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Articles 211 - 240 of 247
Full-Text Articles in Other Computer Engineering
Leveraging Large Language Models For Transforming Student Information Into Actionable Data, Sree Hari Karri
Leveraging Large Language Models For Transforming Student Information Into Actionable Data, Sree Hari Karri
Master's Projects
Admission season places significant demands on university committees, necessitating the review of vast arrays of documents to assess students’ competence. This project advances the development of an automated system designed to streamline this process by evaluating application materials such as Letters of Recommendation (LoRs), Statements of Purpose (SoPs), and resumes. Utilizing a variety of advanced Natural Language Processing (NLP) techniques, the system compares the performance of several Large Language Model (LLM) approaches. It also experiments with different data handling strategies, including the use of vector stores versus traditional context-based processing, to optimize model efficiency and accuracy. Special attention is given …
Implicit Personality Detection From User Behaviour In Recommendation Systems, Uzma Zubair Shaikh
Implicit Personality Detection From User Behaviour In Recommendation Systems, Uzma Zubair Shaikh
Master's Projects
Recommendation systems are an integral part of any business, and a crucial factor in determining their success as these systems help businesses in marketing their products to the right kind of audience. Conventional methods of building recommendation systems such as collaborative filtering and content-based recommendation, although effective, suffer from limitations such as cold start and the data sparsity problems. Moreover, these methods aim at finding similar products as user’s past interactions rather than personalizing the recommendations. The upsurge in use of social media, over-the-top content (OTT), and e-commerce platforms has made the task of personalizing recommendations imperative, leading to the …
Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez
Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez
Master's Projects
Large Language Models (LLMs) have quickly gone from simple rule-based systems to complex knowledge bases capable of tackling many different tasks across a variety of fields. What began as an exercise in human-computer interaction has become the basis for artificial intelligence in a variety of mediums. When attached to larger systems, LLMs become generative assistants that can perform highly on human proficiency assessments and other benchmark skill assessments. This increase in proficiency has led these systems to be deployed in fields such as cybersecurity, business, and programming to help improve productivity and efficiency. However, such a wide availability has allowed …
Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan
Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan
Master's Projects
Knowledge-based tests are widely used to assess knowledge on a specific subject and have many applications in education and professional certifications. These tests usually consist of Multiple Choice Questions (MCQs), where a question with a few possible answers is given. Along with the correct answer, three or more incorrect answers are provided, which are called distractors. MCQs are a popular method for these tests because they are easy to grade. These tests can check different levels of comprehension ranging from beginners to advanced by creating distractors that may confuse unprepared test takers. This project proposes the Knowledge Graph Multiple Choice …
Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy
Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy
Master's Projects
With the advancements in the stream of AI in the recent time and the evolution of Generative AI, it is a given that there is a need to effectively integrate AI into daily tasks, including Coding. When talking about Generative AI, one important thing to consider is prompting, which is that way to talk to the AI. Depending on specific needs and tasks the way we need to prompt AI can vary. With rapid development in the field, there are a lot of new benchmarks that evaluate the AI coders on correctness, but to effectively adapt AI into actual coding …
Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja
Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja
Master's Projects
The main objective of this research is to improve the quality of software code that is produced by the Qwen2.5-Coder model specifically in terms of maintainability, complexity, and reliability. Our approach is going to be a more specific one that will involve the Parameter-Efficient Fine Tuning (PEFT) framework combined with quantization through Low-Rank Adaption (LoRA). This approach involves fine-tuning only some of the parameters of a model to make it suitable for software programming with the general structure of the model largely intact. In this paper, SonarQube is used as a tool to help quantify the improvements made to the …
Facial Expression Mood Classification Using Machine Learning, Tiantong Li
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, …
Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande
Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande
Master's Projects
Coral reefs, made up of thousands of polyps - tiny sac-like marine invertebrates sea anemones and jellyfish, are important to marine ecosystems and prevent loss of life by acting as a natural barrier against storms, floods, and waves. These reefs support a wide range of species, many of which are underexplored and new species being discovered regularly. Crustose coralline algae (CCA) is one of the vital algal species that provides reef structure. Studying the abundance of CCA is important in helping marine biologists analyze coral reef health while understanding the impact of climate change on the marine lifeforms. This study …
Cluster Analysis For Concept Drift Detection In Malware, Aniket Mishra
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 …
Extending A Graphical User Interface For Evidential Reasoning, Vaidehi Sanjay Joshi
Extending A Graphical User Interface For Evidential Reasoning, Vaidehi Sanjay Joshi
Master's Projects
Systems like Capri are used for large-scale graph modeling and integration and PyGrapher aims to do that in a simplified manner. This project is an extension of PyGrapher which was a tool created by previous students at the university. The enhancements include adding customizable default parameters for nodes and edges, automating JSON conversion, and enabling real-time highlighting. These features specifically aim to improve usability, streamline workflows, and provide interactive feedback for the users. The enhancement of the project also added additional and rigorous testing of the platform's compatibility and user interaction. It demonstrates significant improvements in functionality and user experience. …
Enhanced Inter-Satellite Routing With Multi-Path Selection And Congestion Modeling, Jaesung Yoo
