Hyperparameter Optimization for Deep NeuralNetwork Models: A Comprehensive Study onMethods and Techniques DOI Creative Commons
Sunita Roy, Ranjan Mehera, Rajat Kumar Pal

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Июнь 23, 2023

Abstract Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machinelearning (ML) experts. Although a generic model can be used search fora near-optimal solution any problem domain, what makes these DL modelscontext-sensitive is combination training data hyperparameters. Due lack inherent explainability HyperparameterOptimization (HPO) or tuning specific each art,science, experience. In this article, we explored various existing methods ways identify optimal set values for hyperparameters specificto along with techniques realize those real-lifesituations. The article also includes detailed comparative study variousstate-of-the-art HPO using Keras Tuner toolkit highlights observations describing how performance improvedby applying techniques.

Язык: Английский

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review DOI Open Access
Mehrdad Kaveh, Mohammad Saadi Mesgari

Neural Processing Letters, Год журнала: 2022, Номер 55(4), С. 4519 - 4622

Опубликована: Окт. 31, 2022

Язык: Английский

Процитировано

137

Sparrow search algorithm with adaptive t distribution for multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time DOI

Huizhen Zhang,

Qin Huang, Liang Ma

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122042 - 122042

Опубликована: Окт. 12, 2023

Язык: Английский

Процитировано

27

Storm-time ionospheric model over Yunnan-Sichuan area of China based on the SSA-ConvLSTM-BiLSTM algorithm DOI
Wang Li, H. Zhu,

Fangsong Yang

и другие.

GPS Solutions, Год журнала: 2025, Номер 29(2)

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

1

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization DOI Creative Commons
Marcos Avilés, José M. Álvarez-Alvarado, J.B. Robles-Ocampo

и другие.

Bioengineering, Год журнала: 2024, Номер 11(1), С. 77 - 77

Опубликована: Янв. 13, 2024

Accurate classification of electromyographic (EMG) signals is vital in biomedical applications. This study evaluates different architectures recurrent neural networks for the EMG associated with five movements right upper extremity. A Butterworth filter was implemented signal preprocessing, followed by segmentation into 250 ms windows, an overlap 190 ms. The resulting dataset divided training, validation, and testing subsets. Grey Wolf Optimization algorithm applied to gated unit (GRU), long short-term memory (LSTM) architectures, bidirectional networks. In parallel, a performance comparison support vector machines (SVMs) performed. results obtained first experimental phase revealed that all RNN evaluated reached 100% accuracy, standing above 93% achieved SVM. Regarding speed, LSTM ranked as fastest architecture, recording time 0.12 ms, GRU 0.134 Bidirectional showed response 0.2 while SVM had longest at 2.7 second phase, slight decrease accuracy models observed, 98.46% LSTM, 96.38% GRU, 97.63% network. findings this highlight effectiveness speed task.

Язык: Английский

Процитировано

9

Novel hybrid success history intelligent optimizer with Gaussian transformation: application in CNN hyperparameter tuning DOI
Hussam N. Fakhouri, Sadi Alawadi, Feras M. Awaysheh

и другие.

Cluster Computing, Год журнала: 2023, Номер 27(3), С. 3717 - 3739

Опубликована: Ноя. 6, 2023

Язык: Английский

Процитировано

16

Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach DOI Creative Commons
Cun Chen,

Xiaoli Han,

Yong Zhang

и другие.

Computational Materials Science, Год журнала: 2024, Номер 239, С. 112976 - 112976

Опубликована: Март 29, 2024

Язык: Английский

Процитировано

5

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification DOI Open Access
Tsu‐Yang Wu, Haonan Li, Saru Kumari

и другие.

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2024, Номер 79(1), С. 19 - 46

Опубликована: Янв. 1, 2024

Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving highprecision remains significant challenge.In response to this challenge, Spectral Convolutional Neural Network model based on Adaptive Fick's Law Algorithm (AFLA-SCNN) is proposed.The (AFLA) constitutes novel metaheuristic algorithm introduced herein, encompassing three new strategies: weight factor, Gaussian mutation, and probability update policy.With adaptive can adjust weights according change in number iterations improve performance algorithm.Gaussian mutation helps avoid falling into local optimal solutions improves searchability algorithm.The strategy exploitability adaptability algorithm.Within AFLA-SCNN model, AFLA employed optimize two hyperparameters SCNN namely, "numEpochs" "miniBatchSize", attain their values.AFLA's initially validated across 28 functions 10D, 30D, 50D for CEC2013 29 CEC2017.Experimental results indicate AFLA's marked superiority over nine other prominent optimization algorithms.Subsequently, was compared with (FLA-SCNN), Harris Hawks Optimization (HHO-SCNN), Differential Evolution (DE-SCNN), (SCNN) Support Vector Machines (SVM) using Indian Pines dataset Pavia University dataset.The experimental show that outperforms models terms Accuracy, Precision, Recall, F1-score University.Among them, Accuracy reached 99.875%, 98.022%.In conclusion, our proposed deemed significantly enhance precision hyperspectral classification.

Язык: Английский

Процитировано

3

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems DOI
Shuang Liang, Minghao Yin, Geng Sun

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101603 - 101603

Опубликована: Май 18, 2024

Язык: Английский

Процитировано

3

Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm DOI Creative Commons
Amna Bamaqa, Waleed M. Bahgat, Yousry AbdulAzeem

и другие.

Results in Engineering, Год журнала: 2024, Номер 24, С. 102985 - 102985

Опубликована: Сен. 30, 2024

Язык: Английский

Процитировано

3

Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model DOI Creative Commons

Fanchao Zeng,

Q. Gao, Lifeng Wu

и другие.

Atmosphere, Год журнала: 2025, Номер 16(4), С. 419 - 419

Опубликована: Апрель 4, 2025

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework short-term forecasting using SPEI time series (1979–2020) evaluates three predictive models: (1) baseline XGBoost model (XGBoost1), (2) feature-optimized variant incorporating Pearson correlation analysis (XGBoost2), (3) an enhanced CPSO-XGBoost integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection parameter tuning. Key findings reveal spatiotemporal patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R2: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking SPEI-12 where attains optimal performance 0.85–0.90, 0.33–0.43) 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak (R2 ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) dramatic improvement from 0.35, > 1.0) to 0.85, 52%). Multivariate probability density confirms the model’s robustness through capture nonlinear atmospheric-land interactions reduced parameterization uncertainties via intelligence optimization. The CPSO-XGBoost’s superiority stems synergistic optimization: enhances input relevance adaptive tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These establish advanced early warning systems, providing critical support climate-resilient management risk mitigation spatiotemporally predictions.

Язык: Английский

Процитировано

0