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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty
Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty
VMASC Publications
Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …
Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal
Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal
Mechanical & Aerospace Engineering Faculty Publications
This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping time-averaged, unsteady Reynold’s-averaged Navier-Stokes (URANS) simulations to higher resolution results informed by time-averaged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patch-wise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4-digit airfoils …
Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Computer Science Faculty Publications
In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating …
Joint Congestion And Contention Avoidance In A Scalable Qos-Aware Opportunistic Routing In Wireless Ad-Hoc Networks, Ali Parsa, Neda Moghim, Sasan Haghani
Joint Congestion And Contention Avoidance In A Scalable Qos-Aware Opportunistic Routing In Wireless Ad-Hoc Networks, Ali Parsa, Neda Moghim, Sasan Haghani
VMASC Publications
Opportunistic routing (OR) can greatly increase transmission reliability and network throughput in wireless ad-hoc networks by taking advantage of the broadcast nature of the wireless medium. However, network congestion is a barrier in the way of OR's performance improvement, and network congestion control is a challenge in OR algorithms, because only the pure physical channel conditions of the links are considered in forwarding decisions. This paper proposes a new method to control network congestion in OR, considering three types of parameters, namely, the backlogged traffic, the traffic flows' Quality of Service (QoS) level, and the channel occupancy rate. Simulation results …
A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen
A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …
Detecting Deceptive Dark-Pattern Web Advertisements For Blind Screen-Reader Users, Satwick Ram Kodandaram, Mohan Sunkara, Sampath Jayarathna, Vikas Ashok
Detecting Deceptive Dark-Pattern Web Advertisements For Blind Screen-Reader Users, Satwick Ram Kodandaram, Mohan Sunkara, Sampath Jayarathna, Vikas Ashok
Computer Science Faculty Publications
Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users' experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via …
A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala
A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala
Computer Science Faculty Publications
Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …
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 …
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Electrical & Computer Engineering Faculty Publications
Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …