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time implementation » _ implementation (Expand Search), policy implementation (Expand Search), effective implementation (Expand Search)
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Python Code for Fine-Tuned BERT Model in High School Student Advising
Published 2025“…<p dir="ltr">This repository contains the Python implementation and fine-tuned BERT model for answering high school student questions on college and career advising, based on the methodology described in Assayed et al. (2024). …”
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ALAAM models for the Deezer networks, with liking “alternative” music as the outcome variable.
Published 2024Subjects: -
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Models estimated using ALAAMEE for the Deezer networks, with liking jazz as the outcome variable.
Published 2024Subjects: -
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Exploring the integration of metaverse technologies in engineering education through the SAMR model
Published 2025“…The final deliverable is a plan for phased integration of metaverse learning into a Python programming course following this model, building on existing best practices.…”
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Survey Dataset and Python Code for Preprocessing, Statistical Tests
Published 2025“…Supporting figures and tables are available in the repository</p><p dir="ltr">Data processing was implemented in Python 3.11 and attached are all the python scripts used for preprocessing , and statistical Analysis and also the questionnaire used for the study.…”
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Workflow of a typical Epydemix run.
Published 2025“…<div><p>We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. …”
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Python code for a rule-based NLP model for mapping circular economy indicators to SDGs
Published 2025“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”
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Cost functions implemented in Neuroptimus.
Published 2024“…To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. …”
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Type II error rate from estimation of simulated outcomes using the EE algorithm.
Published 2024Subjects: -
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Type I error rate from estimation of simulated outcomes using the EE algorithm.
Published 2024Subjects: -
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