation. The paper proposes a novel ELDE-Net network designed to… More >

Tong Wu^*, Dawei Zhou

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3067-3094, 2025, DOI:10.32604/cmc.2025.066525 - 23 September 2025

Abstract Fault attacks have emerged as an increasingly effective approach for integrated circuit security attacks due to their short execution time and minimal data requirement. However, the lack of a unified leakage model remains a critical challenge, as existing methods often rely on algorithm-specific details or prior knowledge of plaintexts and intermediate values. This paper proposes the Fault Probability Model based on Hamming Weight (FPHW) to address this. This novel statistical framework quantifies fault attacks by solely analyzing the statistical response of the target device, eliminating the need for attack algorithm details or implementation specifics. Building… More >

Musulmon Lolaev¹, Anand Paul^2,*, Jeonghong Kim¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4155-4171, 2025, DOI:10.32604/cmc.2025.064758 - 23 September 2025

Abstract Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights… More >

Hui Yi^1,2,#, Hongjian Shi^2,3,#, Fuquan Kan^2,4,#, Fan Han², Lei Wan⁵, Xiaoyang Hong¹, Zhe Zhao¹, Junjie Shao², Gang Wang¹, Hui Wang¹, Hua Yan⁵, Xiujuan Shi¹, Ran Zhang^2,6,*, Gengxu Zhou^1,*

Congenital Heart Disease, Vol.20, No.4, pp. 519-530, 2025, DOI:10.32604/chd.2025.067164 - 18 September 2025

Abstract Background: To investigate the impact of preoperative extracorporeal membrane oxygenation (ECMO) on clinical outcomes in pediatric heart transplantation (PHT). Methods: This retrospective cohort analysis was conducted on 19 pediatric heart transplant recipients, divided into two groups: ECMO and non-ECMO, based on whether preoperative ECMO was utilized. We evaluated the patients’ surgical conditions, postoperative complications, and survival rates. Additionally, the analysis focused on the differences and correlations in clinical characteristics, inflammatory markers, and long-term survival outcomes. Results: There was no statistically significant difference in perioperative survival rates between the ECMO group (85.7%) and the non-ECMO group (83.3%). However,… More >