DNA Sequencing using Machine Learning and Deep Learning Algorithms
Varada Venkata Sai Dileep1, Navuduru Rishitha2, Rakesh Gummadi3, Natarajan.P4
1Varada Venkata Sai Dileep, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Navuduru Rishitha, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3Rakesh Gummadi, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
4Prof. Natarajan.P, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 28 August 2022 | Revised Manuscript received on 04 September 2022 | Manuscript Accepted on 15 September 2022 | Manuscript published on 30 September 2022 | PP: 20-27 | Volume-11 Issue-10, September 2022 | Retrieval Number: 100.1/ijitee.J927309111022 | DOI: 10.35940/ijitee.J9273.09111022
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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: DNA Sequencing, Machine Learning, Random Forest, Decision Tree, Naive Bayes, Deep Learning, Transform Learning, CNN.
Scope of the Article: Machine Learning