Digital Commons Network^™

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Video Comprehension Score (Vcs): A Metric For Long-Form Video Description Evaluation, Harsh Dubey

Electronic Theses and Dissertations

Existing video description evaluation metrics fail to capture the long-range chronology and semantic alignment essential for long-form descriptions. An effective evaluation metric for long-form descriptions must (i) assess global thematic alignment, (ii) measure local semantic alignment, and (iii) evaluate chronological alignment while detecting corrupted content. We introduce Video Comprehension Score (VCS), a reference-based metric, which directly addresses these evaluation requirements through three components: Global Alignment Score for thematic alignment, Local Alignment Score for local semantic alignment, and Narrative Alignment Score for chronological alignment with adjustable tolerance. We evaluate VCS on two large-scale synthetic datasets designed to test corruption detection and …

Unwritten Rules And Oral Transmission In Architectural Proportion Systems: Case Study Of Bubungan Tinggi House, Huzairin. Muhammad Deddy, Dila Nadya Andini, Rudi Hartono, Anna Oktaviana, Dahliani Dahliani

ASEAN Journal on Science and Technology for Development

The proportional systems in vernacular architecture often exist without formal written codification, raising questions about their transmission across generations. Bubungan Tinggi House which is a replica of the Banjar Kingdom palace was built by Banjarese merchants and aristocrats from the 16th to the 19th centuries, showing a uniform aesthetic quality, which indicates a regularity in the proportions of its form. However, no historical written manuals detailing these proportions are known to exist. To prove the existence of regularity in the Bubungan Tinggi House proportion system, research was conducted by taking 7 Bubungan Tinggi House samples spread across South Kalimantan province, …

Twitter-Based Osint For Cyber Event Analytics, Dakota S. Dale, Kylie Mcclanahan, W. Sky Elder, Qinghua Li

Electrical Engineering and Computer Science Faculty Publications and Presentations

Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system's applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events …

Assessing The English Language Needs Of Student Pilots: A Comprehensive Analysis For Effective Aviation English Training, Triyani Rps Dewi, Lilis Kurnianingsih

International Journal of Aviation, Aeronautics, and Aerospace

This research aims to identify the English language material needs for pilot students at the Indonesian Civil Aviation Polytechnic in order to improve communication skills that are essential for flight safety and operational effectiveness. In data collection, the researcher distributed questionnaires to students and conducted a Focus Group Discussion (FGD) involving students, flight instructors, English lecturers, and experienced pilots. The research results show that pilot students need two main skills in English learning. First, listening skills, especially in understanding instructions from ATC. Second, speaking skills used to respond to ATC instructions and to make requests or ask for information from …

Integrating Protein Language Model And Molecular Dynamics Simulations To Discover Antibiofouling Peptides, Ibrahim A. Imam, Shea Bailey, Duolin Wang, Shuai Zeng, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antibiofouling peptide materials prevent the nonspecific adsorption of proteins on devices, enabling them to perform their designed functions as desired in complex biological environments. Due to their importance, research on antibiofouling peptide materials has been one of the central subjects of interfacial engineering. However, only a few antibiofouling peptide sequences have been developed. This narrow scope of antibiofouling peptide materials limits their capacity to adapt to the broad spectrum of application scenarios. To address this issue, we searched for antibiofouling peptides in the vast sequence pool of the microbiome library using a combination of deep learning-based high-throughput search and molecular …

Suicidal Ideation Detection On Reddit Using Llm-Annotated Data And Graph Neural Networks, Ikbal Singh Gurdev Singh Dhanjal

Master's Projects

Suicide is the fourth leading cause of death among people aged 15-29. More than 720, 000 people commit suicide every year. During the COVID-19 pandemic, we saw an increase in people seeking out mental health support on anonymous forums like Reddit. These anonymous forums allow people to express their suicidal ideation without judgment and give them a support structure that not everyone has. The aim of this project is to detect suicidal ideation using Reddit. In this work, we propose SIRGEL (Suicidal Ideation on Reddit using Graph Embeddings and LLMs), a dual-pipeline approach that combines large language model (LLM)- based …

Transforming Chatbot Text: A Sequence-To-Sequence Approach To Human-Like Text Generation, Natesh Reddy

Master's Projects

With the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show …

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Exploring User Sentiment On Social Issues Via Neural Network Transfer Learning In Digital Communities, Ananya R. Vangoor

2025 Research Poster Competition

Understanding public sentiment on social issues is crucial for gauging the stance of the general population. Traditionally, surveys have been a common approach for this. However, to capture more candid opinions, social media provides a rich source of unadulterated opinions. By analyzing social media statements, we can gain insights into the perspectives of specific groups. More specifically, we will investigate the attitudes of the public into the relationship between hard work and success in the workplace.

