Open AccessA Survey of Machine Learning models for the wide Spectrum of Computational Biology
Author(s) -
Gangadhar Immadi,
Akashansh Jain,
R Bhavya,
K.V. R.V. Prasanth Kumar
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2063149
Subject(s) - artificial intelligence , machine learning , computer science , field (mathematics) , pace , unsupervised learning , process (computing) , reinforcement learning , supervised learning , deep learning , computational learning theory , artificial neural network , mathematics , geodesy , pure mathematics , geography , operating system
With the Advent of advancement in the field of Artificial Intelligence the computer is made more intelligent and can enable to think and make prediction accurately. The machine learning being a subfield of Artificial Intelligence is used in numerous research works. Different analysts feel that enormous data generated in field of biology have to be sorted in an intelligent way to yield best model. There are numerous kinds of Machine Learning Techniques like Unsupervised, Semi Supervised, Supervised, Reinforcement, and Evolutionary Learning and Deep Learning. These learning’s are used to classify huge data at a rapid pace. This paper discusses about the wide spectrum of Biology and the process of pre-processing data and the best suitable Machine learning model for each of them.