MACHINE LEARNING WITH APPLICATIONS
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With the increasing amount of data that are generated everyday worldwide, there can be security problems with them. So, we have to extend the security needs beyond the traditional approach which emerges the field of cybersecurity. In the first chapter we describe some of the techniques of Deep learning for cybersecurity. Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. In the second chapter we have given an application example of sentiment analysis using R, mainly, that of the Tolstoy's book, "Anna Karenina". For our analysis, we have u...
With the increasing amount of data that are generated everyday worldwide, there can be security problems with them. So, we have to extend the security needs beyond the traditional approach which emerges the field of cybersecurity. In the first chapter we describe some of the techniques of Deep learning for cybersecurity. Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. In the second chapter we have given an application example of sentiment analysis using R, mainly, that of the Tolstoy's book, "Anna Karenina". For our analysis, we have used the AFINN, BING and NRC lexicons. In Chapter 3, we analyze the SVM from the viewpoint of mathematical programming, solving a numerical example using the grossone. Today, IoT drives the world and changes people lives with its wide range of services and applications. The fourth chapter is dedicated to IoT security using ML&DL. FHE has been considered as the "holy grail" of cryptography forits adaptability as a cryptographic primitive and wide range of potential applications. We have studied it in the Chapter 5.
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