Digital Commons Network^™

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 …

The Role Of Nature As A Protective Factor In Father-Child Relationships, Sarah J. Barker

Master's Theses

The present study strived to understand how fathers spend time with their children outdoors and how this shared time together may positively influence their relationship within the Family-Based Nature Activities Theoretical Framework. The objectives of this thesis are to understand more about (a) Why fathers participate in outdoor activities with their children; and (b) How spending time outdoors with their children influences their relationship. Participants included 26 fathers (M=40.6 years old) and their children between the ages of 5-12 years old. All urban fathers resided on the West Coast, while rural fathers lived in the southeast United States. Participants were …

Chapter 17. Approaches To Inclusive Recruitment: Practical And Hopeful, Adriana Poo, Anamika Megwalu, Nick Szydlowski, Kathryn Blackmer Reyes, Peggy Cabrera, Ann Agee

Faculty Research, Scholarly, and Creative Activity

San José State University (SJSU) is located in downtown San José, a city of over one million residents in California’s San Francisco Bay Area. Established in 1857, SJSU is the oldest public university on the west coast of the United States. It is also the founding campus of the California State University (CSU) system that comprises twenty-three campuses, making it the largest four-year public university system in the country. SJSU has nine colleges and sixty-seven departments, which offer bachelor’s, master’s, and a growing number of doctoral degree programs. It enrolls over 36,000 students and employs more than 2,100 faculty members. …

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 …

Opening The Black Box: Carbon-Footprint Calculators, Meat Consumption, And The “Wicked Problem” Of Metric Governance, E. Melanie Dupuis, Dustin Mulvaney

Faculty Research, Scholarly, and Creative Activity

Carbon-footprint calculators are widely used as communication and education tools, designed to make people aware of the ways their personal actions contribute to greenhouse-gas emissions. However, they have come under criticism for emphasizing individual behavior versus structural change. We extend this critique to argue that carbon-footprint calculators are a form of “metric governance” that not only disciplines users but also constrains their abilities to engage with the complexities of climate change and social action. We focus on a particularly wicked problem of carbon-metric governance: livestock greenhouse-gas emissions. We show that carbon-footprint calculators black box and fix the complex politics and …

Research On Teachers Of Color And Indigenous Teachers: Developing And Fortifying Policies That Diversify The Educator Workforce, Conra Gist, Travis J. Bristol, Saili Kulkarni

Faculty Research, Scholarly, and Creative Activity

This special issue expands upon the Handbook of Research on Teachers of Color and Indigenous Teachers (Gist & Bristol, 2022a) and the practitioner-focused Phi Delta Kappan special issue, “Learning from the Voices of Black, Indigenous and People of Color Educators: Charting New Directions for Research, Policy, and Practice” (Gist & Bristol, 2021). The co-created articles in this special issue highlight the combined expertise of policy makers and education researchers. While the Handbook featured empirical research, and the Phi Delta Kappan special issue featured research briefs and teacher testimonies, the policy-driven articles in this special issue explore how research can be …

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 …

Leading From The Middle: Arts Administrators' Beliefs About The Impact Of Arts Education On High-Needs Students, Sofia Fojas

Dissertations

Research on arts education notes the positive impact of arts education on educational outcomes for students, particularly students of color, students in poverty, and students needing additional academic support. Students living in low-income communities receive different educational opportunities than those in high-wealth districts. This disparity is especially true for highly mobile and high-needs students of color. Arts education has made positive differences in student outcomes. This qualitative study examined the beliefs of county arts leads, leaders in the middle, situated between the California Department of Education and their local school districts, and how they can be empowered to be change …

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 …

Nft Price Prediction Using Machine Learning, Akhil Patil Bagili

Master's Projects

In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. …

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 …

Adaptive Bounded-Confidence Model For Opinion Maximization, Jacob Ortiz

Master's Projects

Social networks have become a significant source of information due to their easy accessibility, low cost, and ability to spread information quickly. Opinions are crucial in shaping our communication and decision-making processes, and our social connections significantly influence them. We model opinions as continuous values from 0 to 1, i.e., 0 means strong disagree and 1 means strong agree. Each agent has an initial opinion as well as a confidence about her opinion and through interactions with the other agents both are updated. Opinion maximization has gained popularity due to social media’s growing impact on our daily lives, where we …

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, …

Mitigating Learning Bias In Healthcare Datasets, Samyak Jagdish Kumbhalwar

Master's Projects

CVDs have been a major cause of deaths worldwide with WHO reporting 17.9 million deaths annually. Although there are advancements in the treatment of these diseases, most of the fatalities are a result of untimely diagnosis. Active research is going on to collect data points and risk factors related to these diseases, which can enable early diagnosis. Of the datasets available, many researchers have employed different ML models to predict/detect the prevalence of heart diseases. Many employed Tree based, regression models [3, 6]. Few also tried ensemble approaches [1, 2, 4]. These healthcare datasets are generally found to be imbalanced. …

Frame Rate Enhancement Using Gans: A Deep Learning Approach, Shanmukah Sri Harsha Anivilla

Master's Projects

Videos are sequences of frames that are displayed continuously within a time frame, which creates the illusion. FPS is defined as the number of frames per second, and is crucial to determine the smoothness of motion or scene changes in the video. To improve the appearance of the videos, we can a technique called Frame Rate Enhancement. This is an approach to augment generated frames between pairs of frames using Generative Adversarial Networks. There are a few traditional techniques using Convolution Neural Networks and Optical Flow based methods, but they create unwanted artifacts such as blurring or ghosting and might …

Unveiling Gender Bias: An Eye-Tracking Analysis Of Scene Perception, Naga Srija Gopisetty

Master's Projects

Gender bias deeply affects how we perceive and interact with the world around us. This study examined how gender bias affects scene perception by using eye-tracking technology. The relation between gender bias and pupil dilation is examined utilizing art as a stimuli and also studied the underlying cognitive processes. This experiment combines image presentations along with input from participants, drawing on previous research in gender bias, cognitive psychology, and eye-tracking methodologies. The experiment design showcases a series of images to participants, subtly replacing one image during the trial, and then asks participants whether the

replacement took place. This study uses …

Intelligent Caching Using Continuous Machine Learning In Named Data Networking, Sai Sameer Yanamandra

Master's Projects

Our project focuses on improving Named Data Networking (NDN), an alternative network architecture to traditional IP networks, particularly in unstable conditions where connections frequently drop, and data movement is unpredictable. In NDN, data is cached at various routers across the network, enhancing accessibility even amidst unstable connections. A key challenge we address is determining the optimal level of data redundancy in unstable scenarios. We aim to balance the need for data availability with the risk of excessive data duplication. Our solution involves developing a novel data caching approach for the NDN’s content store based on continuous machine learning. This method …

Gender Classification Through Postural Analysis: A Comparative Study Of 2d Images And 3d Reconstructions., Prathamesh Dixit

Master's Projects

In computer vision, gender classification has become a vital task having applications in human-computer interaction, healthcare, and surveillance. In this study, we look at a two-step approach based on human joint information for gender classification. In this research, we use convolutional neural networks (CNNs).

With Leeds Sports Pose (LSP) dataset, we use a C5 pre-trained model to map and extract joint information from 2D RGB images and after pre-processing and background removal, we use PiFUHD to transform these 2D images into 3D representations. Next, we train our models on RGB images and joint images for both 2D and 3D representations. …