Enhanced Inter-Satellite Routing With Multi-Path Selection And Congestion Modeling, Jaesung Yoo
Master's Projects
Satellite networks play a crucial role in global connectivity today and making efficient routing algorithms is crucial for optimal performance. While existing routing algorithms have made significant progress using machine learning techniques, they often overlook network congestion and multiple path availability. This report introduces an enhanced routing framework that builds upon LSTM-based predictive routing using dynamic congestion modeling and multi-path selection. Our approach introduces a busy state metric that tracks satellite memory utilization, allowing for adaptive path selection based on both distance and current network load. Through simulations using a constellation of 20 satellites, our enhanced algorithm demonstrates significant improvements …
Llamatalk: Empowering Conversations With Retrieval-Augmented Generation, Aravind Rokkam
Llamatalk: Empowering Conversations With Retrieval-Augmented Generation, Aravind Rokkam
Master's Projects
This research report talks about the implementation and a comparative study of Llama 7B model’s fine-tuning technique and Retrieval Augmented Generation (RAG) capabilities in the context of creating a reliable AI therapist. This study focuses on training these models using diverse datasets consisting of doctor-patient conversations predominantly addressing general health issues. Using a technique like fine-tuning within the Llama 7B model, the project focuses on training the model with a diverse dataset comprising doctor-patient interactions primarily addressing general health concerns. Additionally, carefully organized mental health dataset from HOPE dataset, ensuring the bot's responsiveness to mental health inquiries. Through integration with …
Teaching Children Programming Concepts Through Video Games, Kayla Musleh
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 …
Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks, Neenu Antony
Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks, Neenu Antony
Master's Projects
Van der Wel & Van Steenbergen mention that there has been a surge in pupillometry research in the past two decades, particularly in the area of task-evoked pupil dilation in the context of cognitive control tasks. The goal of most of these studies has been focused on finding a link between pupil dilation and effort exerted by an individual [10]. The review by authors Van der Wel & Van Steenbergen, aimed to assess the potential of pupil dilation as an indicator of effort rather than task complexity. Their analysis revealed that heightened task demands in domains such as updating, switching, …
Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman
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 …
Malware Detection Using Qr And Aztec Code Representations, Atharva Khadilkar
Malware Detection Using Qr And Aztec Code Representations, Atharva Khadilkar
Master's Projects
In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as convolutional neural networks (CNNs) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process that consists of transforming executable files into QR and Aztec codes. These codes capture structural patterns in a format that may enhance the learning capabilities of CNNs. We design and implement CNN architectures tailored to the unique properties of these codes and apply them to a comprehensive analysis …
Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo
Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo
Dartmouth College Ph.D Dissertations
The 21st century has seen dramatic shifts in human interactions with information, peers, and the environment, primarily driven by the proliferation of online platforms and social media. These advancements offer more access to information and global connectivity, but also present challenges such as information overload, misinformation, online harms, and biased reporting that can negatively impact user well-being. This thesis examines the role of Large Language Models (LLMs) — advanced forms of artificial intelligence that understand and generate human-like text — in enhancing well-being in the digital age. The study begins by exploring the potential of LLMs to detect early signs …
Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali
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 …
Multimodal Emotion Detection In Conversations And Dialogues: A Fusion Model Approach, Abhinay Jatoth
Multimodal Emotion Detection In Conversations And Dialogues: A Fusion Model Approach, Abhinay Jatoth
Master's Projects
Emotion recognition is gaining traction due to its wide range of potential applications across different fields. With the rise of social media, chat platforms, and voice assistants, there is a vast increase in data through which humans implicitly and explicitly carry emotional cues. With new algorithms being developed for understanding the nuances of human language and emotion, businesses can tailor more personalized and empathetic service. Sentiment analysis, expresses a positive, negative, or neutral viewpoint laid the foundation of Emotion classification. Emotion classification in conversations represents the most advanced stage of classification. It is also challenging due to the existence and …
Few-Shot Learning For Ner Using Maml, Nourchene Bargaoui
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 …
Machine Learning For Environmental Sustainability, Syeda Nyma Ferdous
Machine Learning For Environmental Sustainability, Syeda Nyma Ferdous
Graduate Theses, Dissertations, and Problem Reports (ETD)
This research proposes a comprehensive approach to address pressing challenges in environmental sustainability, agricultural residue management, using machine learning based approaches. Machine learning (ML) techniques have emerged as powerful tools for addressing environmental sustainability challenges by facilitating the analysis and prediction of ecological phenomena, and optimization of resource management strategies. The study explores the synergies between environmental sustainability and machine learning to develop a framework that leverages artificial intelligence techniques covering a wide range of tasks including crop residue management, soil CO2 flux prediction, and forest carbon system prediction for sustainable development. The study analyze various ML models, such as, …
Robotic Gas Source Localization And Distribution Mapping Via Deep Reinforcement Learning, Iliya Kulbaka
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, Manikantha Varaprasad Inakollu
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., Manikantha Varaprasad Inakollu
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, Apoorva Mohite
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, Likhith Nemani
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, Mayuri Shimpi
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, Rakshit Gupta
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, Rashmi Boddukuri
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, Gauri Anil Godghase
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 …