Assessing Effectiveness of Exercised Variants of Machine Learning Techniques

Abstract

Currently, the research field of machine learning is receiving more attention due to the automatic visual inspection of different tasks. Thus, the machine learning scheme is incorporated with deep learning and artificial intelligence technology. However, there are different schemes (perception based, instance-based and logic based) to provide an effective classification, prediction, and data recognition in terms of characterizing the features at different conditions to design a suitable module. This investigational study provides a comprehensive study on different algorithmic approaches which are categorized in terms of classification-based, prediction-based and segmentation based approaches. Also, this study evaluates the performance of various machine learning methods and their applications in different fields and also their limitations. Finally, this study leads to identifying the research gaps in the machine learning approach, which can be addressed by further research.