Machine Learning For Electronic Structure Prediction,
2024
Michigan Technological University
Machine Learning For Electronic Structure Prediction, Shashank Pathrudkar
Dissertations, Master's Theses and Master's Reports
Kohn-Sham density functional theory is the work horse of computational material science research. The core of Kohn-Sham density functional theory, the Kohn-Sham equations, output charge density, energy levels and wavefunctions. In principle, the electron density can be used to obtain several other properties of interest including total potential energy of the system, atomic forces, binding energies and electric constants. In this work we present machine learning models designed to bypass the Kohn-Sham equations by directly predicting electron density. Two distinct models were developed: one tailored to predict electron density for quasi one-dimensional materials under strain, while the other is applicable …
Joint Learning Of Unknown Safety Constraints And Control Policies In Reinforcement Learning,
2024
West Virginia University
Joint Learning Of Unknown Safety Constraints And Control Policies In Reinforcement Learning, Lunet Abiye Yifru
Graduate Theses, Dissertations, and Problem Reports (ETD)
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. However, this reliance on predefined safety constraints poses limitations in dynamic and unpredictable real-world settings where such constraints may not be available or sufficiently adaptable. Bridging this gap, we propose a novel approach that concurrently learns a safe RL control policy and identifies the unknown safety constraint parameters of a given environment. …
Leveraging Large Language Models For Enhancing Well-Being In The Digital Age,
2024
Dartmouth College
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 …
Machine Learning For Environmental Sustainability,
2024
West Virginia University
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, …
Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles,
2024
West Virginia University
Machine Learning Based Intrusion Detection Framework For Can Bus Vulnerabilities In Modern Vehicles, Obinna C. Agbo
Graduate Theses, Dissertations, and Problem Reports (ETD)
The Controller Area Network (CAN) bus is a crucial communication backbone in modern vehicles, connecting various Electronic Control Units (ECUs). However, inherent design weaknesses such as the lack of encryption and authentication make CAN networks vulnerable to cyber-attacks, including spoofing, Denial of Service (DoS), and fuzzing attacks. This thesis thoroughly evaluates these vulnerabilities and the limitations of existing security frameworks like Message Authentication Codes (MACs) and encryption, advocating for the adoption of Intrusion Detection Systems (IDS) as a more practical solution for CAN bus security. The proposed IDS leverages advanced machine learning techniques to accurately detect intrusions, even under complex …
Multirelational Twitter Bot Detection Using Graph Neural Networks,
2024
San Jose State University
Multirelational Twitter Bot Detection Using Graph Neural Networks, Ketan Jadhav
Master's Projects
Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation …
Deciphering Speech Through Vision: A Deep Learning Lip Reading System,
2024
San Jose State University
Deciphering Speech Through Vision: A Deep Learning Lip Reading System, Srujith Rao Ambati
Master's Projects
Lip-reading, a ubiquitous field between computer vision and speech processing, focuses on identifying what spoken words a person generates depending on their uttering lip movements. This paper presents a streamlined lip-reading solution that employs machine learning and deep learning. First, Our work utilizes the Multi-Task Cascaded Convo- lutional Networks to detect facial “landmarks,” including the face and lips region, and the aligns the face. The aligned faces are segmented to get the lip images. Lip images are preprocessed using the Real-Enhanced Super Resolution Generative Adversarial Network to enhance image resolution to identify subtle lip movement in video images: a critical …
Deception Detection Models From Speech,
2024
San Jose State University
Deception Detection Models From Speech, Tien Nguyen
Master's Projects
Recently, researchers have shown an increased interest in automatically detecting deceptive actions. The attention given to this area can be attributed to the many potential applications of deception detection, especially in the field of criminology. To contribute to the deception detection research, this project investigates textual and audio data extracted from spoken and written words. We evaluated and compared the traditional linguistic models with advanced Large Language Models (LLMs) while using Natural Language Processing (NLP) techniques. Additionally, various feature selection techniques were applied to assess the importance of linguistic features. We conducted extensive experiments to evaluate the effectiveness of both …
Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning,
2024
San Jose State University
Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning, Chinmayi Lokeshwar Hegde
Master's Projects
The issue of cyberbullying is growing due to the online anonymity and due to online platforms having less repercussions. This research proposes for proactive measures to detect and prevent such behavior before it reaches the victim. By using data from various social media platforms and employing machine learning techniques, this research proposes an innovative system aimed at identifying and thwarting cyberbullying incidents preemptively. While existing methods have primarily focused on prediction and detection of cyberbullying incidents, there remains a significant gap in research regarding prevention strategies. This project aims to address this gap by leveraging machine learning, natural language processing …
Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn,
2024
San Jose State University
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 …
Extending A Graphical User Interface For Evidential Reasoning,
2024
San Jose State University
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. …
Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks,
2024
San Jose State University
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, …
Noteblocklib: A Library For Physics- And Animation-Driven Virtual Midi Instruments For Use In Video Games,
2024
San Jose State University
Noteblocklib: A Library For Physics- And Animation-Driven Virtual Midi Instruments For Use In Video Games, Kevin Rotunni
Master's Projects
To better capture the relationship between the performer of a piece of music and the music itself in a video game context, I have designed NoteBlockLib, a system by which MIDI instructions are generated and processed in real time based on the motion and collision data of in-game objects. Ultimately, the movement of instruments made using this system would be driven by the animations of a character in the game. This system would thus allow the player character to interact with the performer in the game without sacrificing the relationship between the performer’s actions and the resulting music or to …
Instagram Data Analysis Using Machine Learning,
2024
San Jose State University
Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati
Master's Projects
With enormous amount of social media content, we can draw valuable insights. In this paper, we apply different Machine Learning and Deep Learning techniques on Instagram data to determine the techniques that work well to discover the engagement class of a social media post. Out of all the social media platforms, Instagram is growing rapidly not just in the number of users but also in terms of Advertisement and marketing surpassing YouTube’s advertisement revenue. The end goal of this paper is to propose a technique to predict the engagement class. We applied Random Forest (RF), Stacking Classifier, Extreme Gradient Boost …
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, …
Considering A Unified Model Of Artificial Intelligence Enhanced Social Work: A Systematic Review,
2024
Faculty of Applied Social Sciences, University of Applied Sciences Erfurt
Considering A Unified Model Of Artificial Intelligence Enhanced Social Work: A Systematic Review, Michael Garkish, Lauri Goldkind
Social Service Faculty Publications
Social work, as a human rights–based profession, is globally recognized as a profession committed to enhancing human well-being and helping meet the basic needs of all people, with a particular focus on those who are marginalized vulner- able, oppressed, or living in poverty. Artificial intelligence (AI), a sub-discipline of computer science, focuses on develop- ing computers with decision-making capacity. The impacts of these two disciplines on each other and the ecosystems that social work is most concerned with have considerable unrealized potential. This systematic review aims to map the research landscape of social work AI scholarship. The authors analyzed the …
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 …
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 …
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 …
