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Articles 1 - 30 of 2395
Full-Text Articles in Physical Sciences and Mathematics
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Theses and Dissertations
New technologies are being introduced at a rate faster than ever before and smaller in size. Due to the size of these devices, security is often difficult to implement. The existing solution is a firewall-segmented “IoT Network” that only limits the effect of these infected devices on other parts of the network. We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system for malware detection. We perform transfer learning using a generalized model trained on multiple IoT device sources to learn network traffic on new devices with minimal computational resources. We further extend our proposed system to utilize federated learning …
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Theses and Dissertations
Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …
Classification And Explanation Of Iron Deficiency Anemia From Complete Blood Count Data Using Machine Learning, Siddartha Pullakhandam
Classification And Explanation Of Iron Deficiency Anemia From Complete Blood Count Data Using Machine Learning, Siddartha Pullakhandam
Theses and Dissertations
Anemia is a global health problem, and over 2 billion people are affected. Although, the major cause of anemia is iron deficiency (IDA), global estimates suggest that only about half of anemia could be attributed to ID. The typical test of anemia involves measurement of hemoglobin using Complete Blood Count (CBC) test, which also gives additional information on blood cell numbers and morphology. The diagnosis of iron deficiency anemia (IDA, both anemic and ID co-exist in a subject) requires additional expensive serum ferritin test. However, blood cell count, and morphology can also be utilized for diagnosis of IDA. The goal …
Predicting Energy Expenditure From Physical Activity Videos Using Optical Flows And Deep Learning, Gayatri Kasturi
Predicting Energy Expenditure From Physical Activity Videos Using Optical Flows And Deep Learning, Gayatri Kasturi
Theses and Dissertations
This thesis presents a novel approach for predicting energy expenditure of physical activity from videos using optical flows and deep learning. Conventional approaches mainly rely on wearable sensors, which, despite being widely used, are constrained by practicality and accuracy concerns. This proposal introduces a new strategy that utilizes a three-dimensional Convolutional Neural Network (3D-CNN) to evaluate video data and accurately estimate energy costs in metabolic equivalents (METs). Our model utilizes optical flow extraction to analyze video, capturing complex motion patterns and their changes over time. The results are good indicating potential for this method to be deployed in various healthcare …
Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu
Deep Learning In Indus Valley Script Digitization, Deva Munikanta Reddy Atturu
Theses and Dissertations
This research introduces ASR-net(Ancient Script Recognition), a groundbreaking system that automatically digitizes ancient Indus seals by converting them into coded text, similar to Optical Character Recognition for modern languages. ASR-net, with an 95% success rate in identifying individual symbols, aims to address the crucial need for automated techniques in deciphering the enigmatic Indus script. Initially Yolov3 is utilized to create the bounding boxes around each graphemes present in the Indus Valley Seal. In addition to that we created M-net(Mahadevan) model to encode the graphemes. Beyond digitization, the paper proposes a new research challenge called the Motif Identification Problem (MIP) related …
Space Transformation For Open Set Recognition, Atefeh Mahdavi
Space Transformation For Open Set Recognition, Atefeh Mahdavi
Theses and Dissertations
Open Set Recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In OSR, only a limited number of known classes are available at the time of training the model and the possibility of unknown classes never seen at training time emerges in the test environment. In such a setting, the unknown classes and their risk should be considered in the algorithm. Such systems require not only to identify and discriminate instances that belong to the source domain (i.e., the seen known classes contained in the training dataset) but also to reject unknown …
Investigating The Impact Of Human-Centered Interface Design On The User Experience Of Mobile Device Users, Ruchir Gupta
Investigating The Impact Of Human-Centered Interface Design On The User Experience Of Mobile Device Users, Ruchir Gupta
Theses and Dissertations
In order to investigate the intricate interaction between interface design, user technological proficiency, and other components of the user experience, this research study used a mixed-method approach. The beginner user group—those with little experience or expertise with technology - were the main target audience. The important discovery emphasizes the substantial influence that careful design can have on improving the effectiveness and usability of interfaces for non-tech-savvy individuals. When using the suggested Interface B instead of the current Interface A, beginner participants' task completion times significantly improved, according to the user study. This underlines the significance of creating with the needs …
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Theses and Dissertations
Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Theses and Dissertations
Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …
Blockchain For Computational Integrity And Privacy, Rahul Raj
Blockchain For Computational Integrity And Privacy, Rahul Raj
Theses and Dissertations
