Digital Commons Network^™

Investigating The Differential Effects Of Smote Variants On Class Imbalance And Exploring Their Applicability To A Thalassemia Prediction Model, Hussam Mezher Merdas, Ayad Hameed Mousa

AUIQ Technical Engineering Science

Researchers work around the clock on many datasets provided by various institutions. These researchers strive to come up with highly efficient Artificial Intelligence models. Often, researchers face the problem of imbalance in the distribution of classes in a particular feature in the selected dataset, which creates an Artificial Intelligence model biased towards one class at the expense of another class that is no less important than the first. On the other hand, thalassemia is a disease that affects people of different ages. The degree of disease varies according to the thalassemia class. This study proposes an improved Machine Learning model …

A Comprehensive Review On Intrusion Detection In Internet Of Things (Iot) And Industrial Internet Of Things (Iiot) Systems, Umar Ibrahim, Mutasem Jarrah

Journal of King Abdulaziz University: Computing and Information Technology Sciences

As computer networks and technologies become increasingly embedded in our daily lives, there is growing concern about detecting intrusions and cyberattacks. It’s still difficult to detect intrusions in computer networks effectively. This is due to the recent trend of cyber attackers posing as intrusion detection systems (IDS) by altering packet contents. Additionally, a large number of new devices are added to computer networks daily. Security concerns in computer networks are also being brought up by these new devices. In the context of the Internet of Things (IoT) and the industrial Internet of things (IIoT), this study aims to present an …

A Comprehensive Analysis Of Recognition Of Hand Gestures Using Machine Learning, Shivani Shivani, Satinder Bal Gupta

Makara Journal of Technology

Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of …

Developer Analytics: A Novel Approach To Analyzing Developer Performance Through Football Comparisons, Ahmed Harbaoui, Waseem Ahmed

Journal of King Abdulaziz University: Computing and Information Technology Sciences

This article explores a novel approach to evaluating developer performance and software quality by drawing parallels between programmers and football players. We highlight the inefficiencies in current assessment methods, which often rely on years of experience rather than quantitative metrics that truly reflect a developer's capabilities. By examining the historical evolution of both fields, we identify the significant advancements in performance analysis in football, which are rooted in statistical data. We propose a framework for developer analytics that utilizes performance metrics derived from programming contests, allowing for a more nuanced classification of programmers based on their skills and roles. Our …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

How Effective Are Machine Learning Algorithms In Enhancing Marketing Strategies?, Kassidy Dunn

Harriet L Wilkes Honors College Theses 2020 - Present

In recent years, machine learning algorithms have become a vital tool in marketing, yet their impact remains underexplored. This study investigates the mechanisms and applications of machine learning algorithms in marketing strategies, assessing whether they benefit or restrict the effectiveness of marketing strategies. Through relevant research and case studies, we argue that while machine learning-driven marketing offers measurable benefits in personalization and return on investment, its implementation must be balanced with ethical considerations to avoid biased outcomes.

Fractal Dimension And Entropy Analysis Of Medical Images For Knn-Based Disease Classification, Krishna Mahapatra, Selvakumar R

Baghdad Science Journal

The study of medical image-based disease detection has witnessed a notable surge in interest and success with computational methods. This work proposes a novel framework to detect diseases at earlier stages from medical images, using mathematical model and Machine Learning. Introduce two new quantitative measures for COVID-19 and Tumor disease detection: image uncertainty based on Shannon entropy and image complexity based on fractal dimension. Our result demonstrated that in COVID-19 positive images exhibited skewed pixel distribution due to the hazy regions resulting in lower entropy values for diseased cases compared to healthy ones. The second quantity, fractal dimension was measured …

Real-Time Active-Learning Method For Audio-Based Anomalous Event Identification And Rare Events Classification For Audio Events Detection, Farkhund Iqbal, Ahmed Abbasi, Ahmad Almadhor, Shtwai Alsubai, Michal Gregus

All Works

Introduction: Audio event detection, the application of scientific methods to analyze audio recordings, can be helpful in examining and analyzing audio recordings to preserve, analyze, and interpret sound evidence. Furthermore, it can be helpful in safety and compliance, security, surveillance, maintenance, and predictive analysis. Audio event detection aims to recover meaningful information from audio recordings, such as determining the authenticity of the recording, identifying the speakers, and reconstructing conversations. However, filtering out noise for better accuracy in audio event detection is a major challenge. A greater sense of public security can be achieved by developing automated event detection systems that …

