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  • Open Access


    A Hybrid Machine Learning Framework for Security Intrusion Detection

    Fatimah Mudhhi Alanazi*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401

    Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

  • Open Access


    Digital Text Document Watermarking Based Tampering Attack Detection via Internet

    Manal Abdullah Alohali1, Muna Elsadig1, Fahd N. Al-Wesabi2, Mesfer Al Duhayyim3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 759-771, 2024, DOI:10.32604/csse.2023.037305

    Abstract Owing to the rapid increase in the interchange of text information through internet networks, the reliability and security of digital content are becoming a major research problem. Tampering detection, Content authentication, and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies. The authors’ difficulties were tampering detection, authentication, and integrity verification of the digital contents. This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection (ADMDTW-TAD) via the Internet. The DM concept is exploited in the presented… More >

  • Open Access


    Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

    Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080

    Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

  • Open Access


    A Framework for Driver Drowsiness Monitoring Using a Convolutional Neural Network and the Internet of Things

    Muhamad Irsan1,2,*, Rosilah Hassan2, Anwar Hassan Ibrahim3, Mohamad Khatim Hasan2, Meng Chun Lam2, Wan Mohd Hirwani Wan Hussain4

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 157-174, 2024, DOI:10.32604/iasc.2024.042193

    Abstract One of the major causes of road accidents is sleepy drivers. Such accidents typically result in fatalities and financial losses and disadvantage other road users. Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system. Most studies have examined how the mouth and eyelids move. However, this limits the system’s ability to identify drowsiness traits. Therefore, this study designed an Accident Detection Framework (RPK) that could be used to reduce road accidents due to sleepiness and detect the location of accidents. The drowsiness detection model used three facial… More >

  • Open Access


    Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)

    Abeer Abdullah Alsadhan1, Abdullah A. Al-Atawi2, Hanen karamti3, Abid Jameel4, Islam Zada5, Tan N. Nguyen6,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 135-155, 2024, DOI:10.32604/iasc.2023.041130

    Abstract IoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power. In this study, we investigate the possibility of detecting IoT malware using recurrent neural networks (RNNs). RNN is used in the proposed method to investigate the execution operation codes of ARM-based More >

  • Open Access


    Bicuspidization Using the Open-Sleeve Technique for Congenital Aortic Stenosis during Infancy

    Toshi Maeda*, Hiroki Ito, Keiichi Hirose, Kisaburo Sakamoto

    Congenital Heart Disease, Vol.19, No.2, pp. 177-183, 2024, DOI:10.32604/chd.2024.050945

    Abstract Congenital aortic stenosis (cAS) frequently requires intervention during the neonatal or infantile period. However, surgical repair is challenging because of the narrow surgical space. We performed bicuspidization using the open-sleeve technique for cAS with a unicuspid aortic valve in two patients. Postoperatively, the patients were doing well without reintervention for the aortic valve for 8 and 6 years, respectively. Their aortic annular diameter increased along with somatic growth. Bicuspidization for neonates or infancy can be performed safely using the open-sleeve technique as its midterm results have been satisfactory. More > Graphic Abstract

    Bicuspidization Using the Open-Sleeve Technique for Congenital Aortic Stenosis during Infancy

  • Open Access


    High Prevalence of Anatomical Variations and Anomalies of the Coronary Arteries Detected by CT Angiography in Symptomatic Patients

    Ghazi A. Alshumrani*

    Congenital Heart Disease, Vol.19, No.2, pp. 197-206, 2024, DOI:10.32604/chd.2024.049401

    Abstract Objective: Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations. This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography (CCTA). Methods: This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years. Results: The total number of included patients was 507 (60% males) with a mean age of 57.4 years. Approximately 41% had luminal stenoses, averaging 49.7%. The total number… More >

  • Open Access


    Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease: Application and Development

    Yang Wang#, Xun Yang#, Mingtang Ye, Yuhang Zhao, Runsen Chen, Min Da, Zhiqi Wang, Xuming Mo, Jirong Qi*

    Congenital Heart Disease, Vol.19, No.2, pp. 219-231, 2024, DOI:10.32604/chd.2024.048314

    Abstract Congenital heart disease (CHD), the most prevalent congenital ailment, has seen advancements in the “dual indicator” screening program. This facilitates the early-stage diagnosis and treatment of children with CHD, subsequently enhancing their survival rates. While cardiac auscultation offers an objective reflection of cardiac abnormalities and function, its evaluation is significantly influenced by personal experience and external factors, rendering it susceptible to misdiagnosis and omission. In recent years, continuous progress in artificial intelligence (AI) has enabled the digital acquisition, storage, and analysis of heart sound signals, paving the way for intelligent CHD auscultation-assisted diagnostic technology. Although More > Graphic Abstract

    Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease: Application and Development

  • Open Access


    Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

    Weijian Fan1,*, Ziwei Shi2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

    Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

  • Open Access


    Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

    Zhenxiang He*, Zhenyu Zhao, Ke Chen, Yanlin Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

    Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… More >

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