Advances in Sparrow Search Algorithm: A Comprehensive Survey

Archives of Computational Methods in Engineering, Год журнала: 2022, Номер 30(1), С. 427 - 455

Опубликована: Авг. 22, 2022

Язык: Английский

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Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model

Expert Systems with Applications, Год журнала: 2023, Номер 219, С. 119648 - 119648

Опубликована: Фев. 3, 2023

Язык: Английский

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A hybrid wind speed forecasting model with two-stage data processing based on adaptive neuro-fuzzy inference systems and deep learning algorithms

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm

IEEE Access, Год журнала: 2021, Номер 9, С. 169135 - 169155

Опубликована: Янв. 1, 2021

Artificial neural networks are one of the most commonly used methods in machine learning. Performance network highly depends on learning method. Traditional algorithms prone to be trapped local optima and have slow convergence. At other hand, nature-inspired optimization proven very efficient complex problems solving due derivative-free solutions. Addressing issues traditional algorithms, this study, an enhanced version artificial bee colony metaheuristics is proposed optimize connection weights hidden units networks. Proposed improved method incorporates quasi-reflection-based guided best solution bounded mechanisms original approach manages conquer its deficiencies. First, tested a recent challenging CEC 2017 benchmark function set, then applied for training five well-known medical datasets. Further, devised algorithm compared metaheuristics-based methods. The efficiency measured by metrics - accuracy, specificity, sensitivity, geometric mean, area under curve. Simulation results prove that outperforms terms accuracy convergence speed. improvement over different datasets between 0.03% 12.94%. quasi-refection-based mechanism significantly improves speed together with bounded, exploitation capability enhanced, which better accuracy.

Язык: Английский

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A novel prediction model for wind power based on improved long short-term memory neural network

Energy, Год журнала: 2022, Номер 265, С. 126283 - 126283

Опубликована: Дек. 3, 2022

Язык: Английский

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Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies

Energy Conversion and Management, Год журнала: 2022, Номер 259, С. 115590 - 115590

Опубликована: Апрель 11, 2022

Язык: Английский

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Energy forecasting model based on CNN-LSTM-AE for many time series with unequal lengths

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 113, С. 104998 - 104998

Опубликована: Июнь 2, 2022

Язык: Английский

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A Wind Power Forecasting Model Using LSTM Optimized by the Modified Bald Eagle Search Algorithm

Energies, Год журнала: 2022, Номер 15(6), С. 2031 - 2031

Опубликована: Март 10, 2022

High-precision forecasting of short-term wind power (WP) is integral for farms, the safe dispatch systems, and stable operation grid. Currently, data related to maintenance farms mainly comes from Supervisory Control Data Acquisition (SCADA) with certain information about operating characteristics turbines being readable in SCADA data. In WP forecasting, Long Short-Term Memory (LSTM) a commonly used in-depth learning method. present study, an optimized LSTM based on modified bald eagle search (MBES) algorithm was established construct MBES-LSTM model, model make predictions, so as address problem that selection hyperparameters may affect results. After preprocessing acquired by SCADA, forecast WP. The experimental results reveal that, compared PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, BES-LSTM models, could effectively improve accuracy farms.

Язык: Английский

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An improved temporal convolutional network with attention mechanism for photovoltaic generation forecasting

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 123, С. 106273 - 106273

Опубликована: Апрель 17, 2023

Язык: Английский

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A novel air quality index prediction model based on variational mode decomposition and SARIMA-GA-TCN

Process Safety and Environmental Protection, Год журнала: 2024, Номер 184, С. 961 - 992

Опубликована: Фев. 10, 2024

Язык: Английский

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