Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 55(4), P. 4519 - 4622

Published: Oct. 31, 2022

Language: Английский

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Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 283, P. 116916 - 116916

Published: March 16, 2023

Language: Английский

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Ultra-short-term forecasting of wind power based on multi-task learning and LSTM

International Journal of Electrical Power & Energy Systems, Journal Year: 2023, Volume and Issue: 149, P. 109073 - 109073

Published: March 5, 2023

Language: Английский

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A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

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An ensemble method for short-term wind power prediction considering error correction strategy

Applied Energy, Journal Year: 2022, Volume and Issue: 322, P. 119475 - 119475

Published: June 22, 2022

Language: Английский

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A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer

Sensors, Journal Year: 2023, Volume and Issue: 23(6), P. 2932 - 2932

Published: March 8, 2023

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation can help to diagnose at an early stage, lowering workload of radiologists and boosting rate diagnosis. Artificial intelligence-based neural networks promising technologies for automatically detecting employing patient monitoring data acquired from sensor technology through Internet-of-Things (IoT)-based system. However, standard rely on manually features, which reduces effectiveness detection. In this paper, we provide novel IoT-enabled healthcare platform improved grey-wolf optimization (IGWO)-based deep convulution network (DCNN) model The Tasmanian Devil Optimization (TDO) algorithm utilized select most pertinent features diagnosing nodules, convergence grey wolf (GWO) modified, resulting in GWO algorithm. Consequently, IGWO-based DCNN trained optimal obtained IoT platform, findings saved cloud doctor's judgment. built Android with DCNN-enabled Python libraries, evaluated against cutting-edge detection models.

Language: Английский

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Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework

Energy, Journal Year: 2023, Volume and Issue: 278, P. 127864 - 127864

Published: May 19, 2023

Language: Английский

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Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM

Energy Reports, Journal Year: 2023, Volume and Issue: 9, P. 6449 - 6460

Published: June 16, 2023

Accurate prediction of short-term wind power plays an important role in the safe operation and economic dispatch grid. In response to current single algorithm that cannot further improve accuracy, this study proposes a combined model based on data processing, signal decomposition, deep learning. First, outliers original can affect accuracy. This detects by Z-score method fills them with cubic spline interpolation ensure integrity data. Second, for volatility power, time series is decomposed using complete ensemble empirical modal decomposition adaptive noise (CEEMDAN). The component complexity calculated sample entropy (SE), components are reconstructed according SE size Finally, traditional convolutional neural network (CNN) structure improved bi-directional long memory (BiLSTM) used extract feature links between superimpose results each obtain final value. experimental demonstrate hybrid proposed has better performance terms performance.

Language: Английский

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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 205 - 205

Published: Jan. 16, 2025

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding management, and optimizing resource allocation. This study proposes a method predicting yields in China’s major winter wheat-producing regions using MOD13A1 data deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of Convolutional Neural Network (CNN) with IGWO, accuracy significantly enhanced. Additionally, explores potential Green Normalized Difference Vegetation Index (GNDVI) prediction. The research utilizes collected from March to May between 2001 2010, encompassing vegetation indices, environmental variables, statistics. results indicate that IGWO-CNN outperforms traditional machine approaches standalone CNN models terms accuracy, achieving highest performance R2 0.7587, RMSE 593.6 kg/ha, MAE 486.5577 MAPE 11.39%. finds April optimal period early wheat. validates effectiveness combining remote sensing prediction, providing technical support precision agriculture contributing global food security sustainable development.

Language: Английский

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RDERL: Reliable deep ensemble reinforcement learning-based recommender system

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 263, P. 110289 - 110289

Published: Jan. 11, 2023

Language: Английский

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