Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

TL;DR

This paper reviews how machine learning enables intelligent systems across various fields by addressing challenges, highlighting recent applications, and proposing future research opportunities.

Contribution

It provides a comprehensive survey of ML applications, challenges, and research opportunities in diverse domains like healthcare, finance, and social media.

Findings

ML addresses domain-specific challenges effectively.

Numerous research opportunities exist for ML in various fields.

ML applications improve decision-making and system efficiency.

Abstract

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.