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Full-Text Articles in Physical Sciences and Mathematics
Improving Pain Assessment Using Vital Signs And Pain Medication For Patients With Sickle Cell Disease: Retrospective Study, Swati Padhee, Gary K. Nave Jr, Tanvi Banerjee, Daniel M. Abrams, Nirmish Shah
Improving Pain Assessment Using Vital Signs And Pain Medication For Patients With Sickle Cell Disease: Retrospective Study, Swati Padhee, Gary K. Nave Jr, Tanvi Banerjee, Daniel M. Abrams, Nirmish Shah
Computer Science and Engineering Faculty Publications
Background: Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient's pain intensity level. Objective: This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods: This study used electronic health record data collected …
Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner
Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner
Browse all Theses and Dissertations
A phonon is a discrete unit of vibrational motion that occurs in a crystal lattice. Phonons and the frequency at which they propagate play a significant role in the thermal, optical, and electronic properties of a material. A phononic material/device is similar to a photonic material/device, except that it is fabricated to manipulate certain bands of acoustic waves instead of electromagnetic waves. Phononic materials and devices have been studied much less than their photonic analogues and as such current materials exhibit control over a smaller range of frequencies. This study aims to test the viability of machine learning, specifically neural …
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
Browse all Theses and Dissertations
The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …