Karl Timothy LeRoy Pazdernik

Works (7)

Updated: November 28th, 2024 05:01

2024 article

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

Degnan, D. J., Strauch, C. W., Obiri, M. Y., Vonkaenel, E. D., Kim, G. S., Kershaw, J. D., … Bramer, L. M. (2024, November 11). JOURNAL OF PROTEOME RESEARCH.

By: D. Degnan *, C. Strauch *, M. Obiri *, E. Vonkaenel *, G. Kim *, J. Kershaw*, D. Novelli*, K. Pazdernik n, L. Bramer *

author keywords: natural language processing; relationship extraction; biological text mining; machine learning; largelanguage models; BERT; GPT; LLM
Source: Web Of Science
Added: November 25, 2024

2022 journal article

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

PATHOGENS, 11(2).

By: S. Dixon*, R. Keshavamurthy *, D. Farber *, A. Stevens *, K. Pazdernik n & L. Charles *

author keywords: infectious disease forecasting; prediction; big data; multi-feature fusion; machine learning; deep learning; GLARMA; campylobacteriosis; typhoid; Q-fever
TL;DR: The power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches is demonstrated. (via Semantic Scholar)
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Source: Web Of Science
Added: July 11, 2022

2022 journal article

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches

ONE HEALTH, 15.

By: R. Keshavamurthy *, S. Dixon*, K. Pazdernik n & L. Charles *

author keywords: Systematic review; Infectious diseases; Disease prediction; Disease forecast; Machine learning; Deep learning
TL;DR: To fully utilize ML and DL for improved ID forecasting, models should include the full disease ecology in a One-Health context, important food and agricultural diseases, underrepresented hotspots, and important metrics and considerations required for operational deployment. (via Semantic Scholar)
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Source: Web Of Science
Added: November 14, 2022

2021 journal article

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

INTERNATIONAL JOURNAL OF FORECASTING, 38(2), 648–661.

By: J. Bakerman n, K. Pazdernik n, G. Korkmaz * & A. Wilson n 

author keywords: Civil unrest; Dynamic logistic regression; Forecasting; Polya-Gamma latent variable; Penalized credible regions
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16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: September 4, 2021

2021 article

Estimating basis functions in massive fields under the spatial mixed effects model

Pazdernik, K., & Maitra, R. (2021, July 31). STATISTICAL ANALYSIS AND DATA MINING.

By: K. Pazdernik n & R. Maitra *

author keywords: alternating expectation conditional maximization algorithm; bandwidth; basis functions; fixed rank kriging; maximum likelihood estimation; range parameter
TL;DR: This work develops an alternative method that utilizes the Spatial Mixed Effects (SME) model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an Alternating Expectation Conditional Maximization (AECM) algorithm. (via Semantic Scholar)
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2. Zero Hunger (Web of Science)
13. Climate Action (OpenAlex)
Source: Web Of Science
Added: August 16, 2021

2020 journal article

Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods

COMPUTATIONAL MATERIALS SCIENCE, 181.

By: K. Pazdernik*, N. LaHaye *, C. Artman n & Y. Zhu *

author keywords: Deep convolutional neural network; Scanning electron microscopy; Spatial point process; Image segmentation
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Source: Web Of Science
Added: July 20, 2020

2018 journal article

Twitter geolocation: A hybrid approach

ACM Transactions on Knowledge Discovery from Data, 12(3).

By: J. Bakerman, K. Pazdernik, A. Wilson , G. Fairchild & R. Bahran

Source: NC State University Libraries
Added: August 6, 2018

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