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Search Results (7)
  • Open Access

    ARTICLE

    Developing a Secure Framework Using Feature Selection and Attack Detection Technique

    Mahima Dahiya*, Nitin Nitin

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430

    Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based… More >

  • Open Access

    ARTICLE

    Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

    Mohamed Abdel-Basset1, Reda Mohamed1, Ripon K. Chakrabortty2, Michael J. Ryan2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2883-2901, 2021, DOI:10.32604/cmc.2021.017854

    Abstract This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feature selection. This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization (GNDO) supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values. Further, a novel restarting strategy (RS) is proposed… More >

  • Open Access

    ARTICLE

    Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution

    Farouq Mohammad A. Alam1, Sharifah Alrajhi1, Mazen Nassar1,2, Ahmed Z. Afify3,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089

    Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and 95% asymptotic confidence intervals are… More >

  • Open Access

    ARTICLE

    Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

    Huifang Qian1, Xuan Zhou1, *, Mengmeng Zheng1

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2153-2167, 2020, DOI:10.32604/cmc.2020.011843

    Abstract The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately. The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture, so as to solve the problem of recognizing them. In response to this difficulty, this paper introduces an adjustable jump link coefficients model based on the residual network. The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior. A convolution kernel of 1×1 size is added to reduce… More >

  • Open Access

    ARTICLE

    Statistical Analysis of Fatigue Life Data of A356.2-T6 Aluminum Alloy

    Ramamurty Raju P.1, Rajesh S.1, Satyanarayana B.2, Ramji K.3

    Structural Durability & Health Monitoring, Vol.7, No.1&2, pp. 139-152, 2011, DOI:10.3970/sdhm.2011.007.139

    Abstract This paper presents the details of method of sample size determination to estimate the characteristic fatigue life of aluminum alloy, A356.2-T6. The characteristic fatigue life of the alloy has been estimated by assuming log normal distribution model. A step wise procedure is outlined to determine the number of specimens required at predetermined stress amplitude to estimate the fatigue life with an acceptable error at 50% probability and various confidence levels, 90%, 95% and 99%. Maximum percentage of errors has also been calculated for the above probability and confidence levels. Details of generation of S-N curve for aluminum alloy A356.2-T6 using… More >

  • Open Access

    ARTICLE

    Advanced analysis of uncertain cracked structures

    P. Bocchini, C. Gentilini, F. Ubertini, E. Viola1

    Structural Durability & Health Monitoring, Vol.2, No.2, pp. 109-122, 2006, DOI:10.3970/sdhm.2006.002.109

    Abstract This paper provides a simple and reliable method for the probabilistic characterization of the linear elastic response of frame structures with edge cracks of uncertain depth and location. A statistical analysis of the structural response allows consideration of the reliability of the investigated structure. A numerical example provides an indication of the performance of the approach proposed. More >

  • Open Access

    ARTICLE

    Comprehensive Investigation into the Accuracy and Applicability of Monte Carlo Simulations in Stochastic Structural Analysis

    Taicong Chen1, Haitao Ma1, Wei Gao2

    CMES-Computer Modeling in Engineering & Sciences, Vol.87, No.3, pp. 239-270, 2012, DOI:10.3970/cmes.2012.087.239

    Abstract Monte Carlo simulation method has been used extensively in probabilistic analyses of engineering systems and its popularity has been growing. While it is widely accepted that the simulation results are asymptotically accurate when the number of samples increases, certain exceptions do exist. The major objectives of this study are to reveal the conditions of the applicability of Monte Carlo method and to provide new insights into the accuracy of the simulation results in stochastic structural analysis. Firstly, a simple problem of a spring with random axial stiffness subject to a deterministic tension is investigated, using normal and lognormal distributions. Analytical… More >

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