Investigating The Role Of Blood Models In Predicting Rupture Status Of Intracranial Aneurysms, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Nan Mu, Jingfeng Jiang

Michigan Tech Publications

Purpose. Selecting patients with high-risk intracranial aneurysms (IAs) is of clinical importance. Recent work in machine learning-based (ML) predictive modeling has demonstrated that lesion-specific hemodynamics within IAs can be combined with other information to provide critical insights for assessing rupture risk. However, how the adoption of blood rheology models (i.e., Newtonian and Non-Newtonian blood models) may influence ML-based predictive modeling of IA rupture risk has not been investigated.Methods and Materials.In this study, we conducted transient CFD simulations using Newtonian and non-Newtonian rheology (Carreau-Yasuda [CY]) models on a large cohort of 'patient-specific' IA geometries (>100) under pulsatile flow conditions to …

The Conditioning Of Reservoir Computing And Ng-Rc For Forecasting And The Effects Of Temporal Overparameterization, Daniel Jensen

Theses and Dissertations

Reservoir computing and "Next-generation reservoir computing" built on nonlinear vector autoregression are algorithms for predicting time series given past observations. Both algorithms have been successful in predicting some chaotic dynamical systems, but this success is not theoretically well understood. This paper considers the conditioning of both algorithms and the nature of the solutions found. With either algorithm, the least squares problem which is solved to give parameters as a function of the time series data is ill-conditioned, leading to parameter non-identifiability. Both algorithms fall into the class of "sloppy models." Standard practice to regularize either algorithm includes a bias towards …

Measurement Of Groundwater And Contaminant Fluxes In Fractures Using A Combined System Of Passive Flux Meter And Multiport Sampler, Qasim Raza Khan

Thesis/ Dissertation Defenses

Groundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results …

Using Generative Artificial Intelligence To Improve Software-Defined Network Security: A Brief Survey, Anthony Smith

Honors Theses

This paper aims to explore various approaches to using generative artificial intelligence (GenAI) to improve network security in software-defined networking. While software-defined networks provide a more programmable infrastructure, they are not immune to network security threats. Through a combination of Software-Defined Networking (SDN) technologies and generative AI, it is possible to facilitate improved SDN security approaches that promise enhanced network efficiency and protection. Among these approaches, generative adversarial networks (GAN) based models can be employed to generate adversarial traffic samples to train the proposed AI engines proven to be effective in detecting malicious network traffic. Additionally, generative artificial intelligence can …

Securing Edge-Iiot Networks: A Comprehensive Ensemble-Based Ddos Detection System, Fariba Laiq, Feras Al-Obeidat, Adnan Amin, Fernando Moreira

All Works

As the number of Internet of Things (IoT) devices increases daily due to the rapid growth in technology, every device and network is vulnerable to attacks because it is exposed to the internet. Denial of Service (DoS) is a prevalent type of intrusion on the IoT network in which the server becomes down due to flooding requests. Distributed Denial of Service (DDoS) is a special type of DoS attack where the network of malicious computers called botnet consumes the target's system resources by flooding the requests. Edge computing is closely related to Industrial Internet of Things (IIoT), and industry 4.0. …

Heavy Ion Testing Of Versal Acap Ai Engines, Caleb S. Price

Theses and Dissertations

With recent developments in artificial intelligence (AI), there has been an ever increasing need for computation power in space. These AI models are traditionally implemented on GPUs. However, these units come at a high cost in terms of weight and power usage. Due to these limitations, many organizations are turning to the Versal ACAP device, a system on chip (SOC) with hundreds of AI engines. This option delivers more computation per watt than a traditional GPU. The desire to use Versal ACAP in space has created a need to perform radiation testing on the device. Extensive testing has been performed …

Using Human Computation And Harmonic Expansions For Characterization And Design Of Grain Boundary Networks, Christopher W. Adair

Theses and Dissertations

Grain Boundary Networks (GBNs) are a high-dimensional feature in polycrystalline microstructural materials that are formed from the crystallographic degrees of freedom. This feature affects multiple material properties such as diffusivity, fracture, elasticity, heat transfer, and others. The link between the crystallography and the material properties is called the structure-property linkage, and allows for simulation and design of materials. While it is possible to use the structure-property linkage to design materials for better performance, the high-dimensional nature of the interconnected grain boundaries preclude the use of many common optimization algorithms for efficiency or local minima considerations. This work shows how the …

