Abstract
Writing software (programs) is an obstruction, we do not have so many good developers who can develop much more enhanced models and so, for this purpose today many use the data instead of people to perform the same task. According to the generations’ needs, the programmers developed the machine learning approach to make the programming much more scalable and expandable in this domain. Before, machine learning traditional programming is a much more famous approach where programmers used to code each and every single line with their own and its main drawback is that it is not so much scalable. Here, in this chapter we are going to discuss various applications of machine learning and the algorithms they are using along with their advantage, disadvantage, and its working model of how much the particular application is scalable. Like we are going to discuss virtual personal assistants, email spam, online fraud detection, traffic predictions, social media personalization, and many more. In coming to algorithms, we will get to know about Naive Bayes algorithms, neural networks, KNN algorithms, linear regression model, logistic regression model, etc. On coming to today’s need we are also going to discuss its applications in detection of COVID-19 defaulters by the use of semantic segmentation algorithms.
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Singh, S., Anand, A., Mukherjee, S., Choudhury, T. (2022). Machine Learning Applications in Decision Intelligence Analytics. In: Jeyanthi, P.M., Choudhury, T., Hack-Polay, D., Singh, T.P., Abujar, S. (eds) Decision Intelligence Analytics and the Implementation of Strategic Business Management. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-82763-2_15
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