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Conditional Variational Transformer For Bearing Remaining Useful Life Prediction, Yupeng Wei, Dazhong Wu Jan 2024

Conditional Variational Transformer For Bearing Remaining Useful Life Prediction, Yupeng Wei, Dazhong Wu

Faculty Research, Scholarly, and Creative Activity

Transformer, built on the self-attention mechanism, has been demonstrated to be effective in numerous applications. However, in the context of prognostics and health management, the self-attention mechanism in the Transformer is not effective in selecting the most important features that are highly correlated with the remaining useful life (RUL) of a component. To address this issue, we developed a novel conditional variational transformer architecture consisting of four networks: two generative networks and two predictive networks. The first generative network uses the transformer encoder–decoder as well as both condition monitoring data and RUL as input to extract the most important features …


Image Segmentation By Convolutional Neural Networks In Coral Resilience Research, Jennifer Benbow Jan 2024

Image Segmentation By Convolutional Neural Networks In Coral Resilience Research, Jennifer Benbow

Master's Projects

As ocean temperatures rise, coral bleaching is becoming more frequent and severe. Selective breeding experiments show promise for enhancing coral resilience, but scaling these projects is hindered by the labor-intensive nature of taking numerous time series measurements as corals grow. Automating this process with computer vision is one solution to this bottleneck, and to our knowledge, no such tool exists at present. To fill this gap, we have trained a set of machine learning models, based on the Mask R-CNN framework, for segmenting juvenile corals in lab-based coral resilience research. This work shows that retraining the Mask R-CNN architecture through …


Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Yan Chu, Kaichen Tang, Yu Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I. Savitz, Xiaoqian Jiang, Shayan Shams Dec 2023

Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Yan Chu, Kaichen Tang, Yu Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I. Savitz, Xiaoqian Jiang, Shayan Shams

Faculty Research, Scholarly, and Creative Activity

Background: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct …


Remaining Useful Life Prediction Of Bearings With Attention-Awared Graph Convolutional Network, Yupeng Wei, Dazhong Wu Oct 2023

Remaining Useful Life Prediction Of Bearings With Attention-Awared Graph Convolutional Network, Yupeng Wei, Dazhong Wu

Faculty Research, Scholarly, and Creative Activity

Graph Convolutional Networks (GCNs) have recently been used to predict the remaining useful life (RUL) of bearings due to its effectiveness in revealing correlations in condition monitoring data. However, traditional GCNs use a single graph only, either a temporal-correlated graph or a feature-correlated graph without considering both temporal and feature correlations of condition monitoring data. Additionally, traditional GCNs rely heavily on pre-defined graphs to aggregate correlated features. However, the topology of these pre-defined graphs may vary depending on a pre-defined threshold for cosine similarity or covariance which might affect prediction accuracy and robustness. To address these issues, we introduce a …


Enhancing Deep Learning Classifiers For Dynamic Keystroke Authentication Via Gans, Jonathan A. Bazan Jan 2023

Enhancing Deep Learning Classifiers For Dynamic Keystroke Authentication Via Gans, Jonathan A. Bazan

Master's Projects

Leveraging machine learning for biometric authentication is an area of research that has seen a lot of progress within the past decade. Keystroke authentication based on machine and deep learning classifiers aims to develop a robust model that can distinguish a user from an adversary based on typing metrics (keystrokes). While keystroke authentication started with static text, where people type the same data, the shift has been to dynamic data where every user’s data varies. Recent literature has shown that with enough data, deep learning classifiers have the capacity to authenticate users with a low Equal Error Rate (EER).

However, …


Multi-Label Text Classification With Transfer Learning, Likhitha Yelamanchili Jan 2023

Multi-Label Text Classification With Transfer Learning, Likhitha Yelamanchili

Master's Projects

Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. This project's goal is to assess how well several deep learning models perform on a real-world dataset for multi-label text classification. We employed data augmentation techniques like Synonym Substitution and Random Word Substitution to address the problem of data imbalance. We conducted experiments on a toxic comment classification dataset to evaluate the effectiveness of several deep learning models including Bi-LSTM, GRU, and Bi-GRU, as well as fine- tuned pre-trained BERT models. Many metrics, including log loss, recall@k, and …


Convolutional Long-Short Term Memory Network With Multi-Head Attention Mechanism For Traffic Flow Prediction, Yupeng Wei, Hongrui Liu Oct 2022

Convolutional Long-Short Term Memory Network With Multi-Head Attention Mechanism For Traffic Flow Prediction, Yupeng Wei, Hongrui Liu