Improving Heart Attack Prediction Accuracy Performance Using Machine Learning And Deep Learning Algorithms, Mosleh Hmoud Al-Adhaileh, Mohammed Ibrahim Ahmed Al-Mashhadani, Eidah M Alzahrani, Theyazn H.H. Aldhyani

Iraqi Journal for Computer Science and Mathematics

Accurate classification of cardiovascular diseases (CVDs) is of utmost importance for cardiologists to provide appropriate treatments. Diagnosing and predicting cardiovascular conditions are crucial medical responsibilities in this context. The healthcare sector is increasingly utilizing deep learning (DL) and machine learning (ML) algorithms due to their ability to identify patterns in data. Diagnosticians may reduce the number of misdiagnoses by using DL and ML techniques for the categorization of cardiovascular disease incidence. To reduce the mortality linked to CVDs, this research offers a unique model that properly predicts and classifies these problems. This research presents approaches such as deep learning, random …

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd E. Hollander, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J. Lindsell, David E. Newby, Alan G. Japp, Anoop S. V. Shah, Humberto Villacorta, A. Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills, Gordon Moe, Carlos Fernando, Hanna K. Gaggin, Antoni Bayes-Genis, Roland Rj Van Kimmenade, Yigal Pinto, Joost H. W. Rutten, Anton H. Van Den Meiracker, Luna Gargani, Nicola R. Pugliese, Pemberton Christopher, Michael Neumaier, Michael Behnes, Ibrahim Akin, Michele Bombelli, Guido Grassi, Peiman Nazerian, Giovanni Albano, Philipp Bahrmann

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd Hollander, Md, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J Lindsell, David E. Newby, Alan G. Japp, Anoop S V Shah, Humberto Villacorta, A Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Data-Driven Strategy For Contact Angle Prediction In Underground Hydrogen Storage Using Machine Learning, Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

In response to the surging global demand for clean energy solutions and sustainability, hydrogen is increasingly recognized as a key player in the transition towards a low-carbon future, necessitating efficient storage and transportation methods. The utilization of natural geological formations for underground storage solutions is gaining prominence, ensuring continuous energy supply and enhancing safety measures. However, this approach presents challenges in understanding gas-rock interactions. To bridge the gap, this study proposes a data-driven strategy for contact angle prediction using machine learning techniques. The research leverages a comprehensive dataset compiled from diverse literature sources, comprising 1045 rows and over 5200 data …

Beyond Data Deletion: Addressing Anticompetitive Conduct In The Era Of Machine Learning, Vaibhav Srikaran

Washington Journal of Law, Technology & Arts

Like a hamster wheel, the “virtuous cycle” of data collection, collation, and computation spins to create algorithms to assist modern business operations. Companies accumulate data by the terabyte with hopes that their army of data scientists can make sense of all the noise using machine learning. Potential profits encourage companies to seek out data—sometimes unlawfully through collusion. The use of unlawfully collected data in algorithms harms competition. Such data provides a competitive edge that is not accessible to all marketplace participants. Antitrust laws can police unlawful data sharing, but existing remedies are not effective for restoring competition.

Merely deleting ill-gotten …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Ai Foundations And Applications: Summary Of A Panel Discussion At Loyola University Chicago, George K. Thiruvathukal, Dmitry Dligach, Shilpika, Michael B. Burns, Joseph Vukov, Fraser Turner, Mary Usher

Computer Science: Faculty Publications and Other Works

This document summarizes the panel discussion titled "AI Foundations and Applications," held at Loyola University Chicago as part of the "Forum on Global Affairs: Artificial Intelligence in a Globalized World" series. The panel brought together interdisciplinary experts to discuss the foundational aspects of artificial intelligence (AI), its applications, ethical considerations, and implications for education and society.

