This study suggests that perfusion MR imaging can be used in assessing the cardiac transplant patient for rejection related microvascular changes. The high specificity and sensitivity recorded from the ROC curve illustrates the potential utility of this diagnostic test for future studies.
Abstract-It is through experience one could as certain that the classifier in the arsenal or machine learning technique is the Nearest Neighbour Classifier. Automatic melakarta raaga identification system is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance are not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for finding distance between neighbours using Cosine Distance, Earth Movers Distance and formulas are used to identify nearest neighbours, algorithm for classification in training and testing for identifying Melakarta raagas in Carnatic music. From the derived results it is concluded that Earth Movers Distance is producing better results than Cosine Distance measure.
Background/objectives: To propose a suitable method for marking attendance automatically. Automating the attendance is necessary these days because many of the universities/institutions are particular about their student's attendance in the classroom. But the usual method of marking attendance is proven to be tedious, inaccurate and time-consuming when the number of students in a class is more. Proxy attendance is another big issue for the teachers. Methods: This study proposes a method for marking attendance automatically using Principle Component Analysis (PCA) as a face recognition technique. The image of all the students seated in the classroom is captured and then compared with the student's database, based on which attendance of individual students is marked. An e-mail is sent to those parents whose wards were not present in the class. Findings: The students are successfully detected using Viola Jones Algorithm and recognized/identified using PCA analysis method. The database constantly updates the status of attendance of every student. This system works well even in different light settings. It can identify a person even if some of his/her features are changed. For example, it could identify a person with different haircuts, a person with or without spectacles, a reasonably old photo vs new photo etc. Novelty/improvements: Thus, the classroom time can be effectively utilized without compromising attendance. Viola Jones algorithm accurately detects faces. PCA analysis algorithm utilizes the detected faces to recognize the students by comparing it with the database.
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