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 18 of 18 for “"k-nearest neighbor (KNN)"”.
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Evaluation of content-based acoustic features for musical genre classification
… various acoustic features 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 …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… collected using 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 …
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PRIVACY-AWARE AND HARDWARE-BASED ACCLERATION AUTHENTICATION SCHEME FOR INTERNET OF DRONES
… Gate Array (FPGA) that accelerates the k-Nearest Neighbor (kNN) encryption algorithm making it a hardware component. This will allow me to synthesis and implement the three parts of my privacy-aware and hardware-based authentication scheme for internet of drones. I use Vivado and Vivado …
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INCORPORATING EMR AND GENOMIC DATA USING NLP AND MACHINE LEARNING TO REFINE CANCER TREATMENT
… algorithms including Naive Bayes (NB), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among …
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Effects of training methods on classification on surface electromyographic signals for myoelectric control
… we explored how 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 …
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Privacy-preserving query processing over encrypted data in cloud
… well-defined scope. These queries included the k-Nearest Neighbor (kNN) query, advanced analytical query, and correlated range query. The proposed protocols utilize an additive homomorphic cryptosystem and/or a garbled circuit technique at different stages of query processing to achieve the best …
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Adaptive Intelligent User Interfaces With Emotion Recognition
… experiencing specific 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 …
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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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Psychophysiological Monitoring of Crew State for Extravehicular Activity
… Predictive models were created using the K Nearest Neighbor (KNN) algorithm. The contributions of this dissertation span the simulation, characterization, and modeling of cognitive state. Ultimately, this work tests the limits of extending laboratory psychophysiological monitoring to more …
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Depth Correction++ for pseudo-LiDAR
… pseudo-LiDAR point cloud. Pseudo-LiDAR++ uses K-nearest-neighbor (KNN) to classify the 3D points, and then uses a linear system to adjust the depth estimation. Based on this algorithm, we proposed a new algorithm “Depth Correction++” (DC++) that is able to further correct the depth information. …
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Investigation on advanced image search techniques
… classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. Experimental results on four large scale, grand challenge datasets have shown that the proposed oRGB-SIFT descriptor improves recognition …
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Modeling, Designing and Applying Machine Learning Algorithms for Driver Drowsiness Detection
… both the support vector machine (SVM) and K- nearest neighbor (KNN) method. This achieves an accuracy of 80% or higher. In this research, the accuracy output for the SVM method is 83.8%, 82.5% when using STFT, and 87.5% when applying the WT technique. The algorithm with highest accuracy helps …
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Transferable Coarse-Grained Models: From Hydrocarbons to Polymers, and Backmapped by Machine Learning
… algorithms, artificial neural networks (ANN), k-nearest neighbor (kNN), gaussian process regression (GPR), and random forest (RF) were developed to improve the accuracy of the backmapped all-atom structures. These optimized four ML models showed R2 scores of more than 0.99 when testing the …
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A comparative evaluation of unsupervised anomaly detection techniques in smart water metering networks
… Networks (SMWNs). These techniques are k-Nearest Neighbor (kNN), cluster-based local outlier factor (CBLOF), and the histogram-based outlier score (HBOS). The comparative study aims at providing a better unsupervised anomaly detection technique that can be adopted in SWMNs. This work aimed …
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Machine Learning and Additive Manufacturing Based Antenna Design Techniques
… (lasso), artificial neural networks (ANN) and k-nearest neighbor (kNN) methods, for antenna design optimization for antennas. The automated techniques provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole …
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The Exploration of KNN-based Neural Control of Pneumatically Actuated Artificial Muscle
… a promising method 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 …
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Multiple-implementation testing of supervised learning software
… implementations and 4 faults from 20 k-nearest neighbor implementations, and the proposed technique of multiple-implementation monitoring substantially reduces the need of running multiple implementations with high prediction accuracy.
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A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is …