Machine learning algorithm parameters. by Namal Rathnayake (15361124)

“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”

The frame of the WKPCA dimension reduction algorithm. by Wen Bo Liu (11559222)

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WKPCA dimension reduction algorithm flow. by Wen Bo Liu (11559222)

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V-shaped transfer functions. by Nebojsa Bacanin (13932723)

Cost function calculated by QA with different hyperparameters. by Takuro Matsuta (21608505)

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Cost function calculated by SA with different hyperparameters. by Takuro Matsuta (21608505)

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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. …”

Cost function with VPLE. by Ijaz Ahmed (1545961)

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Multi-scale t kernel function under different parameters. by Wen Bo Liu (11559222)

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Friedman ranks of tested methods on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

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

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Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. by Mahmoudreza Moein (22127153)

Mean fitness and standard deviation results of compared approaches on CEC2019 benchmark functions. by Nebojsa Bacanin (13932723)

Convergence graphs for ten CEC 2019 benchmark functions and direct comparison between COFFO and FFO. by Nebojsa Bacanin (13932723)

PSO parameters and objective function. by André G. Coimbra (21354135)

GA parameters and objective function. by André G. Coimbra (21354135)

Combining Deep Learning Neural Networks with Genetic Algorithms to Map Nanocluster Configuration Spaces with Quantum Accuracy at Low Computational Cost by Johnathan von der Heyde (16828390)

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Table 1_Machine learning-based gait adaptation dysfunction identification using CMill-based gait data.docx by Hang Yang (577653)

“…ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. …”

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.…”

Fitness function curve at weight factor 0.3. by Dhulfiqar Hakeem Dhayef (17729684)

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