An optimal neural network to design generators and stabilizers for multi-machine power systems based on a promoted firefly algorithm by Xiujun Nie, Nan Sun, Buqin Wang, Ganbar Akbari

“…Abstract The purpose of this article is to investigate power system stabilizing (PSS) in multi-machine power systems. In this study, special attention has been given to the role of generator and network modelling which has a direct impact on PSS design. …”
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Ensemble-based model to investigate factors influencing road crash fatality for imbalanced data by Nazmus Sakib, Tonmoy Paul, Nafis Anwari, Md. Hadiuzzaman

“…It is the first to train eight distinct binary classification models: Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) under three strategies: in isolation, with bagging, and with optimized bagging techniques (Grid Search CV, Random Search CV, and Bayesian Optimization). …”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

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Effective workflow from multimodal MRI data to model-based prediction by Kyesam Jung, Kevin J. Wischnewski, Simon B. Eickhoff, Oleksandr V. Popovych

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The impact of social security systems on public health outcomes: an economic perspective on machine translation applications by Shuhua Niu, Weihua Li, Li-Li

“…However, traditional methods for evaluating these systems often fail to capture the complex dynamics of policy interventions over time.MethodsTo address this, we propose an advanced economic policy modeling framework that integrates dynamic optimization techniques with machine translation applications. …”
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Cultural Heritage Color Regeneration: Interactive Genetic Algorithm Optimization Based on Color Network and Harmony Models by Zhonghua Jiang, Qianlong Xia, Zhizhou Wang, Kaiwei Zhu, Qianyu Su, Jiajun Wang, Yirui Huang, Bo Wu, Yan Hong

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Optimization of Rockburst Grade Prediction Model Based on Multidimensional Feature Selection: Integrated Learning and Index System Correlation Analysis by Jiayang Chen, Xuebin Xie

“…This study proposes an optimization method based on multidimensional feature selection and integrated learning that systematically evaluates the impact of different indicator dimensions by constructing an indicator–indicator system and an indicator–rockburst hierarchy using a combination of seven-, six-, five-, four-, and three-dimensional indicators in conjunction with six machine-learning models, such as XGBoost, LightGBM, and CatBoost. …”
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Optimization of Reservoir Operation using a Bioinspired Metaheuristic Based on the COVID-19 Propagation Model by Alireza Donyaii, Amirpouya Sarraf

“…The climatic variables downscaled and predicted by the Bias Correction Spatial Disaggregation (BCSD) method of MIROC-ESM model, was introduced into the Extreme Learning Machine (ELM) modelto evaluate the future runoff flowing into the reservoir. …”
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A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective by Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

“…This paper presents an overview of recent developments in optimization and design automation techniques for EM-component design and modeling. …”
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Research on optimization of temperature sensitive points of machine tool thermal error based on independent variable selection criteria by Huadong Li, Jingjun Liu, Yang Li

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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

“…In this study, we conducted a comprehensive meta-analysis to analyze the relationship between the machine learning model performance and variables such crop type, the type of aerial phenotyping platform, the phenological stage, etc A trait-based comparison of the efficiency and popularity of machine learning algorithms was conducted. …”
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A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir HimalayaZenodo by Syed Danish Rafiq Kashani, Faisal Zahoor Jan, Imtiyaz Ahmad Bhat, Nadeem Ahmad Najar, Irfan Rashid

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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Research on Monthly Runoff Forecast in Dry Seasons Based on GEO-RVM Model by ZHANG Yajie, CUI Dongwen

“…To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a from a hydrological station in Yunnan Province,the monthly runoff with good correlation before the forecast month is selected as the influencing factor of forecasts,and the influencing factor is reduced in dimension by principal component analysis (PCA).The kernel width factor and hyperparameters of RVM are optimized by the GEO algorithm,and the GEO-RVM model is built to forecast the monthly runoff of the station during the dry season from November to April of the following year.Moreover,the forecast results are compared with those of the GEO-based support vector machine (SVM) model (GEO-SVM).The results demonstrate that the average relative errors of the GEO-RVM model for the monthly runoff forecasts from November to April of the following year are 8.59%,7.34%,5.97%,6.07%,5.99%,and 5.04%,respectively,which means the accuracy is better than that of the GEO-SVM model.The GEO algorithm can effectively optimize the kernel width factor and hyperparameters of RVM,and the GEO-RVM model has better forecast accuracy,which can be used for monthly runoff forecasting during dry seasons.…”
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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models by Jitendra Khatti, Mohammadreza Khanmohammadi, Yewuhalashet Fissha

“…Each SRVM model has been optimized by each genetic (GA_SRVM) and particle swarm optimization (PSO_RVM) algorithm. …”
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Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior by Luis H. Quitian-Ardila, Yamid J. Garcia-Blanco, Angel De J. Rivera, Raquel S. Schimicoscki, Muhammad Nadeem, Oriana Palma Calabokis, Vladimir Ballesteros-Ballesteros, Admilson T. Franco

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Rheological behavior of MWCNT-SnO2/SAE50 hybrid nanolubricant: Experimental evaluation and viscosity prediction using optimized machine learning model by Mojtaba Sepehrnia, Hamid Maleki, Alireza Hamidi, Ali Davoodi, Hamidreza Golmohammadi

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A hybrid machine learning method of support vector regression with particle swarm optimization for predicting IRI in continuously reinforced concrete pavement by Ali Alnaqbi, Waleed Zeiada, Ghazi Al-Khateeb

“…In order to forecast IRI using data taken from the Long-Term Pavement Performance (LTPP) database, this study proposes a hybrid machine learning model that combines Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). …”
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Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review by Mohit Malik, Vijay Kumar Gahlawat, Rahul S Mor, Vijay Dahiya, Mukheshwar Yadav

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