Digital Commons Network^™

Using Machine Learning And Deep Learning Algorithms To Improve Low Birthweight Prediction, Yang Ren

Theses and Dissertations

Low birthweight (LBW) is a major public health issue resulting in increased neonatal mortality and long-term health complications. Traditional LBW analysis methods, focusing on incidence rates and risk factors through statistical models, often struggle with complex unseen data, and thus, their effectiveness is limited in early prevention of LBW, requiring more advanced LBW prediction models. Therefore, this dissertation delves into this important research area by proposing and examining novel machine learning (ML) and deep learning (DL) algorithms, aiming to predict LBW more accurately during the early stage of pregnancy. This dissertation consists of three studies, strategically designed to build upon …

A Hybrid Method Of 1d-Cnn And Machine Learning Algorithms For Breast Cancer Detection, Ahmed Adil Nafea, Manar Al-Mahdawi, Khattab M Ali Alheeti, Mustafa S. Ibrahim Alsumaidaie, Mohammed M Al-Ani

Baghdad Science Journal

Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to …

Etdsuite: A Toolkit To Mine Electronic Theses And Dissertations To Enrich Scholarly Big Data Using Natural Language Processing And Computer Vision, Muntabir Hasan Choudhury

Computer Science Theses & Dissertations

In the past decades, there has been a growing interest in mining scientific documents to obtain domain knowledge automatically. One of the understudied types of scientific documents is Electronic Theses and Dissertations (ETDs), as ETDs have distinct features compared with conference proceedings and journal articles. ETDs usually serve as partial requirements of academic degrees for students pursuing higher education. They are book-length documents (i.e., 100 – 400 pages long), and the topics may shift across chapters, exhibit the significant contribution of a student’s research over the entire degree pursuing period, and have unique metadata schema and page layouts. However, the …

Can Federated Learning Solve Ai’S Data Privacy Problem?: A Legal Analysis, Warren B. Chik, Florian Gamper

Research Collection Yong Pung How School Of Law

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive …

Data Ownership And Control: Ethical Perspectives In The Digital Age, Roya Ebadi

Theses

The rapid advancement of machine learning (ML) technologies has significantly impacted various sectors, with healthcare standing out as a prominent area for transformation. Machine learning holds the potential to revolutionize healthcare, particularly in the areas of diagnosis, treatment plans, drug development, and personalized medicine. However, with the growing integration of ML into healthcare systems, ethical concerns regarding data privacy, security, and fairness have emerged, requiring comprehensive governance frameworks to ensure the responsible use of these technologies. This research investigates the ethical considerations associated with the application of machine learning in healthcare, focusing on data governance practices that are crucial for …

Understanding Necrotizing Enterocolitis Endotypes And Acquired Intestinal Injury Phenotypes From A Historical And Artificial Intelligence Perspective., Alain Cuna, Navin Kumar, Venkatesh Sampath

Manuscripts, Articles, Book Chapters and Other Papers

Necrotizing enterocolitis (NEC) remains a devastating disease in preterm and term neonates. Despite significant progress made in understanding NEC pathogenesis over the last 50 years, the inability of current definitions to discriminate the various pathophysiological processes underlying NEC has led to an umbrella term that limits clinical and research progress. In this mini review, we provide a historical perspective on how NEC definitions and pathogenesis have evolved to our current understanding of NEC endotypes. We also discuss how artificial intelligence-based approaches are influencing our knowledge of risk-factors, classification and prognosis of NEC and other neonatal intestinal injury phenotypes.

Chatgpt Provides Acceptable Responses To Patient Questions Regarding Common Shoulder Pathology, Umar Ghilzai, Benjamin Fiedler, Abdullah Ghali, Aaron Singh, Benjamin Cass, Allan Young, Adil Shahzad Ahmed

Faculty, Staff and Students Publications

BACKGROUND: ChatGPT is rapidly becoming a source of medical knowledge for patients. This study aims to assess the completeness and accuracy of ChatGPT's answers to the most frequently asked patients' questions about shoulder pathology.

