Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Qingtao Meng, Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069970 - 10 November 2025

Abstract This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >

Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Mintao Xu^1,#, Zichao Jin^1,#, Yangyang Tian¹, Jingcheng Zhang^1,*, Huiqin Ma¹, Yujin Jing¹, Jiangxing Wu², Jing Zhai²

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4059-4078, 2025, DOI:10.32604/phyton.2025.074422 - 29 December 2025

Abstract The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security. Data-driven forecasting models have emerged as an effective approach to support early warning and management, yet the lack of user-friendly tools for model development remains a major bottleneck. This study presents the Multi-Scenario Crop Disease Forecasting Modeling System (MSDFS), an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training, evaluation, and deployment-across four representative scenarios: static point-based, static grid-based, dynamic point-based, and dynamic grid-based. Unlike conventional frameworks, MSDFS emphasizes modeling flexibility, allowing… More >

Seerat Singla¹, Gunjan Shandilya¹, Ayman Altameem², Ruby Pant³, Ajay Kumar⁴, Ateeq Ur Rehman^5,*, Ahmad Almogren^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4021-4057, 2025, DOI:10.32604/phyton.2025.073354 - 29 December 2025

Abstract Turmeric Leaf diseases pose a major threat to turmeric cultivation, causing significant yield loss and economic impact. Early and accurate identification of these diseases is essential for effective crop management and timely intervention. This study proposes DenseSwinGNNNet, a hybrid deep learning framework that integrates DenseNet-121, the Swin Transformer, and a Graph Neural Network (GNN) to enhance the classification of turmeric leaf conditions. DenseNet121 extracts discriminative low-level features, the Swin Transformer captures long-range contextual relationships through hierarchical self-attention, and the GNN models inter-feature dependencies to refine the final representation. A total of 4361 images from the… More >

Di Wu¹, Chunguang Ren¹, Liangliang Li¹, Chongpei Zheng², Wenwen Su^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4079-4090, 2025, DOI:10.32604/phyton.2025.072856 - 29 December 2025

Abstract To identify the pathogen responsible for fruit rot disease in Rosa roxburghii Tratt. from Guiding County, Guizhou Province, China, diseased fruit samples were collected. The pathogen was isolated, purified, and identified through morphological, molecular, and pathogenic analyses. Subsequently, its biological characteristics were evaluated. Furthermore, to determine the agent with the strongest toxicity against the identified pathogen, the antifungal activity of six chemical and biological agents was evaluated through indoor toxicity assays. Finally, Neopestalotiopsis clavispora was identified as the pathogen responsible for fruit rot disease in R. roxburghii Tratt. The diameter of the pathogen grown under different carbon and… More >

Vinay Durbhakula^1,*, Ziv Savin¹, Einat Savin-Shalom², Stephanie L. Gold², Kavita Gupta¹, Eve Frangopoulos¹, Blair Gallante¹, William M. Atallah¹, Mantu Gupta¹

Canadian Journal of Urology, Vol.32, No.6, pp. 659-668, 2025, DOI:10.32604/cju.2025.067614 - 30 December 2025

Abstract Background: A well-established correlation exists between Inflammatory Bowel Disease (IBD) and urolithiasis. However, the influence of surgical history on the severity of urolithiasis in IBD patients remains underexplored. This study aims to investigate the association between gastrointestinal (GI) bowel resection and urolithiasis severity in patients with IBD. Methods: This retrospective cohort study analyzed 42 patients diagnosed with both IBD and urolithiasis between 2016 and 2024. Patients were categorized based on their history of bowel resection. Primary outcomes included maximal stone burden, need for urolithiasis surgery, and stone recurrence. Secondary outcomes were stone-related clinical events, multiple… More >

Joon Yau Leong¹, Tyler Gaines², Zachary J. Prebay¹, David Ebbott¹, Paul H. Chung^1,*

Canadian Journal of Urology, Vol.32, No.6, pp. 589-595, 2025, DOI:10.32604/cju.2025.064125 - 30 December 2025

Abstract Introduction: Despite the diagnostic value of intracavernosal injections (ICI) and penile Doppler ultrasound (PDUS), there remain barriers to widespread clinical adaptation of these methods. The study aimed to evaluate the practice patterns of utilization of ICI and PDUS in the assessment of erectile dysfunction (ED) and Peyronie’s disease (PD). Methods: Using the TriNetX database (Cambridge, MA, USA), adult (≥18 years) male patients with a diagnosis of ED on oral phosphodiesterase-5 (PDE5) inhibitors were identified. Current Procedural Terminology codes were utilized to identify patients who underwent further evaluation with ICI or PDUS, as well as penile… More >