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 20 of 127 for “"KNN"”.
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KNN Optimization for Multi-Dimensional Data
<p>The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data analytics. It uses a distance metric to identify existing samples in a dataset which are similar to a new sample. The new sample can then be classified via a class majority voting of its most …
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Ponderación local evolutiva de la regla kNN
… más cercanos (en ingles, k Nearest Neighbours o kNN). Caben destacar aquellas que se centran en la selección del número de vecinos (parámetro k) y las que establecen unos pesos para el conjunto de características de los ejemplos que conforman los datos a clasificar. En este trabajo, exponemos una …
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KNN Query Processing in Wireless Sensor and Robot Networks
… report needs to find exact k nearest robots (KNN) to react to the event, among those connected to it. We are interested in localized solutions, which avoid message flooding to the whole network. Several existing methods restrict the search within a predetermined boundary. Some network …
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A study of kNN using ICU multivariate time series data
… of this research is to study the performance of kNN (k Nearest Neighbor) classification approach to determine patients’ mortality rate using ICU (Intensive Care Unit) medical records. The ICU data contains medical records collected during the patients’ first 48 hours stay at the ICU. The …
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The Exploration of KNN-based Neural Control of Pneumatically Actuated Artificial Muscle
… of neural control through K-Nearest Neighbor (KNN) sorting and the incremental development of a biomimetic actuator in the form of Pneumatic Artificial Muscles (PAMs). KNN classification is a lightweight non-parametric learning algorithm that can rapidly identify and sort an input into …
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Kombination von terrestrischen Aufnahmen und Fernerkundungsdaten mit Hilfe der kNN-Methode zur Klassifizierung und Kartierung von Wäldern
… lassen. Die k-nächsten-Nachbarn-Methode (kNN-Methode), die terrestrische Aufnahmen mit Fernerkundungsdaten kombiniert, stellt eine Möglichkeit dar, diese Datengrundlage mit Hilfe der Fernerkundung zu verwirklichen. Deshalb beschäftigt sich die vorliegende Dissertation eingehend mit der …
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Microfabrication and electrical characterization of lead-free sodium potassium niobate thin film ribbons on conformal substrates
… alternative, Sodium Potassium Niobate (KNN). Thin films of KNN ribbons with gold interconnects are microfabricated on various conformal substrates such as Kapton, Ecoflex, Polydimethylsiloxane (PDMS), and Silbione/fabric and characterized electrically using the Keithley Semiconductor …
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High Performance Lead--free Piezoelectric Materials
… potassium niobate (KxNa1-xNbO3, abbreviated as KNN) is one of the most interesting candidates in the class of lead-free piezoelectrics. Absence of any poisonous element makes it unique among all the other lead-free candidates having presence of bismuth. Curie temperature of 400"C, even higher …
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Improving Performance of Spatial Network Queries
Spatial network queries, for example KNN or range, operate on systems where objects are constrained to locations on a network. Current spatial network query algorithms rely on forms of network traversal which have a high complexity proportional to the size of the network making, them poor for large …
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Adaptive Intelligent User Interfaces With Emotion Recognition
… emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. Emotion Elicitation with …
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Evaluation of content-based acoustic features for musical genre classification
… are compared using a k-nearest neighbor (KNN) classifier. By utilizing the combinations of acoustic features, an average classification accuracy of $89\%$ for GTZAN database is achieved, which is comparable to prior work. A statistical test, McNemar's test, is applied to support the idea …
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Effects of training methods on classification on surface electromyographic signals for myoelectric control
… two methods of training a K-Nearest Neighbor (KNN) classifier, used to control a MyoPro arm orthosis, affect two subjects' performance on various experimental tasks and their measured sEMG activation throughout the tasks. It was found that for subject 1, the assisted training method, where …
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Brief Study of Classification Algorithms in Machine Learning
… Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an …
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Machine Learning Classification of Gas Chromatography Data
… and compared, including K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Convolutional Neural Network (CNN), Decision Tree, and Random Forest (RF). Feature Selection is tested by applying window sweeps with the KNN algorithm. Feature Engineering is applied via the Principal Component …
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Advancing Rockfall Hazard Assessment through Data-Driven Modelling Based on Laboratory-Scale Experiments
… data. Three models-K-Nearest Neighbors (KNN), perceptron, and deep neural networks (DNNs)-were evaluated for their ability to handle the nonlinear and irregular patterns inherent in rockfall behavior. While perceptron models struggled with the complexity of the data and DNNs faced …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor …
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Data Mining: Modellierung, Methodik und durchführung ausgewählter Fallstudien mit dem SAS Enterprise Miner
… steht hierbei die Modellierung der Verfahren KNN, Entscheidungsbaum und Regression. Bei der Durchführung von Fallstudien wird u.a. auf das Pre-Processing eingegangen.
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The comparative study of model-based and appearance based gait recognition for leave bag behind
… which is Support Vector Machine (SVM) and KNN (K nearest Neighbour) based on accuracy and misclassification rates (MER) metrics. The experiment results show that the accuracy and misclassification rate (MER) of Appearance-based approaches obtained is 93.66% and 6.33% respectively tested on …
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Classification of ADHD Using Heterogeneity Classes and Attention Network Task Timing
… temporal information from evoked EEG signals. KNN has been shown in prior research to be an effective tool in discriminating between ADHD and non-ADHD, therefore several different KNN models are created using features derived in a variety of fashions. One takes into account the heterogeneity of …
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Coupled similarity analysis in supervised learning
… the problem. Firstly, we propose a coupled fuzzy kNN to classify imbalanced categorical data which have strong relationships between objects, attributes and classes in Chapter 3. It incorporates the size membership of a class with attribute weight into a coupled similarity measure, which …
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