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Articles 1 - 8 of 8
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Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott
Enabling Iov Communication Through Secure Decentralized Clustering Using Federated Deep Reinforcement Learning, Chandler Scott
Electronic Theses and Dissertations
The Internet of Vehicles (IoV) holds immense potential for revolutionizing transporta- tion systems by facilitating seamless vehicle-to-vehicle and vehicle-to-infrastructure communication. However, challenges such as congestion, pollution, and security per- sist, particularly in rural areas with limited infrastructure. Existing centralized solu- tions are impractical in such environments due to latency and privacy concerns. To address these challenges, we propose a decentralized clustering algorithm enhanced with Federated Deep Reinforcement Learning (FDRL). Our approach enables low- latency communication, competitive packet delivery ratios, and cluster stability while preserving data privacy. Additionally, we introduce a trust-based security framework for IoV environments, integrating a central authority …
Application Of Functionalized Organosilicas In Adsorption Of Nitrates, Stephen Amoako
Application Of Functionalized Organosilicas In Adsorption Of Nitrates, Stephen Amoako
Electronic Theses and Dissertations
This study addresses the critical environmental issue of elevated nitrate levels in water bodies, primarily due to excessive use of nitrogenous fertilizers and improper waste disposal. It focuses on reducing nitrate concentrations in polluted water to permissible levels through the effectiveness of hybrid materials in nitrate adsorption. We synthesized nine amino-functionalized adsorbents using grafting and sol-gel techniques. Batch adsorption tests confirmed the high nitrate adsorption capacities of these adsorbents, with sol-gel materials showing the highest efficiency due to their abundant amino group contents. Among these, the surfactant-free, sol-gel adsorbent was the most effective, combining ease of synthesis with cost-efficiency. Our …
An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari
An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari
Electronic Theses and Dissertations
Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, …
Synthesis And Characterization Of Indole-Based Zinc Dipyrrin Photosensitizers, Jean-Pierre Sanza
Synthesis And Characterization Of Indole-Based Zinc Dipyrrin Photosensitizers, Jean-Pierre Sanza
Electronic Theses and Dissertations
Metal complexes of dipyrromethene (dipyrrins) used as sensitizers in photocatalysis offer a way to harness solar energy in chemical bonds to create new fuels. This offers the dual role of reducing fossil fuel dependence and atmospheric CO2 levels. Traditionally, metal dipyrrin complexes are synthesized using substituted pyrroles, aldehydes, and transition metals. Indoles have a more expanded pi-electron system and their dipyrrin-type complex may exhibit visible light absorption, suggesting that they can act as photosensitizers for CO2 reduction processes. A novel indoledipyrromethene was synthesized using unsubstituted indole and mesitaldehyde. The complex exhibits visible light absorption at 422 nm. Its …
Comparative Analysis Of Surrogate Models For The Dissolution Of Spent Nuclear Fuel, Dayo Awe
Comparative Analysis Of Surrogate Models For The Dissolution Of Spent Nuclear Fuel, Dayo Awe
Electronic Theses and Dissertations
This thesis presents a comparative analysis of surrogate models for the dissolution of spent nuclear fuel, with a focus on the use of deep learning techniques. The study explores the accuracy and efficiency of different machine learning methods in predicting the dissolution behavior of nuclear waste, and compares them to traditional modeling approaches. The results show that deep learning models can achieve high accuracy in predicting the dissolution rate, while also being computationally efficient. The study also discusses the potential applications of surrogate modeling in the field of nuclear waste management, including the optimization of waste disposal strategies and the …
Detection Of Jamming Attacks In Vanets, Thomas Justice
Detection Of Jamming Attacks In Vanets, Thomas Justice
Undergraduate Honors Theses
A vehicular network is a type of communication network that enables vehicles to communicate with each other and the roadside infrastructure. The roadside infrastructure consists of fixed nodes such as roadside units (RSUs), traffic lights, road signs, toll booths, and so on. RSUs are devices equipped with communication capabilities that allow vehicles to obtain and share real-time information about traffic conditions, weather, road hazards, and other relevant information. These infrastructures assist in traffic management, emergency response, smart parking, autonomous driving, and public transportation to improve roadside safety, reduce traffic congestion, and enhance the overall driving experience. However, communication between the …
Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji
Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji
Electronic Theses and Dissertations
Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …
Interpreting Shift Encoders As State Space Models For Stationary Time Series, Patrick Donkoh
Interpreting Shift Encoders As State Space Models For Stationary Time Series, Patrick Donkoh
Electronic Theses and Dissertations
Time series analysis is a statistical technique used to analyze sequential data points collected or recorded over time. While traditional models such as autoregressive models and moving average models have performed sufficiently for time series analysis, the advent of artificial neural networks has provided models that have suggested improved performance. In this research, we provide a custom neural network; a shift encoder that can capture the intricate temporal patterns of time series data. We then compare the sparse matrix of the shift encoder to the parameters of the autoregressive model and observe the similarities. We further explore how we can …