Digital Commons Network^™

Cloud-Assisted Multi-Channel Data Broadcasting, Debabrata Maji

Master’s Dissertations

The convergence of Internet of Things (IoT) and cloud computing has transformed technology, impacting commerce, industrial production, data management, etc. Multi- Channel Broadcast Encryption (MCBE), first introduced by Phan et al. (ASIACCS 2013), is a cryptographic encryption primitive used for both IoT and Cloud that permits a sender to e!ciently and securely encrypt several messages for di”erent groups of receivers. After thoroughly exploring the existing literature, we observe that none achieves the robust provable security within the standard model. This paper addresses this gap, aiming to achieve adaptive INDistinguishable under full-IDentity Chosen-Ciphertext Attack (IND-ID-CCA) security by constructing an e!cient identity-based …

Shock Induced Droplet Aerobreakup For Newtonian And Non-Newtonian Fluids, James Leung

LSU Doctoral Dissertations

This research investigates shock-induced aerodynamic breakup (aerobreakup) of fluids with different stress-strain behaviors. The central hypothesis is that shear stress from highspeed gas flow manifests differently depending on a fluid’s strain response, influencing droplet breakup morphology, fragment size distribution, and drag-induced acceleration. To test this, experiments and simulations were performed on μm–mm sized droplets of Newtonian (water) and shear-thinning fluids (nanofluids or xanthan gum). Two experimental setups were used: an open-ended shock tube and a closed conventional shock tube. In the openended setup, a normal shock impacts a stationary droplet held by an acoustic levitator. Droplet breakup and gas dynamics …

From Devices To The Cloud: Digital Forensics In The Changing Social Media Landscape, Joseph Brown

LSU Master's Theses

This thesis presents a comprehensive digital forensic analysis of emerging and alternative social media platforms, including Truth Social, Threads, Bluesky, Nextdoor, and Neighbors. These platforms, which range from politically aligned alt-tech networks to hyperlocal neighborhood apps, present unique forensic challenges and security vulnerabilities. Across all case studies, established forensic techniques were applied using a hybrid methodology combining mobile device analysis, network traffic monitoring, and API interrogation. Findings include the discovery of plaintext credentials, session tokens, and other sensitive artifacts, particularly in platforms with weaker security postures such as Truth Social, Bluesky, Nextdoor, and Neighbors. Threads, by contrast, demonstrated greater resilience …

A Descriptive Case Study On How Suburban Texas Police Department Drone Use Conforms To Public Opinion, Terrill Brett Spell

Doctoral Dissertations and Projects

The purpose of this qualitative case study was to describe the use of drones by suburban police departments in Texas, and its central research question asked how that drone use aligns with what prior research found the public supports. The research design used a holistic multiple-case approach that involved interviewing police officers who use drones as part of their law enforcement responsibilities. Relevant theoretical frameworks were social contract theory, Packer’s theory on law enforcement, and evidence-based policing theory. Data collection involved semistructured interviews of 13 officers representing the same number of Texas suburban police departments. The data collected and analyzed …

Incorporating Industry-Standard Technical Writing Into A Materials Testing Laboratory, Caleb Levi Head

Theses and Dissertations

The fluid analysis and materials testing laboratories are crucial for engineering students to learn experimental procedures and data interpretation. Engineers spend a significant portion of their workday on report writing, yet many feel their undergraduate education did not adequately prepare them for this task. At the University of Arkansas at Little Rock, students were given an industry-influenced lab report template to expand their knowledge of the necessary information required in technical writing, thereby teaching and improving their technical writing skills actively. These reports were evaluated qualitatively and quantitatively against previous submissions over two semesters in the mechanical engineering fluids and …

Single-Step Conversion Of Metal Impurities In Cnts To Electroactive Metallic Nitride Nanoclusters For Electrochemical Co2 Reduction, Ahmed Badreldin, John Pellessier, Francisco Alejandro Ospina Acevedo, Siyuan Fang, Carter Racine, Jin Feng, Alvin Chang, Yulu Ge, Yang Gang, Shengyao Wang, Xiaokun Yang, Sergei Ivanov, Zhenxing Feng, Yun Hang Hu, Perla B. Balbuena, Ying Li

