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Scopus Indexed Publications

Title: Machine Learning Techniques to Precaution of Emerging Disease in the Poultry Industry

Abstract: Nowadays poultry is the best production of animal protein. With the amazing food diversity of Bangladesh, poultry chicken has a great impact on our daily life. But some major diseases are hampering this industry frequently. Serpentine illness such as infected bursal disease is more prevalent followed by colibacillosis, Newcastle disease, salmonellosis, chronic breathing disease, Avian Influenza, coccidiosis, aspergillosis, omphalitis, fowl pox, nutritional deficiency. Machine learning can be a useful health care way and also poultry disease precaution and detection. In advanced computer science diseases like Avian Influenza, Newcastle Disease are harmful to chicken. In order to prevent harmful consequences, it is important to concentrate about poultry infection on our very initial stage. We use a few qualities to evaluate our analysis regarding poultry illness and this attribute is one of the key items of the following disease. Perhaps we implement eleven machine classifiers to measure analysis by employing the following technologies, Logistic Regression Classifier, Naive Bayes Classifier, Multilayer Classifier, Stochastic Gradient Classifier, r Random Forest classifier, Bagging Classifier, Decision Tree Classifier, K Nearest Neighbor Classifier, XGB Classifier, AdaBoost Classifier & Gradient Boosting Classifier. The method we employed here gives maximum precision. Decision Tree Classifier has the best outcome yet.

Keywords: Poultry Disease , Avian Influenza , K Nearest Neighbor Classifier Newcastle Disease , Decision Tree Classifier , Logistic Regression classifier , Naive Bayes classifier , Multilayer classifier , Stochastic Gradient Boosting classifier , Random Forest classifier Adaptive Boosting and Bagging

Journal or Conference Name: 2021 24th International Conference on Computer and Information Technology (ICCIT)