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Unsupervised Deep Learning for Classification Of Bats Calls Using Acoustic Data
Published 2021Subjects: “…Unsupervised Deep Learning…”
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doctoralThesis -
2
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Published 2019“…Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. …”
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3
Breast Density Estimation in Mammograms Using Unsupervised Image Segmentation
Published 2023Get full text
doctoralThesis -
4
Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach
Published 2024“…This study follows a different path by proposing a two-stage approach leveraging binary classification and clustering to comprehensively analyze and classify dysarthria severity levels. …”
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InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification
Published 2021“…We demonstrate the capabilities of our framework in the context of visual analysis and unsupervised classification of 2D histology images and 3D nuclear envelopes extracted from serial section electron microscopy stacks.…”
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6
Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
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doctoralThesis -
7
Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
Published 2024“…<p dir="ltr">Clustering has proved to be a successful classification method when it comes to dealing with multiview data. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis
Published 2023“…This work introduces a novel, unsupervised detection methodology for detecting adversarial inputs to NLP classifiers. …”
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Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis
Published 2023“…This work introduces a novel, unsupervised detection methodology for detecting adversarial inputs to NLP classifiers. …”
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A spectral-ensemble deep random vector functional link network for passive brain–computer interface
Published 2023“…The cross-subject <u>classification results</u> obtained demonstrated its effectiveness. …”
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The Use of Supply Chain Metrics in Lebanon
Published 2020“…The results of the survey are analyzed via two machine learning techniques – an unsupervised clustering technique (kMeans) to identify companies with similar behavior relative to the SCOR metrics and a supervised learning technique (Classification Trees) to ascertain which company demographics (ie industry, age, size, age of employees, and SCOR familiarity) dictate cluster membership.…”
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masterThesis -
13
An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering
Published 2023“…AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. …”
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A Machine Learning Approach to Predicting Diabetes Complications
Published 2021Get full text
doctoralThesis -
15
Use of Data Mining Techniques to Detect Fraud in Procurement Sector
Published 2022“…The method used in this research is a classification of models and algorithms used in data mining. …”
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Genome‐wide DNA methylation analysis of colorectal adenomas with and without recurrence reveals an association between cytosine‐phosphate‐guanine methylation and histological subty...
Published 2019“…In conclusion, our methylation data could assist in establishing a more robust and reproducible histological adenoma classification, which is a prerequisite for improving surveillance guidelines.…”
