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In this study, we analyze the algorithms that is used for face recognition and make performance comparison of two algorithms. The methods that is analyzed are k-nearest neighbors, Naive Bayes, eigenfaces, principle component analysis (PCA) and k-means are implemented on ORL face dataset. As a result of the analysis, k-nearest neighbors algorithm and eigenfaces algoritm are the most successful and...
Diabet is one of the metabolic trouble which is generally occurs genetic and environmental components. It happens increasing of blood level. In this study, diabet illness has been diagnosed with its features by classification with support vector machines (SVM) and artificial neural networks (multi layer perceptron). The method used for diagnosis is aritificial neural networks multi layer perceptron...
This report describes the Monte Carlo approach to the localization of a robot or autonomous system. Localization in robot or autonomous systems is the problem of position determination using sensor data. The Monte Carlo method is estimated by making statistical inferences. However, The noisy data from the sensors can change the instantaneous state of the robot or an autonomous system. To overcome...
Predictive Cruise Control (PCC) is one of the most popular functionality on today vehicles. Briefly, it controls vehicle speed at the desired speed value determined by driver. In almost every vehicle sold today, cruise control could be found because it makes drivability manner easier and besides that it decreases fuel consumption with holding vehicle speed stable. Because of high popularity of cruise...
Developments in technology, especially in computer science created the need of storing data in variety of areas. This need created the term database where the data is stored in a useful form. In the database, data is logically integrated in file/files according to relations among them. One of the important issues is to extract knowledge from these databases that hold data in a useful and complete...
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensionality of the data. This presentation compares the method with other popular matrix decomposition approaches for various pattern analysis tasks. Among others, NMF has been also widely applied for clustering and latent feature extraction. Several types of the objective functions have been used for NMF...
Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function...
This paper deals with the global robust asymptotic stability of the equilibrium point of class of delayed neural networks having uncertain parameters whose values are unknown but bounded. By introducing a new upper bound norm for the interconnection matrix of the neural system and employing suitable Lyapunov functionals, we obtain new delay independent sufficient conditions for the uniqueness and...
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