Pradeep Singh - Fundamentals and Methods of Machine and Deep Learning

More than a 100 million users use Netflix, and there is no doubt that it is the most-streamed web service in the whole world. Netflix application use ML algorithms which collect a massive amount of data about the users, when the user pauses, rewinds, or fast forwards. It also takes data depending on the day you watch the content, the date and time, and mainly the rating pattern and search pattern. The application collects these data from each of their users they have and use their recommendation systems and a lot of algorithms related to ML approaches.

Currently, online credit card fraud detection is 32 billion dollars market in 2020. That is approximately higher than the profit made by many MNC companies combined. Nowadays, the number of payment channels like a credit card, debit card, numerous wallets, UPI, and much more has increased the number of criminals. ML approaches fraud detection as a classification problem.

In the anticipating years, ML will embrace a significant reason in the divulgence of data from the abundance of information that is at present open in a different zone of utilization. The supervised learning strategies are developing constantly by the information researchers, which contain an enormous arrangement of algorithms. This zone has the consideration of numerous engineers and has picked up generous advancement in the most recent decade. The learning strategies accomplish magnificent execution that would have been hard to get in the earlier many years. Given the reckless development, there is a lot of room for the engineers to work effectively and to develop the SML strategies.

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1 * Corresponding author: shruthihshetty02@gmail.com

2 † Corresponding author: sumikshashetty7@gmail.com

3 ‡ Corresponding author: chandrasingh146@gmail.com

4 § Corresponding author: ashu1280@gmail.com

Bhargavi K.

Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, India

Abstract

Zonotic diseases are a kind of infectious disease which spreads from animals to humans; the disease usually spreads from infectious agents like virus, prion and bacteria. The identification and controlling the spread of zonotic disease is challenging due to several issues which includes no proper symptoms, signs of zoonoses are very similar, improper vaccination of animals, and poor knowledge among people about animal health. Ensemble machine learning uses multiple machine learning algorithms, to arrive at better performance, compared to individual/stand-alone machine learning algorithms. Some of the potential ensemble learning algorithms like Bayes optimal classifier, bootstrap aggregating (bagging), boosting, Bayesian model averaging, Bayesian model combination, bucket of models, and stacking are helpful in identifying zonotic diseases. Hence, in this chapter, the application of potential ensemble machine learning algorithms in identifying zonotic diseases is discussed with their architecture, advantages, and applications. The efficiency achieved by the considered ensemble machine learning techniques is compared toward the performance metrics, i.e., throughput, execution time, response time, error rate, and learning rate. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high in identifying of the zonotic diseases.

Keywords :Zonotic disease, ensemble machine learning, Bayes optimal classifier, bagging, boosting, Bayesian model averaging, Bayesian model combination, stacking

Zonotic diseases are a kind of infectious disease which spreads from animals to human beings; the disease usually spreads from infectious agents like virus, prion, virus, and bacteria. The human being who gets affected first will, in turn, spread that disease to other human beings likewise the chain of disease builds. The zonotic disease gets transferred in two different mode of transmission, one is direct transmission in which disease get transferred from animal to human being, and the other is intermediate transmission in which the disease get transferred via intermediate species that carry the disease pathogen. The emergence of zonotic diseases usually happens in large regional, global, political, economic, national, and social forces levels. There are eight most common zonotic diseases which spread from animal to humans on a wider geographical area which include zonotic influenza, salmonellosis, West Nile virus, plague, corona viruses, rabies, brucellosis, and lyme disease. Early identification of such infectious disease is very much necessary which can be done using ensemble machine learning techniques [1, 2].