METHODS: ChatGPT (version 3.5) was queried to produce the five most common shoulder pathologies: biceps tendonitis, rotator cuff tears, shoulder arthritis, shoulder dislocation and adhesive capsulitis. Subsequently, it generated the five most common patient questions regarding these pathologies and was queried to respond. Responses were evaluated by three shoulder and elbow fellowship-trained orthopedic surgeons with a mean of 9 years of independent practice, on Likert scales …

Attention-Based Load Forecasting With Bidirectional Finetuning, Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, Pavel Matrenin

All Works

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our …

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

The Evaluation Of Machine Learning Techniques For Isotope Identification Contextualized By Training And Testing Spectral Similarity, Aaron P. Fjelsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe

Faculty Publications

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset …

Text-To-Model Transformation: Natural Language-Based Model Generation Framework, Aditya Akundi, Joshua Ontiveros, Sergio Luna

Mechanical Engineering Faculty Publications

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model …

Regulating Algorithmic Harms, Sylvia Lu

Law & Economics Working Papers

In recent years, the rapid expansion of artificial intelligence (AI) innovations has led to a rise in algorithmic harms—harms emerging from AI operations that pose significant threats to civil rights and democratic values in today’s technological landscape. A facial recognition system for improving criminal detection wrongly collected sensitive personal data and flagged racial minorities as shoplifters. A risk-prediction algorithm adopted to identify patients denied medical treatment to Black individuals with poor health conditions. A social media algorithm intended to boost social engagement exacerbated addictive behavior and mental illness in teenagers. These harms are becoming increasingly ubiquitous yet often manifest in …

An Artificial Intelligence Report Card For Judicial Review, Zoe E. Niesel

Michigan Journal of Environmental & Administrative Law

The rapid advancement of technology, including artificial intelligence (AI), is creating new challenges for judicial review under the Administrative Procedure Act (APA). In late 2023, federal administrative agencies publicly disclosed over 700 use cases of AI that employ sophisticated techniques like machine learning and natural language processing. While the APA's flexible judicial review framework certainly allows agencies to utilize new technologies, the APA also requires explainability of agency decisions; thus, agencies must be able to articulate the reasoning and methodology behind AI-enabled decisions for the purpose of judicial review. This Article examines APA judicial review as it applies to agency …

The K-Profile Parameterization Augmented By Deep Neural Networks (Kpp_Dnn) In The General Ocean Turbulence Model (Gotm), Jianguo Yuan, Jun Hong Liang, Eric P. Chassignet, Olmo Zavala-Romero, Xiaoliang Wan, Meghan F. Cronin

Faculty Publications

This study utilizes Deep Neural Networks (DNN) to improve the K-Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11-year turbulence-resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN-augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long-term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each …

2024 Summer Proceedings Teuscher Lab, Teuscher Group, Christof Teuscher, Chelsea Ogbede, Lauren Sanday, Sofia Vargas, Artem Arefev

altREU Projects

How will computation evolve in the coming years? What problems can be tackled using artificial intelligence, in a world increasingly driven by data? And how can that data be used to better inform our decisions as a society? In this unique collection of research projects, each chapter represents a distinct work undertaken by a single individual or a group of students as part of the altREU program led by Christof Teuscher. The projects, rooted in applications of artificial intelligence and innovative computation techniques, examine impactful solutions to numerous pressing challenges affecting communities around the world.

Stronger Practical Game-Playing Ai For Duelyst Ii, Bryan Mckenney

Master's Theses and Capstones

Duelyst II is an online collectible card game (CCG) that features a 9 × 5 grid board,making it a cross between the popular CCG Hearthstone and chess. It is a partially- observable stochastic game (POSG) with a large branching factor and the ability to take several actions in a time-limited turn, making it a challenging domain for game-playing AI. On top of that, successor generation is very slow, limiting the effectiveness of traditional planning approaches such as Monte Carlo tree search (MCTS).

In this thesis, I build on my previous work on Duelyst II AI, creating a stronger game-playing agent …

Automatic Vessel Attenuation Measurement For Quality Control Of Contrast-Enhanced Ct: Validation On The Portal Vein, Kevin Mccoy, Sujay Marisetty, Dominique Tan, Corey T Jensen, Jeffrey H Siewerdsen, Christine B Peterson, Moiz Ahmad

Faculty, Staff and Student Publications

Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.

Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CT examinations.