Michigan Tech Publications

A novel single-step low-temperature pyrolysis method is developed to efficiently remove encapsulated Ni nanoparticles (NPs, 10–50 nm) from both regular-grade (< 5 wt.% metal impurities) and industrial-grade (< 10 wt.% metal impurities) carbon nanotubes (CNTs). This approach eliminates the need for conventional multi-step purification processes, which often involve high-temperature corrosive gas oxidation and acid washing. The new strategy transforms and redistributes encapsulated Ni-NPs into homogeneously sized nanoclusters (NCs, ≈1 nm) that are evenly dispersed on the surface of CNTs. Surface and bulk sensitive spectroscopic analyses reveal the predominant formation of Ni3N-NCs, along with some metallic Ni-NCs. The treated materials demonstrate exceptional electroactivity toward CO2 reduction to CO, with the best-performing CNT-PTFE-Mel-650 sample achieving an ultra-low onset overpotential of −19 mV and 98% CO selectivity in a current density range of 100–700 mA cm⁻². This NC catalyst demonstrates 25% lower voltage at 700 mA cm⁻² compared to the single atom catalyst (SAC) control. Experimentally verified ab initio molecular dynamics (AIMD) models are simulated, and subsequent density functional theory (DFT) calculations further support the thermodynamic stability of Ni3N-NCs and their favorability for CO2 reduction. This work establishes a new method for creating ligand-free electroactive NCs for efficient electrochemical reactions.

A Deep-Learning-Based Dehazing Framework For Non-Homogenous Scenes, Shimaa Mohammed Abd Elghany, Doaa A. Altantawy, Hossam El-Din Moustafa Moustafa

Mansoura Engineering Journal

The advancement of single-image dehazing techniques has been rapid in recent years. Several existing algorithms that are based on deep learning have shown remarkable efficiency for dealing with homogeneous hazing-free issues, but convolutional neural networks (CNNS) frequently fail on non-homogeneous dehazing datasets. Meanwhile, dehaze results from dense haze regions are often blurry because the information of these regions is typically unknown and difficult to estimate. To address these issues, an efficient image enhancement dehazing algorithm that utilizes deep learning techniques, and a non-uniform atmospheric scattering model had been proposed. Unlike the majority of existing dehazing methods, the medium transmission function …

Integrated Evaluation Of Airtightness, Acoustics, Thermal Performance, And Life-Cycle Carbon Emissions In Code-Compliant Wood-Framed And Thin-Shell Concrete Dome Residential Structures, Eduardo Ibanez Vasquez

Theses and Dissertations

The residential construction industry faces increasing demands for energy-efficient and sustainable housing solutions. In the U.S., residential buildings significantly contribute to energy consumption and greenhouse gas (GHG) emissions, highlighting the need for enhanced building envelope performance. This study compares four residential structures: a traditional code-compliant wood-framed structure built in 1970, a traditional code-compliant wood-framed structure built in 2016, a modular code-compliant wood framed structure built in 2024, and a thin-shell concrete dome built in 2022. The analysis focuses on airtightness, acoustic insulation, passive thermal performance, and life-cycle carbon emissions, including both embodied (cradle-to-gate) and operational energy use. Field-based methods included …

A Longitudinal Analysis Of Morphological Shape Variation Of Spleen In Patients With Fontan Surgery, Varatharajan Nainamalai, Håvard Bjørke Jenssen, Mostafa Rezaeitaleshmahalleh, Djinaud Prophete, Jordan Gosnell, Sarah Khan, Marcus Haw, Jingfeng Jiang, Joseph Vettukattil

Michigan Tech Publications

Background: Splenic size serves as a surrogate biomarker for predicting portal vein hyper-tension and liver abnormalities in subjects with Fontan Associated Liver Disease (FALD). We analyze the long-term shape variation of the spleen in FALD subjects using morphological shape features of radiomic features. Methods: We used 154 (84 from computed tomography and 70 from magnetic resonance) image volumes obtained from 36 individuals who underwent stage 3 Fontan procedure and 145 computed tomography images from controls to assess splenomegaly. To understand the splenomegaly, thirteen shape features of the spleen over three 10-year intervals, and variations between controls and FALD subjects were …

