Digital Commons Network^™

Deep-Learning Approaches To Predict Remaining Useful Life Of Hard Disks, Rohan Mohapatra

Master's Projects

On a daily basis, data centers process huge volumes of data using inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life (RUL) of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. We …

Domain Expert Bot, Amrutha Dondemadahalli Ramegowda

Master's Projects

The fast growth of artificial intelligence in human-computer interaction has been aided significantly by the introduction of conversational AI systems. This project presents a Domain Expert Bot, a multi-domain conversational bot built with advanced NLP techniques incorporated through Sentence-BERT and MapReduce to allow the bot to analyze and comprehend challenging user queries on various topics. The bot can converse on different subjects ranging from technology topics to healthcare, environment, politics, and casual discussions. It excels in understanding deep language contexts and efficiently processes large datasets, ensuring prompt and accurate responses. Furthermore, it uses advanced ranking algorithms to perform real- time …

Regional Sea Level Rise Prediction In Monterey Bay With Lstms And Vertical Land Motion, Branden Lopez

Master's Projects

Earth system data is vast in volume and variety, and is used to forecast weather,

hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of

clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of …

Advancing Discourse Analysis In Multiparty Meetings: Comprehensive Classification Of Argument And Relation Types, Vishal Vaitla

Master's Projects

In multi-party meetings, accurately analyzing dialogue is crucial for enhancing communication effectiveness and decision-making. However, the informal and dynamic nature of these discussions presents complex challenges for computational analysis. Dialogues in such settings often include non-standard language, interruptions, and rapid topic changes, making it difficult to extract useful information with conventional text analysis tools. To tackle this challenge, two specific methods were developed:

Argument Classification: We use machine learning models like Gradient Boosting to identify and categorize the main points people make in their discussions. This helps us understand what each person is trying to say, making it easier to …

Breaking The Cycle: Countering Popularity Bias For Diverse Content Discovery, Brandon J. Weaver

Master's Projects

The ways most people consume the media have become very much driven by some pre-set algorithms. It is increasingly important to examine the outcome of these artificial intelligence (AI) models and ensure that any potentially dangerous long-term effects are addressed before they have a significant negative impact in our society. Popularity bias is one of these potentially harmful impacts, which stemmed from the shift from human intelligence to AI, or machine intelligence/machine learning (ML), when one explores the media and receives recommendations (often without requesting). In ML, three key steps usually occur; i.e, pre-processing, in-processing, and post- processing steps. The …

Hindi Image Captioning Using Indictrans2 And Encoder-Decoder Architecture, Anahita Vayalombrone Dinesh

Master's Projects

One of the most prominent tasks that lie on the conjunction of Natural Language Processing (NLP) and computer vision, is image captioning. Image captioning is the generative task of achieving textual descriptions from images. Its application finds use in many real-world scenarios like aiding the visually impaired, editing applications, recommendation systems, and medical imaging. This research focus lies in Hindi image captioning, the official language of India, as it has not been explored as far as its need. Several challenges such as the lack of substantial Hindi text data for training models, the need for human annotators to verify the …

A Rule-Based Hybrid Translation Model For Context-Aware Speech To Indian Sign Language Tasks, Dania Jaison

Master's Projects

In India, one of the most significant tools to communicate with the Deaf and Hard of Hearing (DHH) communities is Indian Sign Language(ISL). The issue of a scarcity of computational resources for ISL is even more pronounced when it comes to the translation of written or spoken English into ISL. This paper proposes a rule-based model for translation where the spoken english sentences are translated into ISL by focusing on the specific syntactic and grammatical differences between these two languages. ISL uses a simplified syntax unlike the nuanced sentence structures in English — we omit most of the auxiliary verbs …

Facial Expression Mood Classification Using Machine Learning, Tiantong Li

Master's Projects

Facial expression classification is a powerful tool for understanding human emotions, with applications spanning human-computer interaction, healthcare, and entertainment. By analyzing facial cues, systems can interpret emotional states and adapt their responses, creating more personalized and emotionally aware experiences. One emerging application of facial expression classification is in music recommendation systems, where user emotions are integrated to suggest music that aligns with their current mood. While prior research has primarily classified facial expressions into four emotion categories, this study broadens the scope to seven emotions: angry, disgust, fear, happy, neutral, sad, and surprise. The project evaluates four machine learning techniques—CNN, …

