This article introduces a novel ensemble method named eAdaBoost (Effective Adaptive Boosting) is a meta classifier which is developed by enhancing the existing AdaBoost algorithm and to handle the time complexity and also to produce the best classification accuracy. The eAdaBoost reduces the error rate when compared with the existing methods and generates the best accuracy by reweighing each feature for further process. The comparison results of an extensive experimental evaluation of the proposed method are explained using the UCI machine learning repository datasets. The accuracy of the classifiers and statistical test comparisons are made with various boosting algorithms. The proposed eAdaBoost has been also implemented with different decision tree classifiers like C4.5, Decision Stump, NB Tree and Random Forest. The algorithm has been computed with various dataset, with different weight thresholds and the performance is analyzed. The proposed method produces better results using random forest and NB tree as base classifier than the decision stump and C4.5 classifiers for few datasets. The eAdaBoost gives better classification accuracy, and prediction accuracy, and execution time is also less when compared with other classifiers.
In this article, the discussions reflect on medical AI research on maturity and influence that has been achieve. Artificial intelligence (AI) aims to imitate human cognitive functions. It is bringing a pattern transfer to healthcare, power-driven by growing accessibility of healthcare records and fast development of analytics methods. This article describes a technique for representing medical performance instructions and facilitating their beginning into the clinical routine. As this technique it be exploited in internet location, it can correspond to the foundation for distributing clinical instructions both connecting dissimilar institutions and between human and software, brokers are cooperating inside a clinical background. AI can be functional to a variety of healthcare records (structured and unstructured). AI methods contain machine learning for structured data, such as the usual support vector mechanism and neural network, and the modern deep learning, since natural language processing for unstructured data. Main disease areas that use AI tools include cancer, neurology and cardiology. This article presents a review in more information of AI applications in Cancer, in the three most important areas of premature detection and diagnosis, treatment, as well as result prediction and prognosis assessment.
Cardiac arrest and other cardiovascular problems are the most prevalent issue among millions of men, and there are numerous causes that function as the basis of this crisis, such as people’s wellbeing, mainly because of job stress, exhaustion, bad food quality, and an elevated cholesterol level as a consequence of the lack of technology cardiac disease. Many scientific and medical support programs change every day, yet every program has its own special features, advantages and disadvantages. The goal of this article is to research the probability of cardiac arrest based on various regulated or unregulated variables in specific data set machine learning algorithms.
This paper deals with a low cost solution to problem avoidance for a mobile machine using just a single Artifical Intelligennce. It allows the machine to navigate smoothly in an unknown environment, avoiding collisions, without having to stop in front of problems. The problem avoidance process is made up of three distinct stages - the mapping algorithm, the core problem avoidance algorithm, and the steering algorithm. The mapping algorithm takes the raw Artifical Intelligennce readings and processes them to create higher resolution maps from the wide-angle Artifical Intelligennce. The problem avoidance algorithm is based on the potential field theory which considers the machine to be a test charge that is repelled by all the problems around it, and which moves in the direction of the resultant of the forces acting on it. An algorithm which steers a mobile machine based on the differential drive system is also discussed.
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