Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, The Netherlands, 2007.
5. Osisanwo, F.Y., Akinsola, J.E.T., Awodele, O., Hinmikaiye, J.O., Olakanmi, O., Akinjobi, J., Supervised Machine Learning Algorithms: Classification and Comparison. Int. J. Comput. Trends Technol. (IJCTT), 48, 3, 128–138, June 2017.
6. Brownlee, J., Linear Regression for Machine Learning – Machine Learning Mastery, [online] Machine Learning Mastery https://machinelearningmastery.com/linear-regression-for-machine-learning/.
7. Brownlee, J., Logistic Regression for Machine Learning – Machine Learning Mastery, [online] Machine Learning Mastery Available at: https://machine-learningmastery.com/logistic-regression-for-machine-learning/.
8. Mitchell, T., Machine Learning, 432pp., McGraw Hill, Carnegie Mellon University, 2015.
9. https://www.javatpoint.com/machine-learning
11. https://www.edureka.co/blog/supervised-learning/
12. https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
13. Fabris, F., Magalhães, J.P., Freitas, A.A., A review of supervised machine learning applied to ageing research. Biogerontology, 18, 2, 171–188, 2017.
14. Ayodele, T., Types of Machine Learning Algorithms, InTech Open, 2010, https://www.intechopen.com/chapters/10694
15. Musumeci, F. et al., An Overview on Application of Machine Learning Techniques in Optical Networks. IEEE Commun. Surv. Tutorials, 21, 2, 1383– 1408, Secondquarter 2019.
16. https://en.wikipedia.org/wiki/Logistic_regression
17. https://www.edureka.co/blog/decision-trees/
18. Mohri, M., Rostamizadeh, A., Talwalker, A., Foundations of machine learning, MIT Press, Cambridge, MA, 2012.
19. Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., De Felice, F., Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions. Sustainability, 12, 492, 2020.
20. Behera, R. and Das, K., A Survey on Machine Learning: Concept, Algorithms and Applications. Int. J. Innov. Res. Comput. Commun. Eng., 5, 2, 1301–1309, 2017.
21. Domingos, P., A few useful things to know about machine learning. Commun. ACM, 55, 10, 14–20, 2012.
1 * Corresponding author: [email protected]
2 † Corresponding author: [email protected]
3 ‡ Corresponding author: [email protected]
4 § Corresponding author: [email protected]
2
Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms
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
2.1 Introduction
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].
The identification and controlling of spread of zonotic disease is challenging due to several issues which includes no proper symptoms, signs of zoonoses are very much similar, improper vaccination of animals, poor knowledge among the peoples about animal health, costly to control the world wide spread of the disease, not likely to change the habits of people, prioritization of symptoms of disease is difficult, lack of proper clothing, sudden raise in morbidity of the humans, consumption of spoiled or contaminated
