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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

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

الموضوعات:

Pseudocode for machine learning models. حسب 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. حسب 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. حسب 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. حسب Paul van Lent (16876977)

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

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

Melbournevirus protein structure prediction - AlphaFold3 حسب 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 حسب 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> حسب 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' حسب 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 حسب 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. حسب Alemu Birara Zemariam (17540938)

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

Stunting final dataset. حسب Alemu Birara Zemariam (17540938)

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

A mean SHAP value report. حسب Alemu Birara Zemariam (17540938)

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

A waterfall plot analysis. حسب Alemu Birara Zemariam (17540938)

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

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF حسب Shivani V. Pawar (20355171)

"…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …"

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF حسب Shivani V. Pawar (20355171)

"…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …"