Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.
The use of electric cars has the potential to reduce transportation-related pollution. Their adoption has been hampered by the lack of and/or high cost of charging facilities. As a result, small on-board chargers are often used as the main charging system in cars. We know that batteries can be charged in two ways: conductive and inductive. Here we are proposing a dual motor based charging. We know that when we power a motor with electricity, the electrical energy is converted into mechanical energy and vice versa. Here we are using rear motors for the movement of our car. This movement is used by front motors for producing electrical signals again
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