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Full-Text Articles in Engineering
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Turkish Journal of Electrical Engineering and Computer Sciences
Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a …
Abstract Feature Extraction For Text Classification, Göksel Bi̇ri̇ci̇k, Banu Di̇ri̇, Ahmet Coşkun Sönmez
Abstract Feature Extraction For Text Classification, Göksel Bi̇ri̇ci̇k, Banu Di̇ri̇, Ahmet Coşkun Sönmez
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection and extraction are frequently used solutions to overcome the curse of dimensionality in text classification problems. We introduce an extraction method that summarizes the features of the document samples, where the new features aggregate information about how much evidence there is in a document, for each class. We project the high dimensional features of documents onto a new feature space having dimensions equal to the number of classes in order to form the abstract features. We test our method on 7 different text classification algorithms, with different classifier design approaches. We examine performances of the classifiers applied on …
Hybrid Feature Selection For Text Classification, Serkan Günal
Hybrid Feature Selection For Text Classification, Serkan Günal
Turkish Journal of Electrical Engineering and Computer Sciences
Feature selection is vital in the field of pattern classification due to accuracy and processing time considerations. The selection of proper features is of greater importance when the initial feature set is considerably large. Text classification is a typical example of this situation, where the size of the initial feature set may reach to hundreds or even thousands. There are numerous research studies in the literature offering different feature selection strategies for text classification, mostly focused on filters. In spite of the extensive number of these studies, there is no significant work investigating the efficacy of a combination of features, …
Static Hand Gesture Recognition Of Indonesian Sign Language System Based On Backpropagation Neural Networks, Farida Asriani, Hesti Susilawati
Static Hand Gesture Recognition Of Indonesian Sign Language System Based On Backpropagation Neural Networks, Farida Asriani, Hesti Susilawati
Makara Journal of Technology
Static Hand Gesture Recognition of Indonesian Sign Language System Based on Backpropagation Neural Networks. The main objective of this research is to perform pattern recognition of static hand gesture in Indonesian sign language. Basically, pattern recognition of static hand gesture in the form of image had three phases include: 1) segmentation of the image that will be recognizable form of the hands and face, 2) feature extraction and 3) pattern classification. In this research, we used images data of 15 classes of words static. Segmentation is performed using HSV with a threshold filter based on skin color. Feature extraction performed …