Faculty Research, Scholarly, and Creative Activity

Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the …


Tntdetect.Ai: A Deep Learning Model For Automated Detection And Counting Of Tunneling Nanotubes In Microscopy Images, Yasin Ceran, Hamza Ergüder, Katherine Ladner, Sophie Korenfeld, Karina Deniz, Sanyukta Padmanabhan, Phillip Wong, Murat Baday, Thomas Pengo, Emil Lou, Chirag B. Patel Oct 2022

Tntdetect.Ai: A Deep Learning Model For Automated Detection And Counting Of Tunneling Nanotubes In Microscopy Images, Yasin Ceran, Hamza Ergüder, Katherine Ladner, Sophie Korenfeld, Karina Deniz, Sanyukta Padmanabhan, Phillip Wong, Murat Baday, Thomas Pengo, Emil Lou, Chirag B. Patel

Faculty Research, Scholarly, and Creative Activity

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions …


Enhancing The Cognition And Efficacy Of Machine Learning Through Similarity, Vishnu Pendyala, Rakesh Amireddy Aug 2022

Enhancing The Cognition And Efficacy Of Machine Learning Through Similarity, Vishnu Pendyala, Rakesh Amireddy

Faculty Research, Scholarly, and Creative Activity

Similarity is a key element of machine learning and can make human learning much more effective as well. One of the goals of this paper is to expound on this aspect. We identify real-world concepts similar to hard-to-understand theories to enhance the learning experience and comprehension of a machine learning student. The second goal is to enhance the work in the current literature that uses similarity for transcoding. We uniquely try transcoding from Python to R and vice versa, something that was not attempted before, by identifying similarities in a latent embedding space. We list several real-world analogies to show …


A Comparison Of Univariate And Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals During The Covid-19 Pandemic, Egbe Etu Etu, Leslie Monplaisir, Sara Masoud, Suzan Arslanturk, Joshua Emakhu, Imokhai Tenebe, Joseph B. Miller, Tom Hagerman, Daniel Jourdan, Seth Krupp Jun 2022

A Comparison Of Univariate And Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals During The Covid-19 Pandemic, Egbe Etu Etu, Leslie Monplaisir, Sara Masoud, Suzan Arslanturk, Joshua Emakhu, Imokhai Tenebe, Joseph B. Miller, Tom Hagerman, Daniel Jourdan, Seth Krupp

Faculty Research, Scholarly, and Creative Activity

The COVID-19 pandemic has heightened the existing concern about the uncertainty surrounding patient arrival and the overutilization of resources in emergency departments (EDs). The prediction of variations in patient arrivals is vital for managing limited healthcare resources and facilitating data-driven resource planning. The objective of this study was to forecast ED patient arrivals during a pandemic over different time horizons. A secondary objective was to compare the performance of different forecasting models in predicting ED patient arrivals. We included all ED patient encounters at an urban teaching hospital between January 2019 and December 2020. We divided the data into training …


Spartan Face Mask Detection And Facial Recognition System, Ziwei Song, Kristie Nguyen, Tien Nguyen, Catherine Cho, Jerry Gao Jan 2022

Spartan Face Mask Detection And Facial Recognition System, Ziwei Song, Kristie Nguyen, Tien Nguyen, Catherine Cho, Jerry Gao

Faculty Research, Scholarly, and Creative Activity

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection …


Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp Sep 2021

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …


Hyperglycemia Identification Using Ecg In Deep Learning Era, Renato Cordeiro, Nima Karimian, Younghee Park Sep 2021

Hyperglycemia Identification Using Ecg In Deep Learning Era, Renato Cordeiro, Nima Karimian, Younghee Park

Faculty Research, Scholarly, and Creative Activity

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the …


Automating Text Encapsulation Using Deep Learning, Anket Sah May 2021

Automating Text Encapsulation Using Deep Learning, Anket Sah

Master's Projects

Data is an important aspect in any form be it communication, reviews, news articles, social media data, machine or real-time data. With the emergence of Covid-19, a pandemic seen like no other in recent times, information is being poured in from all directions on the internet. At times it is overwhelming to determine which data to read and follow. Another crucial aspect is separating factual data from distorted data that is being circulated widely. The title or short description of this data can play a key role. Many times, these descriptions can deceive a user with unwanted information. The user …


