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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    NEW DEVELOPED METHODS USED FOR DATA SCIENCE OPTIMIZATION AS STATE-OF-THE-ART
    Mohamed Abdeldaiem Abdelhadi Mahboub1*
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1505-1515 . 

    Abstract

    Nowadays, there is a potential need in the new era of data science for introducing newly developed methods and algorithms to be used to formulate data science models by optimization solutions. We are very much concerned with studying and improving extraordinary work as Stat-of-the-Art methods and/or algorithms for solving data science problems with respect to its scalability, and efficiency; which mainly include gradient-descent based algorithms, derivative free algorithms. We do really believe that the best method which has the ability to meet our goals for optimization solutions; is to benefit from using machine learning capabilities. Optimization formulations and algorithms are both possible to lead the development of new optimization approaches that make significant changes presented by machine learning applications.

    Keyword

    Optimization Solutions, Data Science, new Soft-Set theory applications, Machine Learning, Deep Learning.


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ISSN 1004-9037

         

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