Abdullah Alsaleh^1,2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 431-449, 2024, DOI:10.32604/csse.2023.043107

Abstract With the increasing number of connected devices in the Internet of Things (IoT) era, the number of intrusions is also increasing. An intrusion detection system (IDS) is a secondary intelligent system for monitoring, detecting and alerting against malicious activity. IDS is important in developing advanced security models. This study reviews the importance of various techniques, tools, and methods used in IoT detection and/or prevention systems. Specifically, it focuses on machine learning (ML) and deep learning (DL) techniques for IDS. This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles. To speed… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Abdelrahman Maharek^1,2,*, Amr Abozeid^2,3, Rasha Orban¹, Kamal ElDahshan²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436

Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

Arpit Jain¹, Nageswara Rao Moparthi¹, A. Swathi², Yogesh Kumar Sharma¹, Nitin Mittal³, Ahmed Alhussen⁴, Zamil S. Alzamil^5,*, MohdAnul Haq⁵

Computer Systems Science and Engineering, Vol.48, No.2, pp. 341-362, 2024, DOI:10.32604/csse.2023.036973

Abstract Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face… More >

Wuyang Fan, Shisheng Zhong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2525-2555, 2024, DOI:10.32604/cmes.2023.046951

Abstract The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model, which… More >

Yuanle Chen¹, Haobo Wang¹, Chunyu Liu¹, Linyi Wang², Jiaxin Liu¹, Wei Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2985-3009, 2024, DOI:10.32604/cmes.2023.046837

Abstract Recently, there have been significant advancements in the study of semantic communication in single-modal scenarios. However, the ability to process information in multi-modal environments remains limited. Inspired by the research and applications of natural language processing across different modalities, our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos. Specifically, we propose a deep learning-based Multi-Modal Mutual Enhancement Video Semantic Communication system, called M3E-VSC. Built upon a Vector Quantized Generative Adversarial Network (VQGAN), our system aims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission. With it,… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

Thanh-Lam Nguyen¹, Hao Kao¹, Thanh-Tuan Nguyen², Mong-Fong Horng^1,*, Chin-Shiuh Shieh^1,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2181-2205, 2024, DOI:10.32604/cmc.2024.047387

Abstract Since its inception, the Internet has been rapidly evolving. With the advancement of science and technology and the explosive growth of the population, the demand for the Internet has been on the rise. Many applications in education, healthcare, entertainment, science, and more are being increasingly deployed based on the internet. Concurrently, malicious threats on the internet are on the rise as well. Distributed Denial of Service (DDoS) attacks are among the most common and dangerous threats on the internet today. The scale and complexity of DDoS attacks are constantly growing. Intrusion Detection Systems (IDS) have been deployed and have demonstrated… More >

Longle Cheng^1,2, Xiru Li¹, Shiyu Fang², Wansu Pan¹, He Zhao^1,*, Haibo Tan¹, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2491-2509, 2024, DOI:10.32604/cmc.2024.047166

Abstract Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely… More >

Zhenhua Yu, Liangxue Bai, Ou Ye^*, Xuya Cong

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1773-1795, 2024, DOI:10.32604/cmc.2023.047130

Abstract Social robot accounts controlled by artificial intelligence or humans are active in social networks, bringing negative impacts to network security and social life. Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships, which makes it difficult to accurately describe the difference between the topological relations of nodes, resulting in low detection accuracy of social robots. This paper proposes a social robot detection method with the use of an improved neural network. First, social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social… More >