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

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties by Tijana Šušteršič (14519970)

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Auto-tuning algorithm of the weight factors in the cost function. by Syed Umaid Ali (14486372)

“…<p>Auto-tuning algorithm of the weight factors in the cost function.…”

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

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

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The probability density function for cost function g(X). by Matheus Sant’Ana Lima (9937809)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

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

The obtained cost function values with respect to each run of different algorithms. by Davut Izci (15927452)

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Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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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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Confusion matrix of tree algorithm analysis. by Thiago Orion Simões Amorim (6808244)

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Performance comparison of recent and effective algorithms for minimizing error-based cost functions. by Davut Izci (15927452)

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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.…”

Cost function with VPLE. by Ijaz Ahmed (1545961)

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Cost function depending on the waiting time. by Ludovica Adacher (11238189)

Visualization and balancing the cost function with the Pauli term. by Alexander Borst (32619)

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Cost function of storage facility construction. by Qian Huang (127640)

Comparative statistical performance of various algorithms in minimizing the <i>F</i> cost function. by Davut Izci (15927452)

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