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 29 for “"k-nearest neighbours"”.
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Network Kriging - Predicting the Attributes of Nodes in a Network
… and compares results to those using the K-Nearest Neighbours algorithm. We found that for important roles, such as Emir (Leadership), Network Kriging performs better than K-Nearest Neighbours.
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Ponderación local evolutiva de la regla kNN
… de los k vecinos 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 …
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Data-driven clustering for new garment forecasting
… Regress} (CWR) and \textit{k-Nearest Neighbours} (kNN) and show that with enough data the algorithms should achieve human-level accuracy and automate the comparables-finding process.
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Detecting worm mutations using machine learning
… work also compares Support Vector Machines to K-nearest neighbours, known for its simplicity and solid results in other domains. The third part of this dissertation investigates the automatic generation of training data. Classifier accuracy depends on good quality training data -- the wider the …
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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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Machine learning in astronomy
… Classifier (MEC), a naive Bayes classifier, a k-Nearest Neighbours (kNN) algorithm, a Support Vector Machine (SVM) and the SkyNet artificial neural network.
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Adaptive Scheduling in Heterogeneous Distributed Computing Systems
… method is presented, which utilizes a k-Nearest Neighbours algorithm, a smoothed average and an analytical benchmark. These estimated properties are then used by two different scheduling techniques, which make less restrictive assumptions than the current state-of-the-art methods. A …
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Machine Learning for Radio Frequency Interference Flagging
… learning algorithms; Naive Bayes Classifier, K-Nearest Neighbours Classifier, Random Forest Classifier, the U-Net convolution neural network and the Multilayer Perceptron. These algorithms are trained on real data, in which the ground truth includes inherent false positives, and simulated data …
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Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
… exploration of the architecture, the weighted k-nearest neighbours algorithm used for the classification module is found to be prone to misclassification as it relies solely on the majority voting rule to determine the class for new data vector. Additionally, it does not consider the …
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Comparative Analysis of Machine Learning Algorithms on Activity Recognition from Wearable Sensors’ MHEALTH dataset Supported with a Comprehensive Process and Development of an Analysis Tool
… Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forests (RF). Beside using original MHEALTH data as input, reduced dimensionality subsets and reduced features subsets were also analysed. The comparison is made on overall accuracies, class-wise sensitivity and …
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Unconstrained Road Sign Recognition
… (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road …
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Artificial immune system based on real valued negative selection algorithms for anomaly detection
… namely the Support Vector Machine and K-Nearest Neighbours were used for benchmarking the performance of the Real-Valued Negative Selection Algorithms. Experimental results illustrate that RNSA and V-Detector algorithms are suitable for the detection of anomalies, with SVM and KNN …
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Development of Machine Learning Models to Detect Dynamic Disturbances in Human Gait
… accuracy of 88.27% on average, compared to the K-Nearest Neighbours (KNN) approach's accuracy of 67.7%. The proposed SVM models use the receiver operating characteristics (ROC) and the area under the ROC metrics to evaluate their overall performance. The results of this research suggest that the …
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Static and dynamic analysis of near infra-red dorsal hand vein images for biometric applications
… linear discriminant analysis (LDA) and k-nearest neighbours (KNN) were adopted for comparative discussions. The experimental results turned out to be satisfactory and the two groups were well classified, using statistical intensity based features. For dynamic analysis, mean grey levels …
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Comparative Analysis of Machine Learning Algorithms on Activity Recognition from Wearable Sensors’ MHEALTH dataset Supported with a Comprehensive Process and Development of an Analysis Tool
… Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forests (RF). Beside using original MHEALTH data as input, reduced dimensionality subsets and reduced features subsets were also analysed. The comparison is made on overall accuracies, class-wise sensitivity and …
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Operando characterisation techniques for graphene-based lithium-sulfur batteries
… component analysis and de-noising using K-nearest neighbours classification, and is demonstrated in a series of case studies to elucidate reasons for inter-sample variability. The data-processing pipelines developed here can be readily adapted to other heterogenous, spatially and/ or …
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Money Laundering Alert Prioritisation Using Machine Learning and Benford’s Law
… Regression, Na¨ıve Bayes, Decision Trees, K-Nearest Neighbours, and Random Forests, was systematically evaluated. To assess models against the multiple objectives, a bespoke evaluation approach with a businessoriented focus was developed. Standard statistical metrics, such as accuracy, are …
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Classification of Fallow and Perennial Fields in High-Resolution Multispectral Aerial Images
… and instance-based classifiers (such as k-nearest neighbours) are better suited at identifying agricultural land covers with correlated spectral responses. These classifiers yield precision and recall scores ≥ 90% with error rates less than 10%. It is shown that a deterministic sampling …
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Analytical study of computer vision-based pavement crack quantification using machine learning techniques
… Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability …
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The Development of a Reduced Order Model for Prediction of Haemodynamic and Biochemical Changes in a Computational Cerebral Aneurysm Thrombosis Model
… used to predict clot size in patients. The K-nearest neighbours algorithm was used to develop a model that classifies patients' clotting profiles. The biochemistry was found to be more sensitive to mesh size compared to the haemodynamics. Large timesteps overpredicted clot size in pulsatile …
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