Computer Engineering Commons^™

Asl-Catboost Method For Wind Turbine Fault Detection Integrated With Digital Twin, Hongtao Liang, Lingchao Kong, Guozhu Liu, Wenxuan Dong, Xiangyi Liu

Journal of System Simulation

Abstract: In view of the low visibility of the current wind farm status monitoring and insufficient realtime operation and maintenance, based on the concept of digital twin five-dimensional model, the framework of wind farm digital twin five-dimensional model is constructed. Aiming at the insufficient fault detection capability of traditional algorithms and unbalanced positive and negative samples in fan fault data set, the improved ASL-CatBoost algorithm is proposed to achieve the accurate detection of fan fault status. Based on the digital twinning platform, combined with MATLAB/Simulink, the simulation mathematical model of doubly-fed wind turbine under the condition of blade mass imbalance …

Use Of Mobile Technology To Identify Behavioral Mechanisms Linked To Mental Health Outcomes In Kenya: Protocol For Development And Validation Of A Predictive Model, Willie Njoroge, Rachel Maina, Frank Elena, Lukoye Atwoli, Anthony Ngugi, Srijan Sen, Stephen Wong, Linda Khakali, Andrew Aballa, James Orwa, Moses Nyongesa, Jasmit Shah, Amina Abubakar, Zul Merali

Brain and Mind Institute

Objective:This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.

Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya.

Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Ai-Assisted Reconfiguration Of Battery Packs For Cell Balancing To Extend Driving Runtime, Yuqin Weng, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

State of charge (SoC) cell balancing is one of the most important roles of battery management systems (BMS). The performance and lifespan of a battery pack can be significantly degraded and reduced by the presence of imbalance in cells SoC. Recently, we have shown that using a machine learning driven battery pack reconfiguration technique based on a network of controllable switches, one can periodically change the battery pack topology to effectively achieve better cell SoC equalization. As a result, the driving runtime achieved with a better balanced battery pack is increased. In this paper, we build on these promising results …

Comparing Anova And Powershap Feature Selection Methods Via Shapley Additive Explanations Of Models Of Mental Workload Built With The Theta And Alpha Eeg Band Ratios, Bujar Raufi, Luca Longo

Articles

Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At …

Construction Of Machine Learning Data Set For Analyzing The Replay Of The Wargaming, Dayong Zhang, Jingyu Yang, Jun Ma, Chenye Song

Journal of System Simulation

Abstract: The first problem to be solved in the application of machine learning to the analysis of the replay of the wargaming is the construction of data sets. Due to the standardization requirements of machine learning for data structure, as well as the limitations of computing power and storage, building a machine learning data set through the wargaming data still faces many problems in terms of how to describe the wargaming situation, how to describe the wargaming process, how to handle high dimensional data, and how to prevent data distortion. To solve these problems, this paper constructs a mapping model …

A Machine Learning Approach For Multipath Characterization And Mitigation Using Chipshape Observations, Sean A. L. Quiterio

Theses and Dissertations

Multipath continues to be a significant error source in satellite navigation. Recent solutions with Neural Networks (NN) model the effects of multipath on the autocorrelation function to predict errors in the Delay Lock Loop (DLL). Chipshape correlation provides a detailed look into the spreading code transitions in the time domain. It is useful in applications such as Signal Quality Monitoring (SQM) and is much more sensitive to multipath than autocorrelation. This research proposes NN models that each predict pseudorange or carrier range errors due to multipath by monitoring the chipshape correlation output. For a simulation with 50 MHz precorrelation bandwidth …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …

Robustness Of Learning Models To Label Flipping Attacks, Sarvagya Bhargava

Master's Projects

In this paper we compare traditional machine learning and deep learning models

trained on a malware dataset when subjected to adversarial attack based on label- flipping. Specifically, we investigate the robustness of different models when faced

with varying percentages of misleading labels, assessing their ability to maintain their accuracy in the face of such adversarial manipulations of the training data. This research aims to provide insights into which models are more robust, in the sense of being better able to resist intentional disruptions to the training data. We find that traditional machine learning models and boosting techniques are more robust …

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

Benchmarking And Enhancing Generalization In Multilingual Speech Emotion Recognition, Mohamed Osman Ismael

Theses and Dissertations

Speech Emotion Recognition (SER) is pivotal in advancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite significant progress with self-supervised learning models, SER systems often struggle with generalization across diverse languages and unseen data distributions, limiting their real-world applicability. This thesis addresses these challenges by first introducing a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. The benchmark includes a diverse set of multilingual datasets, emphasizing cross-lingual and out-of-domain evaluations to assess model generalization. Surprisingly, we find that the Whisper model, originally designed for automatic …

Improvements In Biomedical Image Analysis With Computational Intelligence And Data Fusion Techniques, Akanksha Maurya

Doctoral Dissertations

"An estimated 2 million new cases of basal cell carcinoma (BCC) are diagnosed each year in the United States, making it one of the most common skin cancers. Earlier detection of these cancers enables less invasive biopsies. Clinical detection consists of a preliminary visual observation of these skin lesions by an experienced dermatologist making it a specialized task highly dependent on their time, availability, and resources. Hence, there is a need for automating this process that can assist healthcare staff. In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. Telangiectasia …

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.