Yoga Pose Classification Using Deep Learning, Shruti Kothari May 2020

Yoga Pose Classification Using Deep Learning, Shruti Kothari

Master's Projects

Human pose estimation is a deep-rooted problem in computer vision that has exposed many challenges in the past. Analyzing human activities is beneficial in many fields like video- surveillance, biometrics, assisted living, at-home health monitoring etc. With our fast-paced lives these days, people usually prefer exercising at home but feel the need of an instructor to evaluate their exercise form. As these resources are not always available, human pose recognition can be used to build a self-instruction exercise system that allows people to learn and practice exercises correctly by themselves. This project lays the foundation for building such a system …


Non-Invasive Hyperglycemia Detection Using Ecg And Deep Learning, Renato Silveira Cordeiro Dec 2019

Non-Invasive Hyperglycemia Detection Using Ecg And Deep Learning, Renato Silveira Cordeiro

Master's Theses

Hyperglycemia is characterized by an elevated level of glucose in the blood. It is normally asymptomatic, except for an extremely high level, and thus a person can live in that state for years before the negative - sometimes irreversible - health impacts appear. Unexpected hyperglycemia can also be an indication of diabetes, a chronic disease that, when not treated, can lead to serious consequences, including limb amputations and even death. Therefore, identifying hyperglycemic state is important. The most common and direct way to measure a person’s glucose level is by directly assessing it from a blood sample by pricking a …


Music Mood Classification Using Convolutional Neural Networks, Revanth Akella May 2019

Music Mood Classification Using Convolutional Neural Networks, Revanth Akella

Master's Projects

Grouping music into moods is useful as music is migrating from to online streaming services as it can help in recommendations. To establish the connection between music and mood we develop an end-to-end, open source approach for mood classification using lyrics. We develop a pipeline for tag extraction, lyric extraction, and establishing classification models for classifying music into moods. We investigate techniques to classify music into moods using lyrics and audio features. Using various natural language processing methods with machine learning and deep learning we perform a comparative study across different classification and mood models. The results infer that features …


Intelligent Log Analysis For Anomaly Detection, Steven Yen May 2019

Intelligent Log Analysis For Anomaly Detection, Steven Yen

Master's Projects

Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We …


Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia May 2019

Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia

Master's Projects

The preservation of the world’s oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped …


Robust Lightweight Object Detection, Siddharth Kumar May 2019

Robust Lightweight Object Detection, Siddharth Kumar

Master's Projects

Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising in- crease in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object de- tection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required …


Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models, Pratik Patil May 2019

Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models, Pratik Patil

Master's Projects

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random …


Deep Learning For Image Spam Detection, Tazmina Sharmin May 2019

Deep Learning For Image Spam Detection, Tazmina Sharmin

Master's Projects

Spam can be defined as unsolicited bulk email. In an effort to evade text-based spam filters, spammers can embed their spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply various machine learning and deep learning techniques to real-world image spam datasets, and to a challenge image spam-like dataset. We obtain results comparable to previous work for the real-world datasets, while our deep learning approach yields the best results to date for the challenge dataset.


Machine Learning Versus Deep Learning For Malware Detection, Parth Jain May 2019

Machine Learning Versus Deep Learning For Malware Detection, Parth Jain

Master's Projects

It is often claimed that the primary advantage of deep learning is that such models can continue to learn as more data is available, provided that sufficient computing power is available for training. In contrast, for other forms of machine learning it is claimed that models ‘‘saturate,’’ in the sense that no additional learning can occur beyond some point, regardless of the amount of data or computing power available. In this research, we compare the accuracy of deep learning to other forms of machine learning for malware detection, as a function of the training dataset size. We experiment with a …


Chatbots With Personality Using Deep Learning, Susmit Gaikwad May 2019

Chatbots With Personality Using Deep Learning, Susmit Gaikwad

Master's Projects

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, …


Image Retrieval Using Image Captioning, Nivetha Vijayaraju May 2019

Image Retrieval Using Image Captioning, Nivetha Vijayaraju

Master's Projects

The rapid growth in the availability of the Internet and smartphones have resulted in the increase in usage of social media in recent years. This increased usage has thereby resulted in the exponential growth of digital images which are available. Therefore, image retrieval systems play a major role in fetching images relevant to the query provided by the users. These systems should also be able to handle the massive growth of data and take advantage of the emerging technologies, like deep learning and image captioning. This report aims at understanding the purpose of image retrieval and various research held in …


Optimizing E-Commerce Product Classification Using Transfer Learning, Rashmeet Kaur Khanuja May 2019

Optimizing E-Commerce Product Classification Using Transfer Learning, Rashmeet Kaur Khanuja

Master's Projects

The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not …