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Review
. 2021;2(3):160.
doi: 10.1007/s42979-021-00592-x. Epub 2021 Mar 22.

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

Iqbal H Sarker  1   2
Affiliations
Review

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

Iqbal H Sarker. SN Comput Sci. 2021.

Abstract

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 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.

Keywords: Artificial intelligence; Data science; Data-driven decision-making; Deep learning; Intelligent applications; Machine learning; Predictive analytics.

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Conflict of interest statement

Conflict of interestThe author declares no conflict of interest.

Figures

Fig. 1
Fig. 1
The worldwide popularity score of various types of ML algorithms (supervised, unsupervised, semi-supervised, and reinforcement) in a range of 0 (min) to 100 (max) over time where x-axis represents the timestamp information and y-axis represents the corresponding score
Fig. 2
Fig. 2
Various types of machine learning techniques
Fig. 3
Fig. 3
A general structure of a machine learning based predictive model considering both the training and testing phase
Fig. 4
Fig. 4
An example of a decision tree structure
Fig. 5
Fig. 5
An example of a random forest structure considering multiple decision trees
Fig. 6
Fig. 6
Classification vs. regression. In classification the dotted line represents a linear boundary that separates the two classes; in regression, the dotted line models the linear relationship between the two variables
Fig. 7
Fig. 7
A graphical interpretation of the widely-used hierarchical clustering (Bottom-up and top-down) technique
Fig. 8
Fig. 8
An example of a principal component analysis (PCA) and created principal components PC1 and PC2 in different dimension space
Fig. 9
Fig. 9
Machine learning and deep learning performance in general with the amount of data
Fig. 10
Fig. 10
A structure of an artificial neural network modeling with multiple processing layers
Fig. 11
Fig. 11
An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers
See this image and copyright information in PMC

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References

    1. Canadian institute of cybersecurity, university of new brunswick, iscx dataset, http://www.unb.ca/cic/datasets/index.html/ (Accessed on 20 October 2019).
    1. Cic-ddos2019 [online]. available: https://www.unb.ca/cic/datasets/ddos-2019.html/ (Accessed on 28 March 2020).
    1. World health organization: WHO. http://www.who.int/.
    1. Google trends. In https://trends.google.com/trends/, 2019.
    1. Adnan N, Nordin Shahrina Md, Rahman I, Noor A. The effects of knowledge transfer on farmers decision making toward sustainable agriculture practices. World J Sci Technol Sustain Dev. 2018.