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.
Results
Showing 1 to 20 of 26 for “"binary classifier"”.
-
Distributionally robust binary classifier under Wasserstein distance
… optimization perspective to robustify a class of binary classifiers. Our model considers the worst-case distribution within a pre-determined uncertainty ball that centers at the given benchmark distribution with the radius calculated as per the Wasserstein distance. We derive the tractable …
-
Knowledge modeling of phishing emails
… representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing …
-
Novel low-complexity MIMO detection based on constellation shift binary classification
… MIMO detection known as Constellation Shift Binary Classification (CSBC). The proposed method utilizes the constellation structure to deduce a constellation shift that is used to reduce the problem of detecting the symbol into binary classification. The proposed method is proven to outperform …
-
Undersampling GA-SVM for network intrusion detection
… ratio. This thesis work intends to build a classifier to achieve high classification accuracy. It proposes an undersampling Genetic Algorithm-Support Vector Machine (GA-SVM) method to handle this problem. It applies an undersampling method in GA-SVM. To solve the multiclassification problem …
-
Development of a process modelling methodology and condition monitoring platform for air-cooled condensers
… in the form of a regression network and binary classifier network. For the test sets, the regression network had an average relative error of 0.3%, while the binary classifier had a 99.85% classification accuracy. The surrogate model was validated to site data over a 3 week operating …
-
Reliable spin-based computing systems
… In particular, we demonstrate that, for a simple binary classifier, 33× improvement in accuracy over conventional design can be achieved while tolerating device error rate of 10%. This work paves a way towards the design of reliable spin-based systems using highly error prone, but energy-efficient …
-
Defending Against GPS Spoofing by Analyzing Visual Cues
… and testing datasets. We utilize LSTM to build a binary classifier which is the key for our Anti-GPS spoofing system. Finally, we evaluate the system performance by simulating driving tests. We prove that our system can achieve more than 98% detection accuracy when the ratio of attacked points in …
-
Ensemble of binary classifiers: combination techniques and design issues
In this thesis the problem of the combination of binary classifiers ensamble is faced. For each pattern a binary classifier (or binary expert) assigns a similarity score, and according to a decision threshold a class is assigned to the pattern (i.e., if the score is higher than the threshold the …
-
Deep learning for digitized histology image analysis
… extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based …
-
Evaluation and validation of multiple predictive models applied to post-wildfire debris-flow hazards
… is applicable to any probability-based binary classifier model, and can be used to evaluate predictive models that address a wide range of natural hazards. The systematic framework established in this research uses statistical and objective measures to guide the selection, evaluation, …
-
Covariate-adjusted ROC regressions and the extensions in trend tests.
… (ROC) curve's diagnostic ability as a binary classifier for continuous outcomes. A generalized linear model (GLM) framework enabled one to investigate covariate effects to model the ROC using parametric and semi-parametric regression methods. The latest addition to this ongoing …
-
Deep Transfer Learning for Macroscale Defect Detection in Semiconductor Manufacturing
… the creation of synthetic data is deployed. The binary classifier model achieves an out-of-distribution area under curve (AUC) of 0.909 for detecting hotspot defects. Detection for other classes of central defects is also explored but limited by even greater data sparsity. Models for catching …
-
Detecting grammatical errors with treebank-induced, probabilistic parsers
… and ungrammatical corpora and trains a binary classifier to distinguish grammatical from ungrammatical sentences. The three approaches are evaluated on a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting common …
-
A real-time 12-lead electrocardiogram remote patient monitoring and analytics framework
… configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on an edge node and alerts can be configured to notify designated healthcare providers. …
-
Artifact removal in digital retinal images
… the mathematical model were then used to train a binary classifier to distinguish pixels affected by distortions within the image without the need for interpretive knowledge of the image itself and, on the basis of this, to establish a validation criterion for quality improvement in retinal …
-
Bioacoustic classification of Hainan gibbon call types using deep learning
… networks (CNNs) were developed, the first was a binary classification model to detect gibbon calls from non-gibbon calls, and the second was a group classifier to distinguish between the social groups in BNNR. The audio data was converted into mel-scale spectrograms, resulting in images used as …
-
Optimizing Real-Time ECG Data Transmission in Constrained Environments
… need for a full-fidelity ECG signal. We use a binary classifier to inform the decision to switch between different operational strategies. In addition, we provide a new approach to support energy-efficient ECG monitoring in real-time through the adaptive selection of ECG leads after applying …
-
Multi-target Prediction Methods for Bioinformatics: Approaches for Protein Function Prediction and Candidate Discovery for Gene Regulatory Network Expansion
… predictions. The second contribution is VSC, a binary classifier designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. A locality-based confidence measure is used to weight the contribution of maximum-margin …
-
Application of Fluid Inclusions and Mineral Textures in Exploration for Epithermal Precious Metals Deposits
… obtained in this study were analyzed using the binary classifier within SPSS Clementine. The models that correctly predicted high versus low grade samples most consistently (~70-75% of the tests) for both Ag and Au were the neural network, the C5 decision tree and Quest decision tree models. For …
-
Analysis of Firmware Security in Embedded ARM Environments
… when running modified firmware. We train a binary classifier with samples of both versions and are able to consistently discriminate between genuine firmware and modified firmware, even despite changes in external factors such as temperature and supplied power.
Page 1 of 2