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Articles 1531 - 1560 of 8518
Full-Text Articles in Physical Sciences and Mathematics
Online Sexual Predator Detection, Muhammad Khalid
Online Sexual Predator Detection, Muhammad Khalid
Electronic Theses and Dissertations
Online sexual abuse is a concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning and deep learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose an approach that first tackles the problem of imbalance and then improves the effectiveness …
Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina
Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina
Theses and Dissertations--Computer Science
Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.
Trading energy among users in a decentralized fashion has been referred …
Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian
Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian
Theses and Dissertations--Computer Science
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature …
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
UNF Graduate Theses and Dissertations
Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …
Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes
Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes
UNF Graduate Theses and Dissertations
Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes.
One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is …
Extracting Road Surface Marking Features From Aerial Images Using Deep Learning, Michael Kimollo
Extracting Road Surface Marking Features From Aerial Images Using Deep Learning, Michael Kimollo
UNF Graduate Theses and Dissertations
The traffic and roadway safety agencies spend significant efforts each year collecting roadway data, including lane configurations and other road surface marking data, such as areas with school zone markings, sidewalks, left turns, right turns, bicycle lanes, etc., for safety analysis and planning purposes. The current manual data collection methods pose significant operational and quality control challenges as they are costly and prone to errors. In addition to that the manual data collection is labor intensive and takes too much time involving high equipment costs, questionable data accuracy guarantees, and concerns about the safety of the crew.
This study aims …
Algorithmic Bias Automation: The Effects Of Proxy On Machine-Learned Systems, Emely J. Galeano
Algorithmic Bias Automation: The Effects Of Proxy On Machine-Learned Systems, Emely J. Galeano
Senior Projects Spring 2023
Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College.
A Symbolic Music Transformer For Real-Time Expressive Performance And Improvisation, Arnav Shirodkar
A Symbolic Music Transformer For Real-Time Expressive Performance And Improvisation, Arnav Shirodkar
Senior Projects Fall 2023
With the widespread proliferation of AI technology, deep architectures — many of which are based on neural networks — have been incredibly successful in a variety of different research areas and applications. Within the relatively new domain of Music Information Retrieval (MIR), deep neural networks have also been successful for a variety of tasks, including tempo estimation, beat detection, genre classification, and more. Drawing inspiration from projects like George E. Lewis's Voyager and Al Biles's GenJam, two pioneering endeavors in human-computer interaction, this project attempts to tackle the problem of expressive music generation and seeks to create a Symbolic Music …
Combating Fake News: A Gravity Well Simulation To Model Echo Chamber Formation In Social Media, Jeremy E. Thompson
Combating Fake News: A Gravity Well Simulation To Model Echo Chamber Formation In Social Media, Jeremy E. Thompson
Dartmouth College Ph.D Dissertations
Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a …
Timeseries Analysis To Characterize Complex Phenomena In Environmental And Human Health Applications, Jeremy Ernest Matt
Timeseries Analysis To Characterize Complex Phenomena In Environmental And Human Health Applications, Jeremy Ernest Matt
Graduate College Dissertations and Theses
Studying and evaluating time series signals that emerge when monitoring complex phenomena requires fusing, visualizing, and often reducing the dimensionality of large amounts of data to reveal the patterns and relationships that appear at different scales. In this work, we develop methods for monitoring, visualizing, and identifying the complex relationships that appear in time series data collected from two very different domains – health care conversations and river networks – to facilitate large-scale understanding of these systems. Fostering connection between clinicians and patients and their families in the context of serious illness is a fundamental component of good clinical communication …
Effects Of Morphology On Genetic Assimilation Of Learned Behavior, Natalie L. Tolley
Effects Of Morphology On Genetic Assimilation Of Learned Behavior, Natalie L. Tolley
Graduate College Dissertations and Theses
The Baldwin effect is an evolutionary theory regarding the assimilation of ontogenetic changes into a population's genome via selection pressure to entrench beneficial phenotypes discovered through learning. In evolutionary computation, the incorporation of learning into non-embodied agents allows them to navigate otherwise rough fitness landscapes by allowing for local exploration at particular points in that landscape. Prior work investigating the specific mechanisms by which learned behavior is genetically assimilated is almost entirely limited to non-situated, non-embodied simulations such as bitstring manipulation. However, recent research has demonstrated that genetic assimilation can be observed in embodied agents. Learning more about the ways …
Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud
Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud
Honors Undergraduate Theses
This paper investigates the impact the LASSO, mRMR, SHAP, and Reinforcement Feature Selection techniques on random forest models for the breast cancer subtypes markers ER, HER2, PR, and TN as well as identifying a small subset of biomarkers that could potentially cause the disease and explain them using explainable AI techniques. This is important because in areas such as healthcare understanding why the model makes a specific decision is important it is a diagnostic of an individual which requires reliable AI. Another contribution is using feature selection methods to identify a small subset of biomarkers capable of predicting if a …
Practical Secure Aggregation In Federated Learning Using Additive Secret Sharing, Hamid Fazli Khojir
Practical Secure Aggregation In Federated Learning Using Additive Secret Sharing, Hamid Fazli Khojir
Electronic Theses and Dissertations
Federated learning is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. In FedAvg, the main federated learning algorithm, clients train machine learning models locally and share the trained model with the server. While the sensitive data will never be sent to the server, a malicious server can construct the original training data by having access to the clients’ models in each training round. Secure aggregation techniques such as cryptography, trusted execution environment, or differential privacy are used to solve this problem. However, these techniques incur computation and communication overhead or affect …
Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar
Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar
CMC Senior Theses
This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …
Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou
Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou
MSU Graduate Theses
In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …
An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma
MSU Graduate Theses
Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …
Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull
Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull
Philosophy Faculty Publications
Hypothetical thought experiments allow researchers to gain insights into widespread moral intuitions and provide opportunities for individuals to explore their moral commitments. Previous thought experiment studies in virtual reality (VR) required participants to come to an on-site laboratory, which possibly restricted the study population, introduced an observer effect, and made internal reflection on the participants’ part more difficult. These shortcomings are particularly crucial today, as results from such studies are increasingly impacting the development of artificial intelligence systems, self-driving cars, and other technologies. This paper explores the viability of deploying thought experiments in commercially available in-home VR headsets. We conducted …
Predicting Housing Prices Using Ai, Eric Sconyers
Predicting Housing Prices Using Ai, Eric Sconyers
Williams Honors College, Honors Research Projects
I have created an AI model that can predict housing prices with 70 percent accuracy in Ames Iowa. I was able to use data from a website called Kaggle.com which is a website that provides datasets to the public so they can create AI models with the data. I found the dataset pertaining to housing prices in Ames Iowa. With this data, I was able to create an AI model that can predict the housing price of these homes. The technology I used in this project was Python as the programming language, and I used the scikit-learn library which has …
Human Tracking Function For Robotic Dog, Andrew Sharkey
Human Tracking Function For Robotic Dog, Andrew Sharkey
Williams Honors College, Honors Research Projects
With the increase the increase in automation and humans and robots working side by side, there is a need for a more organic way of controlling robots. The goal of this project is to create a control system for Boston dynamics robotic dog Spot that implements human tracking image software to follow humans using computer vision as well as using hand tracking image software to allow for control input through hand gestures.
Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu
Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu
Information Technology & Decision Sciences Faculty Publications
Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in …
Digital Twin For Railway: A Comprehensive Survey, Sara Ghaboura, Rahatara Ferdousi, Fedwa Laamarti, Chunsheng Yang, Abdulmotaleb El Saddik
Digital Twin For Railway: A Comprehensive Survey, Sara Ghaboura, Rahatara Ferdousi, Fedwa Laamarti, Chunsheng Yang, Abdulmotaleb El Saddik
Computer Vision Faculty Publications
Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, …
Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, Jr-Shin Li
Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, Jr-Shin Li
Publications
Learning tasks involving function approximation are preva- lent in numerous domains of science and engineering. The underlying idea is to design a learning algorithm that gener- ates a sequence of functions converging to the desired target function with arbitrary accuracy by using the available data samples. In this paper, we present a novel interpretation of iterative function learning through the lens of ensemble dy- namical systems, with an emphasis on establishing the equiv- alence between convergence of function learning algorithms and asymptotic behavior of ensemble systems. In particular, given a set of observation data in a function learning task, we …
Novel Architectures And Optimization Algorithms For Training Neural Networks And Applications, Vasily I. Zadorozhnyy
