Abstract
The digitally-enhanced environment is susceptible to massive data, such as information security data, internet technology data, cellular internet, patient records, media data, corporate data, and so on, in the current era of Industry 4.0. Understanding of Machine Learning (ML) is essential for intelligently evaluating these sets of data and developing related "intelligent" and "automated" solutions. Different forms of ML algorithms e.g. reinforcement learning, semi-supervised, unsupervised and supervised learning exist in this segment. In addition, deep learning, which is a wider segment of ML techniques, can smartly evaluate datasets on a massive scale. In this research, a comprehensive analysis of ML techniques and classification analysis algorithms that are applicable to develop capabilities and intelligence of applications are analyzed. Therefore, this research’s contribution is illustrating the key principles of various ML techniques and their application in different real-life application realms e.g. e-commerce, healthcare, agriculture, smart cities, cyber-security systems etc. Lastly, this paper presents a discussion of the challenges and future research based on this research.
Highlights
Our lives are digitally recorded, and everything around us is linked to a data source
It's critical to comprehend the concepts of diverse machine learning techniques and their relevance to a variety of real-world applications, such as Internet of Things (IoT) network, information security solutions, business or recommendations structures, intelligent buildings, universal health care and COVID-19, context-aware applications, sustainable farming, etc
Choosing a classification model, which is appropriate for a particular application, might be problematic since the results of various Machine Learning (ML) algorithms might be different based on the quality of datasets
Summary
Our lives are digitally recorded, and everything around us is linked to a data source. The data may be organized, semi-structured, or unstructured, and it is increasing at an exponential pace This dataset's information may be used to build a number of smart applications in the relevant areas. It's critical to comprehend the concepts of diverse machine learning techniques and their relevance to a variety of real-world applications, such as IoT network, information security solutions, business or recommendations structures, intelligent buildings, universal health care and COVID-19, context-aware applications, sustainable farming, etc. Unsupervised: Unsupervised learning is a data-driven method that examines unmarked datasets but without user intercession This is commonly used for generating feature extraction, detecting relevant configurations, classifications in findings, and experimental reasons. Semi-supervised learning is utilized in a variety of applications, including language processing, intrusion detection, dataset labelling, and text categorization
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