To begin, I will be training a neural network on X, formerly known as Twitter, tweets to categorize each tweet as either pro-luck or …

Exploring The Stem Career Identity Development Of Black Women Across Their Lifespan, Veronica Hurd

Theses and Dissertations

Although STEM is the fastest-growing career sector, Black women are grossly underrepresented as they account for 2.5% of the workforce. Research highlights that this underrepresentation is due to racialized structures in K-12, postsecondary, and career settings that restrict Black girls’ and women’s STEM opportunities. While macrosystems such as hegemonic ideologies, attitudes, and social conditions shape Black girls’ and women’s opportunities in STEM, they continue to persist and achieve their career goals. To explore these barriers and Black women’s persistence in this industry, this study draws from the autobiographical memories of 10 Black women in the field or formerly in the …

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

Browse all Theses and Dissertations

Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel

Browse all Theses and Dissertations

This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

Browse all Theses and Dissertations

The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

Rift - Reddit Information Falsity Tagger, Parth Joshi

Master's Projects

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity …

Multilingual Sentiment Analysis Using Ensemble Learning, Farhan Ansari

Master's Projects

The widespread use of multiple social media platforms has amplified the expression of public opinions over the Internet in languages such as English, Hindi and Spanish. With the aid of technological advancements in machine learning, we can analyze opinions posted on the Internet and gauge public sentiments. There are organizations and businesses that are interested in the evaluation of these sentiments as these type of data can generally be used to obtain the opinion of a product, restaurant, a candidate, etc. In this study, we perform a comparative analysis of three popular ensemble learning methodologies (Boosting, Bagging and Stacking) based …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Crop2cloud Platform: Real-Time Data Integration For Agricultural Water Monitoring, Bryan Nsoh, Abia Katimbo, Kendall Dejonge, Wei-Zhen Liang, Hongzhi N. Guo, Yufeng Ge, Derek M. Heeren, Yeyin Shi, Xin Qiao, Daran R. Rudnick, Hope Njuki Nakabuye, Birru Girma, Isa Kabenge, Joshua Wanyama

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Efficient water management is vital for sustainable agriculture, yet integrating real-time data for precise irrigation remains a challenge. This study designed the Crop2Cloud (C2C) platform, a system that leverages advanced sensors using Internet of Things (IoT), edge and cloud computing techniques, and computed Water Stress Indices (WSIs) and machine learning models (i.e., fuzzy logic), to provide scalable and real-time irrigation decisions. The C2C platform aggregates several data including Volumetric Water Content (VWC) from TDR sensors (Acclima Inc., US) installed at four multiple depths, canopy temperatures (Tc) measured by Infrared Radiometers (IRTs) (Apogee Instruments, US), as well as weather information and …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

Medilightrag: A System For Medical Query Response Using Fine-Tuned Llms And Graph Based Retrieval, Rajiv Karthik Reddy Kodimala

Master's Projects

The exponential increase in medical data has created a greater demand for precise and efficient information retrieval systems. Existing Large Language Models (LLMs) face domain-specific difficulties such as sophisticated medical jargon, situational comprehension, and the continual advancement of healthcare knowledge. To tackle these challenges, we present MediLightRAG, an innovative two-stage system which integrates parameter-efficient fine-tuning of Large Language models with LightRAG’s graph-based retrieval. The first stage focuses on enabling accurate resource-efficient model adaptation for the medical domain through QLoRA fine-tuning. In the second stage, LightRAG’s two-tiered retrieval architecture that combines graph-based indexing with dynamic knowledge retrieval is employed to enhance …

Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering, Devi Surya Kumari Akula

Master's Projects

Graph neural networks (GNNs) have emerged as a powerful paradigm for collaborative filtering. However, they often fall short in fully leveraging side textual content, resulting in suboptimal recommendations. To address this limitation, we explore the synergy between GNNs and deep contextual embeddings of item descriptions, aiming to enhance recommendation quality on the Amazon-Books dataset. We propose SemanticGraphRec, which combines GNNs with Large Language Models (LLMs) to leverage both collaborative filtering and textual item content. Experimental results demonstrate that incorporating semantic item embeddings produced by fine-tuning LLMs consistently improves performance. Our approach enhances recommendation relevance in sparse data scenarios by leveraging …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Bot Detection In Social Media Using Graphsage And Bert, Abhishek Deshmukh

Master's Projects

This project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.

The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …