The Importance Of Cryptography In Cloud Computing,
2024
Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq
The Importance Of Cryptography In Cloud Computing, Saja Alaam Talib
Al-Esraa University College Journal for Engineering Sciences
Cloud computing has grown as a widely accepted concept simultaneously, as the alarms for the security of data and information. This paper seeks to establish the comprehensive roles of cryptography in tackling these issues in the Cloud environment. The study begins with the historical background of cryptography and then moves on to analyze trends in information technology before discussing on the challenges to security in cloud computing. The goal of its publication is to highlight the importance of using cryptographic technologies for the protection of information confidentiality, its integrity and compliance. The combination of literature review and the case study …
Privacy And Security Of The Windows Registry,
2024
University of Central Florida
Privacy And Security Of The Windows Registry, Edward L. Amoruso
Graduate Thesis and Dissertation 2023-2024
The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …
Streamlining Public Engagement In Transportation Projects Using Text Analytics,
2024
University of Texas at Arlington
Streamlining Public Engagement In Transportation Projects Using Text Analytics, Alireza Shamshiri
Civil Engineering Dissertations - Archive
Infrastructure projects impact a broad range of stakeholders, particularly local communities, whose engagement is critical for successful outcomes. Despite the importance of public engagement in these projects, traditional methods of capturing and analyzing public opinion often fail to fully represent the diverse, genuine perspectives involved. This has led to conflicts between community members and project sponsors. On the other hand, despite advancements in text analytics, including natural language processing (NLP) and its subfields such as topic modeling, sentiment analysis, and neural networks, their functionalities and effectiveness in analyzing public opinion in the domain of infrastructure projects have not been fully …
The Effects Of Ai Image Synthesis On Graphic Design,
2024
Sotheby's Institute of Art
The Effects Of Ai Image Synthesis On Graphic Design, Ji Ren
MA Theses
From 1763 when Thomas Bayes developed a framework to infer event probabilities, to the end of 2022 when the world-renowned AI research laboratory Open AI launched Chat GPT, a language model based on AI technology, statistical computing-based AI has revolutionized human life. AI image synthesis can simulate the processes and methods of human painting through machine learning, deep learning, and other methods, thereby generating high-fidelity images. There is growing concern about how AI image synthesis will affect the art world as it advances. The art market could be reimagined, authorship and creativity concepts challenged, and traditional artistic practices disrupted by …
A Metaverse Of Chinese Traditional Folk Villages And Houses With Great Aesthetic Pleasure: A Virtual World Dedicated To The Enthusiasts Of Life With Art, Culture, History And Architectures,
2024
Sotheby's Institute of Art
A Metaverse Of Chinese Traditional Folk Villages And Houses With Great Aesthetic Pleasure: A Virtual World Dedicated To The Enthusiasts Of Life With Art, Culture, History And Architectures, Xiaobin Lin
MA Projects
Rapid growth of 3D visualisation technology has led to the maturation of metaverse, bringing innovative and immersive experiences to travellers and representing tremendous potential for revolutionising the tourism industry. Advantages of metaverse, plummeting cost of technology and emerging supportive policies are jointly building up a very promising future of metaverse in tourism. Big companies worldwide are heavily investing in metaverse. In China, Tencent SSV Digital Culture Lab has initiated the annual project “Explore Metaverse Plan”, providing a communication platform and exhibition opportunity for the small and midsize players working on the digitalisation of cultural and artistic heritage. Google Art & …
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine,
2024
University of Dayton
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks,
2024
University of Dayton
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …
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 …
Deep-Learning Approaches To Predict Remaining Useful Life Of Hard Disks,
2024
San Jose State University
Deep-Learning Approaches To Predict Remaining Useful Life Of Hard Disks, Rohan Mohapatra
Master's Projects
On a daily basis, data centers process huge volumes of data using inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life (RUL) of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. We …
Integrating Chatgpt With A-Frame For User-Driven 3d Modeling,
2024
San Jose State University
Integrating Chatgpt With A-Frame For User-Driven 3d Modeling, Ivan Hernandez
Master's Projects
ChatGPT is a large language model that is capable of creating conversational text and functional code that can be integrated into various technologies, including computer graphics software. Currently, 3D modeling applications can be relatively difficult for novices to learn and understand due to the overwhelming amount of graphical user interfaces. However, we can remedy this issue by leveraging ChatGPT’s conversational language capabilities. Our project described in this report integrates ChatGPT with A-Frame, an online framework for developing virtual reality experiences, to create an immersive and user-friendly 3D modeling environment where users can create and modify 3D models through natural language …
Comparing Balancing Techniques For Malware Classification,
2024
San Jose State University
Comparing Balancing Techniques For Malware Classification, Ranjit John
Master's Projects
There have been many breakthroughs over the years in the field of Machine Learning to detect and classify malware threats. However, training a holistic machine learning model to effectively classify malware has been an ongoing topic of research. Datasets represent some malware types disproportionately, which can affect the performance of machine learning classifiers. Without ample data, less common but highly dangerous malware can go undetected by classifiers, leading to devastating outcomes. Data balancing techniques have proven to be effective in representing minority classes better and lessening the bias towards the majority class. Also, recent research showed that generative modeling effectively …
Secured Data Storage Management With Deduplication In Cloud Computing,
2024
San Jose State University
Secured Data Storage Management With Deduplication In Cloud Computing, Ganesh Regoti
Master's Projects
