Birdsong Classification Using Deep Learning And Mixit,
2024
San Jose State University
Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru
Master's Projects
The identification of bird species using deep learning techniques presents a novel approach in bioacoustics, by significantly advancing our understanding and enhancing our capabilities in bird species recognition from audio recordings. The value of audio over visual data for monitoring ecological patterns in birds can be highlighted with the deployment of automated recording devices in remote wildlife sensing, offering a more cost-effective, non-invasive, and practical solution. However, the methods of processing and classifying the audio remain challenging due to the complexity of bird audio, characterized by diverse vocalizations and imminent environmental noise, which poses difficult challenges to perform effective classification. …
Novel Approach To Music Analysis Using Apache Spark,
2024
San Jose State University
Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare
Master's Projects
Music is one of the most common source of entertainment. Every user has their own taste of music and prefer to listen music that adheres to their taste and mood. There are various categories, called as music genres in which music can be classified. This research project addresses the challenge in music genre classification by using various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Very Deep Convolutional Networks (VGGNet), ResNet and others. The primary objective of this research is to enhance the accuracy of music genre classification using a distributed computing framework Apache Spark. …
Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model,
2024
San Jose State University
Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare
Master's Projects
Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, …
Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning,
2024
San Jose State University
Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre
Master's Projects
The quest for AI systems that can mirror the intricate aspects of human emotion and personality is crucial for enhancing their performance. This project delves into the capabilities of Large Language Models (LLMs) to mimic the Big Five personality traits in human-written essays by utilizing contextual prompts and fine-tuning methods. Diverging from traditional research in this domain, this project explores smaller, open-source LLMs, including LLaMA 2 7B chat, LLaMA 2 13B chat, and Vicuna v.15 13B, to assess their potential in personality prediction tasks, thereby making high-level personality emulation more accessible and practical for application integration. Through meticulous prompt engineering, …
Sudoku As A Proof Of Useful Work Protocol On The Blockchain,
2024
San Jose State University
Sudoku As A Proof Of Useful Work Protocol On The Blockchain, Abishek Padaki
Master's Projects
The Proof of Work (PoW) consensus used by many blockchain networks like Bitcoin has been criticized for its excessive energy consumption and lack of tangible utility beyond maintaining the network. This report proposes a Proof of Useful Work (uPoW) protocol as an alternative consensus mechanism that utilizes computational resources to solve intrinsically valuable problems. Specifically, it explores the implementation of uPoW on the SpartanGold blockchain test network, where miners must solve Sudoku puzzles to validate new blocks. The report examines the shortcomings of traditional Proof-of-Work protocols, such as their environmental impact and inefficient use of computing power. It then delves …
Ranking-Based Hashtag Recommendation With Collaborative And Content-Based Filtering,
2024
San Jose State University
Ranking-Based Hashtag Recommendation With Collaborative And Content-Based Filtering, Fei Pan
Master's Projects
The purpose of this project is recommending relevant hashtags for users using both Collaborative Filtering (CF) and Content-based filtering with Twitter dataset. The Twitter dataset was collected by leveraging Twitter API v2. After data preprocessing, 40,806 tweets posted by 278 users with 3,107 hashtags from 01/01/2022 to 04/30/2022 are used for model training and testing. For CF models, we will mainly focus on generating embeddings to learn about user and hashtag latent factors and finally predict a probability for unseen hashtags with most possibility will be ranked as topK items for corresponding users. In this project, Matrix Factorization (MF), Neural …
Explaining The Maliciousness Of Urls Using Shap And Lime,
2024
San Jose State University
Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair
Master's Projects
No system has ever reached the levels of proliferation that the Internet now enjoys. It stands as the most widely spread distributed system across the globe; yet this evolution has given rise to an ever-growing wave of malintent that challenges every user and entity on the vast expanse of cyberspace. Malicious URLs loom large as vulnerabilities leaving users naked as they traverse online landscapes, but cybersecurity experts craft models with esoteric algorithms in a bid to stem this tide and shield users from cybercrime. However, peering into the decision-making corridors of these models holds key importance, it’s through understanding such …
