Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Summary of classical and CEC-based benchmark test functions used in this study. by Mahmood A. Jumaah (22272371)

Simulations details for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

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Simulations details for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

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Pseudocode for the CORDIC algorithm to compute multiplication. by Mehdi Nadiri Andabili (22097275)

Convergence curve of each DSM algorithms. by Manoj Kumar V. (18196645)

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Summarized load characteristics of each algorithm. by Manoj Kumar V. (18196645)

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Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…<div><p>The most frequently used approach for protein structure prediction is currently homology modeling. …”

Visualizing the scatter plot of the drought (red) versus no-drought (green) and the support vectors (black) using the SVM section 4 classification algorithm. by J. Emmanuel Johnson (9579567)

Specific information on the 7 benchmark functions. by M. A. JiMing (19207527)

Overview of the algorithm used to calculate Lyapunov exponents for assessing the stability and chaotic behavior of dynamical systems. by Adil Jhangeer (9711917)

Comparisons of the artifact indices (AIs) determined for in-focus plane images procured via the denoising convolutional neural network metal artifact reduction hybrid reconstructio... by Tsutomu Gomi (7382183)

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(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

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The main steps of the general algorithm. by Dror Mughaz (18017583)

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…First, physico-chemical inputs as bulk density (BD), initial water content (W_i), saturated water content (W_s), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

Algorithm for generation of fractals as Julia sets. by Khairul Habib Alam (20521419)

Algorithm for generation of fractals as Mandelbrot sets. by Khairul Habib Alam (20521419)

Acurracy comparison of CCA and FBCCA algorithms. by Hossein Hamidi Shishavan (19688384)

Reprojection of the validation points (9×6 lattice points) using the models obtained from the symbolic regression algorithm on the 6 calibration patterns. by Almoctar Hassoumi (6470354)