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Examining Wildfire Dynamics Using Ecostress Data With Machine Learning Approaches: The Case Of South-Eastern Australia's Black Summer, Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher Nov 2024

Examining Wildfire Dynamics Using Ecostress Data With Machine Learning Approaches: The Case Of South-Eastern Australia's Black Summer, Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre-fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. …

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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 Nov 2024

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 …

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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 Nov 2024

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 …

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Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar Nov 2024

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.

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Artificial Intelligence And Liver Transplantation; Literature Review, Maria Serban, Irina Balescu, Sorin Petrea, Bogdan Gaspar, Lucian Pop, Valentin Varlas, Marilena Stoian, Camelia Diaconu, Cristian Balalau, Nicolae Bacalbasa Oct 2024

Artificial Intelligence And Liver Transplantation; Literature Review, Maria Serban, Irina Balescu, Sorin Petrea, Bogdan Gaspar, Lucian Pop, Valentin Varlas, Marilena Stoian, Camelia Diaconu, Cristian Balalau, Nicolae Bacalbasa

Journal of Mind and Medical Sciences

Liver transplantation is the last life-saving solution for patients with end stage liver disease. The discrepancy between waiting list and available organs has led to the appearance of extended donation criteria and the development of several scores (Child-Pugh score, MELD score, DRI score, SOFT score), in order to find the most suitable donor-recipient match. But none of these scores can predict survival after transplantation. Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver and comes in this aid with its various methods (random forest, artificial neural networks, decision tree, Bayesian networks, and …

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A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis Oct 2024

A Large Scale Multi Institutional Study For Radiomics Driven Machine Learning For Meningioma Grading, Mert Karabacak, Shiv Patil, Rui Feng, Raj K. Shrivastava, Konstantinos Margetis

Department of Medicine Faculty Papers

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade …

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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 Oct 2024

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, …

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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 Oct 2024

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 …

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Sedimentary Environments, Mechanical Properties, And Rock Burst Risk Identification Of Overburden In Deep Mines, Qiao Wei, Cheng Xianggang, Dou Linming, He Hu, Meng Xiangsheng, Ren Yangyang, Xiao Chong, Cai Jin Oct 2024

Sedimentary Environments, Mechanical Properties, And Rock Burst Risk Identification Of Overburden In Deep Mines, Qiao Wei, Cheng Xianggang, Dou Linming, He Hu, Meng Xiangsheng, Ren Yangyang, Xiao Chong, Cai Jin

Coal Geology & Exploration

Objective Disaster risk identification and prediction serve as a prerequisite for disaster control. An engineering geological environment is identified as the fundamental condition inducing dynamic disasters like rock bursts in mines. Exploring the sedimentary genetic mechanisms of engineering geological environments holds great significance for predicting rock burst risks. Methods With the Gaojiapu Coal Mine in the Binchang mining area, Shaanxi Province, as a case study, this study analyzed the engineering geological characteristics of rock masses under different sedimentary microfacies. Furthermore, it explored the deformation and failure characteristics of rocks in different sedimentary environments, along with the energy release patterns of …

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Automation Of Medical Facilities, Automation Of Diagnostic Processes For Intelligent Primary Diagnosis Of Diseases Of The Oral Cavity., Otabek Ismailov, X.F. Temirova Oct 2024

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.

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Development Of Structures Of Intellectual Control Systems Of The Rectification Process, Shodievich Avazov Yusuf Oct 2024

Development Of Structures Of Intellectual Control Systems Of The Rectification Process, Shodievich Avazov Yusuf

Chemical Technology, Control and Management

Rectification processes, which are considered complex technological processes, and the development of structures of intelligent systems for controling the work of rectification devices and columns, which serve to organize them, were considered. The need to build intelligent control systems is based on the fact that the progress of the rectification process is affected by a large number of factors, including temperature, pressure, consumption, temperature difference, concentration of mixtures, properties of light and heavy volatile components. The structure of an intelligent control system based on the situational control method for rectification devices is proposed. The architecture of the proposed intelligent control …

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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 Oct 2024

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. …

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Ddos Attack Detection With Machine Learning, Wei-Wu Tay, Siew-Chin Chong, Lee-Ying Chong Oct 2024

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 …

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The Association Between Oral Microbiome, Oral Inflammation, Periodontal Disease, And Systemic Metabolism In Individuals With/Without Type 1 Diabetes, Mianli Xiao Oct 2024

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 …

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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 Oct 2024

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 …

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Predictive Residual Neural Networks For Optical Trapping Of Small Particles, Nasim Mohammadi Estrakhri, Ponthea Zahraii, Saman Kashanchi, Nooshin M. Estakhri Oct 2024

Predictive Residual Neural Networks For Optical Trapping Of Small Particles, Nasim Mohammadi Estrakhri, Ponthea Zahraii, Saman Kashanchi, Nooshin M. Estakhri

