Now showing items 1-10 of 14
Needle in the Noise: Detecting Public Safety Events Over Twitter
Author(s):Paulson, Keith A.
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
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
Author(s):Alzhrani, Khudran Maeed
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
Author(s):Sheneamer, Abdullah Mohammad
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 ...
OPEN-SET INTRUSION RECOGNITION USING EXTREME VALUE MACHINE
Machine learning is continuously expanding as a sub-field of computer science which evolved from pattern recognition and computational learning theory. Advancements in machine learning, combined with the availability of ...
Machine Learning Model for Elite Athletes, A
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
Author(s):Worsham, Joseph Michael
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
Author(s):Sosa Barillas, Bryan Samuel
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
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
Author(s):Jain, Lalit Prithviraj
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 ...