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Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …

Enhancing Frp-Concrete Interface Bearing Capacity Prediction With Explainable Machine Learning: A Feature Engineering Approach And Shap Analysis, Yanping Zhu, Woubishet Zewdu Taffese, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study introduces a novel approach to predict the shear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms are employed: three standalone models (Artificial Neural Network, Support Vector Regression, and Decision Tree) and five ensemble learning models (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting). Four scenarios with varying input features, including engineered features inspired by mechanics-based bearing capacity equations, are examined. Notably, the inclusion of engineered features such as the stiffness of the FRP strip (Kf) significantly enhanced prediction accuracy and efficiency, although the width correction coefficient (bf/bc) did not yield significant benefits, …

A Novel Machine Learning Model To Predict Revision Acl Reconstruction Failure In The Mars Cohort, Kinjal Vasavada, Vrinda Vasavada, Jay Moran, Sai Devana, Changhee Lee, Sharon L. Hame, Laith M. Jazrawi, Orrin H. Sherman, Laura J. Huston, Amanda K. Haas, Christina R. Allen, Daniel E. Cooper, Thomas M. Deberardino, Kurt P. Spindler, Michael J. Stuart, Annunziato Ned Amendola, Christopher C. Annunziata, Robert A. Arciero, Bernard R. Bach, Champ L. Baker, Arthur R. Bartolozzi, Keith M. Baumgarten, Jeffrey H. Berg, Geoffrey A. Bernas, Stephen F. Brockmeier, Robert H. Brophy, Charles A. Bush-Joseph, J. Brad Butler V, James L. Carey, James E. Carpenter, Brian J. Cole, Jonathan M. Cooper, Charles L. Cox, R. Alexander Creighton, Tal S. David, Warren R. Dunn, David C. Flanigan, Robert W. Frederick, Theodore J. Ganley, Charles J. Gatt, Steven R. Gecha, James Robert Giffin, Jo A. Hannafin, Norman Lindsay Harris, Keith S. Hechtman, Elliott B. Hershman, Rudolf G. Hoellrich, David C. Johnson, Timothy S. Johnson, Morgan H. Jones, Christopher C. Kaeding, Ganesh V. Kamath, Thomas E. Klootwyk, Bruce A. Levy, C. Benjamin Ma, G. Peter Maiers, Robert G. Marx, Matthew J. Matava, Gregory M. Mathien, David R. Mcallister, Eric C. Mccarty, Robert G. Mccormack, Bruce S. Miller, Carl W. Nissen, Daniel F. O'Neill, Brett D. Owens, Richard D. Parker, Mark L. Purnell, Arun J. Ramappa, Michael A. Rauh, Arthur C. Rettig, Jon K. Sekiya, Kevin G. Shea, James R. Slauterbeck, Matthew V. Smith, Jeffrey T. Spang, Steven J. Svoboda, Timothy N. Taft, Joachim J. Tenuta, Edwin M. Tingstad, Armando F. Vidal, Darius G. Viskontas, Richard A. White, James S. Williams, Michelle L. Wolcott, Brian R. Wolf, Rick W. Wright, James J. York

Rothman Institute Papers

BACKGROUND: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights.

PURPOSE: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure.

STUDY DESIGN: Cohort study; Level of evidence, 3.

METHODS: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Feature Selection And Machine Learning Approaches In Prediction Of Current E-Cigarette Use Among U.S. Adults In 2022, Wei Fang, Ying Liu, Chun Xu, Xingguang Luo, Kesheng Wang

Health & Biomedical Sciences Faculty Publications

Feature selection is essentially the process of picking informative and relevant features from a larger collection of features. Few studies have focused on predictors for current e-cigarette use among U.S. adults using feature selection and machine learning (ML) approaches. This study aimed to perform feature selection and develop ML approaches in prediction of current e-cigarette use using the 2022 Health Information National Trends Survey (HINTS 6). The Boruta algorithm and the least absolute shrinkage and selection operator (LASSO) were used to perform feature selection of 71 variables. The random oversampling example (ROSE) method was utilized to deal with imbalance data. …

Impaired Speech Recognition Of Neurological Disorder Persons Using Machine Learning And Deep Learning Techniques, Vishnika Veni S

Theses and Dissertations

Speech Assistive Tools have emerged in recent years to support individuals with cognitive and neurological disorders in the field of assistive technology. People affected by neurological disorders such as autism, stroke, cerebral palsy, dysarthria, Parkinson’s disease, and brain injury often find it difficult to articulate desired sounds, resulting in impaired speech. As the population of impaired speakers continues to increase every year, there is a strong need to develop intelligent speech recognition systems for affected individuals. The primary objective of this research is to develop an Impaired Speech Recognition (ISR) system for the Tamil language. Word Recognition Accuracy (WRA) is …

