Nahed Tawfik^1,*, Heba M. Emara², Walid El-Shafai³, Naglaa F. Soliman⁴, Abeer D. Algarni⁴, Fathi E. Abd El-Samie⁴

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404 - 15 October 2024

Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

Licheng Sun¹, Heping Li^2,3, Liang Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

Jielin Jiang^1,2,3,4,*, Chao Cui¹, Xiaolong Xu^1,2,3,4, Yan Cui⁵

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

Prasanalakshmi Balaji^1,*, Omar Alqahtani¹, Sangita Babu², Mousmi Ajay Chaurasia³, Shanmugapriya Prakasam⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 443-458, 2024, DOI:10.32604/cmes.2024.053158 - 20 August 2024

Abstract Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an More >

Min-Gyu Kim¹, Hwankuk Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221 - 20 August 2024

Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Youshuai Yang², Bo Yang², Shan Liu², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Xiaobing Chen⁶, Wenfeng Zheng^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 87-106, 2024, DOI:10.32604/cmes.2024.051083 - 20 August 2024

Abstract This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer… 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 >

Qiurong Qin, Yueqin Li^*, Yajie Mi, Jinhui Shen, Kexin Wu, Zhenzhao Wang

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 843-874, 2024, DOI:10.32604/cmc.2024.051769 - 18 July 2024

Abstract In the era of the Internet, widely used web applications have become the target of hacker attacks because they contain a large amount of personal information. Among these vulnerabilities, stealing private data through cross-site scripting (XSS) attacks is one of the most commonly used attacks by hackers. Currently, deep learning-based XSS attack detection methods have good application prospects; however, they suffer from problems such as being prone to overfitting, a high false alarm rate, and low accuracy. To address these issues, we propose a multi-stage feature extraction and fusion model for XSS detection based on… 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 >

Saad Abdalla Agaili Mohamed^*, Sefer Kurnaz

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 819-841, 2024, DOI:10.32604/cmc.2024.050474 - 18 July 2024

Abstract VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world. However, increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorize VPN network data. We present a novel VPN network traffic flow classification method utilizing Artificial Neural Networks (ANN). This paper aims to provide a reliable system that can identify a virtual private network (VPN) traffic from intrusion attempts, data exfiltration, and denial-of-service assaults. We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns. Next, we create an ANN architecture that can… More >