Machine Learning Techniques Application: Social Media, Agriculture, and Scheduling in Distributed Systems

Machine Learning Techniques Application: Social Media, Agriculture, and Scheduling in Distributed Systems

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)
DOI: 10.4018/978-1-5225-9902-9.ch020

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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Introduction

Machine learning (ML) is a discipline of information technology that gives machines the ability to “learn” with data, without being expressly programmed, this means in other words that these programs change their behavior by learning from data. Machine learning is the future of computing. From home automation to landing a rover on a comet, everything is digitalized, and these are made possible only because of intelligent systems that use Machine learning and their associated applications (Wernick, Yang, Brankov, Yourganov, & Strother, 2010). With advances in ML, probabilistic standards and deep neural network machine can now promptly convert spoken conversion and written, identify and precisely caption photos, recognize faces and be a personal assistant for the consumers. The latest inventions in Artificial Intelligence (AI) are the result of core advances in AI, including improvements in ML, and perception, on a stage set by improvements in multiple fields of computer science. Processing power has raised and has compared to cloud computing. For the meantime, the extension of the web has produced chances to collect, store and share massive volumes of data (Hatcher & Yu, 2018). There also have been prominent strides in probabilistic modeling, in which processing system examine probabilities and make the best solution or suggestion, and ML in which a computer gets better at something based on the data that it collects. This process proves that the future of machine learning is heading in many ways making the lives of millions of people more comfortable, productive, and effective. Figure 1 depicts the Application of various Machine Learning Techniques.

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