• T.Nikitha
• B.Hadhvika
• Dr.K.Vaidehi

Outwardly SONAR technique has exploited the discovery of rocks and minerals which would have been very difficult otherwise. The technique exploits certain parameters which will aid to detect the surface targets or obstacle such as a rock or a mine. Machine learning has drawn the attention of maximum part of today’s emerging technology, related from banking to many consumer and product based industries, by showing the advancements in the predictive analytics. The main aim is to emanate a capable prediction representative method united by the machine learning algorithmic characteristics, which can deduce if the target of the sound wave is a rock, a mine, any other organism or any kind of foreign body. This proposed work is a clear-cut case study which comes up with a machine learning plan for the grading of rocks and minerals, executed on a huge, highly spatial and complex SONAR dataset. The attempts are done on highly spatial SONAR dataset and achieved an accuracy of 83.17% and area under curve (AUC) came out to be 0.92. With random forest algorithm, the results are further optimized by feature selection to get the accuracy of 90%. Persuade results are found when the fulfillment of the designed groundwork is set side by side with the standard classifiers like SVM, random forest, etc. Different evaluation metrics like accuracy, sensitivity, etc are investigated. Machine learning is performing a major role in improving the quality of detection of underwater natural resources and will tend be better paradigm.

feature selection, data analytics, rocks and mines, machine learning, prediction, SONAR.

"Prediction of Underwater Surface Target through SONAR by using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.159-164, June 2019, Available :http://www.jetir.org/papers/JETIR1907H24.pdf

"Prediction of Underwater Surface Target through SONAR by using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp159-164, June 2019, Available at : http://www.jetir.org/papers/JETIR1907H24.pdf