Mo Hou^1,2,3,#,*, Bin Xu^4,#, Wen Shang^1,2,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071282 - 09 December 2025

Abstract Image captioning, a pivotal research area at the intersection of image understanding, artificial intelligence, and linguistics, aims to generate natural language descriptions for images. This paper proposes an efficient image captioning model named Mob-IMWTC, which integrates improved wavelet convolution (IMWTC) with an enhanced MobileNet V3 architecture. The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module. This innovative neural network significantly reduces the memory space required and model training time, while maintaining a high level of accuracy in generating image descriptions. IMWTC facilitates large receptive… 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 >

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 >

Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Chi Zhang^1,#, Xi Huang^2,#, Cheng Hu¹, Bowen Tang¹, Jianjie Wu¹, Zhuolun Sun¹, Weian Zhu¹, Xiangfu Zhou¹, Hengjun Xiao^1,*, Hua Wang^1,*

Oncology Research, Vol.33, No.9, pp. 2331-2351, 2025, DOI:10.32604/or.2025.065119 - 28 August 2025

Abstract Background: U2AF homology motif kinase 1 (UHMK1) has been associated with RNA processing and protein phosphorylation, thereby influencing tumor progression. The study aimed to explore its regulatory mechanisms and biological functions in human prostate cancer (PCa). Methods: In this study, we systematically evaluated the expression and prognostic significance of UHMK1 in public databases, followed by validation through immunohistochemistry (IHC) in PCa specimens. Both gain-of-function and loss-of-function experiments were conducted to elucidate the role of UHMK1 in vitro and in vivo. Additionally, a series of molecular and biochemical assays were performed to investigate the regulatory mechanisms underlying UHMK1… More >

Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… 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 >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 537-557, 2025, DOI:10.32604/cmc.2025.061379 - 26 March 2025

Abstract Tires are integral to vehicular systems, directly influencing both safety and overall performance. Traditional tire pressure inspection methods—such as manual or gauge-based approaches—are often time-consuming, prone to inconsistency, and lack the flexibility needed to meet diverse operational demands. In this research, we introduce an AI-driven tire pressure detection system that leverages an enhanced GoogLeNet architecture incorporating a novel Softplus-LReLU activation function. By combining the smooth, non-saturating characteristics of Softplus with a linear adjustment term, this activation function improves computational efficiency and helps stabilize network gradients, thereby mitigating issues such as gradient vanishing and neuron death.… More >

Shanmeng Zhao^1,2, Yaxue Peng^1,*, Yaqing Wang³, Gang Li^3,*, Mohammed Al-Mahbashi¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4995-5017, 2025, DOI:10.32604/cmc.2025.059972 - 06 March 2025

Abstract In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network… More >

Diana Abi-Nader¹, Hassan Harb², Ali Jaber¹, Ali Mansour³, Christophe Osswald³, Nour Mostafa^2,*, Chamseddine Zaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 279-298, 2025, DOI:10.32604/cmes.2024.056816 - 17 December 2024

Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >