Sonar Rock vs Mine Prediction Using Machine Learning

Registration ID: IJNRD_218677

Published ID: IJNRD2404548

DOI: Click Here to Get

Subhasini , Agnes , Shruthika , Vaishnavi

sonar rock, prediction, logistic regression, accuracy, precision, underwater.

This study employs logistic regression for predicting underwater objects as rocks or mines using sonar technology. The dataset consists of labeled acoustic signals, with each signal categorized as either a rock or a mine. The research involves essential steps such as data preprocessing, feature extraction, and logistic regression model implementation. The research involves preparing and cleaning data, extracting relevant features, and implementing a logistic regression model to predict outcomes. Feature selection techniques are explored to identify critical acoustic features contributing to the predictive accuracy of the model. Performance is evaluated using metrics like accuracy, precision, recall, and F1 score, providing a comprehensive assessment of the logistic regression model. The objective is to enhance underwater security by improving the reliability of sonar-based systems in differentiating between harmless geological formations and potentially dangerous mines.

Published Paper   E-Certificate

Subhasini, Agnes, Shruthika, & Vaishnavi (April-2024). Sonar Rock vs Mine Prediction Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), f385-f389. https://ijnrd.org/papers/IJNRD2404548.pdf

Volume 9 Issue 4, April-2024

Pages : f385-f389

Paper Reg. ID: IJNRD_218677

Published Paper Id: IJNRD2404548

Downloads: 000121985

Research Area: Information Technology 

Country: Tirunelveli, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404548.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404548

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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