Rock vs Mine Prediction and Detection for Aquatic Systems: A Comparative Analysis of Different Machine Learning and Deep Learning Algorithms

Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and efficiency in rock and mineral prediction. The researchers employed a series of watermarks that appeared and then utilized a convolutional neural network (CNN) to extract significant characteristics. Using transfer learning approaches and state-of-the-art optimization algorithms, researchers suggested a multi-class solution to identify between rocks and minerals. The suggested technique accurately predicted and recognized rocks and mines in underwater imagery, even in complicated settings with complicated against and illumination circumstances. The finding was essential for water protection and maritime safety systems, as it can assist in the automatic identification of rocks and minerals, decreasing the time and effort necessary for manual inspection