Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Raquel Hidalgo-Sanz, María-Ángeles Del-Castillo-Alonso, Laura Monforte, Rafael Tomás-Las-Heras, Encarnación Núñez-Olivera, Javier Martínez-Abaigar^*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3453-3470, 2025, DOI:10.32604/phyton.2025.070087 - 01 December 2025

Abstract Grapevine (Vitis vinifera L.) is one of the main crops worldwide, and ultraviolet-B (UV-B, 280–315 nm) radiation is emerging as a promising technical tool to enhance secondary metabolites that can contribute to the quality and health-promoting properties of both grapes and the resulting wines. However, few studies have assessed the effectiveness of UV-B supplements under field conditions. Here, we compared the effects of two different field UV-B treatments (a single supplement applied at pre-harvest, and a double supplement applied at both veraison and pre-harvest) on the phenolic composition of Tempranillo grape skins and the resulting wines.… More >

Adeel Akram¹, Tallha Akram², Ghada Atteia^3,*, Ayman Qahmash⁴, Sultan Alanazi⁵, Faisal Mohammad Alotaibi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697 - 26 November 2025

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

Mohamed Hammad^1,2,*, Mohammed ElAffendi¹, Souham Meshoul^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1055-1086, 2025, DOI:10.32604/cmes.2025.072650 - 30 October 2025

Abstract Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy. Traditional diagnostic methods often rely on subjective visual assessments, which can lead to misdiagnosis. This study addresses these challenges by developing HybridFusionNet, a novel model that integrates Convolutional Neural Networks (CNN) with 1D feature extraction techniques to enhance diagnostic accuracy. Utilizing two extensive datasets, BCN20000 and… More >

Heba A. S. El-Nashar^1,*, Ahmed T. Negmeldin^2,3,*, Aziza El Baz⁴, Marizé Cuyler⁵, Brandon Alston⁵, Namrita Lall^5,6,7, Naglaa S. Ashmawy^1,8,*

Phyton-International Journal of Experimental Botany, Vol.94, No.10, pp. 3269-3281, 2025, DOI:10.32604/phyton.2025.067998 - 29 October 2025

Abstract Pimenta dioica is a tropical Caribbean tree belonging to the family Myrtaceae, widely used in various human activities, including perfume production, food flavoring, natural pesticides, and medicine. This study aimed to explore the chemical composition of Pimenta dioica flower essential oil obtained via hydrodistillation using GC-MS analysis. Additionally, the oil’s tyrosinase inhibitory activity was investigated. The effectiveness of the oil’s major constituents in binding to tyrosinase was also evaluated through molecular docking simulations. GC-MS analysis identified fifteen compounds, with eugenol (70.59%) as the major component, followed by β-myrcene (10.54%), limonene (8.55%), β-ocimene (4.92%), α-phellandrene (1.39%), and linalool… More >

Areeba Masood Siddiqui^1,2,*, Hyder Abbas^3,4, Muhammad Asim^5,6,*, Abdelhamied A. Ateya⁵, Hanaa A. Abdallah⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3135-3168, 2025, DOI:10.32604/cmes.2025.069926 - 30 September 2025

Abstract Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors. Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance. To address these challenges, we propose a novel Squid Game Optimization-Dimension Reduction-based Ensemble (SGO-DRE) method for the precise diagnosis of skin diseases. Our approach begins by selecting pre-trained models named MobileNetV1, DenseNet201, and Xception for robust feature extraction. These models are enhanced with dimension reduction blocks to improve efficiency. To tackle the aggregation problem of various models, we leverage the Squid Game Optimization… More >

Yiming Zhang¹, Wei Zhang^1,2,*, Cong Shen³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3185-3211, 2025, DOI:10.32604/cmc.2025.067563 - 23 September 2025

Abstract Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement… More >

Alex Varghese¹, Achin Jain², Mohammed Inamur Rahman³, Mudassir Khan^4,*, Arun Kumar Dubey², Iqrar Ahmad⁵, Yash Prakash Narayan¹, Arvind Panwar⁶, Anurag Choubey⁷, Saurav Mallik^8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1767-1791, 2025, DOI:10.32604/cmes.2025.067999 - 31 August 2025

Abstract Skin cancer remains a significant global health challenge, and early detection is crucial to improving patient outcomes. This study presents a novel deep learning framework that combines Convolutional Neural Networks (CNNs), Transformers, and Gated Recurrent Units (GRUs) for robust skin cancer classification. To address data set imbalance, we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques. The hybrid architecture effectively captures both local and global dependencies in dermoscopic images, while the GRU component models sequential patterns. Evaluated on the HAM10000 dataset, the proposed model achieves an accuracy of 90.61%, outperforming baseline architectures such as VGG16 More >

Seo Yeon Shin¹, Nu Ri Song¹, Sa Rang Choi¹, Ki Min Kim¹, Jae Hee Byun¹, Su Jung Kim², Dai Hyun Jung², Seong Sim Kim², Seong Ju Park², So Jeong Chu², Kyung Mok Park^1,*

BIOCELL, Vol.49, No.8, pp. 1435-1448, 2025, DOI:10.32604/biocell.2025.065199 - 29 August 2025

Abstract Background: The efficient transdermal delivery of biologically active molecules remains a major challenge because of the structural barrier of the stratum corneum, which limits the penetration of large or hydrophilic molecules. Low-molecular-weight protamine (LMWP) has a structure similar to that of the HIV TAT protein-derived peptide and is a representative cell-penetrating peptide (CPP) used to increase cell permeability. However, protamine has been reported to have many toxicities and side effects. Objectives: We developed human-derived low-molecular-weight protamine (hLMWP), which is based on fish-derived LMWP but designed using human protein sequences to improve safety and functionality. As… More > Graphic Abstract

Kun Lan¹, Feiyang Gao¹, Xiaoliang Jiang^1,*, Jianzhen Cheng^2,*, Simon Fong³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4805-4824, 2025, DOI:10.32604/cmc.2025.065864 - 30 July 2025

Abstract With the continuous development of artificial intelligence and machine learning techniques, there have been effective methods supporting the work of dermatologist in the field of skin cancer detection. However, object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations, such as bubbles and scales. To address these challenges, we propose a dual U-Net network framework for skin melanoma segmentation. In our proposed architecture, we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net. First, we establish… More >