Zoran Obradović
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View article: Cross-platform spatiotemporal sentiment trends analysis of COVID-19 vaccine discourse
Cross-platform spatiotemporal sentiment trends analysis of COVID-19 vaccine discourse Open
The COVID-19 pandemic has sparked intense global discussions about vaccine safety, efficacy, and distribution on social media. It underscored the need to analyze how vaccine-related sentiments propagate across social media and interact wit…
View article: Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health
Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health Open
Background Frailty is a public health concern linked to falls, disability, and mortality. Early screening and tailored interventions can mitigate adverse outcomes, but community settings require tools that are accurate and explainable. Kor…
View article: Effect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids
Effect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids Open
Power outages in the distribution grid profoundly impact everyday human activity and economic welfare, as numerous infrastructures rely on uninterrupted power for sustained operation. Over the past decade, there has been a significant focu…
View article: Using Machine Learning to Predict Treatment Outcome in a Harmonized Dataset of Youth Anxiety Treatments
Using Machine Learning to Predict Treatment Outcome in a Harmonized Dataset of Youth Anxiety Treatments Open
Machine Learning (ML) is a promising approach to identify predictors/moderators for youth anxiety treatments. To this end, data from nine randomized controlled trials of youth anxiety treatments were harmonized into a dataset (N = 1362; Ma…
View article: A Social Network Analysis of Hemodialysis Clinics: Attitudes Toward Living Donor Kidney Transplant among Influential Patients
A Social Network Analysis of Hemodialysis Clinics: Attitudes Toward Living Donor Kidney Transplant among Influential Patients Open
Key Points Hemodialysis clinic social networks spread attitudes and behaviors toward kidney transplants. Identifying and characterizing influential patients is a first step in future hemodialysis clinic social network interventions to prom…
View article: Understanding Online Attitudes with Pre-Trained Language Models
Understanding Online Attitudes with Pre-Trained Language Models Open
This work investigates how the rich semantic embeddings of pre-trained language models can be used to help understand the general attitudes of an online community. This work describes a novel prediction model that can ingest statements des…
View article: Classifying Severe Weather Events by Utilizing Social Sensor Data and Social Network Analysis
Classifying Severe Weather Events by Utilizing Social Sensor Data and Social Network Analysis Open
Weather-related disruptions have a significant impact on a variety of industries, including agriculture, infrastructure, and public safety. Predicting these unusual weather events remains a significant challenge. The problem is complicated…
View article: Systems and methods for knowledge discovery in spatial data
Systems and methods for knowledge discovery in spatial data Open
Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and model…
View article: Discovering research articles containing evolutionary timetrees by machine learning
Discovering research articles containing evolutionary timetrees by machine learning Open
Motivation Timetrees depict evolutionary relationships between species and the geological times of their divergence. Hundreds of research articles containing timetrees are published in scientific journals every year. The TimeTree (TT) proj…
View article: Enhancing Weather-Related Outage Prediction and Precursor Discovery Through Attention-Based Multi-Level Modeling
Enhancing Weather-Related Outage Prediction and Precursor Discovery Through Attention-Based Multi-Level Modeling Open
Electric grid continually monitors spatiotemporal data from sparse service areas. As power systems grow and get more complex, and with the deployment of more sensors and data collection capabilities, monitoring and analyzing data streams f…
View article: Aligning Comments to News Articles on a Budget
Aligning Comments to News Articles on a Budget Open
Disagreement among text annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms for natural language processing. Using only high agreement annotations …
View article: Incorporating Wind Modeling Into Electric Grid Outage Risk Prediction and Mitigation Solution
Incorporating Wind Modeling Into Electric Grid Outage Risk Prediction and Mitigation Solution Open
Electric grids are vulnerable to the impacts of extreme weather. Utility companies face the necessity to reduce the number of power outages caused by weather. This paper expands the approach of predicting weather outages in the distributio…
View article: Social Media Sensors for Weather-Caused Outage Prediction Based on Spatio–Temporal Multiplex Network Representation
Social Media Sensors for Weather-Caused Outage Prediction Based on Spatio–Temporal Multiplex Network Representation Open
This study investigates severe weather events that lead to power outages. Despite extensive research on using social media during disasters, little work has focused on combining social media information with power outage data. To address t…
View article: Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis
Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis Open
In this exploratory paper, we attempt to address a growing demand for unsupervised machine learning techniques on sleep data by applying a variational autoencoder on respiratory sleep data on a breath-by-breath basis. We transform respirat…
View article: Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data
Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data Open
Electric power system operators monitor large multi-modal data streams from wide service areas. The current data setups stand to get more complex as utilities add more smart-grid sensors to collect additional data from power system substat…
View article: ODP233 Predicting Hospital Readmission for Patients with Diabetes: A Comparison of Models
ODP233 Predicting Hospital Readmission for Patients with Diabetes: A Comparison of Models Open
In patients with diabetes, current models for predicting the risk of readmission within 30 days of hospital discharge vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRI TM), a logistic regress…
View article: A transformer model for learning spatiotemporal contextual representation in fMRI data
A transformer model for learning spatiotemporal contextual representation in fMRI data Open
Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic d…
View article: Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data
Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data Open
As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be…
View article: Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)
Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART) Open
This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Enginee…
View article: Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast
Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast Open
A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to a lower-dimensional representation wh…