Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization,
2025
San Jose State University
Identifying Red Sponges On Arms Plates By Preprocessing Images Using Histogram Equalization, Barry Ng
Master's Projects
Sponges play a vital role in marine ecosystems, being the only organisms capable of converting dissolved organic matter (DOM) into particulate organic matter (POM). They provide nutrients for coral reefs to thrive in oligotrophic waters. Autonomous reef monitoring structures (ARMS) are used to measure the biodiversity of coral reefs by simulating the complex cavities inside reef structures. Organisms settle on them and scientists can retrieve them after a period of time for analysis. Images are taken of ARMS plates after they are retrieved. Human analysis is unsuitable for the analysis of ARMS plates due to the huge number of images. …
Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems,
2025
San Jose State University
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,
2025
San Jose State University
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 …
Mycelia: Cross-Chain Data Oracle Using Frost Signatures,
2025
San Jose State University
Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy
Master's Projects
The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …
Moving Target Defense With Quantized Morphence: Defense Quantification Against Common Adversaries In Image And Time Series Problems,
2025
San Jose State University
Moving Target Defense With Quantized Morphence: Defense Quantification Against Common Adversaries In Image And Time Series Problems, Rithika Dhamala
Master's Projects
In recent years, the vulnerability of deep learning models to adversarial attacks has emerged as a serious threat, particularly in domains where reliability and robustness are critical. This project builds upon the Morphence framework, a Moving Target Defense (MTD) strategy designed to counter adversarial threats by maintaining a dynamic pool of models and introducing randomness at inference time. While Morphence was originally developed for image classification tasks, this work not only reproduces the original architecture using MNIST and CIFAR-10 datasets but also extends the core principles to an entirely new domain: time series forecasting. The project proposes a unified defense …
Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database,
2025
San Jose State University
Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan
Master's Projects
Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …
Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management,
2025
San Jose State University
Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla
Master's Projects
The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is …
Energy Considerations For Large Pre-Trained Neural Networks,
2025
San Jose State University
Energy Considerations For Large Pre-Trained Neural Networks, Leo Mei
Master's Projects
In recent years, neural network models have achieved phenomenal performance due to the increasing parameters and complexity of model architectures. However, these advancements come with high environmental costs as they require massive computational resources and consume substantial amounts of electricity, leading to high carbon emissions. Previous studies have demonstrated that substantial redundancies exist in large pre-trained models, and reducing these redundancies through compression would not compromise model performance. While these studies focused on retaining comparable model performance, the direct impact of compression on energy consumption when training models appears to have received little attention. By quantifying the energy usage associated …
Photoproof: A Mobile Application For Verifying The Authenticity Of Images,
2025
San Jose State University
Photoproof: A Mobile Application For Verifying The Authenticity Of Images, Pruthviraj Urankar
Master's Projects
The easy access to artificial intelligence (AI) technologies, such as deepfakes and generative adversarial networks (GANs), has facilitated the creation of highly realistic artificial images, thereby undermining the authenticity of photos in today’s digital age. Misinformation and manipulation are key dangers to digital content due to this advancement. Therefore, the need for reliable methods of photo verification and authentication has become increasingly important. This report presents a decentralized iOS app that uses blockchain to ensure photo authenticity. The app leverages Ethereum smart contracts and cryptographic hashing to securely log image metadata. When a user takes a photo, the app hashes …
Semanticgraphrec: Lightweight Hybrid Recommendations Powered By Semantic Item Representations And Graph Collaborative Filtering,
2025
San Jose State University
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 …
Optimization Of Permutation Flowshop Scheduling Using An Island Genetic Algorithm For Makespan Minimization,
2025
San Jose State University
Optimization Of Permutation Flowshop Scheduling Using An Island Genetic Algorithm For Makespan Minimization, Sahil Salim
Master's Projects
The Permutation Flowshop Scheduling Problem is a well-known NP-hard combinatorial optimization problem that involves the sequencing of n jobs across m machines in the same order to minimize the total makespan value. This project proposes a Heterogeneous Island Genetic Algorithm framework (HIGA). Each island represents a group of solutions that evolve in parallel using different initialization heuristics, crossover and mutation operators, and adaptive parameters. A dynamic, stagnation-based migration strategy is proposed to maintain targeted communication between the islands. The proposed HIGA approach was compared against the basic Standard Genetic Algorithm (SGA) and a more advanced Niche-based Genetic Algorithm (NEH-NGA) on …
Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings,
2025
San Jose State University
Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings, Tianxiang Chen
Master's Projects
Recommender systems surround us. They shape what we watch, how we buy, and even what our future might look like next. The Amazon Review Dataset and Movielens, those two datasets will help this project explore how to improve recommender systems through the user’s preferences. Two methods were combined: sequence-based models and graph-based models. Sequence models, such as LSTMs and Transformers, look at the order of user actions to find patterns by their sequence. On the other hand, Graphbased models focus on relationships between users, items, and their attributes. Textual embeddings added depth and context. Both methods offer something special, according …
Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach,
2025
San Jose State University
Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre
Master's Projects
Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …
Domain-Specific Graph Rag Pipelines: Optimized Approaches For Building Efficient Personal Knowledge Repositories,
2025
San Jose State University
Domain-Specific Graph Rag Pipelines: Optimized Approaches For Building Efficient Personal Knowledge Repositories, Omkar Yadav
Master's Projects
Managing personal data, including notes, calendar events, to-do lists, and other personal information, has become increasingly complex and challenging. In response to this issue, I propose a framework using RAG that enables a large language model (LLM) to efficiently query this data without requiring training on the personal data itself. Conventional retrieval systems, including those leveraging vector-based retrievalaugmented generation (RAG), are effective at handling basic queries but struggle to deliver coherent global abstractions, integrate diverse knowledge sources, and account for temporal nuances. This research explores domain-specific Graph based RAG frameworks that incorporate a knowledge graph to better model relationships, thereby …
Improving Contextual Retrieval For Long Documents In Q & A Systems,
2025
San Jose State University
Improving Contextual Retrieval For Long Documents In Q & A Systems, Sakshi Garg
Master's Projects
With the vast amount of information available on the internet distributed across several lengthy documents, finding relevant information has become more important and challenging. The goal of this project is to develop advanced techniques to retrieve information from long texts in order to deliver accurate and relevant results while ensuring speed and efficiency. As part of this work, we employ techniques to address unique difficulties posed by large and complex documents. This paper presents a custom Retrieval-Augmented Generation (RAG) framework designed to improve contextual retrieval in long and multi-document settings. In this paper, we employ several techniques like summarization, semantic …
Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs,
2025
San Jose State University
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 …
Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction,
2025
San Jose State University
Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder
Master's Projects
Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …
Link Failure Localization In Hierarchical, Multidomain Optical Networks,
2025
San Jose State University
Link Failure Localization In Hierarchical, Multidomain Optical Networks, Martin Mihailov Bojinov
Master's Projects
Optical networks transport data encoded on light signals over optical fiber cables. Individual optical networks are managed by domain administrators, such as service providers, vendors, or regional bodies. Multidomain optical networking explores the possibility of enabling seamless data transmission across domain boundaries. In this project, we explore how we can perform network monitoring in a hierarchical multidomain optical network while preserving domain security and autonomy. This is done by allocating dedicated monitoring trails across various broker abstractions of our network topologies. We propose three heuristic functions that expedite trail selection. Of the three heuristics, our least monitored algorithm performs the …
Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning,
2025
San Jose State University
Dynamic Pricing For Revenue Maximization In 5g Networks With Elastic Network Slicing And Reinforcement Learning, Naisheel Shah
Master's Projects
Network slicing is a concept that enables a diverse array of network applications with lots of unique service requirements. For the scope of this research, we delve into elastic network slicing, which has a potential benefit for both the providers and users through cost-effective resource utilization. Dynamic pricing of these slice resources is a method for operators to realize the balance of different types of network slices by implicitly communicating the current network state to slice users. We have designed a custom pricing scheme using Deep Reinforcement Learning for elastic network slices that maximizes the revenue of slice providers while …
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms,
2025
San Jose State University
Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi
Master's Projects
Modern search engines and wiki platforms generate vast quantities of user inter-
/="/">action data such as page views, edits, clicks, and session events. This data must be stored and aggregated efficiently to enable scalable analytics and responsive querying. Yioop, an open-source search engine framework, serves as our primary case study, processing millions of such events to power its indexing and recommendation features. This report explores a shift from the conventional database storage based model to a log based model, in order to improve scalability and write efficiency. A size-limited, append-only logging facility was provided to log analytics events: the …