Ai-Enabled Hardware Security Approach For Aging Classification And Manufacturer Identification Of Sram Pufs, Harshdeep Singh

Browse all Theses and Dissertations

Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly …

Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki

Browse all Theses and Dissertations

The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable …

Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning, Chinmayi Lokeshwar Hegde

Master's Projects

The issue of cyberbullying is growing due to the online anonymity and due to online platforms having less repercussions. This research proposes for proactive measures to detect and prevent such behavior before it reaches the victim. By using data from various social media platforms and employing machine learning techniques, this research proposes an innovative system aimed at identifying and thwarting cyberbullying incidents preemptively. While existing methods have primarily focused on prediction and detection of cyberbullying incidents, there remains a significant gap in research regarding prevention strategies. This project aims to address this gap by leveraging machine learning, natural language processing …

Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning, Syed Ibrahim Hassam

Doctoral

Regular inspections of pavements are conducted by civil infrastructure departments to evaluate the surface condition. Pavement surfaces are subject to deterioration caused by several factors such as traffic, weather, and sunlight. This deterioration becomes evident through various distresses, including potholes, rutting, cracking, bleeding, patching, and ravelling, which gradually affects the surface layer over time. It is essential to assess the condition of pavements as it not only ensures their usability but also maximises public safety. Effective pavement maintenance requires substantial resources and capital investment to carry out the most suitable maintenance treatments at the optimal time. Furthermore, the outcomes of …

Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang

Theses and Dissertations

Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …

A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

A Two-Stage Machine Learning Approach For Fake News Detection And News Article Categorization, Snegdha Adusumilli

Master's Projects

This project employs machine learning techniques to develop a sequential model for detecting and categorizing fake news, aiming to mitigate its proliferation in today's digital landscape. The model operates in two phases: in the first phase, the classification algorithms like Naïve Bayes, XGBoost and Random Forest are used to distinguish between true and false news stories and in the second phase the capabilities of Naïve Bayes, XGBoost, Random Forest, and the Transformer-based BERT (Bidirectional Encoder Representations from Transformers) model are leveraged to further categorize the news into specific topics.

The methodology encompasses several key steps: data acquisition, preprocessing, feature extraction, …

Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare

Master's Projects

Music is one of the most common source of entertainment. Every user has their own taste of music and prefer to listen music that adheres to their taste and mood. There are various categories, called as music genres in which music can be classified. This research project addresses the challenge in music genre classification by using various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Very Deep Convolutional Networks (VGGNet), ResNet and others. The primary objective of this research is to enhance the accuracy of music genre classification using a distributed computing framework Apache Spark. …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

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 …

Detection Of Myofascial Trigger Points With Ultrasound Imaging And Machine Learning, Benjamin Formby

All Theses

Myofascial Pain Syndrome (MPS) is a common chronic muscle pain disorder that affects a large portion of the global population, seen in 85-93% of patients in specialty pain clinics [10]. MPS is characterized by hard, palpable nodules caused by a stiffened taut band of muscle fibers. These nodules are referred to as Myofascial Trigger Points (MTrPs) and can be classified by two states: active MTrPs (A-MTrPs) and latent MtrPs (L-MTrPs). Treatment for MPS involves massage therapy, acupuncture, and injections or painkillers. Given the subjectivity of patient pain quantification, MPS can often lead to mistreatment or drug misuse. A deterministic way …

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 …

Learning-Based Ant Colony Optimization Algorithm For Solving A Kind Of Complex 2-Echelon Vehicle Routing Problem, Xue Chen, Rong Hu, Hui Wang, Zuocheng Li, Bin Qian, Yixu Li

Journal of System Simulation

Abstract: Aiming at green 2-echelon vehicle routing problem with simultaneous pick-up and delivery, a learning-based ant colony optimization algorithm combined with clustering decomposition is proposed. The objective function to be minimized is total transportation cost wherein carbon emission cost is specially considered. Associated with the mutual coupling features of the 2-echelon vehicle routing problem, we propose a distance-based clustering method to decompose the original problem into a set of sub-problems. Then, a learning-based ant colony optimization algorithm is presented to find the solutions of the sub-problems based on which the solution of the original problem can be obtained. In the …

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 …