Search alternatives:
process optimization » model optimization (Expand Search)
step optimization » after optimization (Expand Search), swarm optimization (Expand Search), based optimization (Expand Search)
most process » test process (Expand Search), pot process (Expand Search), met process (Expand Search)
primary data » primary care (Expand Search)
binary most » binary mask (Expand Search)
data step » data set (Expand Search)
process optimization » model optimization (Expand Search)
step optimization » after optimization (Expand Search), swarm optimization (Expand Search), based optimization (Expand Search)
most process » test process (Expand Search), pot process (Expand Search), met process (Expand Search)
primary data » primary care (Expand Search)
binary most » binary mask (Expand Search)
data step » data set (Expand Search)
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…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. …”
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Datasets and their properties.
Published 2023“…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. …”
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Parameter settings.
Published 2023“…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. …”
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Dendrogram of the stock prices.
Published 2025“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
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Descriptive statistics on stock prices.
Published 2025“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
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Correlation heatmap of the principal components.
Published 2025“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
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Hyperparameters of the LSTM Model.
Published 2025“…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. …”
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The AD-PSO-Guided WOA LSTM framework.
Published 2025“…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. …”
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Prediction results of individual models.
Published 2025“…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. …”
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Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19.
Published 2021“…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
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SBM 2023 Poster: Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study
Published 2023“…</p> <p><strong>Objectives: </strong>To develop and validate algorithms that predict walking (i.e., >5 minutes) within the next 3 hours, predicted from the participants’ previous five weeks’ steps per minute data.…”