Domain Switch On Sentiment Analysis Using Gradient Reversal Layer,
2024
San Jose State University
Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina
Master's Projects
Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it …
Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing,
2024
San Jose State University
Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing, Manikanta Sanjay Veera
Master's Projects
Serverless edge computing is an emerging technology that realizes the low latency and resource-efficient function calls for responsive computing. In cloud-based serverless computing, it is a common practice to cache sufficiently many function containers for future reuse to reduce the overhead of container initiation. In contrast, the capacity limitation of edge nodes poses a complex problem to the caching strategy in serverless edge computing of selecting an appropriate set of container caches based on the request distribution. Deep Reinforcement Learning (DRL) can play a crucial role in optimizing the caching decisions under dynamic request arrivals. In this paper, we propose …
Few-Shot Learning For Ner Using Maml,
2024
Virginia Commonwealth University
Few-Shot Learning For Ner Using Maml, Nourchene Bargaoui
Theses and Dissertations
This thesis investigates the application of Few-Shot Learning (FSL) using Model-Agnostic Meta-Learning (MAML) to enhance Named Entity Recognition (NER) within the domain of Natural Language Processing (NLP), specifically focusing on chemical datasets. The primary challenge addressed is the impracticality of relying on extensive annotated datasets, especially in specialized fields like chemistry. The research primarily explores the concept of Few-Shot Learning, aiming to train models on minimal data while maintaining performance across diverse tasks. It delves into the N-way K-shot methodology, where "N" represents the number of classes and "K" signifies the number of examples per class. This approach is further …
A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance,
2024
University of North Florida
A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor
UNF Graduate Theses and Dissertations
Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …
Robotic Gas Source Localization And Distribution Mapping Via Deep Reinforcement Learning,
2024
University of North Florida
Robotic Gas Source Localization And Distribution Mapping Via Deep Reinforcement Learning, Iliya Kulbaka
UNF Graduate Theses and Dissertations
This research aims to advance the fields of Gas Source Localization (GSL) and Gas Distribution Mapping (GDM) by developing deep reinforcement learning (DRL) methodologies suitable for complex, real-world environments. GSL and GDM are crucial for applications such as environmental monitoring, hazardous material detection, and search-and-rescue missions, where safe and efficient exploration is essential. Traditional methods often fall short in dynamic settings influenced by factors like wind and obstacles. To address these limitations, this study proposes novel neural network architectures and learning frameworks for adaptive exploration and mapping, integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) layers, and Deep Q-Networks …
Finops-Driven Cloud Optimization Models For Enterprise Applications,
2024
University of South Florida
Finops-Driven Cloud Optimization Models For Enterprise Applications, Manikantha Varaprasad Inakollu
Computer Science and Engineering Faculty Publications
Cloud computing has revolutionized enterprise IT infrastructure, yet escalating costs and resource inefficiencies threaten to undermine these benefits. This research examines FinOps-driven optimization models that enable organizations to balance cloud performance, cost efficiency, and business value. The study addresses the critical challenge enterprises face in managing cloud expenditures while maintaining operational excellence. Through comprehensive analysis of FinOps principles and practical optimization frameworks, we develop models that integrate financial accountability, technical efficiency, and business alignment. Our research demonstrates that organizations implementing structured FinOps practices achieve 25-40% cost reductions without compromising application performance. The study contributes both theoretical frameworks for understanding cloud …
Enhancing Erp Auditability And Compliance Using Permissioned Blockchain, A Framework For Transparent And Immutable Enterprise Resource Planning Systems.,
2024
University of South Florida
Enhancing Erp Auditability And Compliance Using Permissioned Blockchain, A Framework For Transparent And Immutable Enterprise Resource Planning Systems., Manikantha Varaprasad Inakollu
Computer Science and Engineering Faculty Publications
