Digital Commons Network^™

Relationalnet Using Graph Neural Networks For Social Recommendations, Dharahas Tallapally

Master's Projects

Traditional recommender systems create models that can predict user interests based on the user-item relationships. However, these systems often have limited performance due to sparse user behavior data. To address this challenge, researchers are now exploring models for social recommendation that can account for both user- user and user-item relationships based on social networks, and user past behavior, respectively. These models aim to understand each user’s behavior by considering their trusted neighbors and their influence on each other. Specifically, the potential embedding of each user is influenced by their trusted neighbors, who are, in turn, influenced by their own trusted …

Rideshare Using Degrees Of Separation: A Social Network-Based Approach, Gokul Garikipati

Master's Projects

Conventional ride-sharing services, such as Lyft and Uber, routinely match drivers with riders based on their proximity to each other, using GPS coordinates and mapping technology. The application then calculates the cost of the ride based on factors such as distance traveled and time spent in the car. The concept of six degrees of separation suggests that a maximum of 6 steps or relationships can connect any two individuals in the world. This idea could be applied to a ride-share service to provide a more personalized and efficient experience for users. Instead of just matching riders with drivers based on …

Nft Artifact Prediction Using Machine Learning, Rishabh Pandey

Master's Projects

NFT Prediction Systems are web applications that provide their users with valuable insights about the artifact. These insights are useful for investors and collectors to make better decisions about their purchases. This project builds upon the same concept of prediction by developing a web application to dynamically provide recommendations based on user input and training an ML model to predict their cost. Preliminary work for the prediction system involved data collection, pre-processing, analysis, and filtering of large datasets from diverse sources. The project focused on the development of a user- friendly UI to enable seamless categorization of search results generated …

Wikipedia Web Table Interpretation, Keyword-Based Search, And Ranking, Kartikee Dabir

Master's Projects

Information retrieval and data interpretation on the web, for the purpose of gaining knowledgeable insights, has been a widely researched topic from the onset of the world wide web or what is today popularly known as the internet. Web tables are structured tabular data present amidst unstructured, heterogenous data on the web. This makes web tables a rich source of information for a variety of tasks like data analysis, data interpretation, and information retrieval pertaining to extracting knowledge from information present on the web. Wikipedia tables which are a subset of web tables hold a huge amount of useful data, …

Sign Language Recognition Using A Hybrid Machine Learning Model, Peeyusha Shivayogi

Master's Projects

Sign Language is a visual language used by millions of people around the world. American Sign Language (ASL) is one of the most popular sign languages and the third most popular language in the United States. Automatic recognition of ASL signs can help bridge the communication gap between deaf and hearing individuals. In this project, we explore the use of deep learning models for ASL sign recognition, using the MNIST dataset as a benchmark. We preprocessed the data by reshaping the images to the input layer size of the models and normalized the pixel values. We evaluated five popular deep-learning …

Spartan Price Oracle: A Schelling-Point Based Decentralized Pirce Oracle, Sihan He

Master's Projects

Nakamoto’s Bitcoin is the first decentralized digital cash system that utilizes a blockchain to manage transactions in its peer-to-peer network. The newer generation of blockchain systems, including Ethereum, extend their capabilities to support deployment of smart contracts within their peer-to-peer networks. However, smart contracts cannot acquire data from sources outside the blockchain since the blockchain network is isolated from the outside world. To obtain data from external sources, smart contracts must rely on Oracles, which are agents that bring data from the outside world to a blockchain network. However, guaranteeing that the oracle’s off-chain nodes are trustworthy remains a challenge. …

Eye Movements Behaviors In A Driving Simulator During Simple And Complex Distractions, Pradeep Narayana

Master's Projects

Road accidents occur frequently due to driving distractions all around the world. A driving simulator has been created to explore the cognitive effects of distractions while driving in order to address this problem. The purpose of this study is to discover the distraction-causing elements and how they affect driving performance. The simulator offers a secure and regulated setting for carrying out tests while being distracted by different visual distractions, such as solving mathematical equations and number memorizations.

Several trials have been conducted in the studies, which were carried out under varied circumstances like varying driving sceneries and by displaying different …

Driving Simulator : Driving Performance Under Distraction, Kaushik Pilligundla

Master's Projects

This pilot study used a driving simulator experiment to look into how podcast consumption affects driving performance as a continuous distraction. Three volunteers conducted three trials in the study, each with a different driving scenario. Data analysis was done to compare two conditions. The first condition is the Audio, where volunteers listen to podcasts while driving. The second condition is no-audio condition.. The no-audi condition had nothing to play in the background. We used eye-tracking technology to gather gaze data. The study's findings using the post survey and eye fixation data indicate that listening to podcasts leads to continuous distraction …

Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model And Network Flow Analysis, Rucha Mannikar

