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  • Open Access

    ARTICLE

    A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

    Kaili Shao1, Hui Fu1, Ying Song2, Bo Wang3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

    Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… More >

  • Open Access

    ARTICLE

    Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

    Mahmoud Ragab1,2,*, Sultanah M. Alshammari2,3, Abdullah S. Al-Malaise Al-Ghamdi2,4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446

    Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult. This study… More >

  • Open Access

    ARTICLE

    A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

    Tao Liu1, Kejia Zhang1,*, Jingsong Yin1, Yan Zhang1, Zihao Mu1, Chunsheng Li1, Yanan Hu2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228

    Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the… More >

  • Open Access

    ARTICLE

    SlowFast Based Real-Time Human Motion Recognition with Action Localization

    Gyu-Il Kim1, Hyun Yoo2, Kyungyong Chung3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2135-2152, 2023, DOI:10.32604/csse.2023.041030

    Abstract Artificial intelligence is increasingly being applied in the field of video analysis, particularly in the area of public safety where video surveillance equipment such as closed-circuit television (CCTV) is used and automated analysis of video information is required. However, various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging. Video analysis technology applies object classification, detection, and relationship analysis to continuous 2D frame data, and the various meanings within the video are thus analyzed based on the extracted basic data. Motion recognition is key in this analysis. Motion recognition is a challenging… More >

  • Open Access

    ARTICLE

    An Innovative Technique for Constructing Highly Non-Linear Components of Block Cipher for Data Security against Cyber Attacks

    Abid Mahboob1, Muhammad Asif2, Rana Muhammad Zulqarnain3,*, Imran Siddique4, Hijaz Ahmad5, Sameh Askar6, Giovanni Pau7

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2547-2562, 2023, DOI:10.32604/csse.2023.040855

    Abstract The rapid advancement of data in web-based communication has created one of the biggest issues concerning the security of data carried over the internet from unauthorized access. To improve data security, modern cryptosystems use substitution-boxes. Nowadays, data privacy has become a key concern for consumers who transfer sensitive data from one place to another. To address these problems, many companies rely on cryptographic techniques to secure data from illegal activities and assaults. Among these cryptographic approaches, AES is a well-known algorithm that transforms plain text into cipher text by employing substitution box (S-box). The S-box disguises the relationship between cipher… More >

  • Open Access

    ARTICLE

    Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

    S. Srinivasan, K. Rajakumar*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2481-2496, 2023, DOI:10.32604/csse.2023.040817

    Abstract

    The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors. The high correlation between these features and the noises greatly affects the classification performances. To overcome this, dimensionality reduction techniques are widely used. Traditional image processing applications recently propose numerous deep learning models. However, in hyperspectral image classification, the features of deep learning models are less explored. Thus, for efficient hyperspectral image classification, a depth-wise convolutional neural network is presented in this research work. To handle the dimensionality issue in the classification process, an optimized self-organized map model is employed using a water strider optimization… More >

  • Open Access

    ARTICLE

    3D Model Encryption Algorithm by Parallel Bidirectional Diffusion and 1D Map with Sin and Logistic Coupling

    Yongsheng Hu*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1819-1838, 2023, DOI:10.32604/csse.2023.040729

    Abstract 3D models are essential in virtual reality, game development, architecture design, engineering drawing, medicine, and more. Compared to digital images, 3D models can provide more realistic visual effects. In recent years, significant progress has been made in the field of digital image encryption, and researchers have developed new algorithms that are more secure and efficient. However, there needs to be more research on 3D model encryption. This paper proposes a new 3D model encryption algorithm, called the 1D map with sin and logistic coupling (1D-MWSLC), because existing digital image encryption algorithms cannot be directly applied to 3D models. Firstly, this… More >

  • Open Access

    ARTICLE

    Towards Generating a Practical SUNBURST Attack Dataset for Network Attack Detection

    Ehab AlMasri1, Mouhammd Alkasassbeh1, Amjad Aldweesh2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2643-2669, 2023, DOI:10.32604/csse.2023.040626

    Abstract Supply chain attacks, exemplified by the SUNBURST attack utilizing SolarWinds Orion updates, pose a growing cybersecurity threat to entities worldwide. However, the need for suitable datasets for detecting and anticipating SUNBURST attacks is a significant challenge. We present a novel dataset collected using a unique network traffic data collection methodology to address this gap. Our study aims to enhance intrusion detection and prevention systems by understanding SUNBURST attack features. We construct realistic attack scenarios by combining relevant data and attack indicators. The dataset is validated with the J48 machine learning algorithm, achieving an average F-Measure of 87.7%. Our significant contribution… More >

  • Open Access

    ARTICLE

    Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624

    Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated… More >

  • Open Access

    ARTICLE

    Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2119-2134, 2023, DOI:10.32604/csse.2023.040620

    Abstract Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality rates. Chronic Kidney Disease (CKD) is treatable during its initial phases but can become irreversible and cause renal failure. Among the various diseases, the most prevalent kidney conditions affecting kidney function are cyst growth, kidney tumors, and nephrolithiasis. The significant challenge for the medical community is the immediate diagnosis and treatment of kidney disease. Kidney failure could result from kidney disorders like tumors, stones, and cysts if not often identified and addressed. Computer-assisted diagnostics are necessary to support clinicians’ and specialists’ medical assessments due to the rising… More >

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