Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Sami Alrabie^#,*, Ahmed Barnawi^#

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 2025

Abstract In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

Carmen Rubio^1,#, Norma Serrano-GarcíA^1,#, Elisa Taddei¹, Eduardo CastañEda², HéCtor Romo^1,3, MoiséS Rubio-Osornio^4,*

BIOCELL, Vol.49, No.8, pp. 1391-1412, 2025, DOI:10.32604/biocell.2025.066320 - 29 August 2025

Abstract Rotenone is a lipophilic herbicide extensively utilized in experimental neurodegenerative models because of its capacity to disrupt complex I of the mitochondrial electron transport chain. This inhibition results in reduced ATP synthesis, elevated reactive oxygen species (ROS) formation, and mitochondrial malfunction, which instigates oxidative stress and cellular damage, critical elements in neurodegenerative disorders like Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Alzheimer’s disease (AD). In addition to causing direct neuronal injury, rotenone significantly contributes to the activation of glial cells, specifically microglia and astrocytes. Activated microglia assumes a proinflammatory (M1) phenotype, distinguished by the… More >

Mai Ishiwata^1,2, Kohei Ishibashi^1,*, Yoshiaki Kato³, Heima Sakaguchi³, Toshihiro Nakamura¹, Satoshi Oka¹, Yuichiro Miyazaki¹, Akinori Wakamiya¹, Nobuhiko Ueda¹, Kenzaburo Nakajima¹, Tsukasa Kamakura¹, Mitsuru Wada¹, Yuko Inoue¹, Koji Miyamoto¹, Takeshi Aiba¹, Norihiko Takeda², Kengo Kusano¹

Congenital Heart Disease, Vol.20, No.3, pp. 273-286, 2025, DOI:10.32604/chd.2025.067716 - 11 July 2025

Abstract Background: Ventricular arrhythmia is a common cause of mortality in adult congenital heart disease (ACHD). The beneficial effects of implantable cardioverter-defibrillators (ICD) in patients with ACHD have been demonstrated; however, evidence on this topic remains insufficient. This study aimed to assess the long-term outcomes after ICD implantation in the ACHD population. Methods: We retrospectively reviewed 35 consecutive patients with ACHD who underwent ICD implantation between December 2012 and August 2022. ICD implantation was classified as primary or secondary prevention. The long-term outcomes, including all-cause mortality, appropriate and inappropriate ICD therapy, and complications related to ICD implantation, were… More > Graphic Abstract

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 >

Ye Zeng^1,*, Bingmei M. FU^2,*

BIOCELL, Vol.49, No.6, pp. 925-930, 2025, DOI:10.32604/biocell.2025.065879 - 24 June 2025

Abstract This article has no abstract. More >

Siarhei A. Dabravolski^1,*, Michael I. Bukrinsky², Aleksandra S. Utkina³, Alessio L. Ravani⁴, Vasily N. Sukhorukov^5,6, Alexander N. Orekhov⁷

BIOCELL, Vol.49, No.6, pp. 931-959, 2025, DOI:10.32604/biocell.2025.062789 - 24 June 2025

Abstract Neutrophil extracellular traps (NET) have emerged as critical players in the pathogenesis of atherosclerosis and other cardiovascular diseases (CVD). These web-like structures, composed of DNA, histones, and granule proteins released by neutrophils, contribute significantly to both inflammation and thrombosis. This manuscript offers a comprehensive review of the recent literature on the involvement of NET in atherosclerosis, highlighting their interactions with various pathophysiological processes and their potential as biomarkers for CVD. Notably, the impact of radiation on NET formation is explored, emphasising how oxidative stress and inflammatory responses drive NET release, contributing to plaque instability. The… More >

Sameera V Mohd Sagheer¹, Orwel P V², P M Ameer³, Amal BaQais⁴, Shaeen Kalathil^5,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1329-1349, 2025, DOI:10.32604/cmc.2025.063961 - 09 June 2025

Abstract The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods. Conventional approaches rely on visual examination by experts to detect infected palm leaves, which is time intensive and susceptible to mistakes. This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability. The system leverages pretrained convolutional neural network architectures (InceptionV3, DenseNet, and MobileNet) to extract and examine leaf characteristics for classification purposes. A publicly accessible dataset comprising multiple… More >

Chih-Ta Yen^1,*, Jung-Ren Wong², Chia-Hsang Chang²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 347-363, 2025, DOI:10.32604/cmc.2025.063389 - 09 June 2025

Abstract In its 2023 global health statistics, the World Health Organization noted that noncommunicable diseases (NCDs) remain the leading cause of disease burden worldwide, with cardiovascular diseases (CVDs) resulting in more deaths than the three other major NCDs combined. In this study, we developed a method that can comprehensively detect which CVDs are present in a patient. Specifically, we propose a multi-label classification method that utilizes photoplethysmography (PPG) signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions: hypertension, diabetes, cerebral infarction, and cerebrovascular disease. Our approach to multi-disease classification… More >

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >