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). …”

The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). by Solomon Shiferaw Beyene (9143178)

Accuracy, sensitivity, specificity and F-score. by Solomon Shiferaw Beyene (9143178)

<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.…”

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Objective function values of different algorithms on individual problem instances. by Panfei Li (22379086)

“…<p>Objective function values of different algorithms on individual problem instances.…”

Loss function values of target detection algorithms during training. by Guodong Su (7876934)

“…<p>Loss function values of target detection algorithms during training.…”

Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

Initialization parameter values for the comparison algorithms. by Muhammad Zubair Rehman (11243478)

Predictions from a Universal Value Function Approximator (UVFA) algorithm. by Sam Hall-McMaster (10343795)

Performance profile of the four algorithms based on the function value of iteration number. by Sulaiman M. Ibrahim (20614376)

“…<p>Performance profile of the four algorithms based on the function value of iteration number.…”

GD and IGD values of the traditional NSGA-II algorithm. by Ying Wang (11406)

GD and IGD values of the improved NSGA-II algorithm. by Ying Wang (11406)

Objective function values of each algorithm for solving large-scale problems. by Lijun Fan (2007628)

“…<p>Objective function values of each algorithm for solving large-scale problems.…”

Values of the fitness function for different algorithms run on various companies’ datasets. by Bitanu Chatterjee (15295261)

“…<p>Values of the fitness function for different algorithms run on various companies’ datasets.…”

IGD values of different algorithms under different standard test functions. by Jun Sun (48981)

“…<p>IGD values of different algorithms under different standard test functions.…”

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

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

“…<p>The obtained cost function values with respect to each run of different algorithms.…”