Cluster Analysis For Concept Drift Detection In Malware, Aniket Mishra

Master's Projects

The rapid evolution of malware presents significant challenges for detection systems. This is due to malware families adapting through feature manipulation and obfuscation, which causes concept drift. A clustering based approach is used to detect and adapt to these shifts. The KronoDroid dataset is segmented into batch sizes of 50 and analyzed with MiniBatch K-Means clustering. The silhouette coefficient is used to evaluate clustering quality, and help identify drift by detecting significant changes in cluster patterns. Concept drift will cause retraining of supervised classifiers, including Linear SVM, RF, MLP, and XGBoost. Three scenarios are used: static models, periodic retraining, and …

Teaching Children Programming Concepts Through Video Games, Kayla Musleh

Master's Projects

Children have a tendency to lose focus when they are presented with something that does not entertain them or tailor to their personal interests; such as studying [1], [3], [10]. The research performed for this project focuses on studying how much more children can comprehend and focus on learning educational material if they are learning through playing a video game rather than being taught information directly in a typical classroom manner. For this study I created a computer game designed to introduce educational subjects such as mathematics and programming concepts to the child playing the game. By completing the tasks …

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

The Effect Of A Neonatal Palliative Care Education Program In Increasing Knowledge And Communication Self-Efficacy Among Nicu Nurses, Shiou-Huei Joy Wang

Doctoral Projects

Background: Neonatal palliative care is crucial to providing comprehensive, high-quality care to neonates with life-limiting or life-threatening health conditions and their families. One significant barrier to the inconsistent provision of neonatal palliative care is the lack of education. This project aimed to deliver an education training program to increase neonatal palliative care knowledge and self-efficacy in NICU nurses. Method: A pre-post-test study design was used to examine the efficacy of an educational training program on NICU nurses’ knowledge and communication self-efficacy in neonatal palliative care. A post-test was administered immediately after the training and again four weeks later to evaluate …

Posttraumatic Symptoms As Predictors Of Mobile App Engagement: An Evaluation Of Usage Data From Public Users Of An App For Military Sexual Trauma, Shilpa Hampole

Master's Theses

Veterans who have experienced military sexual trauma (MST) often face barriers to help-seeking. Mental health mobile applications (apps) such as Beyond MST, developed by the National Center for PTSD, can expand access to evidence-based coping tools, psychoeducation, and resources. However, user engagement is crucial for these apps to be effective. Researchers are exploring factors impacting app engagement, with emerging evidence suggesting that symptom severity may play a key role. This study examined whether the severity of posttraumatic stress disorder (PTSD) symptoms and posttraumatic negative cognitions, as well as level of well-being, are associated with app engagement. Anonymous app usage data …

Electron-Induced Chemical Transformations In Polymer Films: Understanding The Role Of Electrons In Euv Lithography, Maximillian Mueller

Master's Theses

The next generation of computer processors, memory, and nano-scale technologies all rely on our ability to create patterns at the nanometer scale reliably. Extreme ultraviolet (EUV) lithography can create sub-10 nm features by exposing photoresist materials, typically polymers or organic networks, to 13.5 nm, 92 eV light. At these energies, absorption of photons by the photoresist leads to the emission of a primary electron around 80 eV which inelastically scatters throughout the material, initiating chemistry and creating secondary electrons. The resulting cascade of electrons and chemical reactions remains poorly understood for most photoresist materials systems. This work aims to characterize …

Corrigendum: A Live Fuel Moisture Climatology In California (Front. For. Glob. Change, (2023), 6, 1203536, 10.3389/Ffgc.2023.1203536), Jack R. Drucker, Angel Farguell, Craig B. Clements, Adam K. Kochanski

Faculty Research, Scholarly, and Creative Activity

In the published article, there was an error in Figures 4, 5 as published. The plots originally appearing in Figure 4 should be interchanged with those in Figure 5, and vice versa. The corrected Figure 4 and its caption “Figure 4. The general (annual average) (black dashed line), maximum (green dashed line), and minimum (red dashed line) LFMC trends for the (A) Bay Area, (B) Central Coast, and (C) South Coast regions. The differences in the time ranges stem from the length of the data record which varies across the regions” and the corrected Figure 5 along with its caption …