The Effects Of Anthropogenic Stressors On Above- And Belowground Phytochemical Diversity Of The Wetland Grass, Phragmites Australis, Andrea E. Glassmire, Ana L. Salgado, Rodrigo Diaz, Joseph Johnston, Laura A. Meyerson, Joshua S. Snook, James T. Cronin

Faculty Publications

Coastal wetlands face threats from climate change-induced flooding and biological invasions. Plants respond to these stressors through changes in their phytochemical metabolome, but it is unclear whether stressors affecting one tissue compartment (e.g., leaves) create vulnerabilities in others (e.g., roots) or elicit similar responses across tissues. Additionally, responses to multiple simultaneous stressors remain poorly understood due to the focus on individual metabolites in past studies. This study aims to elucidate how the phytochemical metabolome of three Phragmites australis (Cav.) lineages, common in the Mississippi River Delta, responds to flooding and infestation by the non-native scale insect Nipponaclerda biwakoensis (Kuwana). Among …

Prediction Of Individual Weight Loss Using Supervised Learning: Findings From The CalerieTm 2 Study, Christina Glasbrenner, Christoph Höchsmann, Carl F. Pieper, Paulina Wasserfurth, James L. Dorling, Corby K. Martin, Leanne M. Redman, Karsten Koehler

Clinical Research Faculty Publications

Background: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. Objective: The primary aim of this study was to develop machine learning (ML) models to predict individual WL responses using only variables known before starting the intervention. In addition, we used ML to identify pre-intervention variables influencing the individual WL response. Methods: We used 12-mo data from the comprehensive assessment of long-term effects of reducing intake of energy (CALERIETM) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human …

Development Of Crop2cloud: An Iot-Integrated Platform For Automated Irrigation Scheduling Using Multi-Source Stress Indices, Bryan Nsoh

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

Efficient water management in agriculture is critical, as irrigation accounts for 70% of global freshwater withdrawals. While precision agriculture technologies offer potential solutions, integrating diverse data streams for real-time irrigation scheduling remains challenging, particularly in combining plant and soil-based stress indicators. This research addresses these challenges through three interconnected studies.

A systematic review first examined the current state and future potential of IoT-based automated irrigation management systems, analyzing how these technologies can enhance agricultural water use efficiency and crop productivity. The review identified critical gaps in data integration and real-time processing while highlighting opportunities for combining multiple stress indices for …

Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar

All Works

EnhancedBERT is a software framework designed to disambiguate Arabic polysemous terms using advanced natural language processing techniques. It integrates transformer architectures with ensemble methods to achieve high performance in understanding and processing Arabic text. The framework provides a flexible pipeline that can be directly utilized or fine-tuned according to specific needs. EnhancedBERT stands out for its ease of use, leveraging transformer-based models combined with ensemble strategies to provide superior contextual understanding. This contextual awareness makes it an invaluable tool for researchers and practitioners tackling complexities in Arabic language processing.

Leading Edge Erosion Classification In Offshore Wind Turbines Using Feature Extraction And Classical Machine Learning, Oscar Best, Asiya Khan, Sanjay Sharma, Keri Collins, Mario Gianni

School of Engineering, Computing and Mathematics

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The …

Enhancing Soil Texture And Bulk Density Mapping Using Soil Grids And Machine Learning: A Comparative Analysis With Observed Data, Aram Ali, Ismael O. Ismael, Hewa T. Mustafa, Diman Krwanji, Akram O. Esmail

Research outputs 2022 to 2026

Digital soil mapping plays a crucial role in understanding soil variability and informing sustainable land management practices. This study focuses on the Kurdistan Region of Iraq (KRI), evaluating the accuracy of SoilGrids, a global-scale soil mapping initiative, and exploring the efficacy of machine learning algorithms in refining soil properties estimations. The aim of this research was to assess and represent the physical parameters of soils effectively by comparing ground truth soil sampling data with data obtained from SoilGrids regarding clay, silt, and sand fractions and bulk density. Comparative analyses were conducted between ground truth soil sampling data and SoilGrids predictions, …

Radiomics-Based Machine Learning With Natural Gradient Boosting For Continuous Survival Prediction In Glioblastoma, Mert Karabacak, Shiv Patil, Zachary C. Gersey, Ricardo J. Komotar, Konstantinos Margetis

SKMC Student Presentations and Publications

(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences …

Automation Of Medical Facilities, Automation Of Diagnostic Processes For Intelligent Primary Diagnosis Of Diseases Of The Oral Cavity., Otabek Ismailov, X.F. Temirova

Chemical Technology, Control and Management

A mathematical machine learning model for dental disease diagnosis plays an important role in medicine with high accuracy, speed, and personalization capabilities. This allows for increased efficiency not only for patients but also for medical personnel. For this reason, in this article, an improved mathematical model has been developed to effectively detect dental diseases.