Hourly Sulfur Dioxide Observations Over North America: First Retrieval Results From Tempo, Can Li, Nickolay A. Krotkov, Joanna Joiner, Simon Carn, Vitali Fioletov, Chris Mclinden, Debora Griffin, Xiong Liu, Heesung Chong

Michigan Tech Publications

We present the first sulfur dioxide (SO2) retrievals from Tropospheric Emissions: Monitoring of Pollution (TEMPO), the first geostationary atmospheric composition sensor to cover North America, along with some potential applications of TEMPO SO2 data. We show that high resolution (∼10 km²) TEMPO measurements can be used to produce good quality SO2 retrievals with relatively small noise and biases. We demonstrate that hourly TEMPO data are useful for monitoring volcanic hazards, by providing frequent updates on the plume location and additional information on the plume height or winds. With the large number of measurements from TEMPO, it is also …

The Effect Of Transformer Based Pre-Trained Language Models On Biomedical Relation Extractions, Eman Saad, Sherif Kishk, Amr Ali-Eldin, Ahmed I. Saleh

Mansoura Engineering Journal

Biomedical relation extraction represents a critical advancement in healthcare research. As the volume of biomedical publications continues to grow exponentially, efficient extraction of entity relationships has become essential for accelerating knowledge discovery, drug development, and precision medicine initiatives. The field has evolved from focusing on simple binary relations, such as Protein-Protein Interactions (PPI), which are fundamental to understanding cellular processes and therapeutic development, to addressing more complex multi-class classification challenges like Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI).Pre-trained language models have become the cornerstone of modern approaches of extracting biomedical relations. Models like BioBERT, PubMedBERT, and SciBERT, along with general-purpose …

Research On Requirements And Methods For Intelligent Assessment Of Simulation Credibility, Bingheng Wang, Tingrui Liu, Fan Yang, Huan Zhang, Wei Li, Ping Ma, Ming Yang

Journal of System Simulation

Abstract: The accuracy of simulations in representing real-world systems is a critical concern for users. Simulation credibility assessment ensures trustworthiness by evaluating the correctness and effectiveness of simulations to meet application requirements. As simulation technologies are widely adopted, and new simulation paradigms emerge, traditional assessment methods are increasingly showing limitations in their dependence on experts, data processing capabilities, and assessment efficiency. This paper systematically reviewed the research demands, current progress, new technologies, and future trends of intelligent simulation credibility assessment. Based on the simulation credibility assessment process and problem analysis, the requirements for intelligent credibility assessment were discussed. Intelligent technologies …

Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie

Iraqi Journal for Computer Science and Mathematics

COVID-19, caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization (WHO) and rapidly spread worldwide from late 2019. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic tool, its sensitivity ranges from only 60% to 70%, leading to false negatives. Computed Tomography (CT) imaging has emerged as a valuable alternative for accurate diagnosis; however, the quality of CT images is often degraded by motion-induced blur and additive noise, particularly in children, individuals with mental health conditions, or those with phobias of CT scans. This study aims to enhance COVID-19 CT image quality …

Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar

Iraqi Journal for Computer Science and Mathematics

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

The Future Of Intelligent Industrial Systems: Plc, Node-Red, And Iot/Iiot, Firas Ahmed Hussein, Mohammad Tariq Yaseen, Mohammed Obaid Mustafa

AUIQ Technical Engineering Science

Integrating Programmable Logic Controllers (PLCs) with Node-RED and IoT/IIoT has emerged as a transformative technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and smart manufacturing and …

Recent Advances In Modelling Of Frost Formation For Mechanical Systems, Yong Tao

Mechanical Engineering Faculty Publications

The physics underlying frost and ice formation has been extensively studied over the past few decades, with significant contributions to our understanding of this phenomenon. These insights have primarily been applied to engineering systems with refrigerated surfaces, such as refrigerators, freezers and heat pumps of various sizes. Despite considerable progress, the dynamic and complex mechanisms governing frost and ice formation remain an active area of research, as competing factors continue to challenge predictive accuracy. The increasing interest from stakeholders in reducing energy consumption and carbon footprints in mechanical systems further underscores the importance of advancing modelling and simulation capabilities in …

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Stokes-Dependent Droplet Collection Efficiency On A Naca 0012 Airfoil From Droplet-Informed Simulations With Statistical Overloading, Arash Shad, Hashnayne Ahmed, ‪Nadim Zgheib, S. Balachandar, S. A. Sherif

Mechanical Engineering Faculty Publications

Accurate modelling of ice accretion on aircraft wings requires analysing droplet impingement on the surface to optimize the design of ice-protection systems. We perform Euler–Lagrange simulations of a droplet-laden flow impinging on a NACA 0012 airfoil. Our study includes water droplets with eight discrete sizes ranging from 1 to 160 microns. We vary the free-stream velocity of the incoming airflow in the range 60≤⁢𝑈≤⁢240 m s−1 and the chord length of the airfoil in the range 0.5≤⁢𝑐≤⁢2 m. Due to the dilute nature of supercooled clouds, one-way coupling is used in the simulations. The effects of droplet breakup and collision …

A Rigorous Framework For An Improved Messinger/Myers Model Of Ice Accretion Under Conditions Of Variable Property And Unsteady Aircraft Icing, Hashnayne Ahmed, Arash Shad, ‪Nadim Zgheib, S. A. Sherif, S. Balachandar

Mechanical Engineering Faculty Publications

We analyse the Messinger/Myers model by critically evaluating simplifying assumptions through a rigorous formulation of the rime ice accretion process. We explore the effects of both constant and variable ice density and thermal conductivity, along with the effects of sublimation from the ice surface. The effects of key factors such as droplet impact rate, ambient temperature relative to the freezing temperature and the temperature difference between the ambient air and the airfoil surface are examined. Under these varying conditions, the present rigorous formulation is used to assess the significance of unsteady effects, variable ice properties and sublimation. We observe that …

Mode Engineering And Functional Enhancement In Piezoelectric-On-Silicon Mems Resonators With Magnetic Field Sensing Applications, Ugur Guneroglu

USF Tampa Graduate Theses and Dissertations

This dissertation significantly advances the field of micro-electro-mechanical systems (MEMS) resonators by exploring novel post-fabrication tuning techniques, electrode configuration effects, and innovative sensor applications for thin-film piezoelectric-on-silicon (TPoS) resonator devices. Driven by the increasing demand for tunable, high-precision and robust MEMS resonators in radio frequency (RF) applications such as sensing, timing, and filtering, this research aim to provide foundational knowledge and practical solutions for overcoming existing technological limitations. The dissertation is organized into three main research thrusts, each addressing critical challenges and opportunities in the design, fabrication, and application of TPoS MEMS resonators.

The first research thrust investigates an innovative …

Security Analysis Of Encrypted Mempool, Prabal Das

Master’s Dissertations

With the rapid growth of Decentralized Finance (DeFi), the challenge of Maximum Extractable Value (MEV) has become increasingly significant-particularly on Ethereum. MEV allows malicious actors to manipulate transaction order within blocks, enabling exploitative strategies such as frontrunning and sandwich attacks. In response, recent research has proposed encrypted mempools, which conceal trans- action content until after ordering is finalized, thereby reducing the exploitable surface for MEV. This thesis investigates encrypted mempool, specifically the Shutter protocol, a threshold encryption-based approach designed to mitigate MEV by hiding transac- tion contents during the mempool phase. We analyze its core architecture, under- lying cryptographic mechanisms, …

Towards The Wearable Cardiorespiratory Sensors For Aerospace Applications, Chandan Sheikder

Journal of Aviation/Aerospace Education & Research

In safety-critical aviation operations, adaptive Human-Machine Interfaces (HMI) rely on accurate physiological monitoring to mitigate cognitive overload. While cardiorespiratory sensors are promising for real-time cognitive workload assessment, existing studies lack rigorous validation of consumer-grade devices in high-stress aviation contexts and fail to address measurement uncertainty propagation. This study evaluates the Zephyr BioHarness, a commercial wearable sensor, against clinical-grade equipment during arithmetic tasks simulating aviation cognitive demands. By integrating a neuro-fuzzy system with uncertainty propagation methods, we quantify the reliability of heart rate (HR) and breathing rate (BR) metrics for workload estimation. Results demonstrate moderate HR accuracy (RMSE: 4.85 bpm, CC: …