In the cloud era, cloud storage has become a major service and the security of data and user privacy algorithms are becoming of great importance. This way, we make sure that the encrypted data is kept in the cloud storage. But, the challenges follow: First, storing encrypted data may result in ineffective utilization of cloud resources as in the provision of encrypted data, redundancy cannot be provided. Access control to the encrypted data is difficult as the underlying data is hidden and there is no metric with which the decision to share among users can be easily taken. Deduplication is …
Adversarial Attacks And Defense Mechanisms In Multivariate Time-Series Forecasting For Applications In Smart And Connected Infrastructures,
2024
San Jose State University
Adversarial Attacks And Defense Mechanisms In Multivariate Time-Series Forecasting For Applications In Smart And Connected Infrastructures, Pooja Krishan
Master's Projects
The advent of deep learning models has revolutionized the industry over the past decade, leading to the widespread proliferation of smart devices and infrastructures. They play a crucial role in safety-critical applications like self-driving cars and medical image analysis, sustainable technologies like power consumption prediction, and in health monitoring tools to replace industrial equipment like hard disk drives, semiconductor chips, and lithium-ion batteries. But these indispensable deep learning models can be easily fooled to give incorrect predictions with utmost conviction, leading to catastrophic failures in applications where safety is of utmost importance, and resulting in the wastage of resources in …
Enhancing Environmental Health And Safety: Fine-Tuning Large Language Models For Domain-Specific Applications,
2024
San Jose State University
Enhancing Environmental Health And Safety: Fine-Tuning Large Language Models For Domain-Specific Applications, Mohammad Adil Ansari
Master's Projects
This study aims to simplify Environmental Health and Safety (EHS) by leveraging the power of Large Language Models (LLMs). In this research, we focus on fine-tuning three LLMs — LLaMA, Mistral, and Falcon — using PEFT techniques such as QLoRA and SFT, to address domain-specific needs such as safety compliance, incident reporting, and knowledge dissemination. Our research methodology involves fine-tuning each LLM model on a custom dataset compiled from various regulatory agencies, supplemented by targeted web scraping and manual collection of questionnaires to capture and enrich the models with the latest regulations and guidelines. This study aims to compare the …
Mitigating The Risk Of Reentrancy Attack In Smart Contract Development,
2024
San Jose State University
Mitigating The Risk Of Reentrancy Attack In Smart Contract Development, Eric Ngo
Master's Projects
Smart contracts, while revolutionizing the blockchain with their immutable nature, are prone to attacks such the reentrancy attack. This attack allows malicious adversaries to repeately enter a contract before previous executions are completed. SpartanScript, a custom dialect of Scheme, is a way for developers to write and develop contracts in an experimental blockchain environment like SpartanGold. Compared to cryptocurrencies that use a virtual machine to run on the blockchain, SpartanScript utilizes a simplified interpreter for rapid prototyping. However, SpartanScript does not have a way to detect and warn developers of reentrancy vulnerabilities. Hence, there is a need to implement reliable …
Resume Content Generation Using Llama 2 With Adapters,
2024
San Jose State University
Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu
Master's Projects
The primary objective of this project is to optimize the Llama language model to generate customized resumes containing domain-specific job descriptions and maintain the linguistic capabilities of the large language model. Building upon the prior research by Sumed Kale on Job Tailored Resume content generation using GPT-2, where he employed full fine-tuning of the model and demonstrated the capability of LLMs to generate resume content, it is evident that while effective, full fine-tuning has its limitations. Primarily, it is computationally expensive, which can pose constraints, especially for large models. Additionally, during the fine-tuning process, there is a risk of losing …
Enhancing Medical Chatbots With Image Diagnosis,
2024
San Jose State University
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 …
Domain Expert Bot,
2024
San Jose State University
Domain Expert Bot, Amrutha Dondemadahalli Ramegowda
Master's Projects
The fast growth of artificial intelligence in human-computer interaction has been aided significantly by the introduction of conversational AI systems. This project presents a Domain Expert Bot, a multi-domain conversational bot built with advanced NLP techniques incorporated through Sentence-BERT and MapReduce to allow the bot to analyze and comprehend challenging user queries on various topics. The bot can converse on different subjects ranging from technology topics to healthcare, environment, politics, and casual discussions. It excels in understanding deep language contexts and efficiently processes large datasets, ensuring prompt and accurate responses. Furthermore, it uses advanced ranking algorithms to perform real- time …
Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine,
2024
San Jose State University
Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti
Master's Projects
This project introduces a "dynamic forecasting engine," designed to transform the way restaurants predict sales. The engine dynamically handles seasonal ARIMA_HoltWinter hybrid model, XGBoost, and LSTM algorithms to dynamically select the best forecasting method based on data volume, variety, and customer taste preferences delving upon the spice level categorical sales. This guide differs from traditional crystal ball approaches because it has the ability to improve over time as new data comes in terms of spice levels. It emphasizes the importance of dataset size in the selection of machine learning algorithms through complexity for large datasets and simplicity for smaller ones …
Satellite Handover Optimization Using Predicted Satellite-To-Base-Station Proximity,
2024
San Jose State University
Satellite Handover Optimization Using Predicted Satellite-To-Base-Station Proximity, Pranathi Kunadi
Master's Projects
Modern telecommunications heavily rely on Satellite communication networks to provide global coverage, especially in remote areas which link the whole world in a loop. Conventional handover algorithms methods rely on fixed and predefined rules and thresholds predefined statically to make a handover decision. However, these static handover algorithms may become inefficient under the changing conditions of the network. Therefore, it would be useful to measure the proximity order of satellites to the specific base station. Consequently, the assessed relative proximity helps in optimizing the handovers proactively inside related coverage areas. This results in the service quality and the delays in …
