Effective Prediction for Rock Burst Dataset Using Classification Algorithms with Particle Swarm

Authors(2) :-Sadesh S, Banupriya GK

Rock burst and Slope Stability is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst and Slope Stability classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst and Slope Stability classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst and Slope Stability indicator for classification. In addition, analysis and prediction of slope stability is of great importance in geotechnical engineering. With the development of economics, the number of slopes is increasing and landslides caused by slope instability have become one of the three major geological disasters in the world along with earthquakes and volcanoes. To reduce or prevent landslide damage, slope stability analysis and stabilization are required. However, accurately predicting slope stability is challenging because of the complexity of slope structures and the difficulty to determine the precise input data associated with key geotechnical parameters the proposed methodology PSO feature extraction preserves important distance relationships, such as : The Random forest, Naive Bayes of each object of the original dataset. This leads to preservation of any mining operation that depends on the ordering of distances between objects, such as Random forest, Naive Bays -search, SVM, J.48 and MLP classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, i.e., tight bounds on the contraction/expansion of the original distances.

Authors and Affiliations

Sadesh S
Department of Computer Science and Engineering, Velalar College of Engineering &Technology, Erode, Tamilnadu, India
Banupriya GK
ME, Department of Computer Science and Engineering, Velalar College of Engineering &Technology, Erode, Tamilnadu, India

Raspberry Pi, USB Camera, Motor Driver, Motor, LCD Display, Road Side Speed Sign Controlling.

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 180-184
Manuscript Number : CSEIT195122
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sadesh S, Banupriya GK, "Effective Prediction for Rock Burst Dataset Using Classification Algorithms with Particle Swarm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.180-184, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT195122
Journal URL : http://ijsrcseit.com/CSEIT195122

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