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Needle in the Noise: Detecting Public Safety Events Over Twitter
Contributor:Paulson, Keith A.
Date Submitted:2019-05
Date Submitted:2019-05
The recent advent of using social media and search engine queries to detect and classifying events is an emerging area in data science. This study uses current Natural Language Processing (NLP) techniques for deep learning ...
Post-Traumatic Stress Disorder Severity Prediction on Web-based Trauma Recovery Treatments Through Electrodermal Activity Measurements
Contributor:Mallol-Ragolta, Adria
Date Submitted:2018-05
Date Submitted:2018-05
Recent studies have shown evidences regarding trauma recovery through web-based interventions. Currently, a widespread protocol is to assess trauma severity by answering the PTSD Checklist (PCL) questionnaire, which requires ...
Towards Automating Big Texts Security Classification
Contributor:Alzhrani, Khudran Maeed
Date Submitted:2018-05
Date Submitted:2018-05
The U.S Government has been the target of cyber-attacks from all over the world. Former President Obama accused the Russian government of leaking emails to WikiLeaks and declared that the U.S. might be forced to respond. ...
Detection And Analysis Of Software Clones
Contributor:Sheneamer, Abdullah Mohammad
Date Submitted:2017-08
Date Submitted:2017-08
Effective detection of code clones is important for software maintenance. Code clones introduce difficulty in software maintenance and lead to bug propagation. Detection of duplicated bugs within a piece of software is ...
Machine Learning Model for Elite Athletes, A
Contributor:Hmosze, Maadh
Date Submitted:2018-05
Date Submitted:2018-05
Abstract In this research we have used different machine learning algorithms to predict the performance of the elite athletes riding bicycles. The study of measuring the performance of the athletes is based on different ...
Towards Literary Genre Identification: Applied Neural Networks for Large Text Classification
Contributor:Worsham, Joseph Michael
Date Submitted:2018-12
Date Submitted:2018-12
Recent advances in Natural Language Processing are finding ways to place an emphasis on the hierarchical nature of text instead of representing language as a flat sequence or unordered collection of words or letters. A ...
EFFICIENT MACHINE LEARNING INFERENCE FOR EMBEDDED SYSTEMS WITH INTEGER BASED RESTRICTED BOLTZMANN MACHINES CLASSIFIERS
Contributor:Sosa Barillas, Bryan Samuel
Date Submitted:2019-12
Date Submitted:2019-12
Nowadays, there exist many emerging applications for embedded systems, such as computer vision and speech recognition, which heavily rely on machine learning classification. The typical approach to carry out machine learning ...
Autonomic performance and power control in virtualized datacenters
Contributor:Lama, Palden
Date Submitted:2013
Date Submitted:2013
Virtualized datacenters, the platform for supporting Cloud computing, allow diverse applications to share the underlying server resources. Due to the highly dynamic nature of Internet workloads, increasing complexity of ...
Probabilistic Open Set Recognition
Contributor:Jain, Lalit Prithviraj
Date Submitted:2015
Date Submitted:2015
Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An ...
Learning Indoor Localization using Radio Received Signal Strength
Contributor:Kulkarni, Gauri
Date Submitted:2016-05
Date Submitted:2016-05
With this research we will investigate a novel machine learning approach to the prediction of location from received signal strength indicators (RSSI) values obtained from these transmitting access points. Indoor localization ...