Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Nearest Neighbor Algorithm"”.
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Using a Nearest Neighbor Algorithm and Logistic Regression to Assess Hazard Identification in the U.S. Army Risk Management Process
… using data mining techniques, in particular Nearest Neighbor (NN) algorithm and Logistic Regression Model (LRM). NN determines how similar a participant's case is to an expert case and LRM analyzes the outputs in a way that allows us to see if any of the seven experiential and demographic …
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Model fusion for improving hypoxia forecasts in Corpus Christi Bay, TX, USA: A study of boosting and historical scenario modeling
… scenario modeling and boosting both a k-nearest neighbor (KNN) algorithm and the historical scenario model. Existing data-driven k-nearest neighbor and physics-based valve models are used as the basis for the model fusion. The historical scenario model combines the k-nearest neighbor …
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K x N Trust-Based Agent Reputation
… reason and adapt using a modification of the k-Nearest Neighbor algorithm called (k X n) Nearest Neighbor where k neighbors recommend reputation values for trust during each of n interactions. Reputation allows a single agent to receive recommendations about the trustworthiness of others. One …
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Prosthesis control using a nearest neighbor electromyographic pattern classifier
A prosthesis control strategy using a nearest neighbor electromyographic pattern classifier was investigated with both a real time microprocessor-based controller and offline computational facilities. Four active electrodes for myoelectric signal amplitude detection were interfaced with a …
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Comparing visual features for morphing based recognition
… thin-plate spline. Given these morphs, a simple algorithm, least median of squares (LMEDS), is used to find the best morph. A scoring metric, using both LMEDS and distance transform, is used to classify test images based on a nearest neighbor algorithm. We perform the experiments on the Caltech …
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Classification of ADHD and non-ADHD Using AR Models and Machine Learning Algorithms
… work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. More specifically, the K-nearest Neighbor algorithm and Gaussian-Mixture-Model-based Universal …
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Planification de la trajectoire des drones et gestion de l'énergie dans les réseaux de capteurs sans fil
… problème, deux heuristiques sont proposées : l’algorithme du plus proche voisin (en anglais nearest neighbor algorithm, NNA) et l’algorithme génétique (en anglais genetic algorithm, GA). Pour le deuxième problème deux heuristiques simples sont aussi proposées dans cette thèse, à savoir …
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Genetic mapping of agronomic traits from the interspecific cross of Oryza sativa (L.) and Oryza glaberrima (Steud.)
… values were selected by stepwise DA. Using a k-nearest neighbor algorithm, the largest phenotypic differentiation (3 standard deviations) between two contrasting phenotypic groups resulted in 100% correct classification. Adjustments for population structure resulted in a 5-fold decrease in …
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The Relationship Between Economic Growth and Fossil Fuel Energy Consumption Growth in Net Energy-Importing Emerging Economies
… consumption per capita is linear when using K-Nearest Neighbor algorithm—and not curvilinear as postulated by the environmental Kuznets Curve (EKC) hypothesis.
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Deep Neural Network for Anomaly Detection
… model (CDAEE-KNN) is a hybrid of CDAAE and the K-nearest Neighbor algorithm to generate borderline attack samples. By training on the augmented datasets, the accuracy of the AD problems is enhanced significantly. Third, the thesis designs a Deep Transfer Learning (DTL) model to build an effective …
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Taxonomic classification of genomic sequences : from whole genomes to environmental genomic fragments
… genomic fragments using a kernelized nearest neighbor approach. A combination of machine learning techniques has been employed to realize a classifier that exploits the wealth of knowledge deposited in public databases. The developed classifier uses as features oligonucleotide …