Erratum To: Reconfigurations In Brain Networks Upon Awakening From Slow Wave Sleep: Interventions And Implications In Neural Communication (Network Neuroscience), Cassie J. Hilditch, Kanika Bansal, Ravi Chachad, Lily R. Wong, Nicholas G. Bathurst, Nathan H. Feick, Amanda Santamaria, Nita L. Shattuck, Javier O. Garcia, Erin E. Flynn-Evans

Faculty Research, Scholarly, and Creative Activity

After publication of our paper Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication, we noticed an error in our condition labels that required some revision of figures and text and some new interpretations throughout the manuscript. The error was a consequence of the double-blind nature of the original experimentation which contained two interventions: (1) a blue-enriched light intervention, as originally mentioned; and (2) an olfactory odorant condition. In review of previous analyses and presentations of the data, there was a time when non-descriptive labels (e.g., “A”, “B”) were accidentally swapped, indicating the …

Oculometric Biomarkers Of Visuomotor Deficits In Clinically Asymptomatic Patients With Systemic Lupus Erythematosus Undergoing Long-Term Hydroxychloroquine Treatment, Andrew R. Berneshawi, Kimia Seyedmadani, Rahul Goel, Mark R. Anderson, Terence L. Tyson, Yasmin M. Akay, Metin Akay, Loh Shan B. Leung, Leland S. Stone

Faculty Research, Scholarly, and Creative Activity

Introduction: This study examines a set of oculomotor measurements, or “oculometric” biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. Methods: We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical …

Emergency And Disaster Preparedness For Individuals Who Use Augmentative And Alternative Communication: A Pilot Study On Supported Planning Using A Toolkit, Andrea Barton-Hulsey, Miriam C. Boesch, Yoosun Chung, Tina Caswell, Amy Miller Sonntag, Wendy Quach

Faculty Research, Scholarly, and Creative Activity

Purpose: When emergencies or disasters arise, individuals who use augmenta-tive and alternative communication (AAC) are particularly vulnerable. It is critical that individuals who use AAC are supported to make decisions that impact their own lives and are able to communicate during such a crisis. Preparedness efforts that include a plan around communication needs and supports are necessary for ensuring that individuals who use AAC are able to understand options that ensure personal safety and express their needs during a time of crisis. Method: Qualitative methods were used to identify and describe the experi-ences of two young adults who use AAC …

Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali

Master's Projects

Medical chatbots, at the conjunction of artificial intelligence and healthcare, are the very cornerstone of a transformative force in diagnostic capabilities and communication channels for healthcare professionals. The history of this journey, from early chatbot models to sophisticated systems, is born out of a relentless pursuit of accuracy and contextual understanding. This proposal acknowledges the critical role played by NLTK in raising the interpretability and communicative capabilities of intelligent systems, meeting challenges that arise from varying writing styles and accommodating the standards of the medical field. The integration of NLTK is a linchpin, bridging the gap between sophisticated technological architectures …

Fake Malware Generation Using Gans As Api Calls, Saieswar Reddy Vaka

Master's Projects

With malware threats on the rise, they have also grown more complicated and subtle. Consequently, incorporating cutting-edge machine learning into cybersecurity defenses has never been more crucial. Nevertheless, building resilient machine-learning models is a significant challenge due to the need for existing diversified and complete malware datasets. This project will relieve this difficulty by employing a Generative

Adversarial Network (GAN) to develop artificial malware samples featuring an Appli- cation Programming Interface (API) call series. While traditional generative modeling

has primarily been limited to image-based fields, we offer an “outside the box” domain – malware signature generation – as an API …

Base Station Selection Based On The Predicted Dwell Time In 5g-V2x Handover, Anushree Jayesh Shah

Master's Projects

Vehicle-to-Everything (V2X) networks have facilitated smooth communication in vehicles and their surroundings via 5G technology. As vehicles move into different coverage areas, they tend to switch between cellular base stations in order to talk to these "things". Handovers are crucial to maintaining these networks’ effectiveness. To ensure uninterrupted connectivity and seamless handovers, it is extremely important to select a base station that is most appropriate. A vehicle’s dwell time is the duration it stays connected to a base station before handoff. This research focuses on predicting dwell time using regression techniques to proactively choose the most suitable base station. We …

