Electrical and Computer Engineering Commons^™

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct …

In-Plane And Out-Of-Plane 2-D Microdisplacement Sensor Based On A Single Microwave Resonator With Machine Learning, Shiyu Li, Osamah Alsalman, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Microwave displacement sensors have garnered significant research interest in recent years and have found successful applications in industrial automation and aerospace engineering. However, most microwave sensors are limited to measuring in-plane displacement, with the assumption that out-of-plane displacement remains constant during operation. In this work, we propose and experimentally demonstrate a novel concept for 2-D micro displacement sensing that simultaneously measures both in-plane and out-of-plane displacement. We developed a proof-of-concept sensor system based on a custom-made coaxial cable resonator (CCR) serving as the stator and a rubber-metal heterogeneous plate as the movable part. The in-plane and out-of-plane micromovements of the …

Application Of Multiple Data Augmentation Techniques To Improve Training With Synthetic Sar Data In Common Cnn, Stephanie M.V. Saich

Browse all Theses and Dissertations

To address the issues of limited target data in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) problem set, synthetic data is often used to aid in filling the gap. This paper covers an in depth look at the use of colorization, dynamic range adjustment, and target extraction as data augmentation techniques to improve the accuracy of deep learning networks trained on synthetic SAR data. The use of multiple different data augmentations combine to dramatically improve the accuracy of a common Convolutional Neural Network (CNN) over the use of standard synthetic data. A comparison of increasing fraction of measured …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

Distributed Intelligence: Exploring Federated Paradigms Across Computing And Networking On The Edge, Yang Deng

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

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 …

Fuzzycsampling: A Hybrid Fuzzy C-Means Clustering Sampling Strategy For Imbalanced Datasets, Abdullah Maraş, Çi̇ğdem Erol

Turkish Journal of Electrical Engineering and Computer Sciences

Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches' results not only …

Machine Learning Based Bioinformatics Analysis Of Intron Usage Alterations And Metabolic Regulation In Adipose Browning, Hamza Umut Karakurt, Pinar Pi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Adipose tissue is the major energy depot of the body and is considered an endocrine organ. Adipose tissue involves many different cell types, first and foremost, the adipocytes. White adipose cells that store fat and brown adipocytes that take part in lipid oxidation and heat generation are the most common cell types in adipose tissue. Even though brown adipocytes which have a high number of mitochondria and high fat-burning capacity are rare in adults, they are abundant in newborns and rodents. White adipocytes can gain a temporal brown-like character with a process called browning, which can be induced with cold …

Inverse Engineering Of Absorption And Scattering In Nanoparticles: A Machine Learning Approach, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

We use a region-specified machine learning approach to inverse design highly absorptive multilayer plasmonic nanoparticles. We demonstrate the design of particles with a wide range of absorption to scattering ratios (i.e., cloaked absorbers and bright absorbers) and for different visible wavelengths.

Employment Of Intelligent Predictive Maintenance On Thermal Power Plant Component Parts Taking Condenser Vacuum As A Case Study, Titus Ajewole

Tanzania Journal of Engineering and Technology (TJET)

This work proposes deployment of machine learning in the maintenance of individual constituent parts of steam power plant assemblages. With the condenser vacuum of a steam turbine (in a six-turbine plant assemblage) taken as a case study, information on the past operating parameters of the selected plant component was used to forecast its future working condition. Based on Exponential Gaussian Process of Regression, a model was developed, trained using the diachronic operational data, and employed in determining the future. A quantitative evaluation was employed to provide the distribution of the test values of the data about the lines of regression, …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

On The Prediction Of The Mechanical Properties Of Limestone Calcined Clay Cement: A Random Forest Approach Tailored To Cement Chemistry, Taihao Han, Bryan K. Aylas-Paredes, Jie Huang, Ashutosh Goel, Narayanan Neithalath, Aditya Kumar

Materials Science and Engineering Faculty Research & Creative Works

Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder's carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models' prediction performance and interpretability. This …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Novel Approach To In-Situ Mocvd Oxide/Dielectric Deposition For Iii-Nitride-Based Heterojunction Field Effect Transistors, Samiul Hasan

Theses and Dissertations

III-Nitride-based compound semiconductors have unique properties such as high bandgap and high breakdown field, which make them attractive for a variety of applications, including high-power and high-frequency electronics and optoelectronics. The most common types of III-Nitride-based field effect transistors (FETs) are aluminum gallium nitride (AlGaN)/gallium nitride (GaN) based, which suffer from some inherent problems such as virtual gate effect, current collapse, gate leakage, etc. The solution to this problem can be the inclusion of a dielectric passivation layer under the gate. However, the addition of the dielectric layer impacts one of the most critical device-controlling parameters, “threshold voltage”, which suffers …

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …

Model Optimization And Applications In Deep Learning, Chengchen Mao

Electrical Engineering Dissertations - Archive

ABSTRACT: Machine learning refers to a machine or an algorithm that draws experience from data. A certain pattern is found to build a model, which is used to solve real problems. Deep learning, an important branch and extension of machine learning, employs a neural network structure containing multiple hidden layers. It learns critical features of the data by combining lower-level features to form more abstract higher-level representations of attribute categories or features. In this dissertation, deep learning network models were applied to sense-through-foliage target detection and extended with Rake structure. The deep learning network models had a large number of …

Achieving High Renewable Energy Integration In Smart Grids With Machine Learning, Yaze Li

Graduate Theses and Dissertations

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. …

Using Machine Learning To Assist Auditory Processing Evaluation, Hasitha Wimalarathna, Sangamanatha Veeranna, Minh Vu Duong, Chris Allan Prof, Sumit K. Agrawal, Prudence Allen, Jagath Samarabandu, Hanif M. Ladak

Electrical and Computer Engineering Publications

Introduction: Approximately 0.2–5% of school-age children complain of listening difficulties in the absence of hearing loss. These children are often referred to an audiologist for an auditory processing disorder (APD) assessment. Adequate experience and training is necessary to arrive at an accurate diagnosis due to the heterogeneity of the disorder.

Objectives: The main goal of the study was to determine if machine learning (ML) can be used to analyze data from the APD clinical test battery to accurately categorize children with suspected APD into clinical sub-groups, similar to expert labels.

Methods: The study retrospectively collected data from 134 children referred …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

A Comparative Effectiveness Study On Opioid Use Disorder Prediction Using Artificial Intelligence And Existing Risk Models, Sajjad Fouladvand, Jeffery Talbert, Linda Phyliss Dwoskin, Heather M. Bush, Amy L. Meadows, Lars E. Peterson, Yash R. Mishra, Steven K. Roggenkamp, Fei Wang, Ramakanth Kavuluru, Jin Chen

Markey Cancer Center Faculty Publications

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least …