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  • Open Access


    Incremental Learning Framework for Mining Big Data Stream

    Alaa Eisa1, Nora EL-Rashidy2, Mohammad Dahman Alshehri3,*, Hazem M. El-bakry1, Samir Abdelrazek1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2901-2921, 2022, DOI:10.32604/cmc.2022.021342

    Abstract At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant… More >

  • Open Access


    Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

    Hamid A. Jalab1, Ala’a R. Al-Shamasneh1, Hadil Shaiba2, Rabha W. Ibrahim3,4,*, Dumitru Baleanu5,6,7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170

    Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More >

  • Open Access


    Seasonal Characteristics Analysis and Uncertainty Measurement for Wind Speed Time Series

    Xing Deng1,2, Haijian Shao1,2,*, Xia Wang3,4

    Energy Engineering, Vol.117, No.5, pp. 289-299, 2020, DOI:10.32604/EE.2020.011126

    Abstract Wind speed’s distribution nature such as uncertainty and randomness imposes a challenge in high accuracy forecasting. Based on the energy distribution about the extracted amplitude and associated frequency, the uncertainty measurement is processed through Rényi entropy analysis method with time-frequency nature. Nonparametric statistical method is used to test the randomness of wind speed, more precisely, whether or not the wind speed time series is independent and identically distribution (i.i.d) based on the output probability. Seasonal characteristics of wind speed are analyzed based on self-similarity in periodogram under scales range generated by wavelet transformation to reasonably divide the original dataset and… More >

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