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  • Open Access

    ARTICLE

    A Markov Model for Subway Composite Energy Prediction

    Xiaokan Wang1,2,*, Qiong Wang1, Liang Shuang3, Chao Chen4

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 237-250, 2021, DOI:10.32604/csse.2021.015945 - 20 July 2021

    Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard More >

  • Open Access

    ARTICLE

    iPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC

    ALAA OMRAN ALMAGRABI1, YASER DAANIAL KHAN2, SHER AFZAL KHAN3,*

    BIOCELL, Vol.45, No.5, pp. 1287-1298, 2021, DOI:10.32604/biocell.2021.013770 - 12 July 2021

    Abstract Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspartate sites is critical, and its experimental identification can be expensive, time-consuming, and tedious. For this purpose, we propose iPhosD-PseAAC, a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues: (1) Benchmark dataset. (2) The feature extraction techniques such as pseudo More >

  • Open Access

    ARTICLE

    Prediction of the Corrosion Rate of Al–Si Alloys Using Optimal Regression Methods

    D. Saber1,*, Ibrahim B. M. Taha2, Kh. Abd El-Aziz3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 757-769, 2021, DOI:10.32604/iasc.2021.018516 - 01 July 2021

    Abstract In this study, optimal regression learner methods were used to predict the corrosion behavior of aluminum–silicon alloys (Al–Si) with various Si ratios in different media. Al–Si alloys with 0, 1%, 8%, 11.2%, and 15% Si were tested in different media with different pH values at different stirring speeds (0, 300, 600, 750, 900, 1050, and 1200 rpm). Corrosion behavior was evaluated via electrochemical potentiodynamic test. The corrosion rates (CRs) obtained from the corrosion tests were utilized in the formation of datasets of various machine regression learner optimization (MRLO) methods, namely, decision tree, support vector machine,… More >

  • Open Access

    ARTICLE

    Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

    Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405 - 01 July 2021

    Abstract Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) More >

  • Open Access

    ARTICLE

    Surge Fault Detection of Aeroengines Based on Fusion Neural Network

    Desheng Zheng1, Xiaolan Tang1,*, Xinlong Wu1, Kexin Zhang1, Chao Lu2, Lulu Tian3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 815-826, 2021, DOI:10.32604/iasc.2021.017737 - 01 July 2021

    Abstract Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor More >

  • Open Access

    ARTICLE

    A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

    Dejia Shi1, Hanzhong Zheng2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917 - 28 June 2021

    Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN).… More >

  • Open Access

    ARTICLE

    Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

    Chenjing Su1, Xiaoyu Li1,*, Mengru Li2, Qinsheng Zhu2, Hao Fu2, Shan Yang3

    Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/jqc.2021.016651 - 22 June 2021

    Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this More >

  • Open Access

    ARTICLE

    Prediction of Mechanical Properties of Structural Bamboo and Its Relationship with Growth Parameters

    Pengcheng Liu, Ping Xiang, Qishi Zhou*, Hai Zhang, Jiefu Tian, Misganu Demis Argaw

    Journal of Renewable Materials, Vol.9, No.12, pp. 2223-2239, 2021, DOI:10.32604/jrm.2021.015544 - 22 June 2021

    Abstract Bamboo is a renewable natural building material with good mechanical properties. However, due to the heterogeneity and anisotropy of bamboo stalk, a large amount of material performance testing costs are required in engineering applications. In this work, longitudinal compression, bending, longitudinal shear, longitudinal tensile, transverse compression and transverse tensile tests of bamboo materials are conducted, considering the influence of the bamboo nodes. The mechanical properties of the whole bamboo stalk with the wall thickness and outer circumference are explored. Through univariate and multiple regression analysis, the relationship between mechanical properties and wall thickness and perimeter More > Graphic Abstract

    Prediction of Mechanical Properties of Structural Bamboo and Its Relationship with Growth Parameters

  • Open Access

    ARTICLE

    CNN-Based Voice Emotion Classification Model for Risk Detection

    Hyun Yoo1, Ji-Won Baek2, Kyungyong Chung3,*

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115 - 16 June 2021

    Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions… More >

  • Open Access

    REVIEW

    Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

    Faseeha Matloob1, Shabib Aftab1,2, Munir Ahmad2, Muhammad Adnan Khan3,*, Areej Fatima4, Muhammad Iqbal2, Wesam Mohsen Alruwaili5, Nouh Sabri Elmitwally5,6

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562 - 16 June 2021

    Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are More >

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