Showing 81 - 100 results of 353 for search '(( ((python model) OR (python code)) implementation ) OR ( method proof implementation ))', query time: 0.33s Refine Results
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    Code by Baoqiang Chen (21099509)

    Published 2025
    “…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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    Biomethanation of alkaline waste sludge in haloalkaline conditions: combined proof of concept experiments and technical economic evaluation by Ramon Zwaan (22673727)

    Published 2025
    “…Alkaline fermentation offers an attractive method for supplying increasingly needed high-purity biomethane using various recalcitrant substrates that have undergone alkaline pre-treatment. …”
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    Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming' by Paul Cuffe (2937081)

    Published 2024
    “…<p dir="ltr">These Python notebooks implement the techniques described in the paper "Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming".…”
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    Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

    Published 2025
    “…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”
  16. 96

    Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations by Sascha Mausenberger (22225772)

    Published 2025
    “…We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. …”
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