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  • Open Access

    ARTICLE

    Modelling Supply Chain Information Collaboration Empowered with Machine Learning Technique

    Naeem Ali1,*, Alia Ahmed1, Leena Anum2, Taher M. Ghazal3,4, Sagheer Abbas5, Muhammad Adnan Khan6,7, Haitham M. Alzoubi8, Munir Ahmad5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 243-257, 2021, DOI:10.32604/iasc.2021.018983 - 26 July 2021

    Abstract Information Collaboration of the supply chain is the domination and control of product flow information from the producer to the customer. The data information flow is correlated with demand fill-up, a role delivering service, and feedback. The collaboration of supply chain information is a complex contrivance that impeccably manages the efficiency flow and focuses on its vulnerable area. As there is always room for growth in the current century, major companies have shown a growing tendency to improve their supply chain’s productivity and sustainability to increase customer consumption in complying with environmental regulations. Therefore, in… More >

  • Open Access

    ARTICLE

    Research on Detection Method of Interest Flooding Attack in Named Data Networking

    Yabin Xu1,2,*, Peiyuan Gu2, Xiaowei Xu3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 113-127, 2021, DOI:10.32604/iasc.2021.018895 - 26 July 2021

    Abstract In order to effectively detect interest flooding attack (IFA) in Named Data Networking (NDN), this paper proposes a detection method of interest flooding attack based on chi-square test and similarity test. Firstly, it determines the detection window size based on the distribution of information name prefixes (that is information entropy) in the current network traffic. The attackers may append arbitrary random suffix to a certain prefix in the network traffic, and then send a large number of interest packets that cannot get the response. Targeted at this problem, the sensitivity of chi-square test is used… More >

  • Open Access

    ARTICLE

    A Shadowed Rough-fuzzy Clustering Algorithm Based on Mahalanobis Distance for Intrusion Detection

    Lina Wang1,2,*, Jie Wang3, Yongjun Ren4, Zimeng Xing1, Tao Li1, Jinyue Xia5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 31-47, 2021, DOI:10.32604/iasc.2021.018577 - 26 July 2021

    Abstract Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an… More >

  • Open Access

    ARTICLE

    Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection

    Mohammed Baz1,*, Hatem Zaini1, Hala S. El-sayed2, Matokah AbuAlNaja3, Heba M. El-Hoseny4, Osama S. Faragallah5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 97-111, 2021, DOI:10.32604/iasc.2021.018265 - 26 July 2021

    Abstract In the era of medical technology, automatic scan detection can be considered a charming tool in medical diagnosis, especially with rapidly spreading diseases. In light of the prevalence of the current Coronavirus disease (COVID-19), which is characterized as highly contagious and very complicated, it is urgent and necessary to find a quick way that can be practically implemented for diagnosing COVID-19. The danger of the virus lies in the fact that patients can spread the disease without showing any symptoms. Moreover, several vaccines have been produced and vaccinated in large numbers but, the outbreak does… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Intrusion Detection Model for Fog Computing Environment

    K. Kalaivani*, M. Chinnadurai

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 1-15, 2021, DOI:10.32604/iasc.2021.017515 - 26 July 2021

    Abstract Fog computing extends the concept of cloud computing by providing the services of computing, storage, and networking connectivity at the edge between data centers in cloud computing environments and end devices. Having the intelligence at the edge enables faster real-time decision-making and reduces the amount of data forwarded to the cloud. When enhanced by fog computing, the Internet of Things (IoT) brings low latency and improves real time and quality of service (QoS) in IoT applications of augmented reality, smart grids, smart vehicles, and healthcare. However, both cloud and fog computing environments are vulnerable to… More >

  • Open Access

    ARTICLE

    Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

    Jianming Zhao1,2,3,4, Peng Zeng1,2,3,4,*, Chunyu Chen1,2,3,4, Zhiwei Dong5, Jongho Han6

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 337-350, 2021, DOI:10.32604/iasc.2021.016966 - 26 July 2021

    Abstract As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data… More >

  • Open Access

    ARTICLE

    Intrusion Detection Using a New Hybrid Feature Selection Model

    Adel Hamdan Mohammad*

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 65-80, 2021, DOI:10.32604/iasc.2021.016140 - 26 July 2021

    Abstract Intrusion detection is an important topic that aims at protecting computer systems. Besides, feature selection is crucial for increasing the performance of intrusion detection. This paper employs a new hybrid feature selection model for intrusion detection. The implemented model uses Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms in a new manner. In addition, this study introduces two new models called (PSO-GWO-NB) and (PSO-GWO-ANN) for feature selection and intrusion detection. PSO and GWO show emergent results in feature selection for several purposes and applications. This paper uses PSO and GWO to select features… More >

  • Open Access

    ARTICLE

    An Improved Algorithm for the Detection of Fastening Targets Based on Machine Vision

    Jian Yang, Lang Xin#, Haihui Huang*,#, Qiang He

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 779-802, 2021, DOI:10.32604/cmes.2021.014993 - 22 July 2021

    Abstract Object detection plays an important role in the sorting process of mechanical fasteners. Although object detection has been studied for many years, it has always been an industrial problem. Edge-based model matching is only suitable for a small range of illumination changes, and the matching accuracy is low. The optical flow method and the difference method are sensitive to noise and light, and camshift tracking is less effective in complex backgrounds. In this paper, an improved target detection method based on YOLOv3-tiny is proposed. The redundant regression box generated by the prediction network is filtered… More >

  • Open Access

    ARTICLE

    Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

    Munir Ahmad1, Majed Alfayad2, Shabib Aftab1,3, Muhammad Adnan Khan4,*, Areej Fatima5, Bilal Shoaib6, Mohammad Sh. Daoud7, Nouh Sabri Elmitwally2,8

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2717-2731, 2021, DOI:10.32604/cmc.2021.019013 - 21 July 2021

    Abstract Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending More >

  • Open Access

    ARTICLE

    GPS Vector Tracking Receivers with Rate Detector for Integrity Monitoring

    Dah-Jing Jwo*, Ming-Hsuan Lee

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2387-2403, 2021, DOI:10.32604/cmc.2021.018670 - 21 July 2021

    Abstract In this paper, the integrity monitoring algorithm based on a Kalman filter (KF) based rate detector is employed in the vector tracking loop (VTL) of the Global Positioning System (GPS) receiver. In the VTL approach, the extended Kalman filter (EKF) simultaneously tracks the received signals and estimates the receiver’s position, velocity, etc. In contrast to the scalar tracking loop (STL) that uses the independent parallel tracking loop approach, the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals. Although the VTL scheme… More >

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