− Deep Learning and ML models to predict the percentage of Silica Concentrate in the Fe ore concentrate per min, to get the perSilica Concentrate at faster rate compared to the traditional methods.
− Used different MACHINE LEARNING ALGORITHMS to get the most possible accuracy of 98.56 percent with DECISION TREE to mitigate over-fitting when RANDOM FOREST is used.
− Developed and trained an ARTIFICIAL NEURAL NETWORK with TENSORFLOW having over 2 million parameters and an accuracy just over 91 percent.
