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 73 for “"k-Nearest Neighbor"”.
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A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest
… requirements.</p> <p>This research applies k nearest neighbours with 10-fold cross validation and random forest machine learning algorithms to a network-based intrusion detection system in order to improve the accuracy of the intrusion detection system. This project focused on specific feature …
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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 …
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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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Utilizing data mining techniques and ensemble learning to predict development of surgical site infections in gynecologic cancer patients
… random forest, feed forward neural network, k-nearest neighbor, and support vector machines with linear kernel. Weighted stacked generalization was implemented to improve upon the individual base level model’s performance. The chosen meta level classifiers were support vector machines with …
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DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL
… approach, and a novel approach called K-Nearest-Neighbor distance approach. Both approaches compute spatial statistics for different neighborhood sizes and compare these characteristics with spatial characteristics obtained by placing objects randomly to determine the presence of …
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Statistical classification of item-response patterns into misconception groups in rule space
… discriminant-function approach (DA) and the k-nearest-neighbor approach (KNN) perform for the classification of each bivariate-normally- and non-bivariate-normally-distributed item-response patterns in rule space? (b) Is the use of rule-space model better than not using it for classifications? …
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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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A study of kNN using ICU multivariate time series data
… 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 challenge of …
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Mining multi-granular multivariate medical measurements
… outperform other classification models such as k-Nearest Neighbor and demonstrates that SVM is a viable model for this project. The hope is that going forward other work can build off of this research and one day make an impact in the medical community.</p>
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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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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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Study on Adversarial Robustness of Phishing Email Detection Models
… examples to their true labels using K-Nearest Neighbor with 94% accuracy in our prediction.
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Protein Fold Recognition Using Adaboost Learning Strategy
… using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen validation dataset, in which 90% of the proteins have less than 25% sequence identity in …
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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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Machine Learning to Predict Warhead Fragmentation In-Flight Behavior from Static Data
… After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired fragmentation characteristics. Using Monte Carlo simulations, the K-NN regression is shown to predict the distributions for the total number of fragments intersecting a given surface …
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Information theory meets big data: Theory, algorithms and applications to deep learning
… quantity estimators, especially k-nearest neighbor estimators of differential entropy and mutual information; (2) designing novel algorithms of differential entropy and mutual information estimators for some special and challenging practical scenarios, as well as new …
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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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Deep learning benchmarks on L1000 gene expression data
… methods, random forests, decision trees, and K nearest neighbor classifiers, as well as graph convolutional neural networks (GCNNs), which augment learning via prior biological domain knowledge. We find GCNNs offer performance improvements given sufficient data, excelling at all tasks on our …
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Object tracking and fetal signal monitoring
… signals. Three supervised classifiers, the k nearest neighbor, nearest mean, and a support vector machine, are implemented and their performances are compared. The results show that the method developed is able to effectively classify the dichotomous signals.
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