<b> </b> Precision analysis. by Mohanad Faeq Ali (21354273)

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Convergence rate analysis. by Mohanad Faeq Ali (21354273)

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Computational efficiency. by Mohanad Faeq Ali (21354273)

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FPR and FNR analysis based on different time steps. by Mohanad Faeq Ali (21354273)

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Performance comparison of cyber security techniques in IoMT healthcare devices. by Mohanad Faeq Ali (21354273)

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Impact of cyberattack types on IoMT devices. by Mohanad Faeq Ali (21354273)

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IoMT devices and attack overview in the CICIoMT2024 dataset. by Mohanad Faeq Ali (21354273)

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Pseudocode for machine learning models. by Jingru Dong (14076094)

“…A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”

Data features examined for potential biases. by Harry Hochheiser (3413396)

“…<div><p><b>Objective:</b> To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”

Analysis topics. by Harry Hochheiser (3413396)

“…<div><p><b>Objective:</b> To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.…”

Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease by Zhuoyan Chen (12193358)

“…However, no clinically applicable model currently exists to predict the response of patients with CD to IFX therapy. …”

Flowchart of the study participants. by Saeid Rasouli (20370998)

“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”

Feature importance of variables. by Saeid Rasouli (20370998)

“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”

Melbournevirus protein structure prediction - AlphaFold3 by Lars Mühlberg (21524075)

“…Number of multimer predictions per model was set to 1. The heteromultimeric model of MEL_149 and MEL_368 has been predicted with the AlphaFold3 online search server. pTM and ipTM scores were extracted from .json files using R and PAE data was extracted using python 3. …”

Data Sheet 1_Computation of domination degree-based topological indices using python and QSPR analysis of physicochemical and ADMET properties for heart disease drugs.pdf by Geethu Kuriachan (20865374)

“…QSPR models are developed to assess the ability of these indices to predict key properties, offering insights into their effectiveness for drug design.…”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…</p><p dir="ltr">GPU：NVIDIA GeForce RTX 3090 GPU</p><p dir="ltr">Bert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-cased</p><p dir="ltr">python=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.…”

Data and Code for 'A Comparative Study of Physics-Informed and Data-Driven Neural Networks for Compound Flood Simulation at River-Ocean Interfaces: A Case Study of Hurricane Irene' by Dongyu Feng (21196556)

“…<br><br>conda create --name tf2 --file requirement_tf2.txt<br>conda activate tf2<br><br><br>### Before training<br>Before running the code, need to create folders to save the model output<br><br>For CNN, create /files/CNN<br><br>For PINNs, create /saved_model<br><br>For saving figures from visualization, create /figures<br><br></p><p dir="ltr">Training and Results</p><p dir="ltr"><br>PINNs<br>Training: To train the model, run:</p><p dir="ltr">python PINN_test_bnd_uh_Telemac.py</p><p dir="ltr">python PINN_test_bnd_uh_Telemac_FDM.py<br></p><p dir="ltr">Result Plotting and Comparison: For plotting and comparing results, use:</p><p dir="ltr">python PINN_plot_comparison.py<br><br><br>Data-driven Model<br>CNN Training: To train the CNN model, execute:</p><p dir="ltr">python train_CNN.py<br><br>Result Visualization: To visualize the results of the CNN model, run:</p><p dir="ltr">python predict_CNN.py<br><br>To reproduce all results and figures in the manuscript, please refer to the scripts in analysis/</p>…”

A free tutorial book from NSF Cybertraining C2D: Cybertraining for Chemical Data scientists by Xiangliang Zhang (19460047)

“…In the following chapters, 4 through 9, we focus on specific chemical tasks, dedicating each chapter to solving a distinct problem using these machine learning techniques, including molecular property prediction, molecular optimization, reaction outcome prediction, retrosynthesis, yield prediction and Large Language Models (LLMs) for Chemistry. …”

Study workflow diagram. by Alemu Birara Zemariam (17540938)

“…The performance of the predictive model was evaluated using evaluation metrics value through Python software. …”