Zhiyi Ding, Lei Sun^*, Xiuqing Mao, Leyu Dai, Ruiyang Ding

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4387-4412, 2024, DOI:10.32604/cmc.2024.052196 - 12 September 2024

Abstract Object detection finds wide application in various sectors, including autonomous driving, industry, and healthcare. Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples. This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems. Most existing adversarial attack strategies focus primarily on image classification problems, failing to fully exploit the unique characteristics of object detection models, thus resulting in widespread deficiencies in their transferability. Furthermore, previous research has predominantly concentrated on… More >

Zhanyang Xu^*, Zhe Wang, Jian Xu, Hongyan Shi, Hong Zhao

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3991-4015, 2024, DOI:10.32604/cmc.2024.051620 - 12 September 2024

Abstract System logs, serving as a pivotal data source for performance monitoring and anomaly detection, play an indispensable role in assuring service stability and reliability. Despite this, the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs, utilizing solely a single Recurrent Neural Network (RNN) and its variant sequence models for detection. These approaches have not thoroughly exploited the semantic information embedded in logs, exhibit limited adaptability to novel logs, and a single model struggles to fully unearth the potential features within the log sequence. Addressing these challenges,… More >

Brij Bhooshan Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4895-4916, 2024, DOI:10.32604/cmc.2024.050815 - 12 September 2024

Abstract Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each More >

Muhammad Dawood¹, Shanshan Tu¹, Chuangbai Xiao¹, Muhammad Haris², Hisham Alasmary³, Muhammad Waqas^4,5,*, Sadaqat Ur Rehman⁶

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4513-4542, 2024, DOI:10.32604/cmc.2024.050049 - 12 September 2024

Abstract The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow More >

Ganesh There^*, Rohit Sharma^*

Energy Engineering, Vol.121, No.10, pp. 2851-2892, 2024, DOI:10.32604/ee.2024.049506 - 11 September 2024

Abstract This analysis investigates the widespread use of solar drying methods and designs in developing countries, particularly for agricultural products like fruits, vegetables, and bee pollen. Traditional techniques like hot air oven drying and open sun drying have drawbacks, including nutrient loss and exposure to harmful particles. Solar and thermal drying are viewed as sustainable solutions because they rely on renewable resources. The article highlights the advantages of solar drying, including waste reduction, increased productivity, and improved pricing. It is also cost-effective and energy-efficient. The review study provides an overview of different solar drying systems and… More > Graphic Abstract

Pawat Chunhachatrachai^1,*, Chyi-Yeu Lin^1,2

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 599-618, 2024, DOI:10.32604/iasc.2024.055385 - 06 September 2024

Abstract Angle detection is a crucial aspect of industrial automation, ensuring precise alignment and orientation of components in manufacturing processes. Despite the widespread application of computer vision in industrial settings, angle detection remains an underexplored domain, with limited integration into production lines. This paper addresses the need for automated angle detection in industrial environments by presenting a methodology that eliminates training time and higher computation cost on Graphics Processing Unit (GPU) from machine learning in computer vision (e.g., Convolutional Neural Networks (CNN)). Our approach leverages advanced image processing techniques and a strategic combination of algorithms, including More >

Hussein Alaa Al-Kabbi^1,2, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh³

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 665-682, 2024, DOI:10.32604/iasc.2024.050452 - 06 September 2024

Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… 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 >

ZULONG SHENG^*, YANRU HE, JUNYAN CAI, YUQIN JI, YUYU YAO, GENSHAN MA

BIOCELL, Vol.48, No.9, pp. 1369-1377, 2024, DOI:10.32604/biocell.2024.049905 - 04 September 2024

Abstract Background: Myocardial infarction (MI) is known worldwide for its important disabling features, including myocarditis and cardiomyocyte apoptosis. It is believed that microRNA (miRNA) has a role in the cellular processes of apoptosis and myocarditis, and miR-219a-5p has been found to suppress the inflammatory response. However, unknown is the precise mechanism by which miR-219a-5p contributes to MI. Methods: We measured the expression of miR-219a-5p and evaluated its effects on target proteins, inflammatory factors, and apoptosis in a mouse model of MI. Echocardiography was utilized to examine the MI clinical index, and triphenyl tetrazolium chloride staining was More >

Lijun Zhang, Ying Liu^*, Yushou Ma^*, Xinyou Wang

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1913-1941, 2024, DOI:10.32604/phyton.2024.052621 - 30 August 2024

Abstract Perennial grasses have developed intricate mechanisms to adapt to diverse environments, enabling their resistance to various biotic and abiotic stressors. These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions. Methods such as antisense RNA technology, CRISPR/Cas9 screening, virus-induced gene silencing, and transgenic technology, are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties. This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses. It synthesizes recent studies on mining functional genes, regulatory More >