Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

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Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

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Objective function value statistics. by Chao Li (145513)

Flowchart of step() function. by Raed Alharthi (18340157)

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

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Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Flowchart of P&O-based MPPT algorithm. by Luqman Khan (17921419)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Test functions. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. The DGEP model serves to enhance GEP performance through the effective maintenance of diversity and improved global search functions. …”

Flowchart of the FPA algorithm. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Algorithm convergence curve. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Flowchart of the IFPA algorithm. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Five types of Beta functions considered. by Luan Carlos de Sena Monteiro Ozelim (16914117)

The proposed method for protein function prediction. by Seyyede Fatemeh Seyyedsalehi (10196767)