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 76 for “"k-Nearest Neighbors"”.
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Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
… REQUEST.] In this thesis, a possibilistic K-nearest neighbor classifier is presented to distinguish between and classify mine and non-mine targets on data obtained from wideband electromagnetic induction sensors. The goal of this work is to develop methods for classifying wide-band …
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NBA Sleep Tracking Data Imputation
… four main techniques are evaluated: K-Nearest Neighbors Regression, Linear Interpolation, Linear Regression, and Quadratic Regression. Each technique is applied and evaluated on key sleep metrics such as sleep duration, rMSSD (Root Mean Square of the Successive Differences between …
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Three Essays in Applied Econometrics: with Application to Natural Resource and Energy Markets
… the Nadraya-Watson estimator, and the k-Nearest Neighbors method developed by Hallin et al. (2004b), Lu and Chen (2002), P.M. Robinson (2011) and Li and Tran (2009). With data sampled on a rectangular grid in a nonlinear random field, the results show that nonparametric local linear …
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High Frequency Transmission Spectroscopy
… of the S21 scattering parameter with a k-nearest neighbors clustering approach achieved an accuracy of 88% when classifying devices based on the individual manufacturing lot. Similarly, a test setup for performing HFTS on liquid samples was developed. By applying both k-nearest neighbors …
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Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis
… Two schemes (metric based classification and k nearest neighbors) have been applied to the data set to empirically learn and judge ACL diagnosis.'
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Brief Study of Classification Algorithms in Machine Learning
… commonly used Machine 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 …
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Scalable online nonlinear goal-oriented inference with physics-informed maps
This thesis develops a physics-informed k-nearest neighbors approach, which draws from both physics-based modeling and data-driven machine learning. In doing so, our method achieves robustness and increased accuracy with small datasets, while being cheap to apply. Our method tackles the challenges …
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Automatic fall risk detection based on imbalanced data
… After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. This experiment provides evidence that our approach is more interpretable, with key features from skeleton information, and workable in multi-people scenarios.
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… using prediction intervals from quantile k-nearest neighbors and quantile random forest can be biased to low or too high from the nominal level under various situations of sparsity. We also introduce a test that can be used to see if a new data point lies in an area of sparse data so that …
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Apklausų dalyvių aktyvumo analizė, pritaikant įvairius binarinio klasifikavimo algoritmus /
… algorithms were selected: logistic regression, K-nearest neighbors, decision tree, support vector machine, and XGBoost classifiers. The best results were obtained using the XGBoost classifier, with oversampling applied to the training dataset. The best model achieved a sensitivity (recall) metric …
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A method for detecting nonequilibrium dynamics in active matter
… the KLD from a stationary time series using a k-nearest neighbors estimator, comparing the time-forwards process to the time-reversed process Using time series data of probe particles embedded in the actin cortex, we establish a lower bound for the entropy production of cortical activity. Our …
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Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations
… this platform, four models—Random Forest, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and Prophet—are evaluated with expanding-window cross-validation on 30 stations and Adaptive Charging Network (ACN) datasets. Key findings show that data regularity and density strongly …
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Performance analysis of automatic techniques for tissue classification in magnetic resonance images of the human brain
… (Artificial Neural Networks, Bayesian, k-Nearest Neighbors, C4.5 decision tree, Minimum Distance) and two unsupervised (Hard C Means, Fuzzy C Means) classification algorithms is compared under varying conditions of MR imaging artifacts. The Artificial Neural networks classifier was …
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PREDICTING METRICS FOR NAVAL SUPPLY SYSTEMS COMMAND WHOLESALE INVENTORY OPTIMIZATION MODEL
… We examine two machine learning approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost …
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ANALYZING AND PREDICTING ARMY COMBAT FITNESS TEST PERFORMANCE: A STATISTICAL AND MACHINE LEARNING APPROACH
… (LR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forests (RF), and Artificial Neural Networks (ANN)—to predict ACFT outcomes using raw ACFT scores alongside demographic and body composition data. The analysis evaluates two …
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Recommender System for Audio Recordings
… filtering methods – named weighted-average, K-nearest neighbors, and item-based – which are based on collaborative filtering techniques, which work by recording user preferences on items and by anticipating the future likes and dislikes of users by comparing the records, for prediction of user …
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A standards-based grading model to predict students' success in a first-year engineering course
… of models (i.e., Support Vector Machine, K-Nearest Neighbors, and Naive Bayes Classifier) had the best results among the seven tested models. This study identified possible threshold concepts and learning objectives that are important to students’ success in the course, and learning …
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Classification of Stars from Redshifted Stellar Spectra utilizing Machine Learning
… to standard classification methods such as K-Nearest Neighbors, Random Forest, and Support Vector Machine to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using standard Feature Selection methods such as Chi-Squared and Fisher …
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Performance analysis of machine learning applications on rapid: a highly parallel computer architecture
… common machine learning algorithms: K-Means, K-Nearest Neighbors, Linear Regression, Latent Dirichlet Allocation, Deep Neural Network, and Radix Sort on RAPID. RAPID is a highly parallel computer architecture developed at Oracle Labs for accelerating and improving the performance of database …
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A data mining approach for acoustic diagnosis of cardiopulmonary disease
… excellent recognition performance by using k nearest neighbors, neural networks, and support vector machines to make classifications in pair-wise comparisons. We also extend the research to a multi-class scenario and are able to separate patients with interstitial pulmonary fibrosis with 80% …
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