Research Article
Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas
@ARTICLE{10.4108/eai.13-7-2018.158419, author={Samriti Sharma and Gurvinder Singh and Dhanpreet Singh}, title={Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={6}, number={21}, publisher={EAI}, journal_a={SIS}, year={2019}, month={4}, keywords={Machine Learning, Agriculture, Engineering, Education, Stock Forecasting, Disease Diagnosis}, doi={10.4108/eai.13-7-2018.158419} }
- Samriti Sharma
Gurvinder Singh
Dhanpreet Singh
Year: 2019
Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas
SIS
EAI
DOI: 10.4108/eai.13-7-2018.158419
Abstract
In the last few years, different machine learning techniques such as supervised, unsupervised, and reinforcement learning have been effectively employed to solve distinct real-life multidisciplinary problems. These techniques have been effectively applied to accurately predict the problems related to stock values, disease diagnosis, sentiment analysis, text processing, gene classification, crop prediction, and weather forecasting. The objective of this manuscript is to present the systematic review on the use of these techniques in five major domains i.e. agriculture, finance, healthcare, education and engineering. A standard review methodology has been adapted to include and exclude the related literature. The performance of different supervised and nature-inspired computing techniques have been accessed on the basis of different performance metrics. The publication trend on the use of machine learning techniques in these five research areas has been also explored. Finally, the gaps in the study have been identified that will assist prospective researchers who want to pursue their research in these areas.
Copyright © 2019 Samriti Sharma et al., licensed to EAI. This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.