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 36 for “"network classifier"”.
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Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier
… vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The …
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Machine learning for network based intrusion detection: an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data.
… to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of …
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Automated mammographic mass shape classification using wavelets and neural networks
… of wavelet transforms with artificial neural networks for the classification of mammographic mass shapes. A fully automated mammographic classification system has been developed to distinctly classify mass shapes as either round, which typically indicates the absence of breast cancer, or …
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Comparison of classification techniques for speechaudio applications
… also used machine learning software and neural networks with basic training algorithms. This thesis provides an extensive experimental simulation of the speech classification problem. In this thesis, the Extended Kalman Filter algorithm is proposed to train a neural network classifier. Our …
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Intelligent Vision System for the Detection of Protozoa on Microscope Slides
… with an 80 percent accuracy. Using a neural network classifier, performance ranged from 50 to 100 percent depending on the parameters tested. Overall, the correspondence between the system and expert suggested a strong relationship to classifications of unknown objects.
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Text mining with neural network and MapReduce
… of natural language processing method and neural network classifier. Natural language processing can extract more accurate features from text documents with consideration of syntactical and semantic order at sentence level. Then it summarizes document as reduced dimension features. Neural network …
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Research on the accuracy of Lithuanian speaker’s identification using recurrent neural networks /
… speaker dataset in order to determine a classifier most accurately identifying Lithuanian speaking individuals. The performed experimental research allowed concluding that for a Lithuanian speaker higher identification accuracy is achieved by using a recurrent neural network classifier …
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Optimizing relational search with embedded neural network
… full-text indexes and an embeddable neural network classifier in the query processing pipeline. The classifier is trained with self-supervision. It learns to optimize the partitioned indexes access pattern to accelerate query performance. Using textual features of user queries, the …
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Improving Ultra-Wideband Localization by Detecting Radio Misclassification
… conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.</p>
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Multiresolution wavelet analysis of event-related EEG potentials using ensemble of classifier data fusion techniques for early diagnosis of Alzheimer's disease
… were concatenated and used as inputs to a neural network classifier. This contribution investigates training an ensemble of classifiers on each feature set separately, and combining the ensemble decisions in a data fusion setting. Comparisons of intra-signal and inter-signal ensemble combinations …
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Network anomaly detection using management information base (MIB) network traffic variables
… multi-tier, multiple-observation-window, network anomaly detection system (NADS) is introduced, namely, the MIB Anomaly Detection (MAD) system, which is capable of detecting and diagnosing network anomalies (including network faults and Denial of Service computer network attacks) …
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Deep Learning Based Approaches For Low Cost Defense Detection
… feature representation and processed by a neural network classifier to predict the presence of pulmonary fibro- sis. The proposed framework is evaluated using the PadChest dataset, which contains chest radiographs paired with radiology reports. Experimental results demonstrate that incorporating …
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Use of principal component analysis with linear predictive features in developing a blind SNR estimation system
… were investigated to determine whether a neural network trained with data from corrupt speech signals could accurately estimate the SNR of a speech signal. A MultiLayer Perceptron (MLP) was trained on extracted features for each decibel level from 0dB to 30dB, in an attempt to create 'expert …
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Aggregated Learning: An Information Theoretic Framework to Learning with Neural Networks
… increased the demand for developing deep neural networks and more effective learning approaches. The aim of this thesis is to consider the problem of learning a neural network classifier and to propose a novel approach to solve this problem under the Information Bottleneck (IB) principle. Based …
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Morphometric measurements of the retinal vasculature in ultra-wide scanning laser ophthalmoscopy as biomarkers for cardiovascular disease
… bank of multi-scale matched filters and a neural network classifier. The technique was devised to minimize errors in vessel width estimation, in order to ensure the reliability of width measures obtained from the vessel maps. After a step of refinement of the centrelines, a multi-level …
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Weak-Supervised Deep Learning Methods for the Analysis of Multi-Source Satellite Remote Sensing Images
… a novel spectral index generative adversarial network to augment real training samples for generating class-specific remote sensing data to provide a large number of labeled samples to train a neural-network classifier; (ii) a mono- and dual-regulated contractive-expansive-contractive …
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Multiscale Image Analysis for the Automated Localisation of Taxonomic Landmark Points and the Identification of Species of Parasitic Wasp
… of 95%. Applying these landmarks to a neural network classifier results in a 91% correct identification rate. This represents a significant improvement over the 65% identification rate obtained by taxonomists and is robust to landmark displacement as a result of contour erosion.
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Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.
… and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition …
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