Identifying Individual Factors Which Impact Student Learning In A Desktop Flight Training Simulator, Tim Ryley, Guido Carim Junior, James Mcbroom, Elvira Marques, Chris Campbell, Nnenna Ike

Journal of Aviation/Aerospace Education & Research

With expected global growth in air transport demand and forecasted shortage of pilots, there is a need for faster and cheaper flight training. A range of protocols was used to assess factors such as student’s emotional state, adaptive performance, and situational awareness as possible predictors of flight performance based on the influence of these factors on student flight performance conducted using a Personal Computer-based Aviation Training Devices (PCATD). Factor analysis and regression models were used to identify how these factors related to student performance during a visual flight conducted using a desktop simulator and our findings demonstrated some influence of …

The State Of Uas Operations At Airports, A Perspective From Airport Managers, Damon Lercel, Sarah M. Hubbard

Journal of Aviation/Aerospace Education & Research

As the number of Uncrewed Aircraft Systems (UAS) operating in our National Airspace System (NAS) increases, so do UAS operations near or at an airport. The accelerating technology in Advanced Air Mobility (AAM) and related business opportunities will only further increase UAS operations at airports. This continued growth in new UAS technologies and applications introduces new hazards and risks to the airport environment. This proliferation of UAS highlights the importance of airports developing a robust Safety Management System (SMS) that includes specific UAS risk mitigations. There is currently little empirical data regarding UAS traffic around airports and there is no …

Vibration-Based Detection For The Monitoring Of Neonatal Bcpap Respiratory Support To Minimize Duration Of Disconnections, Kaitlyn G. Calabresi, Meera R. Bhat, Lavinia L. Kong, Eli J. Foster, Jonathan Wang, Sangeeta Koilada, Angela Mak, Aastha Kapadia, Niam Mohseni, W. Keith Leung, Suneetha Desiraju, Constanza Miranda

School of Medicine Faculty Publications

Bubble continuous positive airway pressure (bCPAP) is a minimally invasive respiratory support for neonates experiencing respiratory distress syndrome (RDS). It exerts continuous, oscillatory air pressure to encourage lung function and development. Neonates’ movement may cause disconnections at the nasal interface. These disconnections are characterized by the cessation of bubbling at the bCPAP respiratory circuit output and frequently go unnoticed until hypoxia develops. Common Neonatal Intensive Care Unit (NICU) bCPAP systems lack a built-in alarm or accessory device to monitor for disconnections. As a result, nursing personnel must exercise extreme caution utilizing current bCPAP procedure. If a neonate is disconnected, it …

Advancing Eye-Gaze Writing Systems With Computer Vision, And Dynamic Text Suggestions, Walid Abdallah Shobaki

Theses and Dissertations

Eye gaze writing, a novel interaction modality, has the potential to revolutionize communication for individuals with limited mobility. In our research, we investigated the deep learning algorithms efficiency for real-time eye gaze writing. We have compared many algorithms' performance in many computer vision areas, such as object detection in which we used first YOLOv8, the second algorithm SSD, and the third algorithm is Faster R-CNN, the second computer vision area is the image segmentation in which we used DeepLab and U-Net, and the last computer vision area is self-supervised learning we have used SimCLR algorithm. By evaluating these models on …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

Design & Development Of Efficient Biometric Authentication And Key Agreement Schemes For Wireless Body Area Network, Aarthi S

Theses and Dissertations

Wireless Body Area Networks (WBANs) play a vital role in continuous health monitoring, where sensitive biometric and physiological data must be protected from privacy breaches and emerging quantum-based threats. Conventional security mechanisms are often inadequate due to resource constraints, scalability issues, and vulnerability to advanced attacks. To address these challenges, this research proposes a lightweight, scalable, and future-proof security framework tailored for WBAN applications, focusing on secure communication, privacy preservation, and quantum resilience.

An anonymous Certificate-Based Signcryption–Mutual Authentication and Key Agreement (CBS-MAKE) protocol is introduced to secure extra-body communications while ensuring patient anonymity. The protocol integrates Elliptic Curve Cryptography with …