Physical Sciences and Mathematics Commons^™

Cross Dataset Evaluation For Iot Network Intrusion Detection, Anjum Farah

Theses and Dissertations

With the advent of Internet of Things (IOT) technology, the need to ensure the security of an IOT network has become important. There are several intrusion detection systems (IDS) that are available for analyzing and predicting network anomalies and threats. However, it is challenging to evaluate them to realistically estimate their performance when deployed. A lot of research has been conducted where the training and testing is done using the same simulated dataset. However, realistically, a network on which an intrusion detection model is deployed will be very different from the network on which it was trained. The aim of …

Machine-Learning-Based Prediction Of Sepsis Events From Vertical Clinical Trial Data: A Naïve Approach, Tyler Michael Gaddis

Theses and Dissertations

Sepsis is a potentially life-threatening condition characterized by a dysregulated, disproportionate immune response to infection by which the afflicted body attacks its own tissues, sometimes to the point of organ failure, and in the worst cases, death. According to the Centers for Disease Control and Prevention (CDC) Sepsis is reported to kill upwards of 270,000 Americans annually, though this figure may be greater given certain ambiguities in the current accepted diagnostic framework of the disease.

This study attempted to first establish an understanding of past definitions of sepsis, and to then recommend use of machine learning as integral in an …

Dictionary-Based Data Generation For Fine-Tuning Bert For Adverbial Paraphrasing Tasks, Mark Anthony Carthon

Theses and Dissertations

Recent advances in natural language processing technology have led to the emergence of

large and deep pre-trained neural networks. The use and focus of these networks are on transfer

learning. More specifically, retraining or fine-tuning such pre-trained networks to achieve state

of the art performance in a variety of challenging natural language processing/understanding

(NLP/NLU) tasks. In this thesis, we focus on identifying paraphrases at the sentence level using

the network Bidirectional Encoder Representations from Transformers (BERT). It is well

understood that in deep learning the volume and quality of training data is a determining factor

of performance. The objective of …