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  2. Volume 7, Issue 2
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Online ISSN: 2515-8260

Volume7, Issue2

An Empirical Study of Deep Learning Strategies for Spatial Data Mining

    K. Sivakumar A.S. Prakaash

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5124-5132

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Abstract

The emergence of scalable frameworks for machine learning to efficiently analyse and derive valuable insights from these data has triggered growing volumes of data collected. Huge spatial data frameworks cover a wide variety of priorities, including tracking of infectious diseases, simulation of climate change, etc. Conventional mining techniques, especially statistical frameworks to handling these data, are becoming exhausted due to the rise in the number, volume and quality of spatio-temporal data sets. Various machine learning tasks have recently shown efficiency with the development of deep learning methods. We therefore include a detailed survey in this paper on important impacts in the application of deep learning techniques to the mining of spatial data.
Keywords:
    Big data convolutional neural network Deep Learning machine learning Spatial Data Mining
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(2020). An Empirical Study of Deep Learning Strategies for Spatial Data Mining. European Journal of Molecular & Clinical Medicine, 7(2), 5124-5132.
K. Sivakumar; A.S. Prakaash. "An Empirical Study of Deep Learning Strategies for Spatial Data Mining". European Journal of Molecular & Clinical Medicine, 7, 2, 2020, 5124-5132.
(2020). 'An Empirical Study of Deep Learning Strategies for Spatial Data Mining', European Journal of Molecular & Clinical Medicine, 7(2), pp. 5124-5132.
An Empirical Study of Deep Learning Strategies for Spatial Data Mining. European Journal of Molecular & Clinical Medicine, 2020; 7(2): 5124-5132.
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