Master's Projects

Botnet is a network of infected computer systems called bots managed remotely by an attacker using bot controllers. Using distributed systems, botnets can be used for large-scale cyber attacks to execute unauthorized actions on the targeted system like phishing, distributed denial of service (DDoS), data theft, and crashing of servers. Common internet protocols used by normal systems for regular communication like hypertext transfer (HTTP) and internet relay chat (IRC) are also used by botnets. Thus, distinguishing botnet activity from normal activity can be challenging. To address this issue, this project proposes an approach to detect botnets using peculiar traits in …

Nosql Databases In Kubernetes, Parth Sandip Mehta

Master's Projects

With the increasing popularity of deploying applications in containers, Kubernetes (K8s) has become one of the most accepted container orchestration systems. Kubernetes helps maintain containers smoothly and simplifies DevOps with powerful automations. It was originally developed as a tool to manage stateless microservices that run seamlessly in containers. The ephemeral nature of pods, the smallest deployable unit, in Kubernetes was well-aligned with stateless applications since destroying and recreating pods didn’t impact applications. There was a need to provision solutions around stateful workloads like databases so as to take advantage of K8s. This project explores this need, the challenges associated and …

Insecure Deserialization Detection In Python, Aneesh Verma

Master's Projects

The importance of Cyber Security is increasing every single day. From the emergence of new ransomware to major data breaches, the online world is getting dangerous. A multinational non- profit group devoted to online application security is called OWASP, or the Open Web Application Security Project. The OWASP Top 10 is a frequently updated report that highlights the ten most important vulnerabilities to web application security. Among these 10 vulnerabilities, there exists a vulnerability called Software and Data Integrity Failures. A subset of this vulnerability is Insecure Deserialization. An object is transformed into a stream of bytes through the serialization …

Machine Learning-Based Anomaly Detection In Cloud Virtual Machine Resource Usage, Tarun Mourya Satveli

Master's Projects

Anomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs). Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server's resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server's capacity to give resources to legitimate customers.

To recognize attacks and common occurrences, machine learning techniques such as …

Keystroke Dynamics And User Identification, Atharva Sharma

Master's Projects

We consider the potential of keystroke dynamics for user identification and authentication. We work with a fixed-text dataset, and focus on clustering users based on the difficulty of distinguishing their typing characteristics. After obtaining a confusion matrix, we cluster users into different levels of classification difficulty based on their typing patterns. Our goal is to create meaningful clusters that enable us to apply appropriate authentication methods to specific user clusters, resulting in an optimized balance between security and efficiency. We use a novel feature engineering method that generates image-like features from keystrokes and employ multiclass Convolutional Neural Networks (CNNs) to …

Federated Learning For Protecting Medical Data Privacy, Abhishek Reddy Punreddy

Master's Projects

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access …

Spam Comments Detection In Youtube Videos, Priyusha Kotta

Master's Projects

This paper suggests an innovative way for finding spam or ham comments on the video- sharing website YouTube. Comments that are contextually irrelevant for a particular video or have a commercial motive constitute as spam. In the past few years, with the advent of advertisements spreading to new arenas such as the social media has created a lucrative platform for many. Today, it is being widely used by everyone. But this innovation comes with its own impediments. We can see how malicious users have taken over these platforms with the aid of automated bots that can deploy a well-coordinated spam …

Resilience Training For Healthcare Professionals: A Literature Review, Michelle Dictor

Master's Projects

Purpose: An exploration of various resilience training programs to determine if there is a significant impact on the psychological resilience of health care professionals.
Method: An electronic systematic review using San Jose State University’s OneSearch database search engine was completed. The review contains published research articles between 2011-2022. Nine published articles were reviewed and used for this systematic review.
Results: There is limited data related to resilience training and resources in accredited nursing programs. Resilience training programs are both a feasible and acceptable way of building psychological resilience among interdisciplinary health care professionals.
Conclusion: Mindfulness-based resilience training programs can be …

Pet Loss And Grieving Strategies: A Systematic Review Of Literature, Ruth E. Cervantes

Master's Projects

The impact that companion animals have on human lives has been explored in many aspects including therapy and emotional support. In some cases, these types of pets have been deemed necessary for pet owners with medical problems. However, pets are not only a medical necessity for some humans, but they are also companions, and most are included as family members. The loss of a pet often brings on intense grief that is not often acknowledged in the workforce. Bereavement leave for immediate family members is a benefit offered to employees, and as we will read in this literature review pet …

A Roadmap To Safer Hyaluronic Acid Injections, Mandi Lonergan

Master's Projects

Non surgical aesthetic treatments have become increasingly more popular in the United States (2020 Plastic Surgery Statistics Report 2021). Hyaluronic acid (HA) fillers are the second most popular non-surgical aesthetic treatment behind Botulinum toxin injections (The Aesthetic Society, 2021). The increased use of these fillers has led to increased rates of complications With the release of new long lasting fillers in 2014, providers began to see an increase in delayed onset nodules (DON). A DON is considered in the aesthetic field to be a nodule that occurs 30 days to years after filler injection (Convery et al., 2021). A …

