Iterative optimization convergence curve of each algorithm. حسب Guangwei Liu (181992)

The optimization results of each algorithm for the 8 benchmark functions. حسب Guangwei Liu (181992)

Optimal results of the different algorithms on the speed design reducer problem. حسب Guangwei Liu (181992)

Optimal results of the different algorithms on the pressure vessel design problem. حسب Guangwei Liu (181992)

Optimal results of the different algorithms on the gear train design problem. حسب Guangwei Liu (181992)

The optimization results of each algorithm for the CEC2019 benchmark functions. حسب Guangwei Liu (181992)

Optimal results of the different algorithms on the tension/compression spring design problem. حسب Guangwei Liu (181992)

Binary contingency table. حسب Lu Xiao (314030)

Generator loss of the proposed method. حسب Nanavath Kiran Singh Nayak (22184565)

"…Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. …"

Pseudocode of artificial dragonfly algorithm. حسب Ghassan Ahmed Ali (17041488)

"…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …"

DataSheet1_Optimal capacity configuration of the wind-storage combined frequency regulation system considering secondary frequency drop.docx حسب Dongdong Li (123985)

The Hopfield artificial neural network algorithm. حسب Ghassan Ahmed Ali (17041488)

"…The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EB<i>k</i>SAT logic representation. …"

Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation. حسب Aly A. Valliani (13251484)

"…<b>(C)</b> Schematic of the proposed algorithm. a) Real data from a source domain is translated by the generator to resemble data from a specified target domain while maintaining underlying semantic qualities of the input image. b) Translated data is reconstructed by the generator to resemble data from the source domain to maintain domain-agnostic image characteristics with a semantic consistency constraint ensuring that reconstructed images maintain the semantic characteristics of the source data. c) The discriminator aims to distinguish between real and synthetic images and identify the domain of input images to constrain the generator to produce realistic-looking synthetic images from a specified domain. d) A target discriminator is fine-tuned on synthetic images to better identify opacity in the target domain.…"

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results حسب Se-Hee Jo (20554623)

"…A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. …"

S1 Data - حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Curve of step response signal of 6 algorithms. حسب Guangwei Liu (181992)

"…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …"

Models’ performance without optimization. حسب Muhammad Usman Tariq (11022141)

"…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. …"