Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include Age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as [Formula: see text] Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods.
Abstract-This paper contains an in depth work carried out in the area of real-time vehicle detection from an Unmanned Ariel Vehicle (UAV) to detect the traffic jam by counts the number of cars in the roadway. The optical camera located inside the UAV takes images from a particular angle and analysis is performed on the image to detect the vehicle (static or moving). To obtain the accurate and quick result, the analysis should be in ground station. This paper presents a highly reliable technique to detect many different types of vehicles from images having variant backgrounds. This technique used Haar classifiers to perform this real-time application. The Haar classifier was trained with multiple features to accomplish the task. The task was successfully achieved with minimum false positive rate.
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