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DNA Sequencing using M achine L earning and D eep L earning A lgorithms

Abstract: DNA Sequencing plays a vital role in the modern research. It allows a large number of multiple areas to progress, as well as genetics, meta-genetics, and phylogenetics. DNA Sequencing involves extracting and reading the strands of DNA. This research paper aims at comparing DNA Sequencing using “Machine Learning algorithms (Decision Trees, Random Forest, and Naive Bayes) and Deep Learning algorithms (Transform Learning and CNN)”. The aim of our proposed system is to implement a better prediction model for DNA research and get the most accurate results out of it. The “machine learning and deep learning models” which are being considered are the most used and reputed. A prediction accuracy of the higher range in deep learning is also being used which is also the better performer in different medical domains. The proposed models include “Decision Tree, Random Forest, Naive Bayes, CNN, and Transform Learning”. The Naive Bayes method gave greater accuracy of 98.00 percent in machine learning and the transform learning algorithm produced better accuracy of 94.57 percent in deep learning, respectively.
Keywords: Learning algorithms / Naive Bayes / machine learning / percent / DNA Sequencing / models

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