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  • Open Access


    Optimizing Optical Fiber Faults Detection: A Comparative Analysis of Advanced Machine Learning Approaches

    Kamlesh Kumar Soothar1,2, Yuanxiang Chen1,2,*, Arif Hussain Magsi3, Cong Hu1, Hussain Shah1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

    Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

  • Open Access


    An Ontology Based Test Case Prioritization Approach in Regression Testing

    Muhammad Hasnain1, Seung Ryul Jeong2,*, Muhammad Fermi Pasha1, Imran Ghani3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1051-1068, 2021, DOI:10.32604/cmc.2021.014686

    Abstract Regression testing is a widely studied research area, with the aim of meeting the quality challenges of software systems. To achieve a software system of good quality, we face high consumption of resources during testing. To overcome this challenge, test case prioritization (TCP) as a sub-type of regression testing is continuously investigated to achieve the testing objectives. This study provides an insight into proposing the ontology-based TCP (OTCP) approach, aimed at reducing the consumption of resources for the quality improvement and maintenance of software systems. The proposed approach uses software metrics to examine the behavior… More >

  • Open Access


    Method for Detecting Macroscopic Irregularities in Gears Based on Template Matching and the Nonequivalence Operation

    W.C. Wang1, F.L. Chang2, Y.L. Liu1, X. J. Wu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.107, No.5, pp. 411-431, 2015, DOI:10.3970/cmes.2015.107.411

    Abstract The detection of macroscopic irregularities is an essential procedure during the production of gears, and it helps to guarantee the quality of electromechanical transmission equipment. The working principles of template matching and the image nonequivalence operation are described in detail in this paper. Gray-level transformation, edge-preserving filtering, image segmentation, feature extraction, and pattern recognition were analyzed, leading to the design of a defect detection system based on template matching and the nonequivalence operation, followed by the development of a hardware platform and application software for the system. The experimental results indicate that the proposed detection More >

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