Features selected by optimization algorithms. حسب Afnan M. Alhassan (18349378)

"…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …"

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب Hussein Ali Bardan (21976208)

"…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …"

Accuracy comparison of proposed and other models. حسب Subathra Gunasekaran (19492680)

Hybrid feature selection algorithm of CSCO-ROA. حسب Afnan M. Alhassan (18349378)

"…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …"

Datasets and their properties. حسب Olaide N. Oyelade (14047002)

"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"

Parameter settings. حسب Olaide N. Oyelade (14047002)

"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

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

"…Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. …"

Optimized model prediction performance. حسب Guang Tu (22054865)

MLP vs classification algorithms. حسب Mohd Mustaqeem (19106494)

"…We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. …"

Hyperparameters of the LSTM Model. حسب Ahmed M. Elshewey (21463867)

"…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …"

The AD-PSO-Guided WOA LSTM framework. حسب Ahmed M. Elshewey (21463867)

"…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …"

Prediction results of individual models. حسب Ahmed M. Elshewey (21463867)

"…This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. …"

Data_Sheet_1_Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.pdf حسب Wen Cao (696121)

Data_Sheet_1_Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S..pdf حسب Xiao Zhou (122661)