Cardiovascular diseases (CVD) involving the heart and blood vessels are reported as the leading causes of mortality worldwide. Coronary Artery Disease (CAD) is a major group of CVD in which presence of atherosclerotic plaques in coronary arteries leads to myocardial infarction or sudden cardiac death. In the past decades, several research efforts have been made to better understand the etiology of CAD, which will enable effective CAD diagnosis and treatment strategies. In this study, we have proposed a novel Self Optimized and Adaptive Ensemble Machine Learning Algorithm for the diagnosis of CAD. In our proposed method, the system automatically selects the most appropriate machine learning models. Our main goal is to design an Optimized Adaptive Ensemble Machine Learning Algorithm that works in different CAD datasets with high accuracy even with raw dataset. One of the important aspects of the proposed method is that the solution works on real-time data without using any pre-processing techniques on the datasets. Throughout this research attempt, we obtained 88.38% accuracy using two publicly available CAD diagnosis datasets.
Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.
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