Parameter Estimation Of Binary Black Hole Coalescence Using Lstm Neural Networks, Lane Scheel

Undergraduate Honors Papers

The Advanced Laser Interferometer Gravitational Wave Observatory (aLIGO) made its first detection of gravitational waves in 2015. Since then, the rate of event detection has only increased, with a detection being made every 2-3 days during the current observing run, O4. This rapid influx of data has the potential to create bottle-necks in data analysis efforts, and can delay the scientific progress which require those efforts. Traditional gravitational wave data analysis techniques, such as matched filtering, require extremely large template banks of synthetic gravitational waveforms and can often fail to provide meaningful limits on system parameters. Not only is this …

Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition, Andrew Smith, Kuba Jerzmanowski, Phyllis Raynor Phd, Aprn, Pmhnp-Bc, Cynthia F. Corbett Phd, Rn, Faan, Homayoun Valafar

Publications

The opioid epidemic in the United States has significantly impacted pregnant women with opioid use disorder (OUD), leading to increased health and social complications. This study explores the feasibility of using machine learning algorithms with consumer-grade smartwatches to identify medication-taking gestures. The research specifically focuses on treatments for OUD, investigating methadone and buprenorphine taking gestures. Participants (n = 16, all female university students) simulated medication-taking gestures in a controlled lab environment over two weeks, with data collected via Ticwatch E and E3 smartwatches running custom ASPIRE software. The study employed a RegNet-style 1D ResNet model to analyze gesture data, achieving …

Data-Driven Decision Making For Quality Control In Foundry Applications, Ronit Shetty

Dissertations

In the transformative landscape of Industry 4.0, advanced quality control in manufacturing demands innovative methods for processing and analyzing datasets. This dissertation presents a comprehensive study on leveraging high volume data and machine learning to enhance decision making in the foundry industry. The research explores three crucial challenges in the foundry industry: predicting surface roughness, monitoring refractory coating thicknesses, and comparing feature extraction techniques for surface quality classification. The findings underscore the transformative potential of integrating advanced feature engineering techniques and machine learning into manufacturing quality control.

The first of the three challenges introduces a framework that integrates digital images …

Novel Approaches To Analyze Consumer Behavior And Policies To Promote Sustainable Consumption, Bhagyashree Katare, Xuejian Wang, Shuoli Zhao, Jacqueline Yenerall

Agricultural Economics Faculty Publications

Technological advancements, such as online grocery shopping, have significantly transformed consumer retail environments and experiences. Effectively studying consumer behavior in these new environments requires the use of novel methodological approaches, which will also aid in developing interventions to encourage sustainable consumption. This paper begins by providing an overview of the current literature on novel approaches to analyzing consumer behavior, then provides an empirical application by examining consumer decision-making pathways within an online grocery shopping platform. Specifically, the paper focuses on exploring the consumers’ digital footprints, such as page visits, product additions and removals, and interactions with information labels to identify …

Exploring Electrical Impedance Tomography Techniques For Breast Cancer Detection In Three Dimensions, Emily Jean Mader Corcoran

Dissertations (1934 -)

Electrical Impedance Tomography (EIT) is an imaging modality whose reconstruction problem is severely ill-posed; large changes in the interior conductivity can present as small changes in the data. Due to this ill-posedness, reconstructed images generally have low spatial resolution. However, EIT remains a promising area of study for many medical imaging applications, including breast imaging. In this dissertation, a proof of concept study is discussed in Project 1: "Machine Learning for Breast Cancer Detection" wherein machine learning techniques are used to classify breast tumors as malignant or benign from simulated EIT voltage data. Promising results in terms of accuracy and …

Hybrid Machine Learning And Comparison Error Minimization For Frequency Domain-Based Rapid State Estimation In Structures Subjected To High-Rate Boundary Change, James Scheppegrell

Theses and Dissertations

Dynamic forces and evolving structural boundary conditions pose challenges for various structural systems such as aircraft, orbital infrastructure, and energy harvesting devices. The design, evaluation, and functionality, of such systems can be aided through the collection and analysis of data. However, real-time decision-making for systems experiencing high-rate changes can pose unique challenges, if assessments are to be made accurately and rapidly enough to be relevant. In cases where the systems are well-defined and thoroughly understood, monitoring the frequency response can be instrumental in determining the state of structures subjected to high-rate structural boundary condition changes. This study focuses on investigating …

Cross-Layer Design And Optimization Of Analog In-Memory Computing Systems, Md Hasibul Amin

Theses and Dissertations

There has been a rapid growth in the computational demands of machine learning (ML) workloads in recent days. Conventional von Neumann architectures are not capable of keeping up with the high cost of data movement between the processor and memory, well-known as memory wall problem. In-memory computing (IMC) has been focused as a solution by the researchers, where the computation is performed inside the memory devices such as SRAM, MRAM, RRAM etc. Most commonly, the memory devices are arranged in a crossbar setting where the matrixvector multiplication (MVM) operation is performed through intrinsic parallelism of analog computations. The conventional IMC …