Nevena Stojadinović¹, Goran Mihajlović¹, Marko Spasić¹, Milena Mladenović¹, Darko Hinić^2,*

Psycho-Oncologie, Vol.18, No.3, pp. 159-168, 2024, DOI:10.32604/po.2024.050122 - 12 September 2024

Abstract Breast cancer is considered one of the most frequent causes of morbidity and death in women. Individuals’ response to information regarding health threats and illness can influence the adjustment of the treatment to existing conditions including the issues of non-completion of treatment or non-attendance at medical appointments. The study aimed to examine the relationship between illness perception, body image dissatisfaction and (mal)adaptive coping styles in breast cancer patients. A sample of 197 patients with diagnosed breast cancer hospitalized at the Center for Oncology and Radiology, Kragujevac, Serbia, was surveyed. The instruments included sociodemographic questionnaire, a… More >

Yuanyuan Wang^1,2,*, Jialong Huang¹, Md Sharid Kayes Dipu¹, Hu Zhao³, Shangbing Gao^1,2, Haiyan Zhang^1,2, Pinrong Lv¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4973-4995, 2024, DOI:10.32604/cmc.2024.055839 - 12 September 2024

Abstract Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You… More >

Huiling Yu¹, Yanqiu Hang², Shen Shi¹, Kangning Wu¹, Yizhuo Zhang^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4859-4874, 2024, DOI:10.32604/cmc.2024.055403 - 12 September 2024

Abstract Electrolysis tanks are used to smelt metals based on electrochemical principles, and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures, thus affecting normal production. Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks, an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5 (YOLOv5) is proposed. Firstly, we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) by changing the U-shaped network (U-Net)… More >

Kunkun Tong^1,#, Guchu Zou^2,#, Xin Tan^1,*, Jingyu Gong¹, Zhenyi Qi², Zhizhong Zhang¹, Yuan Xie¹, Lizhuang Ma¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3421-3461, 2024, DOI:10.32604/cmc.2024.055327 - 12 September 2024

Abstract Confusing object detection (COD), such as glass, mirrors, and camouflaged objects, represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds, leveraging deep learning methodologies. Despite garnering increasing attention in computer vision, the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures. As of now, there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks. To fill this gap, our study presents a pioneering review that covers… More >

Aljuaid Turkea Ayedh M^1,2,*, Ainuddin Wahid Abdul Wahab^1,*, Mohd Yamani Idna Idris^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4663-4686, 2024, DOI:10.32604/cmc.2024.055287 - 12 September 2024

Abstract Organizations are adopting the Bring Your Own Device (BYOD) concept to enhance productivity and reduce expenses. However, this trend introduces security challenges, such as unauthorized access. Traditional access control systems, such as Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC), are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources. This paper proposes a method for enforcing access decisions that is adaptable and dynamic, based on multilayer hybrid deep learning techniques, particularly the Tabular Deep Neural Network TabularDNN method. This technique transforms… 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 >

Atta Rahman^1,*, Mustafa Youldash², Ghaida Alshammari², Abrar Sebiany², Joury Alzayat², Manar Alsayed², Mona Alqahtani², Noor Aljishi²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106 - 12 September 2024

Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

Mahmoud E. Farfoura^1,*, Ahmad Alkhatib¹, Deema Mohammed Alsekait^2,*, Mohammad Alshinwan^3,7, Sahar A. El-Rahman⁴, Didi Rosiyadi⁵, Diaa Salama AbdElminaam^6,7

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849 - 12 September 2024

Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

Kwang-Cheol Rim¹, Young-Min Yoon², Sung-Uk Kim³, Jeong-In Kim^4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3903-3916, 2024, DOI:10.32604/cmc.2024.054620 - 12 September 2024

Abstract The identification and mitigation of anomaly data, characterized by deviations from normal patterns or singularities, stand as critical endeavors in modern technological landscapes, spanning domains such as Non-Fungible Tokens (NFTs), cyber-security, and the burgeoning metaverse. This paper presents a novel proposal aimed at refining anomaly detection methodologies, with a particular focus on continuous data streams. The essence of the proposed approach lies in analyzing the rate of change within such data streams, leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy. Through empirical evaluation, our method demonstrates a marked improvement over existing More >