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  • Open Access


    A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

    Amgad Muneer1,2,*, Shakirah Mohd Taib1,2, Suliman Mohamed Fati3, Abdullateef O. Balogun1, Izzatdin Abdul Aziz1,2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5363-5381, 2022, DOI:10.32604/cmc.2022.021113

    Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a… More >

  • Open Access


    A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

    Samar Nour1,*, Sameh A. Salem1,2, Shahira M. Habashy1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4447-4466, 2022, DOI:10.32604/cmc.2022.020775

    Abstract Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance. In this paper, an effective and reliable hybrid model to reduce the energy and makespan in multicore systems is proposed. The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency (Vmin/F) levels. Then, the allocation process is applied based on the available workloads. The hybrid model consists of three stages. The first stage gets the optimum safe voltage while the second stage sets… More >

  • Open Access

    A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

    Mohammad Hadwan1,2,3,*, Basheer M. Al-Maqaleh4 , Fuad N. Al-Badani5 , Rehan Ullah Khan1,3, Mohammed A. Al-Hagery6

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4829-4845, 2022, DOI:10.32604/cmc.2022.017824


    Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient forecasting accuracy. The present study proposes a hybrid forecasting methods to address this need. The proposed method includes three models. The first model is based on the autoregressive integrated moving average (ARIMA) statistical model; the second model is a back propagation neural network (BPNN) with adaptive slope and momentum parameters; and the third model is a hybridization between ARIMA and BPNN (ARIMA/BPNN) and artificial neural networks and ARIMA… More >

  • Open Access


    A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines

    Xiyang Li1,2, Bin Cheng1,2, Hui Zhang1,2,*, Xianghan Zhang1, Zhi Yun1

    Energy Engineering, Vol.118, No.6, pp. 1869-1886, 2021, DOI:10.32604/EE.2021.015542

    Abstract With the continuous increase in the proportional use of wind energy across the globe, the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consideration for research. Therefore, it is crucial to accurately analyze the thickness of icing on wind turbine blades, which can serve as a basis for formulating corresponding control measures and ensure a safe and stable operation of wind turbines in winter times and/or in high altitude areas. This paper fully utilized the advantages of the support vector machine (SVM) and back-propagation neural network (BPNN), with the… More >

  • Open Access


    A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

    Omar Almomani*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 409-429, 2021, DOI:10.32604/cmc.2021.016113

    Abstract Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one… More >

  • Open Access


    Short-term Forecasting of Air Passengers Based on the Hybrid Rough Set and the Double Exponential Smoothing Model

    Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

    Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that the forecasting accuracy of the… More >

  • Open Access


    A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

    Sasan Saqaeeyan1, Hamid Haj Seyyed Javadi1,2,*, Hossein Amirkhani1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848

    Abstract Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone. Compared to the previous studies done on this topic, less attention has been given to hybrid methods. This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home. First, it employs various algorithms with different characteristics to detect anomalies from sensory data. Then, it aggregates their results using a Bayesian network. In this Bayesian network, abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods. Experimental evaluation of a real dataset indicates… More >

  • Open Access


    A Hybrid Local/Nonlocal Continuum Mechanics Modeling and Simulation of Fracture in Brittle Materials

    Yongwei Wang1, Fei Han2,*, Gilles Lubineau1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 399-423, 2019, DOI:10.32604/cmes.2019.07192

    Abstract Classical continuum mechanics which leads to a local continuum model, encounters challenges when the discontinuity appears, while peridynamics that falls into the category of nonlocal continuum mechanics suffers from a high computational cost. A hybrid model coupling classical continuum mechanics with peridynamics can avoid both disadvantages. This paper describes the hybrid model and its adaptive coupling approach which dynamically updates the coupling domains according to crack propagations for brittle materials. Then this hybrid local/nonlocal continuum model is applied to fracture simulation. Some numerical examples like a plate with a hole, Brazilian disk, notched plate and beam, are performed for verification… More >

  • Open Access


    A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

    Assia Maamar1,*, Khelifa Benahmed2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

    Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to… More >

  • Open Access


    Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

    Baojia Wang1, Pingzeng Liu1,*, Zhang Chao1, Wang Junmei1, Weijie Chen1, Ning Cao2, Gregory M.P. O’Hare3, Fujiang Wen1

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 283-296, 2018, DOI:10.32604/cmc.2018.03791

    Abstract Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict the nonlinear part of garlic… More >

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