Cultural Perspectives In Pain And Palliative Care, Loan Lagué

Master's Projects

Culturally and linguistically diverse (CALD) patients are constantly challenged by the different levels on which lack of cultural competency or bias occurs in healthcare. This literature review focuses on the healthcare providers’ experiences when providing pain and palliative care to CALD populations and explores the challenges that may result in health inequalities. A review of current literature was conducted using PubMed, CINHAL, Sage Journals, Ovid, and Science Direct electronic databases. Sixteen published articles between 2015-2023 were reviewed. Findings from this review identified factors that induce poor quality palliative, pain, and end-of-life (EOL) care among culturally diverse groups. Improving quality of …

A Natural Language Processing Approach To Malware Classification, Ritik Mehta

Master's Projects

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a …

Standardizing Patient Transfer Process Among Nurses From Labor & Delivery To Mother-Baby Unit: A Quality Improvement Project, Kiratraj Kaur Grewal

Master's Projects

Purpose: To implement and evaluate the efficacy of a standardized patient transfer process among registered nurses (RN) from Labor & Delivery (L&D) to Mother-Baby Unit (MBU) using PDSA cycle to improve RN satisfaction and reduce patient transfer time
Conceptual Framework: PDSA Cycle
Setting: Labor & Delivery and Mother-Baby Unit of a tertiary care hospital
Methods: The project consisted of collecting both objective (estimated patient transfer time from L&D to MBU) and subjective data (anonymous nursing pre- and post-intervention surveys). Standardization of transfer process included implementing a transfer checklist, patient transfer tool, updating the workflow, and a sample patient transfer video. …

Classification Of Darknet Traffic By Application Type, Shruti Sharma

Master's Projects

The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we …

Implicit Racial Bias: A Human Computer Interaction Study Using Eye Tracker, Wenfan Zhang

Master's Projects

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater …

On The Memory Scalability Of Spectral Clustering Algorithms, Ran Li

Master's Projects

Spectral clustering has lots of advantages compared to previous more traditional clustering methods, such as k-means and Gaussian Mixture Models (GMM), and is popular since it was introduced. However, there are two major challenges, speed scalability and memory scalability, that impede the wide applications of spectral clustering. The first challenge has been addressed recently by Chen [1] [2] in the special setting of sparse or low dimensional data sets. In this work, we will first review the recent study by Chen that speeds up spectral clustering. Then we will propose three new computational methods for the same special setting of …

Differential Gene Expression Analysis Of Rodent Mammary Tissue Reveals Dysregulation Of Greb1 And An Age-Dependent Response To Spaceflight, Cleoma Arnold

Master's Projects

Life aboard spacecraft poses a danger to astronaut health, with hazards including microgravity, radiation, and enclosed spaces. Research into mitigating these health issues includes analysis of the transcriptome of rodents sent to the International Space Station. This project investigates the effects of spaceflight on the mammary tissue of female mice of two age groups, 10-12 and 32 weeks, in order to assess the impact of age and spaceflight on gene expression in a tissue relevant to female health. Analysis of the OSD-511 dataset from NASA’s Open Science Data Repository utilized a containerized implementation of their RNA-Seq pipeline on the San …

Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil

Master's Projects

Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …

Advances In Robustness Of Image-Based Malware Detection, Rishika Pamanji

Master's Projects

In recent years, deep learning has emerged as a powerful tool for image classification tasks. However, the performance of individual deep learning models can be limited by their architecture and training data. In this project, various Convolutional Neural Network (CNN) architectures are proposed to train the malware data for feature extraction for various color coordinates such as L, CMYK, RGB, RGBA, and YCbCr. Different optimization techniques like Stochastic Gradient Descent, Root Mean Square Propagation, Ada Delta, Adam, and Adaptive Gradient are used to minimize errors in the trained data, leading to enhanced accuracy. The proposed ensemble deep learning model for …

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

An Incentivization Mechanism Towards Network-Slice As A Service, Yosha Mundhra

Master's Projects

Next-generation communication networks are expected to support many different types of services. Network slicing virtually divides one physical network into several virtual networks in order to accommodate diverse demands for network connections. Assuming multiple network domains are stitched together to provide end-to-end network slices for such diverse services, it is critical to consider the economic behaviors and revenue models of the stakeholders. This project analyzes the economic behaviors from the perspective of a Global Slice Coordinator (GSC) that receives a slice request and coordinates the end-to-end resource allocations across multiple network domains. The primary role of the GSC is to …