Table 1_Entropy-adaptive differential privacy federated learning for student performance prediction and privacy protection: a case study in Python programming.docx by Shanwei Chen (12031760)

“…This study proposes an Entropy-Adaptive Differential Privacy Federated Learning method (EADP-FedAvg) to enhance the accuracy of student performance prediction while ensuring data privacy. Based on online test records from Python programming courses for Electronic Engineering students (grade 2021–2023) at the School of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, China, the study uses a Multilayer Perceptron (MLP) model and 10 distributed clients for training. …”

The ROC, calibration curve and DCA of the combined prediction model based on tumor diameter combined with the optimal radiomics features. by Jiangfa Li (18345033)

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ANFIS model for the four parameters. by Ahmed G. Ismail (21519374)

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LHS model for the four parameters. by Ahmed G. Ismail (21519374)

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Steering system model. by Honglei Pang (22693724)

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Braking system model. by Honglei Pang (22693724)

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Partial Rank Correlation Coefficient (PRCC) sensitivity analysis results, obtained using LHS with 1000 samples and run in Python version 3.11.0. by Tinashe Victor Mupedza (22022124)

After redistributed load using EMS/ energy storage predicted by LHS with ANFIS. by Ahmed G. Ismail (21519374)

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DeepB3Pred_source_code by Muhammad Arif (17012472)

Impact of sensitivity analysis on model performance. by Ahmed G. Ismail (21519374)

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Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

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Error in percentage for used hybrid prediction method versus actual. by Ahmed G. Ismail (21519374)

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<b>Map 104. </b>Distribution of <i>Python natalensis </i>records in south-eastern Zimbabwe. by Kurt van Wyk (20481341)

Actual versus hybrid prediction method for (Maximum, Average and Minimum) demand. by Ahmed G. Ismail (21519374)

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Python’s Evolution on Stack Overflow: An Empirical Analysis of Topic Trends by Hu Fengqi (20971700)

“…The original data source is from : <b>h</b><b>ttps://archive.org/details/stackexchange</b></p><h2>Training Data</h2><p dir="ltr">Each set contains two files, Training Set and Rater, Training Set is the content of the training set, i.e., the file in which the model is directly trained, and the Grade column is the result of the machine's prediction.The Rater file contains the data of the manual scoring, and contains the following sections:</p><p dir="ltr">1. the rating data for each rater, with the specific column name Rater- rater number (e.g. …”

Comparison of previous predictive models. 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.…”

BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

“…However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. …”

Predicting coding regions on unassembled reads, how hard can it be? - Genome Informatics 2024 by Amanda Clare (98717)

“…The locations and directions of the predictions on the reads are then combined with the information about locations and directions of the reads on the genome using Python code to produce detailed results regarding the correct, incorrect and alternative starts and stops with respect to the genome-level annotation.…”

Finites differences python code to solve CH equation with a source term and Comsol routine to solve Brusselator equation in radial domains. by Giulio Facchini (9031490)

Configurations used in ALAAMs for undirected networks. by Alex Stivala (8356257)

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