Enterprise Resource Planning systems serve as the backbone of modern organizational operations, yet their centralized architecture creates significant challenges for auditability and regulatory compliance. This research proposes a permissioned blockchain framework to enhance ERP auditability by creating immutable, transparent, and traceable records of all system transactions and modifications. The study addresses critical gaps in current ERP systems where transaction histories can be altered, audit trails prove insufficient, and compliance verification remains cumbersome. Through examination of existing ERP limitations and blockchain capabilities, we develop an integrated architecture that maintains operational efficiency while providing cryptographic assurance of data integrity. Our framework employs …
A Framework For Scientific Data Indexing, Searching And Sharing,
2024
San Jose State University
A Framework For Scientific Data Indexing, Searching And Sharing, Apoorva Mohite
Master's Projects
Scientific data continues to grow. Wildfire simulation experiments performed by the WIRC team at SJSU have generated over 138 TB of data so far and it is expected to keep growing. It becomes difficult for researchers to search through that data to find the data of their interest. This data is stored on an HPC cluster that external users do not have access to. The WIRC team also conducts experiments and publishes their research, but the size of data makes it difficult to share these datasets. This project introduces a novel solution to indexing scientific data, searching through the data …
Fine-Tuning Large Language Models For Folder Structure Generation,
2024
San Jose State University
Fine-Tuning Large Language Models For Folder Structure Generation, Likhith Nemani
Master's Projects
Starting a new project is a significant challenge in the software development world. Building a new project skeleton and configurations will require vast amounts of time and effort. This project aims to overcome the difficulty presented by this challenge using advanced large language models, specifically fine-tuning LLMs. Our initial focus with the implementation is to use the powerful capabilities of advanced modern models to simplify and accelerate the complicated process of getting new projects started. The solution process begins with a user posting a README file to a predetermined repository. This README file then is used as a source for …
Credit Score-Based Lending System On The Ethereum Platform,
2024
San Jose State University
Credit Score-Based Lending System On The Ethereum Platform, Mayuri Shimpi
Master's Projects
Traditional banking systems act as intermediaries, assessing risks and profiting from interest rate differentials. Credit scores, provided by trusted bureaus, are commonly used to evaluate the creditworthiness of borrowers. Cryptocurrencies have emerged as a significant and innovative medium due to their decentralized nature, operating without reliance on a central authority, such as a government.
This report describes a project to implement the Autonomous Lending system on the Ethereum Platform (ALOE), as proposed in [1], aiming to seamlessly integrate traditional credit scoring methodologies for evaluating a borrower's risk of default. The objective of this project report is to establish a robust …
Ml-Based User Identification Through Mouse Dynamics,
2024
San Jose State University
Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta
Master's Projects
User authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from …
Analysis And Application Of Adaptive Ml Algorithms For Malware Classification,
2024
San Jose State University
Analysis And Application Of Adaptive Ml Algorithms For Malware Classification, Rashmi Boddukuri
Master's Projects
Malware classification is the process of distinguishing malware samples into categories of malware families that it is associated with and remains a critical step in the process of mitigating malware-related threats. In recent years, machine learning techniques have emerged as a powerful tool for such malware classification tasks. In this study, we explore the application of adaptive machine learning models to malware classification in order to analyze and determine how they compare in performance to similar but non-adaptive algorithms. The results achieved in this study share insight into the strengths and limitations of adaptive learning models when applied towards malware …
Distinguishing Chatbot From Human,
2024
San Jose State University
Distinguishing Chatbot From Human, Gauri Anil Godghase
Master's Projects
There have been many recent advances in the field of Generative Artificial Intelligence and Large Language Models, with GPT 3 or ChatGPT model being one of the frontrunners in this field. These large language models have become so powerful that it has become difficult to differentiate between text written by humans and machine-generated text. This paper proposes a solution to the problem of classification of the origin of data (human or chatbot) by using Machine Learning. In addition, the proposed solution also helps us analyze the text generated by such Language Models and understand the underlying patterns present in the …
