Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. …”

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Images of partial benchmark functions. by ZeSheng Lin (20501356)

Subjects:

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”

Statistical results of various algorithms. by ZeSheng Lin (20501356)

Subjects:

PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

Subjects:

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

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.…”

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

“…<p>We labeled 1,000 frames per cohort under both day and night conditions (total 6,000 human labeled frames across three cohorts), and studied the accuracy of the animal tracking algorithm (SLEAP) as a function of the number of frames (<i>x</i>-axis) trained. …”