Predicting sonar rocks against mines using machine learning
By: Sree, P. Vinaya
.
Contributor(s): Bhavana, Ch.
Publisher: Gurugram IOSR - International Organization of Scientific Research 2022Edition: Vol.24(5), Sep-Oct.Description: 48-51p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: Underwater Mine usage by the Naval Defense System provides great Security but also possesses a threat to the
marine life and submarine vessels as the mines can be easily mistaken for rocks. So, we need a much more
accurate system to predict the object as it is very dangerous if a mistake is made. To have a great accuracy we
need more accurate data to generate accurate results. Our idea presents a method for prediction of underwater
mines and rocks using Sonar Signals. Sonar Signals are used to record the various frequencies of underwater
objects at 60 different angles. We constructed three binary classifier models according to their accuracy. Then,
prediction models are used to predict the mine and rock categories. Python and Supervised Machine Learning
Classification algorithms are used to construct these prediction models.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0084 |
Underwater Mine usage by the Naval Defense System provides great Security but also possesses a threat to the
marine life and submarine vessels as the mines can be easily mistaken for rocks. So, we need a much more
accurate system to predict the object as it is very dangerous if a mistake is made. To have a great accuracy we
need more accurate data to generate accurate results. Our idea presents a method for prediction of underwater
mines and rocks using Sonar Signals. Sonar Signals are used to record the various frequencies of underwater
objects at 60 different angles. We constructed three binary classifier models according to their accuracy. Then,
prediction models are used to predict the mine and rock categories. Python and Supervised Machine Learning
Classification algorithms are used to construct these prediction models.
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