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  • Open Access

    ARTICLE

    An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

    Norwini Zaidi1, Iskandar Ishak2,*, Fatimah Sidi2, Lilly Suriani Affendey2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1175-1188, 2022, DOI:10.32604/csse.2022.021969

    Abstract Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However, there is no standard guideline for data transformation from relational database to NoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processing time when compared to the relational database query processing time. However, these approaches produced redundant tables in the resulted schema that in turn consume large unnecessary storage size and produce high query processing time due to the generated schema with redundant column families in the transformed column-oriented… More >

  • Open Access

    ARTICLE

    Noisy ECG Signal Data Transformation to Augment Classification Accuracy

    Iqra Afzal1, Fiaz Majeed1, Muhammad Usman Ali2, Shahzada Khurram3, Akber Abid Gardezi4, Shafiq Ahmad5, Saad Aladyan5, Almetwally M. Mostafa6, Muhammad Shafiq7,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.022711

    Abstract In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The proposed model uses ECG signals… More >

  • Open Access

    ARTICLE

    Novel Power Transformer Fault Diagnosis Using Optimized Machine Learning Methods

    Ibrahim B.M. Taha1, Diaa-Eldin A. Mansour2,*

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 739-752, 2021, DOI:10.32604/iasc.2021.017703

    Abstract Power transformer is one of the more important components of electrical power systems. The early detection of transformer faults increases the power system reliability. Dissolved gas analysis (DGA) is one of the most favorite approaches used for power transformer fault prediction due to its easiness and applicability for online diagnosis. However, the imbalanced, insufficient and overlap of DGA dataset impose a challenge towards powerful and accurate diagnosis. In this work, a novel fault diagnosis for power transformers is introduced based on DGA by using data transformation and six optimized machine learning (OML) methods. Four data transformation techniques are used with… More >

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