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DAVE is a comprehensive set of event detection techniques to monitor and detect 5 important verbal agitations: asking for help, verbal sexual advances, questions, cursing, and talking with repetitive sentences. The novelty of DAVE includes combining acoustic signal processing with three different text mining paradigms to detect verbal events (asking for help, verbal sexual advances, and questions)...
Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error...
The Microsoft Kinect sensor has brought a new era of Natural User Interface (NUI) based gaming and the associated SDK has provided access to its powerful sensors, which can be utilized in many ways, especially in research purposes. We have already seen its use in robotics, developing assistive technologies, and augmented reality, aside from gaming. Thousands of people around the world are playing...
Recognizing various meaningful patterns from stock market time series data is getting tremendous attention among researcher during the recent years. Much work has been devoted to pattern discovery from stock market time series data using template based approaches and rule based approaches but not much has attempted to combine the power of any of these approaches with the prediction capability of neural...
For last few years many research have been taken place to recognize various meaningful patterns from time series data. These researches are based on recognizing basic time series patterns. Most of these works used template based, rule based and neural network based techniques to recognize basic patterns. But in time series there exist many composite patterns comprise of simple basic patterns. In this...
In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we have implemented Artificial Neural Network...
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