This study proposes a blockchain based system that utilizes fully homomorphic encryption to provide security of data in use as well as computational integrity. This is achieved by leveraging the attributes of blockchain which provides availability and data integrity combined with homomorphic encryption that provides confidentiality. The proposed system is designed to perform statistical operations, including mean, median and variance, on encrypted data, thus providing confidentiality of data while in use. The computations are performed on the smart contract, residing on the blockchain which provides computational integrity. The results indicate that it is possible to perform fully homomorphic computations on …
Towards Energy-Efficient Edge Computing For Tiny Ai Applications, Vamsi Krishna Bhagavathula
Towards Energy-Efficient Edge Computing For Tiny Ai Applications, Vamsi Krishna Bhagavathula
Theses and Dissertations
As artificial intelligence (AI) applications become more common on the edge of networks, like Raspberry Pi servers, it is crucial to optimize their energy use. This research project investigates how AI algorithms affect energy efficiency and resource usage on Raspberry Pi servers. Two models were created: one predicts resource usage, and the other predicts power consumption of AI algorithms on Raspberry Pi. Several factors are considered like CPU and memory use, algorithm speed, dataset size, and types of algorithms and datasets. Using regression-based methods, we model how these factors affect energy use. By converting categorical factors into numerical ones, we …
Graph Coloring Reconfiguration, Reem Mahmoud
Graph Coloring Reconfiguration, Reem Mahmoud
Theses and Dissertations
Reconfiguration is the concept of moving between different solutions to a problem by transforming one solution into another using some prescribed transformation rule (move). Given two solutions s1 and s2 of a problem, reconfiguration asks whether there exists a sequence of moves which transforms s1 into s2. Reconfiguration is an area of research with many contributions towards various fields such as mathematics and computer science.
The k-coloring reconfiguration problem asks whether there exists a sequence of moves which transforms one k-coloring of a graph G into another. A move in this case is a type …
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Theses and Dissertations
Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …
Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser
Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser
Theses and Dissertations
Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.
Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson
Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson
Theses and Dissertations
The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …
Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded
Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded
Theses and Dissertations
Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.
A Conceptual Decentralized Identity Solution For State Government, Martin Duclos
A Conceptual Decentralized Identity Solution For State Government, Martin Duclos
Theses and Dissertations
In recent years, state governments, exemplified by Mississippi, have significantly expanded their online service offerings to reduce costs and improve efficiency. However, this shift has led to challenges in managing digital identities effectively, with multiple fragmented solutions in use. This paper proposes a Self-Sovereign Identity (SSI) framework based on distributed ledger technology. SSI grants individuals control over their digital identities, enhancing privacy and security without relying on a centralized authority. The contributions of this research include increased efficiency, improved privacy and security, enhanced user satisfaction, and reduced costs in state government digital identity management. The paper provides background on digital …
Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen
Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen
Theses and Dissertations
This work investigates digital twin (DT) applications for electric power system (EPS) resilience. A novel DT architecture is proposed consisting of a physical twin, a virtual twin, an intelligent agent, and data communications. Requirements for the virtual twin are identified. Guidelines are provided for generating, capturing, and storing data to train the intelligent agent. The relationship between the DT development process and an existing controller hardware-in-the-loop (CHIL) process is discussed. To demonstrate the proposed DT architecture and development process, a DT for a battery energy storage system (BESS) is created based on the simulation of an industrial nanogrid. The creation …
Big Data Applications And Challenges In Giscience (Case Studies: Natural Disaster And Public Health Crisis Management), Amir Masoud Forati
Big Data Applications And Challenges In Giscience (Case Studies: Natural Disaster And Public Health Crisis Management), Amir Masoud Forati
Theses and Dissertations
This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic …
A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang
A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang
Theses and Dissertations
Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …
Statically Scheduling Circular Remote Attribute Grammars, Seyedamirhossein Hesamian
Statically Scheduling Circular Remote Attribute Grammars, Seyedamirhossein Hesamian
Theses and Dissertations
Classical attribute grammars invented by Knuth have been the subject of extensive study. Over the years there have been various extensions introduced, each with the goal of making attribute grammar more useful for applications such as program analysis. The first extension described here is circular attribute grammar by Farrow. It is followed by remote attribute grammar, which was introduced separately by Boyland and Hedin. More recently, Hedin introduced circular remote attribute grammars and a proof of concept implementation with demand evaluation. Remote attribute grammars make it possible for semantic rules to access attributes of nodes that are not local, and …
Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya
Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya
Theses and Dissertations
High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Theses and Dissertations
Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …
Anxiety In International Graduate Students With U.S. Education, Aeshah Zarraa
Anxiety In International Graduate Students With U.S. Education, Aeshah Zarraa
Theses and Dissertations
Anxiety in international graduate students is a significant concern for most students planning to move out of their home country to pursue higher education in the United States. The primary purpose of this research is to identify the prime causes of anxiety triggers in the targetted Graduate students, thereby determining a step-wise methodology development to address the causes. The study initially identified a set of graduate students who were voluntarily willing to collaborate and address their concerns anonymously to identify the significant issues faced by current students and alumni of the Florida Institute of Technology, Melbourne, Florida. Several questions were …
Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam
Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam
Theses and Dissertations
The security and privacy of IoT devices have become primary concerns as smart home networks are connected to the internet. Ethereum blockchain can be a solution to mitigate or prevent attacks – sniffing attacks, malware attacks, Eavesdropping, and Distributed Denial of Services (DDoS) attacks. Deploying Ethereum in resource constraint IoT devices is challenging due to resultant energy consumption, computational overhead, and delay. We adopted smart home as a case study to examine our methodology as a model for general IoT applications. This thesis work presents the implementation of private Ethereum blockchain that is optimized and installable on smart home IoT. …
Intellibeehive, Christian Ivan Narcia-Macias
Intellibeehive, Christian Ivan Narcia-Macias
Theses and Dissertations
Utilizing computer vision and the latest technological advancements, in this study, we developed a honey bee monitoring system that aims to enhance our understanding of Colony Collapse Disorder, honey bee behavior, population decline, and overall hive health. The system is positioned at the hive entrance providing real-time data, enabling beekeepers to closely monitor the hive's activity and health through an account-based website. Using machine learning, our monitoring system can accurately track honey bees, monitor pollen-gathering activity, and detect Varroa mites, all without causing any disruption to the honey bees. Moreover, we have ensured that the development of this monitoring system …
Robust And Uncertainty-Aware Image Classification Using Bayesian Vision Transformer Model, Fazlur Rahman Bin Karim
Robust And Uncertainty-Aware Image Classification Using Bayesian Vision Transformer Model, Fazlur Rahman Bin Karim
Theses and Dissertations
Transformer Neural Networks have emerged as the predominant architecture for addressing a wide range of Natural Language Processing (NLP) applications such as machine translation, speech recognition, sentiment analysis, text anomaly detection, etc. This noteworthy achievement of Transformer Neural Networks in the NLP field has sparked a growing interest in integrating and utilizing Transformer models in computer vision tasks. The Vision Transformer (ViT) model efficiently captures long-range dependencies by employing a self-attention mechanism to transform different image data into meaningful, significant representations. Recently, the Vision Transformer (ViT) has exhibited incredible performance in solving image classification problems by utilizing ViT models, thereby …
Enhancing Time Series Hashing Performance Via Deep Orthogonal Hashing, Mahmudul Hasan Robin
Enhancing Time Series Hashing Performance Via Deep Orthogonal Hashing, Mahmudul Hasan Robin
Theses and Dissertations
Deep hashing has been widely used for efficient retrieval and classification of high-dimensional data like images and text. However, its application to time series data is still challenging due to the data’s temporal nature. To tackle this issue, a new deep hashing method has been proposed that generates efficient hash codes and enhances the time series hashing performance using a ResNet model with Orthohash (Cosine Similarity Loss). The proposed method uses one loss architecture while using ResNet model for efficient hashing. It uses the Character Trajectories dataset to extract discriminative features from the time series data. These features are then …
Automated Human Activity Recognition From Controlled Environment Videos, Pranay Mandadapu
Automated Human Activity Recognition From Controlled Environment Videos, Pranay Mandadapu
Theses and Dissertations
This thesis explores deep learning methods for Human Activity Recognition (HAR) from videos to automate the annotation of human activities in videos. The research is particularly relevant for continuous monitoring in healthcare settings such as nursing homes and hospitals. The innovative part of the approach lies in using YOLO models to first detect humans in video frames and then isolating them from the rest of the image for activity recognition which leads to an improvement in accuracy. The study employs pre-trained deep residual networks, such as ResNet50, ResNet152-V2, and Inception-ResNetV2, which were found to work better than custom CNN-based models. …
Enhancing Inter-Document Similarity Using Sub Max, Richard Imorobebh Igbiriki
Enhancing Inter-Document Similarity Using Sub Max, Richard Imorobebh Igbiriki
Theses and Dissertations
Document similarity, a core theme in Information Retrieval (IR), is a machine learning (ML) task associated with natural language processing (NLP). It is a measure of the distance between two documents given a set of rules. For the purpose of this thesis, two documents are similar if they are semantically alike, and describe similar concepts. While document similarity can be applied to multiple tasks, we focus our work on the accuracy of models in detecting referenced papers as similar documents using their sub max similarity. Multiple approaches have been used to determine the similarity of documents in regards to literature …