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

• Published in: SN computer science Vol. 2; no. 3; p. 160
• Main Author: Sarker, Iqbal H.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.05.2021; Springer Nature B.V
• Subjects: Advances in Computational Approaches for Artificial Intelligence; Algorithms; Artificial intelligence; Automation; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; COVID-19; Cybersecurity; Data analysis; Data Structures and Information Theory; Datasets; Deep learning; Image Processing, IoT and Cloud Applications; Industrial applications; Industry 4.0; Information Systems and Communication Service; Internet of Things; Machine learning; Metadata; Pattern Recognition and Graphics; Review; Review Article; Smart cities; Smartphones; Software Engineering/Programming and Operating Systems; Vision; Deep learning; Data-driven decision-making; Intelligent applications; Machine learning; Data science; Artificial intelligence; Predictive analytics
• ISSN: 2662-995X; 2661-8907; 2661-8907
• DOI: 10.1007/s42979-021-00592-x

In the current age of the Fourth Industrial Revolution (4 IR 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 smart and automated  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 application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, 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 both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.