INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
Keywords:
sonar rock, prediction, logistic regression, accuracy, precision, underwater.
Cite Article:
"Sonar Rock vs Mine Prediction Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.f385-f389, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404548.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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