Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025

Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >

Yunho Na¹, Munsu Jeon¹, Seungmin Joo¹, Junsoo Kim¹, Ki-Yong Oh^1,2,*, Min Ku Kim^1,2,*, Joon-Young Park³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1013-1044, 2025, DOI:10.32604/cmes.2025.066447 - 31 July 2025

Abstract This paper proposes an automated detection framework for transmission facilities using a feature-attention multi-scale robustness network (FAMSR-Net) with high-fidelity virtual images. The proposed framework exhibits three key characteristics. First, virtual images of the transmission facilities generated using StyleGAN2-ADA are co-trained with real images. This enables the neural network to learn various features of transmission facilities to improve the detection performance. Second, the convolutional block attention module is deployed in FAMSR-Net to effectively extract features from images and construct multi-dimensional feature maps, enabling the neural network to perform precise object detection in various environments. Third, an… More >

Wei Liu¹, Sen Liu^2,3,*, Yinchao He², Jiaojiao Wang¹, Yu Gu¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4863-4880, 2025, DOI:10.32604/cmc.2025.059295 - 06 March 2025

Abstract To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping… More >

Youhe Wang, Yi Sun^*, Yujie Li, Chuanqi Zhou

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1233-1254, 2025, DOI:10.32604/cmc.2024.057710 - 03 January 2025

Abstract With the development of anti-virus technology, malicious documents have gradually become the main pathway of Advanced Persistent Threat (APT) attacks, therefore, the development of effective malicious document classifiers has become particularly urgent. Currently, detection methods based on document structure and behavioral features encounter challenges in feature engineering, these methods not only have limited accuracy, but also consume large resources, and usually can only detect documents in specific formats, which lacks versatility and adaptability. To address such problems, this paper proposes a novel malicious document detection method-visualizing documents as GGE images (Grayscale, Grayscale matrix, Entropy). The… More >

P. Kuppusamy^1,*, M. Sanjay¹, P. V. Deepashree¹, C. Iwendi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 445-466, 2023, DOI:10.32604/cmc.2023.042675 - 31 October 2023

Abstract The infrastructure and construction of roads are crucial for the economic and social development of a region, but traffic-related challenges like accidents and congestion persist. Artificial Intelligence (AI) and Machine Learning (ML) have been used in road infrastructure and construction, particularly with the Internet of Things (IoT) devices. Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing traffic-related problems. This study aims to use You Only Look Once version 7 (YOLOv7), Convolutional Block Attention Module (CBAM), the most optimized object-detection algorithm, to detect and identify traffic signs, and… More >

P. Sabitha^*, G. Meeragandhi

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402 - 21 June 2023

Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet)… More >

Yudong Zhang^1,3,*, Xin Zhang^2,*, Weiguo Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807 - 24 May 2021

Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for COVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracy of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: This proposed ANC method is superior to 9 state-of-the-art approaches. More >