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.
Highlights
Machine Learning(ML) is one of the major multidisciplinary research areas
Different articles related to machine learning and their use in agriculture, finance, healthcare, education, and engineering has been explored by executing different queries on Google Scholar
Supervised learning techniques are important machine learning techniques that assist in data classification
Summary
Machine Learning(ML) is one of the major multidisciplinary research areas. There are three main categories of machine learning called supervised learning, unsupervised learning and reinforcement learning[2]. These techniques have been effectively used to solve a wide variety of classification, clustering and prediction problems. The inputs are labeled and these labels are the desired outputs. These techniques assist in the data classification process. Stock prediction, sentiment analysis are some of the major application areas for supervised learning techniques. An unsupervised learning technique known as clustering techniques deals with unlabeled data. There are some functions associated with these agents and they perform their operations in the specified environment to achieve the reward[3]
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