Methods: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers …

Artificial Intelligence-Based Segmentation Of Residual Pancreatic Cancer In Resection Specimens Following Neoadjuvant Treatment (Isgpp-2): International Improvement And Validation Study, Boris V Janssen, Bart Oteman, Mahsoem Ali, Pieter A Valkema, Volkan Adsay, Olca Basturk, Deyali Chatterjee, Angela Chou, Stijn Crobach, Michael Doukas, Paul Drillenburg, Irene Esposito, Anthony J Gill, Seung-Mo Hong, Casper Jansen, Mike Kliffen, Anubhav Mittal, Jas Samra, Marie-Louise F Van Velthuysen, Aslihan Yavas, Geert Kazemier, Joanne Verheij, Ewout Steyerberg, Marc G Besselink, Huamin Wang, Caroline Verbeke, Arantza Fariña, Onno J De Boer, Pancreatobiliary And Hepatic Artificial Intelligence Research (Phair) Consortium, International Study Group Of Pancreatic Pathologists (Isgpp);

Faculty, Staff and Student Publications

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest …

A Machine Learning Approach To Discovering Physical Models Of Galaxy Formation, Festa Bucinca

Dissertations, Theses, and Capstone Projects

Galaxies are the breathtakingly beautiful starry islands of the Universe. The process of galaxy formation involves the transformation from simple initial conditions in the early Universe to the complex galaxy structures we observe today. Spanning an immense spatial range and tremendous time scales - from the vastness of the Universe to the scale of individual stars - the physics of galaxy formation is both complex and crucial for understanding the Universe we live in. However, despite significant advancements, our theoretical understanding of galaxy formation remains incomplete.

In the era of big data available from hydrodynamical simulations and observations, Machine Learning …

Multi-Label Classification Using Conformal Prediction, Chhavi Tyagi

Dissertations

In many machine learning applications, such as image tagging, document classi-fication, and medical diagnosis, a data instance can be associated with multiple classes in parallel so that each instance is associated with multiple response variables simultaneously defining multi-label classification. Standard multi-label classification methods that provide point predictions have been developed. They lack in quantifying the uncertainty of predictions. These methods also lack in accounting for label dependencies and are very computationally expensive. This dissertation develops two methods of multi-label classification using conformal prediction that quantify the uncertainty of predictions. Chapter 1 introduces notations and tools that have been used in …

Federated Learning Systems For Mobile Sensing Data, Xiaopeng Jiang

Dissertations

Federated Learning (FL) has emerged as a new distributed Deep Learning (DL) paradigm that enables privacy-aware training and inference on mobile devices with help from the cloud. This dissertation presents a comprehensive exploration of FL with mobile sensing data, covering systems, applications, and optimizations.

First, a mobile-cloud FL system, FLSys, is designed to balance model performance with resource consumption, tolerate communication failures, and achieve scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. In addition, FLSys provides advanced privacy-preserving mechanisms and a common API for third-party app developers to …

Scla 521 Ai In Society, Bert Chapman

Libraries Faculty and Staff Presentations

Provides access to information resources on societal impacts of artificial intelligence from multiple libraries databases covering multiple disciplines including government information resources.

Enhancing Predictive Accuracy Of Compressive Strength In Recycled Concrete Using Advanced Machine Learning Techniques With K-Means Clustering, Ziaul Haq Doost, Leonardo Goliatt, Mohammed Suleman Aldlemy, Mumtaz Ali, Bruno Da S. Macêdo

AUIQ Technical Engineering Science

The urgent need to mitigate environmental impacts in the construction industry drives the exploration of sustainable practices, such as the use of recycled materials in concrete production. The primary objective of this study was to enhance the predictability of compressive strength in the concrete through the application of advanced machine learning (ML) techniques, specifically Gradient Boosting Regression (GBR) and Random Forest Regression (RFR). Using a comprehensive dataset of 353 eco-friendly concrete samples, the study carefully developed and validated these models to evaluate their performance. The findings exposed that the GBR model outperformed the RFR model, obtained an R² of 0.97 …

Early Diagnosis Of Parkinson's Disease Using A Hybrid Method Of Least Squares Support Vector Regression And Fuzzy Clustering, Hossein Ahmadi, Lin Huo, Goli Arji, Abbas Sheikhtaheri, Shang-Ming Zhou

School of Nursing and Midwifery

Parkinson's disease (PD) is a neurodegenerative disorder that influence brain's neurological, behavioral, and physiological functions and includes motor and nonmotor manifestations. Although there have been several PD diagnosis systems with supervised machine learning techniques, there are more efforts that need to enhance the accurate detection of PD in its early stage. The current paper developed a novel approach by integrating Least Squares Support Vector Regression (LS-SVR) and Fuzzy Clustering for Unified Parkinson's Disease Rating Scale (UPDRS) diagnosis. This paper used feature selection and Principal Component Analysis (PCA) to overcome the multicollinearity issues in data. This paper used a large medical …

