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  • Open Access


    Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

    K. Akilandeswari1, Nithya Rekha Sivakumar2,*, Hend Khalid Alkahtani3, Shakila Basheer3, Sara Abdelwahab Ghorashi2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1189-1205, 2024, DOI:10.32604/cmc.2023.034815

    Abstract In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression Feature Extraction model is used… More >

  • Open Access


    Statistic Structural Damage Detection Of Functionally Graded EulerBernoulli Beams Based on Element Modal Strain Energy Sensitivity

    Zhongming Hu1,*, Leilei Chen1, Delei Yang1, Jichao Zhang1, Youyang Xin1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-3, 2023, DOI:10.32604/icces.2023.09340

    Abstract Functionally graded materials (FGMs), a kind of composite materials, were proposed to satisfy the requirements of thermal barrier materials initially [1-3]. Compared with traditional composites, the microstructure and mechanical characteristics of FGMs change continuously which make them present excellent performance in deformation resistance or toughness under extreme mechanical and thermal loadings [4]. Therefore, FGMs have been paid much attention and experienced rapid developments in the last decade. Nowadays, various structural components manufactured by FGMs have been used in extensive applications, such as aerospace, bioengineering, nuclear industries, civil constructions etc. [5-7]
    While, FG Euler-Bernoulli beams maybe suffer damage in practical… More >

  • Open Access


    An Efficient Peridynamics Based Statistical Multiscale Method for Fracture in Composite Structure with Randomly Distributed Particles

    Zihao Yang1, Shaoqi Zheng1, Fei Han2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09250

    Abstract This paper proposes a peridynamics-based statistical multiscale (PSM) framework to simulate the macroscopic structure fracture with high efficiency. The heterogeneities of composites, including the shape, spatial distribution and volume fraction of particles, are characterized within the representative volume elements (RVEs), and their impact on structure failure are extracted as two types of peridynamic parameters, namely, statistical critical stretch and equivalent micromodulus. At the microscale level, a bondbased peridynamic (BPD) model with energy-based micromodulus correction technique is introduced to simulate the fracture in RVEs, and then the computational model of statistical critical stretch is established through micromechanical analysis. Moreover, based on… More >

  • Open Access


    Use of Statistical Tools for Comparison between Different Analytical and Semi-Empirical Models of the Bleve Fireball

    Abderraouf Guelzim1,2,*, Baraka Achraf Chakir3, Aziz Ettahir1, Anas Mbarki1,*

    Frontiers in Heat and Mass Transfer, Vol.21, pp. 125-140, 2023, DOI:10.32604/fhmt.2023.041832

    Abstract The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel. The loss of containment results in a large fireball if the stored chemical is flammable. In order to predict the damage generated by a Bleve, several authors propose analytical or semi-empirical correlations, which consist in predicting the diameter and the lifetime of the fireballs according to the quantity of fuel. These models are based on previous experience, which makes their validity arbitrary in relation to the initial conditions and the nature of the product concerned. The article delves into… More > Graphic Abstract

    Use of Statistical Tools for Comparison between Different Analytical and Semi-Empirical Models of the Bleve Fireball

  • Open Access


    A New Scheme of the ARA Transform for Solving Fractional-Order Waves-Like Equations Involving Variable Coefficients

    Yu-Ming Chu1, Sobia Sultana2, Shazia Karim3, Saima Rashid4,*, Mohammed Shaaf Alharthi5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 761-791, 2024, DOI:10.32604/cmes.2023.028600

    Abstract The goal of this research is to develop a new, simplified analytical method known as the ARA-residue power series method for obtaining exact-approximate solutions employing Caputo type fractional partial differential equations (PDEs) with variable coefficient. ARA-transform is a robust and highly flexible generalization that unifies several existing transforms. The key concept behind this method is to create approximate series outcomes by implementing the ARA-transform and Taylor’s expansion. The process of finding approximations for dynamical fractional-order PDEs is challenging, but the ARA-residual power series technique magnifies this challenge by articulating the solution in a series pattern and then determining the series… More >

  • Open Access


    On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications

    Hassan Okasha1,2, Mazen Nassar1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783

    Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum likelihood estimators of the model’s… More >

  • Open Access


    New Configurations of the Fuzzy Fractional Differential Boussinesq Model with Application in Ocean Engineering and Their Analysis in Statistical Theory

    Yu-Ming Chu1, Saima Rashid2,*, Shazia Karim3, Anam Sultan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1573-1611, 2023, DOI:10.32604/cmes.2023.027724

    Abstract The fractional-order Boussinesq equations (FBSQe) are investigated in this work to see if they can effectively improve the situation where the shallow water equation cannot directly handle the dispersion wave. The fuzzy forms of analytical FBSQe solutions are first derived using the Adomian decomposition method. It also occurs on the sea floor as opposed to at the functionality. A set of dynamical partial differential equations (PDEs) in this article exemplify an unconfined aquifer flow implication. This methodology can accurately simulate climatological intrinsic waves, so the ripples are spread across a large demographic zone. The Aboodh transform merged with the mechanism… More >

  • Open Access


    Health Monitoring of Dry Clutch System Using Deep Learning Approach

    Ganjikunta Chakrapani, V. Sugumaran*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1513-1530, 2023, DOI:10.32604/iasc.2023.034597

    Abstract Clutch is one of the most significant components in automobiles. To improve passenger safety, reliability and economy of automobiles, advanced supervision and fault diagnostics are required. Condition Monitoring is one of the key divisions that can be used to track the reliability of clutch and allied components. The state of the clutch elements can be monitored with the help of vibration signals which contain valuable information required for classification. Specific drawbacks of traditional fault diagnosis techniques like high reliability on human intelligence and the requirement of professional expertise, have made researchers look for intelligent fault diagnosis techniques. In this article,… More >

  • Open Access


    Unsupervised Log Anomaly Detection Method Based on Multi-Feature

    Shiming He1, Tuo Deng1, Bowen Chen1, R. Simon Sherratt2, Jin Wang1,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392

    Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes… More >

  • Open Access


    Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model

    Fares Abdulhafidh Dael1,*, Ömer Çağrı Yavuz2, Uğur Yavuz1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903

    Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an interesting area. The purpose of… More >

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