Estimation of sugarcane biomass from Sentinel-2 leaf area index using an improved SAFY model (SAFY-Sugar) by Jingyuan Xu, Xin Du, Taifeng Dong, Qiangzi Li, Yuan Zhang, Hongyan Wang, Miao Liu, Jiong Zhu, Jian Yang

“…The Simple Algorithm for Yield Estimation model (SAFY), a semi-physical crop growth model grounded in light use efficiency theory has been widely adopted for satellite-based biomass estimation in major field crops. …”
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Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. by Huili Dou, Sirui Chen, Fangyuan Xu, Yuanyuan Liu, Hongyang Zhao

“…In order to improve the detection performance of the model, we introduced a multi-scale feature fusion module and an improved non-maximum suppression (NMS) algorithm based on the YOLOv8 model. …”
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Sensitivity Analysis for Dynamic Parameters of High–Speed Train Based on Multimodal–Optimization Improved Kriging Model and Distance Correlation by Jie JIANG, Xufeng YANG, Guofu DING

“…To improve the accuracy of the approximation model, a novel multimodal optimization algorithm is introduced to globally optimize the hyperparameters of these Kriging models. …”
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An Improved Optimal Cloud Entropy Extension Cloud Model for the Risk Assessment of Soft Rock Tunnels in Fault Fracture Zones by Shuangqing Ma, Yongli Xie, Junling Qiu, Jinxing Lai, Hao Sun

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Wireless sensor network positioning technology based on improved sampling box and fuzzy reasoning by Ningyun Dan, Lun Zhao, Wenjin Pan, Yan Cai

“…By using a fuzzy clustering algorithm based on time series optimization and a multidimensional Gaussian model to optimize the sampling box, the accuracy and efficiency of localization are significantly improved. …”
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Global‐Scale Groundwater Recharge Modeling Is Improved by Tuning Against Ground‐Based Estimates for Karst and Non‐Karst Areas by Wenhua Wan, Petra Döll, Hannes Müller Schmied

“…With an increase in the coefficient for the discharge from groundwater to surface water bodies, the updated GWR algorithm results in an improved fit of simulated streamflow to observations, including low flows. …”
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Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks by Haiping LIANG, Haoyan SHI, Yan WANG, Yingpei LIU, Xinming WANG

“…An improved particle swarm optimization (PSO) algorithm is proposed for the optimization model, which uses such methods as the multi-dimensional indefinite length coding, sub-group collaborative optimization, and Monte-Carlo-simulation-based fitness evaluation to improve the standard PSO algorithm. …”
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Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach by Dong Zhan, Ai-Qing Tian, Shao-Quan Ni

“…This study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). …”
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Evaluating energy efficiency in Turkish electric distribution using network DEA and GA models by Serpil Aydin, Talat Şenel

“…For 2015, only Boğaziçi EDC is efficient among the twenty-one distribution companies evaluated in detail. The NDEA model with sub-processes provided more realistic efficiency scores than traditional DEA. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…In this paper, we propose a general framework that combines advanced deep learning models (such as GRU, Bidirectional GRU (BIGRU), Stacked GRU, and Attention-based BIGRU) with a novel hybridized optimization algorithm, GGBERO, which is a combination of Greylag Goose Optimization (GGO) and Al-Biruni Earth Radius (BER). …”
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Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model. by Chao Cao, Ziyu Li, Lingzhi Li, Fanglu Luo

“…This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…To overcome these issues, this text proposes design of a Dual Genetic Algorithm (DGA) based Auto regression model for Correlative prediction (AC) of Air Quality Metrics. …”
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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…This manuscript presents an Enhance Malicious Domain Detection Using an Attention-Based Deep Learning Model with Optimization Algorithms (EMDD-ADLMOA) technique. …”
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A Novel Hyper-Heuristic Algorithm with Soft and Hard Constraints for Causal Discovery Using a Linear Structural Equation Model by Yinglong Dang, Xiaoguang Gao, Zidong Wang

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Squirrel search algorithm-support vector machine: Assessing civil engineering budgeting course using an SSA-optimized SVM model by He Yanqing, Shi Ling, Yao Xiaoqin, Zhang Haojie, Al-Barakati Abdullah A.

“…The above results reveal that the proposed optimization algorithm and course evaluation model have good performance. …”
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Modelling the evolutionary dynamics of drug resistance in tuberculosis using hypercube and Baum-Welch algorithm inference: A case study of Tanzania by Leonce Leandry, Egbert Mujuni, Morten Brun, Eunice W. Mureithi, Mbazi Senkoro, Saidi Mfaume

“…To address some of these issues, the study investigated the evolutionary dynamics of drug resistance using a 6-hypercubic model, Hidden Markov Model (HMM) and the Baum-Welch algorithm for inference. …”
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Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms by Mohammed Maray

“…The EARDP-DLMNOA model mainly relies on improving the activity recognition model using advanced optimization algorithms. …”
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Multi-Agent Communication for Dynamic Job-Shop Scheduling: A Robust Single-Machine Scheduling Model With Genetic Algorithm Optimization by Hong-Phuc Nguyen, Quang-Hieu Ngo, van-Can Nguyen

“…Decisional entities accept extra costs if there is an inconsistency between the start and completion times of two consecutive operations. The genetic algorithm is employed to solve the single-machine scheduling models. …”
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A cross-chain model for warehouse receipts in port supply chain based on notary mechanism and ShangMi cryptographic algorithms by Yangbo Chen, Wei Ou, Mengxue Pang, Jianqiang Ma, Qiuling Yue, Wenbao Han

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