Chao Wang^1,3, Kaijie Zhang^1,2,*, Xiaoyong Yu¹, Dejun Li², Wei Xie², Xinqiao Wang²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4451-4473, 2024, DOI:10.32604/cmc.2024.051764 - 12 September 2024

Abstract In the model of the vehicle recognition algorithm implemented by the convolutional neural network, the model needs to compute and store a lot of parameters. Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance. Therefore, obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research. In this paper, a lightweight densely connected, and deeply separable convolutional network (DCDSNet) algorithm is proposed to achieve this goal. Visual Geometry Group (VGG) More >

Nannan Chong^1,2,*, Fan Yang¹

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2761-2783, 2024, DOI:10.32604/cmc.2024.052217 - 15 August 2024

Abstract In minimally invasive surgery, endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities. However, in clinical operating environments, endoscopic images often suffer from challenges such as low texture, uneven illumination, and non-rigid structures, which affect feature observation and extraction. This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images, leading to treatment and postoperative recovery issues for patients. To address these challenges, this paper introduces, for the first time, a Cross-Channel Multi-Modal… More >

Md. Mahbubur Rahman Tusher¹, Fahmid Al Farid^2,*, Md. Al-Hasan¹, Abu Saleh Musa Miah¹, Susmita Roy Rinky¹, Mehedi Hasan Jim¹, Sarina Mansor², Md. Abdur Rahim³, Hezerul Abdul Karim^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2633-2656, 2024, DOI:10.32604/cmc.2024.049296 - 15 August 2024

Abstract The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name… More >

Yu Wang^1,2, Mengyang Ma^1,2,*, Yong Long^1,2, Qingxiang Zhao^1,2, Zhifei Cheng^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.8, pp. 1773-1784, 2024, DOI:10.32604/fdmp.2024.048695 - 06 August 2024

Abstract Shrinkage-induced cracking is a common issue in concrete structures, where the formation of cracks not only affects the aesthetic appearance of concrete but also potentially reduces its durability and strength. In this study, the effect of ceramsite sand addition on the properties of a ternary system of cement-ground granulated blast furnace slag (GGBFS)-phosphogypsum (PG) is investigated. In particular, the fluidity, rheology, hydration heat, compressive strength, autogenous shrinkage, and drying shrinkage of the considered mortar specimens are analyzed. The results indicate that an increase in PG content leads to a decrease in fluidity, higher viscosity, lower More >

Li Li^*, Qing Zhang, Youran Kong

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916 - 18 July 2024

Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >

Zhilin Li^1,2, Yuxin Li¹, Chunyu Yan¹, Peng Yan¹, Xiutong Li¹, Mei Yu¹, Tingchi Wen^4,5, Benliang Xie^1,2,3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526 - 18 July 2024

Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease More >

Khursheed Aurangzeb^*, Syed Irtaza Haider, Musaed Alhussein

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1381-1405, 2024, DOI:10.32604/cmc.2024.048987 - 18 July 2024

Abstract In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers… More >

Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475 - 17 July 2024

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Haitao Ren², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Wenfeng Zheng^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853 - 08 July 2024

Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760 - 08 July 2024

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >