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  • Open Access

    ARTICLE

    Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001

    Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes… More >

  • Open Access

    ARTICLE

    Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Khalid Masood Khan1, Mohammad A. Al Ghamdi3, Abdur Rehman2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1329-1342, 2020, DOI:10.32604/cmc.2020.011155

    Abstract An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine… More >

  • Open Access

    ARTICLE

    KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117

    Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis… More >

  • Open Access

    ARTICLE

    Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

    T. Jayasree1,*, R. Prem Ananth2

    Sound & Vibration, Vol.54, No.1, pp. 57-74, 2020, DOI:10.32604/sv.2020.08573

    Abstract Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by More >

  • Open Access

    ARTICLE

    Traffic Sign Recognition Method Integrating Multi-Layer Features and Kernel Extreme Learning Machine Classifier

    Wei Sun1,3,*, Hongji Du1, Shoubai Nie2,3, Xiaozheng He4

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 147-161, 2019, DOI:10.32604/cmc.2019.03581

    Abstract Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract… More >

  • Open Access

    ARTICLE

    A Novel Interacting Multiple-Model Method and Its Application to Moisture Content Prediction of ASP Flooding

    Shurong Li1,*, Yulei Ge2, Renlin Zang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.1, pp. 95-116, 2018, DOI:10.3970/cmes.2018.114.095

    Abstract In this paper, an interacting multiple-model (IMM) method based on data-driven identification model is proposed for the prediction of nonlinear dynamic systems. Firstly, two basic models are selected as combination components due to their proved effectiveness. One is Gaussian process (GP) model, which can provide the predictive variance of the predicted output and only has several optimizing parameters. The other is regularized extreme learning machine (RELM) model, which can improve the over-fitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance. Then both of the models are updated continually using meaningful… More >

  • Open Access

    ARTICLE

    Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds

    Liming Yang1,2, Junjian Bai1, Qun Sun3

    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 49-65, 2015, DOI:10.3970/cmes.2015.108.049

    Abstract Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l2-norm and l1-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on More >

  • Open Access

    ARTICLE

    Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

    B. V. Santhosh Krishna1, *, T. Gnanasekaran2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 855-869, 2019, DOI:10.32604/cmc.2019.07585

    Abstract An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages,… More >

  • Open Access

    ARTICLE

    Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

    Susom Dutta1, A. Ramach,ra Murthy2, Dookie Kim3, Pijush Samui4

    CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

    Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage More >

  • Open Access

    ARTICLE

    Prediction of Fracture Parameters of High Strength and Ultra-High Strength Concrete Beams using Minimax Probability Machine Regression and Extreme Learning Machine

    Vishal Shreyans Shah1, Henyl Rakesh Shah2, Pijush Samui3, A. Ramachra Murthy4

    CMC-Computers, Materials & Continua, Vol.44, No.2, pp. 73-84, 2014, DOI:10.3970/cmc.2014.044.073

    Abstract This paper deals with the development of models for prediction of facture parameters, namely, fracture energy and ultimate load of high strength and ultra high strength concrete based on Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM). MPMR is developed based on Minimax Probability Machine Classification (MPMC). ELM is the modified version of Single Hidden Layer Feed Foreword Network (SLFN). MPMR and ELM has been used as regression techniques. Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split More >

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