Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning: Algorithms, Real-World Applications and Research Directions

Version 1 : Received: 7 March 2021 / Approved: 8 March 2021 / Online: 8 March 2021 (12:55:59 CET)

A peer-reviewed article of this Preprint also exists.

Journal reference: SN Computer Science 2021
DOI: 10.1007/s42979-021-00592-x


In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.


machine learning; deep learning; artificial intelligence; data science; data-driven decision making; predictive analytics; intelligent applications;


MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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