The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we address the problem of predicting wind turbine electrical subsystem fault using time series data obtained from multiple sensors on wind turbine. While considering this as a time series classification problem, we are facing with the challenge that there is no explicit label information regarding the temporal location and duration of symptoms of the fault. Besides, significant data...
Neural Network is a powerful pattern recognition algorithm capable of learning complex non-linear patterns. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible or transferable thus making it unsuitable tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensible...
Decision Tree is a widely used supervised learning algorithm due its many advantages like fast non parametric learning, comprehensibility and son. But, Decision Tree require large training set to learn accurately because, decision tree algorithms recursively partition the data set that leaves very few instances in the lower levels of the tree. In order to address this drawback, we present a novel...
Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, an algorithm can be given this knowledge of feature importance based on expert opinion or prior learning. Learning can be faster and more accurate if learners take feature importance into account...
Rural areas of Bangladesh do not have quality healthcare facilities or doctors. However, Internet is widely available everywhere in the country. A web based Clinical Decision Support System (CDSS) is proposed that will serve the rural medical centers. A Clinical Decision Support System is a software that provides diagnostic suggestions based on input patient data. The system will use Artificial Intelligence...
Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt...
Standard hybrid learners that use domainknowledge require stronger knowledge that is hard andexpensive to acquire. However, weaker domainknowledge can benefit from prior knowledge while beingcost effective. Weak knowledge in the form of featurerelative importance (FRI) is presented and explained.Feature relative importance is a real valuedapproximation of a feature's importance provided byexperts...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.