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 23 for “"machine learning classifier"”.
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Optimization of Optical Nanosensor Response for the Detection of Anthracyclines Using a Binary Machine Learning Classifier
… and optimizing SWCNT sensor response is through machine learning. In this study, anthracyclines Daunorubicin, Doxorubicin, Epirubicin, Mitoxantrone and Idarubicin, were used to interrogate 12 SWCNT preparations wrapped with separate oligonucleotide sequences. In triplicate, the near-infrared …
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Authenticating a Known User Through Behavioral Biometrics Using a Smartphone Accelerometer
… 19 unknown—were used in the creation of a MATLAB machine-learning classifier. The classifier accurately distinguished an unknown subject from the known subject. Recommendations for future work include repeating the experiment with the latest smartphone devices as available, incorporating different …
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Structural Damage Classification using Support Vector Machines
… representation method and support vector machines is investigated. Piezoelectric ceramic actuators are utilized to generate guided wave signals on a set of aluminum beam coupons with different damage features, such as types, locations, and thicknesses. The short-time Fourier transform is …
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Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images
… of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust …
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Determining therapeutically actionable genetic interactions in human cancer at scale using multiplexed CRISPR screening
… genomes (Chapter 2), while the second utilised a machine-learning classifier to predict SL among paralog pairs, prioritising those with clinical relevance and therapeutic tractability (Chapter 3). Pooled screens were conducted using the Synergy library across 12 cancer cell lines, including those …
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Statistical methods for locating performance problems in multi-tier applications
… saturation using statistical methods, and uses a machine learning classifier to interpret those results. The algorithm was tested with two test applications in several configurations, with different performance problems synthetically introduced. The algorithm correctly located these problems as …
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MRI microstructure and morphology enable machine learning-based prediction of freezing of gait in Parkinson’s disease.
… biomarkers exist. In this thesis, we developed a machine learning classifier to predict FOG onset from baseline structural neuroimaging of 106 de novo PD patients from the Parkinson’s Progression Marker Initiative. The trained model demonstrated high accuracy (AUC=0.91, Sensitivity=0.94, …
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Towards an automatic predictive question formulation
… labels that can be used to train a supervised machine learning classifier. Using a smaller subset of this language, we developed software that enumerated 1077 prediction problems automatically for the Walmart Store Sales Forecasting dataset found on Kaggle[16], and built models that attempted …
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Recognition and Classification of Aggressive Motion Using Smartwatches
… signals, and subsequently used by multiple classifiers to determine on a machine learning platform six performance metrics (accuracy, sensitivity, specificity, precision, F-score, Matthews correlation coefficient). This thesis demonstrated: 1) the best features for a binary classification; …
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Machine learning classification techniques for non-intrusive load monitoring
… identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load …
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The role of approximate negators in modeling the automatic detection of negation in tweets
… helped engineer specific features that guided a machine learning classifier in predicting negated tweets. The machine learning experiments also modeled negation scope (i.e. in which specific words are negated in the text) by employing lexical and dependency graph information. Promising results …
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A monitoring framework for side-channel information leaks
… software against these common types of attacks. Machine-learning based intrusion detection systems which monitor system activity for suspicious patterns are also available and are commonly deployed in production environments. What is missing, however, is the consideration of implicit information …
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Blacklist filtering for security research: bridging the gap between domain blacklists and malicious web content
… mechanism to collect training data to build a machine learning classifier to identify parked domains in blacklists. They found up to 10.9% of entries are parked. In this work, we reproduced their approach and found that most of the heuristics and features they used have become stale after five …
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Application of Computer Vision Techniques for Railroad Inspection using UAVs
… aerial imagery from a UAV, Computer Vision and Machine Learning based techniques were developed in this thesis to analyze two kinds of defects on the rail tracks. The defects targeted were missing spikes on tie plates and cracks on ties. In order to perform this inspection, the rail region was …
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Identifying End-User Challenges and Mitigation Strategies in Software Ecosystems: A Large-Scale Empirical Study on User Feedback
… as problem categories identified. An XGBoost Machine Learning classifier was trained using this dataset to categorize SECO reviews into six distinct problem categories with an accuracy of 93%. Negative reviews were identified using sentiment analysis, and TF-IDF and Chi-Squared analysis were …
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Classification of Foetal Distress and Hypoxia Using Machine Learning
… hypoxic. This study investigates the use of machine learning classifiers for classification of foetal hypoxic cases using a novel method, in which we are not only considering the classification performance only, but also investigating the worth of each participating parameter to the …
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Transcriptional Heterogeneity in ALS: Patients with Retrovirus-mediated Disease and Potential Cell-type Specific Therapeutic Approaches
… logistic regression, a type of supervised machine learning, classifier for ALS patient or control samples. Finally, the cell type specificity of these results was determined using a publicly available single nucleus RNA sequencing (snRNA-seq) dataset consisting of 23 ALS patients and 17 …
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A New Hybrid Approach to Sentiment Classification
… of the classification approach. This can be machine learning, lexical/lexicon-based, and more recently, hybrid. The machine learning approaches have the benefits of carrying out classification with high accuracies, and efficiently handling large feature sets, which makes them a favourite …
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Curating and summarising data from animal research on neurodevelopment at scale
… programming interface (API), text mining, and machine learning – we may be able to increase the feasibility of producing systematic maps. In this thesis, I investigate how evidence synthesis methods and automation approaches can be combined to create a systematic map and make the most use out …
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