Computer Science Engineer
and Web Developer

ABSTRACT- It is in need of smart mass transit, and currently there really are solutions that really can assist. The efficiency of existing system might be improved in a range of methods, especially with deep learning. Artificial intelligence. The effectiveness of intelligent systems depends on their ability to recognize and categories cars with accuracy. With a fast expanding population and constrained space, countries like India may soon rely heavily on these devices to help us get around. This effort seeks to provide solutions to a few issues that are crucial for India. Motorcycle helmet use is required in many nations; however, many people choose not to follow the law for a variety of reasons. We discuss the development of a framework for identifying bikers who are disobeying helmet laws using sophisticated convolutional neural networks (CNNs). The system uses the YOLO algorithm, motorcycles, helmet detection, categorization, and helmet vs. no-helmet method counting. For the number plate detection process, convolutional neural networks using sequential CNN models are being used. The CNN classification algorithm suggests categorizing the license plate in the image and extracting user information. Next, determine the fine amount. Making SMS alerts available to users is the final step in preventing motorcycle accidents. We evaluate the framework's precision and speed.

Heart Disease (including Coronary Heart Disease, Hypertension, and Stroke) remains the No. 1 cause of death in the US.The Heart Disease and Stroke Statistics—2019 Update from the American Heart Association indicates that:

116.4 million, or 46% of US adults are estimated to have hypertension. These are findings related to the new 2017 Hypertension Clinical Practice Guidelines.

On average, someone dies of CVD every 38 seconds. About 2,303 deaths from CVD each day, based on 2016 data.

On average, someone dies of a stroke every 3.70 minutes. About 389.4 deaths from stroke each day, based on 2016 data.

In this machine learning project, we have collected the dataset from UCI (https://archive.ics.uci.edu/ml/datasets/statlog+(heart)) and we will be using Machine Learning to make predictions on whether a person is suffering from Heart Disease or not.