Advancing Data-Driven Disaster Debris Characterization And Quantification, Jasmine Bekkaye

LSU Doctoral Dissertations

Natural hazards generate tremendous amounts of debris that negatively impact communities and overwhelm waste management infrastructure. The challenge in disaster debris planning and management stems from inconsistent and scarce post-disaster debris quantity data due to the chaotic nature of recovery operations. This leads to a limited understanding of key factors influencing disaster debris generation across hazards and regions, hindering the accuracy and efficiency of modeling. With the increasing availability of comprehensive post-disaster waste datasets, the capacity to understand debris generation is expanding. This dissertation aims to fill knowledge gaps on disaster debris generation as follows: (1) investigate existing technologies for …

Unmasking Neuroendocrine Prostate Cancer With A Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression, Agustina Sabater, Pablo Sanchis, Rocio Seniuk, Gaston Pascual, Nicolas Anselmino, Daniel F Alonso, Federico Cayol, Elba Vazquez, Marcelo Marti, Javier Cotignola, Ayelen Toro, Estefania Labanca, Juan Bizzotto, Geraldine Gueron

Faculty, Staff and Student Publications

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, …

Artificial Intelligence And Machine Learning In Ocular Oncology, Retinoblastoma (Armor): Experience With A Multiracial Cohort, Vijitha S. Vempuluru, Rajiv Viriyala, Virinchi Ayyagari, Komal Bakal, Patanjali Bhamidipati, Krishna Krishore Dhara, Sandor R. Ferenczy, Carol L. Shields, Swathi Kaliki

Wills Eye Hospital Papers

Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. …

Development And Validation Of A Machine Learning Model To Predict Myocardial Blood Flow And Clinical Outcomes From Patients’ Electrocardiogram, Fares Alahdab, Maliazurina Binti Saad, Ahmed Ibrahim Ahmed, Qasem Al Tashi, Muhammad Aminu, Yushui Han, Jonathan B Moody, Venkatesh L Murthy, Jia Wu, Mouaz H Al-Mallah

Faculty, Staff and Student Publications

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET) and 17,649 ECG-single-photon emission computed tomography (SPECT) data pairs, the ML model is trained with a swarm intelligence approach and support vector regression (SVR). The model achieves a receiver-operator curve (ROC) area under the curve (AUC) of 0.83, with a sensitivity and specificity of 0.75. An ECG-MFR value below 2 is significantly associated with MACE, with hazard ratios (HRs) of 3.85 and 3.70 in the discovery …

The Association Between Oral Microbiome, Oral Inflammation, Periodontal Disease, And Systemic Metabolism In Individuals With/Without Type 1 Diabetes, Mianli Xiao

USF Tampa Graduate Theses and Dissertations

This study explores the associations between oral microbiomes, periodontal disease (PD), and systemic metabolism in individuals with and without Type 1 diabetes (T1D). Data were obtained from 208 participants (99 with T1D, 109 non-diabetic) in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) pilot study and 283 participants from the Oral Microbiome ancillary cohort study. Both studies included the collection of saliva or subgingival plaque samples. Microbial DNA was extracted, and the V4 region of the 16S rRNA gene was sequenced using the Illumina MiSeq platform. Machine learning models, including Random Forest (RF) and Multi-layer Perceptron (MLP), were applied …

Ddos Attack Detection With Machine Learning, Wei-Wu Tay, Siew-Chin Chong, Lee-Ying Chong

Journal of Informatics and Web Engineering

Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to …

Navigating Artificial Intelligence In Spine Surgery: Implementation And Optimization Across The Care Continuum, Antony A. Fuleihan, Arjun K. Menta, Tej D. Azad, Kelly Jiang, Carly Weber-Levine, Daniel A. Davidar, Andrew M. Hersh, Nicholas Theodore

SKMC Student Presentations and Publications

The field of spine surgery has long been characterized by innovations and technological advancements. The integration of artificial intelligence (AI) into spine surgery represents one of the latest technical developments in the field. The ability of AI to rapidly analyze datasets improves decision making, risk assessment, intraoperative precision, and postoperative management, all of which contribute to increasing personalized spine care and improving outcomes. However, the successful implementation of AI faces regulatory and privacy challenges that must be addressed before its full potential can be realized. Here, we provide a detailed analysis of the current applications and future prospects of AI …

