MACHINE LEARNING FUTURE DRIVE TOWARDS ENHANCED LEARNING- A REVIEW

Abstract

Real-world applications are strongly associated with data, their storage, their consistency etc. that require Machine Learning (ML) Techniques. While this data is maintained, it may sometimes lead to poor quality of data, insufficient information which has an adverse effect on the applications of Machine Learning techniques. Various data storage techniques may encounter issues in accessing which is adversely correlated to the data privacy, its security and the regulations that encompasses it. Considering these challenges, our study discusses extensive approach to review the existing research that discusses the various modeling techniques of machine learning that can enhance the data regulations. Our study involves various dynamics of data revolutions, Human Computer Integration, Natural Language processing, medical observations and various corporate aspects. We have also discussed the critical analysis of the data privacy integrated with machine learning models and their simulation with neural network and AI based generative models. The short-comings and opportunities that prevail in this field and their potentialities for the future developments are also discussed.