Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas

Sharma, Samriti and Singh, Gurvinder and Singh, Dhanpreet (2019) Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas. EAI Endorsed Transactions on Scalable Information Systems, 6 (21): e2. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Machine Learning, Agriculture, Engineering, Education, Stock Forecasting, Disease Diagnosis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:55
Last Modified: 08 Oct 2020 13:55
URI: https://eprints.eudl.eu/id/eprint/705

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