Supervised Classification Modeling On Louisiana Medicaid Data: A Comparative Study, Yead Rahman

Master's Theses

This thesis systematically optimizes and compares state-of-the-art supervised classification models for Louisiana Medicaid data targeting clinical services, COVID-19 infection, and tobacco use. These target variables are critically important as they represent key health outcomes and behaviors among Medicaid enrollees in Louisiana, a population often characterized by poverty and limited access to education. This study applies advanced machine learning techniques to identify the best model for multinomial and binary classification tasks. These include models such as Logistic Regression, XGBoost, AdaBoost, Random Forest, Decision Tree, Artificial Neural Networks, and Naïve Bayes. Extensive tuning of the hyperparameters and optimization of each classifier were …

Utilizing Machine Learning To Forecast Sector-Level Equity Returns From Sector-Level Financial Ratios, Greg Kuppenheimer

Electronic Theses and Dissertations

Academics and equity analysts often utilize fundamental financial ratios to evaluate an industry's financial and operational performance. However, sophisticated econometric methods could be improved for estimating these ratios' importance in predicting sector-level stock return performance. To address this gap, gradient-boosted decision tree methods were applied to the WRDS sector-level financial ratio database to identify if financial ratios effectively forecast sector-level stock returns in future periods. The findings show that these ratios are predictive and economically material, both stand alone and in combination with other macroeconomic factors. Using these predictions, long and long-short sector-level portfolios were created, and most of these …

Approximate Computing And In-Memory Computing: The Best Of The Two Worlds!, Mohammed Essa Fawzy Essa

Theses and Dissertations

Machine learning (ML) has become ubiquitous, integrating into numerous real-life applications. However, meeting the computational demands of ML systems is challenging, as existing computing platforms are constrained by memory bandwidth, and technology scaling no longer yields substantial improvements in system performance. This work introduces novel hardware architectures to accelerate ML workloads, addressing both compute and memory challenges. In the compute domain, we explore various approximate computing techniques to assess their efficacy in accelerating ML computations. Subsequently, we propose the Approximate Tensor Processing Unit (APTPU), a hardware accelerator that utilizes approximate processing elements to replace direct quantization of inputs and weights …

Application Of Machine Learning Techniques And The Unscented Kalman Filter To Real-Time Gas Turbine Clearance Prediction, Donald Earl Floyd

Theses and Dissertations

The growth in renewable energy sources and retirement of large baseload coal-fired power stations has led to an accompanying decrease in reliability and security of the electrical grid. Since renewable energy sources are typically non-dispatchable, this can lead to blackouts and/or brownouts for customers. Heavy duty gas turbine power plants (HDGT) offer a solution to this problem. HDGTs are dispatchable, clean, and offer flexibility in the fuel they consume, but operational limitations must be well understood to fully exploit their benefits.

One of the main operational limitations is the tip clearances in the gas turbine. In many cases, the gas …

Simultaneous Crack & Wave Propagation And Acoustic Emission Signal Modelling Using Peri-Elastodynamic, Md Mushfiqur Rahman Fahim

Theses and Dissertations

This work presents the use of Peri-Elastodynamic, a guided wave simulation method based on meshfree non-local Peridynamics theory. To model simultaneous crack and wave propagation simulation and its application on Acoustic Emission (AE) signal modelling, a new formulation is presented. The field of nondestructive evaluation (NDE) and structural health monitoring (SHM) is slowly embracing the advancement in Machine Learning (ML) / Artificial Intelligence (AI) for better and cost-effective assessment of structures and damage prediction. AI/ML can revolutionize the NDE/SHM field by automating the data collection and analyzing processes. However, the big challenge is that the model needs a sheer amount …

Image Processing And Machine Learning Approach For Yolk Color Evaluation, Supreeya Thepparak, Chusak Pulmar, Chanwit Kaewtapee

The Thai Journal of Veterinary Medicine

The objective of this study was to develop a predictive model for yolk color measurement using machine learning. A total of eight hundred eggs were randomly collected from thirty laying hens (Lohmann brown, 28–35 weeks). The experimental diets were formulated based on broken rice or corn, and further supplemented with canthaxanthin from 10 to 150 mg/kg. The Digital YolkFan™ was used to classify yolk color scales from 0 to 15, whereas the Hunter Lab was used to measure color values. Furthermore, yolk images were obtained from a digital camera and then extracted into red color, green color and blue (RGB) …