Mates: A Deep Learning-Based Model For Locus-Specific Quantification Of Transposable Elements In Single Cell, Ruohan Wang, Yumin Zheng, Zijian Zhang, Kailu Song, Erxi Wu, Xiaopeng Zhu, Tao P Wu, Jun Ding

Faculty, Staff and Students Publications

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either 'best-mapped' or 'random-mapped' locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell …

Adding The Third Dimension: 3d Convolutional Neural Network Diagnosis Of Temporal Lobe Epilepsy, Erik Kaestner, Reihaneh Hassanzadeh, Ezequiel Gleichgerrcht, Kyle Hasenstab, Rebecca W. Roth, Allen Chang, Theodor Rüber, Kathryn A. Davis, Patricia Dugan, Ruben Kuzniecky, Julius Fridriksson Ph.D., Alexandra Parashos, Anto I. Bagić, Daniel L. Drane, Simon S. Keller, Vince D. Calhoun, Anees Abrol, Leonardo Bonilha, Carrie R. Mcdonald

Faculty Publications

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and …

Ethical Requirements For Achieving Fairness In Radiology Machine Learning: An Intersectionality And Social Embeddedness Approach, Dessislava S. Fessenko

Journal of Health Ethics

Radiodiagnostics by machine-learning (ML) systems is often perceived as objective and fair. It may, however, exhibit bias towards certain patient sub-groups. The typical reasons for this are the selection of disease features for ML systems to screen, that ML systems learn from human clinical judgements, which are often biased, and that fairness in ML is often inappropriately conceptualized as “equality”. ML systems with such parameters fail to accurately diagnose and address patients’ actual health needs and how they depend on patients’ social identities (i.e. intersectionality) and broader social conditions (i.e. embeddedness). This paper explores the ethical obligations to ensure fairness …

Slm Impact Finance Sa: Financial Analysis, The Man And The Machine, Cole Amaya

Switzerland: Banking, Finance, and Social Responsibility

This paper details the insights and learning outcomes from my internship at SLM Impact Finance SA (SLM), where I gained hands-on experience in applying AI and machine learning to investment research. Throughout the internship, I worked on projects involving sentiment natural language processing (NLP) analysis, artificial intelligence (AI) model development, and data processing, directly contributing to SLM's innovative approach to market analysis and investment strategy. The experience allowed me to bridge theoretical knowledge with real-world applications, particularly in the areas of applying computer science to my financial knowledge.

One of the key lessons was overcoming cognitive biases related to the …

A Guideline For Open-Source Tools To Make Medical Imaging Data Ready For Artificial Intelligence Applications: A Society Of Imaging Informatics In Medicine (Siim) Survey, Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine Andriole, Peter Chang, Keyvan Farahani, Mona Flores, Les Folio, Sina Houshmand, Maryellen Giger, Judy Gichoya, Bradley Erickson

Department of Radiology Faculty Papers

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora …

Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, …

Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu

Research Collection School Of Computing and Information Systems

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …

Predicting Consumer Churn In Telecommunications, Amna Yousef Almatrooshi

Theses

The telecommunications industry is highly competitive, with consumer churn being a significant concern due to market saturation. Churn, or customer attrition, refers to customers leaving a service provider due to dissatisfaction or better offers from competitors. This phenomenon is particularly impactful in the telecom sector, where retaining customers is more cost-effective than acquiring new ones. To address this issue, telecom companies increasingly rely on machine learning techniques to predict customer churn and implement strategies to retain at-risk customers. The study highlights the importance of understanding the factors contributing to customer churn, including service quality, pricing, customer service interactions, and competition. …

Evaluation Of Liver Fibrosis On Grayscale Ultrasound In A Pediatric Population Using A Cloud-Based Transfer Learning Artificial Intelligence Platform, Amr Mohammed, Aylin Tahmasebi, Sooji Kim, Mostafa Alnoury, Corinne E. Wessner, Tania Siu Xiao, Sharon W. Gould, Lauren A. May, Heidi Kecskemethy, David T. Saul, John R. Eisenbrey

Department of Radiology Faculty Papers

Objectives: The incidence of chronic liver diseases in children is increasing worldwide due to congenital, metabolic, autoimmune and viral diseases. Currently, liver biopsy for fibrosis assessment is considered the gold standard. However, this procedure is invasive, may result in unavoidable complications and is prone to sampling errors. These limitations have led to an increasing demand for noninvasive methods for fibrosis screening. Artificial intelligence integration in ultrasound diagnosis of liver fibrosis has gained interest in clinical research. In the current study we used a cloud-based artificial intelligence platform utilizing transfer learning to evaluate the accuracy of B-mode ultrasound based AI model …