Physical Sciences and Mathematics Commons^™

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Machine Learning Based Ultra High Carbon Steel Image Segmentation, Sumith Kuttiyil Suresh

Theses and Dissertations

Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultra- high carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work …

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

In this thesis, we investigate the performance of a series of classification methods for the

Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting

LOS for an inpatient in an hospital is a challenging task but is essential for the operational

success of a hospital. Since hospitals are faced with severely limited resources including

beds to hold admitted patients, prediction of LoS will assist the hospital staff for better

planning and management of hospital resources. The goal of this project is to create a

machine learning model that predicts the length-of stay for each patient …

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

In this thesis, we investigate the performance of a series of classification methods for the

Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting

LOS for an inpatient in an hospital is a challenging task but is essential for the operational

success of a hospital. Since hospitals are faced with severely limited resources including

beds to hold admitted patients, prediction of LoS will assist the hospital staff for better

planning and management of hospital resources. The goal of this project is to create a

machine learning model that predicts the length-of stay for each patient …

Evaluating Machine Learning Techniques For Smart Home Device Classification, Angelito E. Aragon Jr.

Theses and Dissertations

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, …

Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano

Theses and Dissertations

Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography …

Automated Essay Evaluation Using Natural Language Processing And Machine Learning, Harshanthi Ghanta

Theses and Dissertations

The goal of automated essay evaluation is to assign grades to essays and provide feedback using computers. Automated evaluation is increasingly being used in classrooms and online exams. The aim of this project is to develop machine learning models for performing automated essay scoring and evaluate their performance. In this research, a publicly available essay data set was used to train and test the efficacy of the adopted techniques. Natural language processing techniques were used to extract features from essays in the dataset. Three different existing machine learning algorithms were used on the chosen dataset. The data was divided into …