Novel Architectures And Optimization Algorithms For Training Neural Networks And Applications, Vasily I. Zadorozhnyy
Theses and Dissertations--Mathematics
The two main areas of Deep Learning are Unsupervised and Supervised Learning. Unsupervised Learning studies a class of data processing problems in which only descriptions of objects are known, without label information. Generative Adversarial Networks (GANs) have become among the most widely used unsupervised neural net models. GAN combines two neural nets, generative and discriminative, that work simultaneously. We introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions. Using the gradient information, we can adaptively choose weights to train a discriminator in the direction …
Integrating The Spatial Pyramid Pooling Into 3d Convolutional Neural Networks For Cerebral Microbleeds Detection, Andre Accioly Veira
Integrating The Spatial Pyramid Pooling Into 3d Convolutional Neural Networks For Cerebral Microbleeds Detection, Andre Accioly Veira
CCE Theses and Dissertations
Cerebral microbleeds (CMB) are small foci of chronic blood products in brain tissues that are critical markers for cerebral amyloid angiopathy. CMB increases the risk of symptomatic intracerebral hemorrhage and ischemic stroke. CMB can also cause structural damage to brain tissues resulting in neurologic dysfunction, cognitive impairment, and dementia. Due to the paramagnetic properties of blood degradation products, CMB can be better visualized via susceptibility-weighted imaging (SWI) than magnetic resonance imaging (MRI).CMB identification and classification have been based mainly on human visual identification of SWI features via shape, size, and intensity information. However, manual interpretation can be biased. Visual screening …
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
CCE Theses and Dissertations
Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …
Maple: Multi-Modal Prompt Learning, Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan
Maple: Multi-Modal Prompt Learning, Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan
Computer Vision Faculty Publications
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we …
The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá
The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá
Academic Posters Collection
Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a succinct overview of these developments. Specifically, we look to bring together all publications that might impact the teaching of AI (for example, teacher expectations in the coming years around AI competencies) and publications that affect the use of AI in the classroom. We mean using tools and systems that incorporate both ‘Good Old Fashioned’ AI and those that can …
Enhancing Early-Stage Xai Projects Through Designer-Led Visual Ideation Of Ai Concepts, Helen Sheridan, Emma Murphy, Dympna O'Sullivan
Enhancing Early-Stage Xai Projects Through Designer-Led Visual Ideation Of Ai Concepts, Helen Sheridan, Emma Murphy, Dympna O'Sullivan
Academic Posters Collection
The pervasive use of artificial intelligence (AI) in processing users’ data is well documented with the use of AI believed to profoundly change users’ way of life in the near future. However, there still exists a sense of mistrust among users who engage with AI systems some of this stemming from lack of transparency, including users failing to understand what AI is, what it can do and its impact on society. From this, the emerging discipline of explainable artificial intelligence (XAI) has emerged, a method of designing and developing AI where a systems decisions, processes and outputs are explained and …
Unlocking The Black Box: Evaluating Xai Through A Mixed Methods Approach Combining Quantitative Standardised Scales And Qualitative Techniques, Helen Sheridan, Dympna O'Sullivan, Emma Murphy
Unlocking The Black Box: Evaluating Xai Through A Mixed Methods Approach Combining Quantitative Standardised Scales And Qualitative Techniques, Helen Sheridan, Dympna O'Sullivan, Emma Murphy
Academic Posters Collection
In 1950 when Alan Turing first published his groundbreaking paper, computing machinery and intelligence and asked “Can machines think?” a new era of research exploring the intelligence of digital computers and their ability to deceive and/or imitate a human was ignited. From these first explorations of AI to modern day artificial intelligence and machine learning systems many advances, breakthroughs and improved algorithms have been developed usually advancing at an exponential pace. This has resulted in the pervasive use of AI systems in the processing of data. Concerns have been expressed related to biased decisions by AI systems around the processing …
Development Of A Simevents Model For Printed Circuit Board (Pcb) Assembly Processes, Siqin Dong, Mileta Tomovic, Krishnanand Kaipa
Development Of A Simevents Model For Printed Circuit Board (Pcb) Assembly Processes, Siqin Dong, Mileta Tomovic, Krishnanand Kaipa
Engineering Technology Faculty Publications
Printed circuit boards (PCBs) are the foundational building blocks of most modern electronic devices. PCB assembly is defined as the process of mounting different electronic components on a PCB. Circuit board assembly utilizes an automated technique with most steps completed by machines for different operations (e.g., pick-and-place components, soldering, etc.). In this paper, details of a student course project, carried out at Old Dominion University, on the design and simulation of PCB assembly processes based on MATLAB discrete-event system are presented. An essential component in the advanced manufacturing technology course is the hands-on experience where students implement multiple software simulation …