Engineering Faculty Articles and Research

Optical tweezers provide a non-contact method to trap, move, and manipulate micro- and nano-sized objects. Using properly designed dielectric and plasmonic nanostructure configurations, optical tweezers have been tailored to create stable and precise trapping for nanoscale objects. Recent advances in numerical optimization techniques allow further enhancement in nanoscale optical traps through inverse optimization of such configurations. One of the main challenges in such optimization approaches is the time-consuming nature of full-wave simulation of nanostructures and postprocessing steps to extract optical forces. To address this challenge, we introduce a surrogate solver based on residual neural networks that can accurately predict the …

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A Comprehensive Ml And Ai Framework For Intersection Safety: Assessing Contributing Factors, Surrogate Safety Measures, Non-Compliance Behaviors, And Cost-Inclusive Methodology, Deep Patel Oct 2024

A Comprehensive Ml And Ai Framework For Intersection Safety: Assessing Contributing Factors, Surrogate Safety Measures, Non-Compliance Behaviors, And Cost-Inclusive Methodology, Deep Patel

Theses and Dissertations

Intersection safety, particularly in traffic management, has emerged as a pressing concern due to its substantial contribution to road crashes and fatalities. In recent years, understanding road users' behavior and conflicts at intersections has become essential for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, over 50% of fatal and injury crashes occurred at or near intersections, necessitating further investigation. This study addresses this issue through an innovative blend of comprehensive literature review, advanced machine learning algorithms, artificial intelligence (AI) technologies, surrogate safety measures (SSMs), non-compliance behavior, and emphasizing the economic aspects of traffic incidents. First, …

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Optimizing Transport Predictive Modeling With Simulation-Based Statistical Inference, Quyen Tran '27, Mamunur Rashid Oct 2024

Optimizing Transport Predictive Modeling With Simulation-Based Statistical Inference, Quyen Tran '27, Mamunur Rashid

Annual Student Research Poster Session

Simulation-based statistical inference (SBI) leverages computer simulations to help scientists understand and analyze complex data. This project explores how SBI techniques can be used to analyze transportation data. We use modern computational methods, including machine learning models, to improve the accuracy of predictions and decision-making in transportation planning. Our study focuses on applying two SBI methods, Approximate Bayesian Computation - Markov Chain Monte Carlo and Synthetic Likelihood, to create synthetic data for training machine learning models. These models show the potential of SBI to handle uncertain data. It also highlights the practical benefits of SBI in making predictions and decisions …

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Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu Oct 2024

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 …

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Improving Out-Of-Distribution Detection With Disentangled Foreground And Background Features, Choubo Ding, Guansong Pang Oct 2024

Improving Out-Of-Distribution Detection With Disentangled Foreground And Background Features, Choubo Ding, Guansong Pang

Research Collection School Of Computing and Information Systems

Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground features (e.g., objects in CIFAR100 images vs. those in CIFAR10 images) and background features (e.g., textural images vs. objects in CIFAR10). Existing methods can confound foreground and background features in training, failing to utilize the background features for OOD detection. This paper considers the importance of feature disentanglement in out-of-distribution detection and proposes the simultaneous exploitation of both foreground and …

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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 Oct 2024

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 …

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Autonomous Real-Time Model Updating Within Digital Twin Frameworks For Thermal Systems, Braden Robert Priddy Oct 2024

Autonomous Real-Time Model Updating Within Digital Twin Frameworks For Thermal Systems, Braden Robert Priddy

Theses and Dissertations

As engineering systems increase in scale and complexity in the era of the Fourth Industrial Revolution, data-driven solutions will become essential in enabling the next generation of these systems. One of the trending tools that can aid in this transition is digital twins. As physical systems degrade throughout their life cycles, their behavior also changes. Digital twins use data assimilation to continuously update virtual models to represent the current state of their physical counterparts. A reliable digital twin can be leveraged by a system operator to perform diagnostics, optimize, and tests without ever needing the physical system. However, implementing effective …

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Using Machine Learning And Deep Learning Algorithms To Improve Low Birthweight Prediction, Yang Ren Oct 2024

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 …

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Can Federated Learning Solve Ai’S Data Privacy Problem?: A Legal Analysis, Warren B. Chik, Florian Gamper Oct 2024

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 …

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Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang Oct 2024

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, …

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Etdsuite: A Toolkit To Mine Electronic Theses And Dissertations To Enrich Scholarly Big Data Using Natural Language Processing And Computer Vision, Muntabir Hasan Choudhury Oct 2024

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 …

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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 Sep 2024

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 …

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Attention-Based Load Forecasting With Bidirectional Finetuning, Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, Pavel Matrenin Sep 2024

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 …

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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 Sep 2024

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 …

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Regulating Algorithmic Harms, Sylvia Lu Sep 2024

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 …

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