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

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 corre...

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Vydáno v:SN computer science Ročník 2; číslo 3; s. 160
Hlavní autor: Sarker, Iqbal H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 01.05.2021
Springer Nature B.V
Témata:
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
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Popis
Shrnutí: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.
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ISSN:2662-995X
2661-8907
2661-8907
DOI:10.1007/s42979-021-00592-x