Ai-Driven Credit Card Fraud Detection With Enhanced User Interaction, Toshi Bhat

Master's Projects

This thesis describes the development and testing of a unique system for detecting credit card fraud. The system employs graph neural networks (GNNs) and a real-time user interaction platform. The primary goal of this study is to use advanced machine learning methods and interactive technologies to improve fraud detection accuracy and the speed with which users can receive assistance. GraphSAGE, a type of GNN, was trained on a simulated set of credit card transactions, allowing the system to detect and predict fraud very accurately. Simulating a real-world transaction scenario is an important aspect of the project. In this case, the …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Detecting Fake Reviews Using Aspect Based Sentiment Analysis And Graph Convolutional Networks, Prathana Phukon

Master's Projects

Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as …

Emotion Detection Using Ensemble Learning, Priya Harika Yerapothu

Master's Projects

Emotion detection is gaining exponential necessity in today’s technological age. This research seeks to delve into ways conversational AI could be enhanced by integrating emotional intelligence using an ensemble learning approach. Traditional machine learning along with advanced neural network architectures are implemented to improve the understanding and intricacies of emotion detection from textual data. The dataset we use is GoEmotions dataset, annotated with 27 emotional labels, to conduct a detailed analysis of emotion recognition. Various machine learning models, such as HistGradientBoosting, LightGBM, CatBoost, and MLP, will be evaluated side by side with advanced models of Bidirectional Long Short-Term Memory (BiLSTM) …

Multirelational Twitter Bot Detection Using Graph Neural Networks, Ketan Jadhav

Master's Projects

Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation …

Ensemble Model With Meta-Learning For Ddos Attack Classification In Sdn, Ankith Indrakumar

Master's Projects

In response to the security threats posed by Distributed Denial of Service (DDoS) attacks, this paper presents an intrusion detection framework with a high-accuracy multi-class classification model. In addition to detecting the existence of DDoS attacks, our framework aims to identify the type of attack (e.g., protocol or message type) so that the system can select the most appropriate countermeasure against the DDoS type. We leverage a meta-learner to build an ensemble model of multiple machine learning models such as LSTM, RF, and KNN to enhance detection and classification accuracy. Tested on the CIC-DDoS 2019 dataset, the proposed model achieves …

Community Detection Using Deep Learning: Variational Graph Autoencoder Enhanced With Leiden And K-Truss Techniques, Jyotika Hariom Patil

Master's Projects

Community detection in networks is essential for understanding the complex structures of connected systems. Traditional deep learning (DL) methods such as Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) have shown promised results in supervised tasks, like classification, but often fail in unsupervised tasks like community detection because of the lack of labels. Self- supervised approaches where we integrate crucial community information offer a solution. This project seeks to explore DL methods for community detection, focusing specifically on using Graph Variational Autoencoders (VGAEs). While classical approaches can efficiently handle small to medium-sized networks, they typically struggle with larger-sized structures. …

Opinion Graphs Construction For Reviews Using Transfer Learning And Large Language Models, Yichen Lin

Master's Projects

With the rapid development of the Internet, reading online reviews before making a purchase, booking a hotel, or making a restaurant reservation has become a part of daily life. Customers often consider reviews as crucial supplementary information before making decisions on how to spend their money. However, reading many reviews to gain helpful information takes time and effort. This project proposes a new method OpinionGraphGenerator that aims to create opinion graphs from hotel reviews to reduce the high volume of text in reviews while preserving essential insights. In an opinion graph, vertices are semantically similar opinions, where each opinion consists …

Skin Cancer Detection Using Reinforcement Learning, Vikas Chercadu

Master's Projects

Advances in medical diagnostics have increasingly harnessed the power of artificial

intelligence, offering substantial improvements in early and accurate disease identifi- cation. This paper elaborates on a novel integration of Multi-Agent Reinforcement

Learning (MARL) with Deep Learning for the early detection of skin cancer, one of the most prevalent and lethal forms of cancer when left unchecked. Our project capitalizes on the sophisticated VGG16 Network for the extraction of detailed features from the widely-utilized HAM10000 dermatoscopic dataset, enhancing these features with additional color, texture, and shape analysis. Utilizing a custom-designed MARL environment, we facilitate a collaboration among multiple intelligent agents, …