Now-a-days a healthcare eld produces a huge amount of data, for processing those data some e cient techniques are required. In this paper, a classi cation model is developed for heart disease prediction and the attribute selection is carried out through a modi ed bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time classi cation. In the entire attributes, some of the attributes have contributed a large amount of information at the time of classi cation and some of the attributes have contributed only a small amount of information during the classi cation task. In this paper, a modi ed bee algorithm is used to identify the best subset of features from the entire features in the dataset i.e., in the training phase of classi cation only retain those features that are contributing more information during classi cation and it will reduce the training time of classi ers. The experiment is analyzed with a obtained reduced subset of features by using the following classi ers such as Support Vector Machine, Navie bayes, Decision tree and Random forest.The experimental result shows that the Support Vector Machine classi er will provide a good classi cation accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Navie bayes and Random forest tree classi er.Engineering and Technology (IJCIET), 10(1)

Now-a-days a healthcare field produces a huge amount of data, for processing those data some efficient techniques are required. In this paper, a classification model is developed for heart disease prediction and the attribute selection is carried out through a modified bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time classification. In the entire attributes, some of the attributes have contributed a large amount of information at the time of classification and some of the attributes have contributed only a small amount of information during the classification task. In this paper, a modified bee algorithm is used to identify the best subset of features from the entire features in the dataset i.e., in the training phase of classification only retain those features that are contributing more information during classification and it will reduce the training time of classifiers. The experiment is analyzed with a obtained reduced subset of features by using the following classifiers such as Support Vector Machine, Navie bayes, Decision tree and Random forest. The experimental result shows that the Support Vector Machine classifier will provide a good classification accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Navie bayes and Random forest tree classifier.

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A virtual assistant, also called AI assistant or digital assistant, is an application that understands voice commands and completes tasks for the user. Popular virtual assistants currently include Amazon Alexa, Apple, Siri, Google Assistant. In our project we'll create an AI system using Neural network algorithm and NLP Techniques. AI System which is able to perform all the actions-speaking, listening task, google search engine tasks, playing YouTube videos, tracking current location, date, time, day, year etc. Additionally, to these features AI Assistant will recognize emotions and communicate back to the speaker ,changing the voice of the assistant and recognize other language