A Machine Learning Approach for the Land-Type Classification-Reference-Cited by-同舟云学术

A Machine Learning Approach for the Land-Type Classification

Published:2021 Issue: Volume: Page:647-656
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Kumar Arvind,Choudhary Tejalal

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-0749-3_51

Reference23 articles.

1. G. Singal, A. Goswami, S. Gupta, C.T. Pitfree, Pot-holes detection on Indian roads using mobile sensors, in 2018 IEEE 8th International Advance Computing Conference (IACC) (IEEE, 2018), pp. 185–190

2. A. Kumar, A. Kakkar, R. Majumdar, A.S. Baghel, Spatial data mining: recent trends and techniques, in 2015 International Conference on Computer and Computational Sciences (ICCCS) (IEEE, 2015), pp. 39–43

3. T. Choudhary, V. Mishra, A. Goswami, J. Sarangapani, A comprehensive survey on model compression and acceleration. Artif. Intell. Rev. 1–43 (2020)

4. A. Kumar, N. Sinha, A. Bhardwaj, A novel fitness function in genetic programming for medical data classification. J. Biomed. Inf. 112, 103623 (2020)

5. A. Srinivasan, UCI machine learning repository, statlog (landsat satellite) data set (2017). Available from: https://archive.ics.uci.edu/ml/datasets/Statlog+(Landsat+Satellite)

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