Predictive analytics of heart disease presence with feature importance based on machine learning algorithms

Heart failure disease is a complex clinical issue which has more impact on life of human begins. Hospitals and cardiac centers frequently employ electrocardiogram (ECG)tool to assess and to identify heart failure at early stages. Healthcare professionals are very concerned about the early identification of heart disease. In this research paper we have focused on predictive analysis of cardiac disease by using machine learning algorithms. We have developed python-based software for healthcare research in this paper. This research has more significant work for tracking and establishing numerous health monitoring apps. We have demonstrated information handling that requires adjusting clear-cut portions and working with absolute factors. A quick overview of the various machine learning technologies based on heart disease diagnosis is described clearly in this research. A more reliable way for diagnosing cardiac problems is the random forest(RF)classification algorithm. This application needs data analysis, which is crucial owing to its about 95% accuracy rate across training data.We have discussed the tests and findings of the RFclassifier method, which improves the accuracy of heart disease research diagnosis

An efficient object detection by autonomous vehicle using deep learning

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%