Tiantian Wang^1,2, Zhihua Hu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2357-2381, 2025, DOI:10.32604/cmes.2025.073640 - 26 November 2025

Abstract Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks. We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details. Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal. The core restoration is achieved via enhanced dual-residual blocks, which stabilize training and mitigate feature degradation across layers. To further refine representations, we integrate cross-dimensional spatial attention supervised by ground-truth guidance, ensuring that More >

Kavita Bodke^1,*, Sunil Bhirud¹, Keshav Kashinath Sangle²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1547-1562, 2025, DOI:10.32604/sdhm.2025.069239 - 17 November 2025

Abstract Structural Health Monitoring (SHM) systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity. There is a need for more efficient techniques to detect defects, as traditional methods are often prone to human error, and this issue is also addressed through image processing (IP). In addition to IP, automated, accurate, and real- time detection of structural defects, such as cracks, corrosion, and material degradation that conventional inspection techniques may miss, is made possible by Artificial Intelligence (AI) technologies like Machine Learning (ML) and Deep Learning… More > Graphic Abstract

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025

Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

Carlos Rojas¹, Cristian Bravo¹, Carlos Enrique Montenegro-Marín¹, Rubén González-Crespo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5489-5507, 2025, DOI:10.32604/cmc.2025.067394 - 23 October 2025

Abstract The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time. While traditional video surveillance relies on human monitoring, this approach suffers from limitations such as fatigue and delayed response times. This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety. Our approach leverages state-of-the-art convolutional neural networks (CNNs), specifically You Only Look Once version 4 (YOLOv4) and EfficientDet, for real-time object detection. The system was trained on a comprehensive… More >

Meenakshi S. Patil^1,*, Rajesh B. Ghongade², Hemant B. Dhonde³

Journal on Artificial Intelligence, Vol.7, pp. 289-300, 2025, DOI:10.32604/jai.2025.069500 - 12 September 2025

Abstract This study seeks to establish a novel, semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes, significantly contributing to the dependability of concrete quality evaluations. The study utilizes image processing and machine learning (ML) methods, namely object detection models such as YOLOv8 and Convolutional Neural Networks (CNNs), to evaluate images of concrete cubes. These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions. Preliminary results indicate a good performance in the classification of concrete More >

Jiexiong Xu^*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2365-2383, 2025, DOI:10.32604/phyton.2025.067124 - 29 August 2025

Abstract Background: Rice grain morphology—including traits such as awn length, hull color, size, and shape—is of central importance to yield, quality, and domestication, yet comprehensive quantification at scale has remained challenging. A promising solution has been provided by the integration of high-throughput imaging with genomic analysis. Methods: A standardized 2D image-processing pipeline was established to extract four categories of traits—awn length, hull color, projected grain area, and shape descriptors via PCA of normalized contours—from high-resolution photographs of 229 Oryza sativa japonica landraces. Genome-wide association analyses were then performed using a mixed linear model to control for population… More >

Fathe Jeribi^1,2, Ayesha Siddiqa^3,*, Hareem Kibriya⁴, Ali Tahir¹, Nadim Rana¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 955-982, 2025, DOI:10.32604/cmc.2025.065853 - 29 August 2025

Abstract Wound classification is a critical task in healthcare, requiring accurate and efficient diagnostic tools to support clinicians. In this paper, we investigated the effectiveness of the YOLO11n model in classifying different types of wound images. This study presents the training and evaluation of a lightweight YOLO11n model for automated wound classification using the AZH dataset, which includes six wound classes: Background (BG), Normal Skin (N), Diabetic (D), Pressure (P), Surgical (S), and Venous (V). The model’s architecture, optimized through experiments with varying batch sizes and epochs, ensures efficient deployment in resource-constrained environments. The model’s architecture… More >

MD Tausif Mallick¹, Saptarshi Banerjee², Nityananda Thakur³, Himadri Nath Saha^4,*, Amlan Chakrabarti¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 121-180, 2025, DOI:10.32604/cmc.2025.065250 - 29 August 2025

Abstract Addressing plant diseases and pests is not just crucial; it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive… More >

Vijaya Choudhary^1,2,3,*, Paramita Guha^1,2, Giovanni Pau⁴

Journal on Big Data, Vol.7, pp. 1-14, 2025, DOI:10.32604/jbd.2025.065122 - 12 August 2025

Abstract Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing More >

Ahmad Taher Azar^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 29-36, 2025, DOI:10.32604/cmes.2025.069309 - 31 July 2025

Abstract This article has no abstract. More >