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Machine Learning Techniques Application: Social Media, Agriculture, and Scheduling in Distributed Systems
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Author(s): Karthikeyan P. (Presidency University Bangalore, India), Karunakaran Velswamy (Karunya Institute of Technology and Sciences, India), Pon Harshavardhanan (VIT Bhopal, India), Rajagopal R. (Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, India), JeyaKrishnan V. (Saintgits College of Engineering, India)and Velliangiri S. (CMR Institute of Technology, India)
Copyright: 2021
Pages: 22
Source title:
Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5339-8.ch068
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Abstract
Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.
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