Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction by Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan, Roobini Maridhas Santhi

“…In order to choose the best features, a modified chicken swarm optimization algorithm (MCSO) was proposed. Aberrant waves caused by cardiac ailments impacted the dataset patients, according to the suggested research’s unique machine learning methods of multi-module neural network system (MMNNS). …”
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A Study on the Impact of Obstacle Size on Training Models Based on DQN and DDQN by Lu Siyu, Tao Ye, Zeng Junwei, Zuo Qihuan

“…Various parameters such as obstacle size and complexity influence the agent's performance, promoting efficient learning and policy optimization using both DQN and DDQN algorithms under different configurations. …”
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An Enhanced Interval Type-2 Fuzzy C-Means Algorithm for Fuzzy Time Series Forecasting of Vegetation Dynamics: A Case Study from the Aksu Region, Xinjiang, China by Yongqi Chen, Li Liu, Jinhua Cao, Kexin Wang, Shengyang Li, Yue Yin

“…Fuzzy time series (FTS) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions. …”
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Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model by Dongge Cui, Chuanqu Zhu, Qingfeng Li, Qiyun Huang, Qi Luo

“…Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. …”
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Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin by SI Qi, JIN Baoming, LU Wangming, CHEN Zhaoqing

“…The remaining eight floods were used to test and validate the model. The results show that compared with that of the conventional BP neural network model, the convergence speed of the model is faster, and the calculation speed is obviously improved; the deterministic coefficient of the model is greater than 0.87, and the relative error of peak flow of six floods is within 10%. …”
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Research on PRBP Neural Network Flood Forecasting Model in Chongyang River Basin by SI Qi, JIN Baoming, LU Wangming, CHEN Zhaoqing

“…The remaining eight floods were used to test and validate the model. The results show that compared with that of the conventional BP neural network model, the convergence speed of the model is faster, and the calculation speed is obviously improved; the deterministic coefficient of the model is greater than 0.87, and the relative error of peak flow of six floods is within 10%. …”
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Optimization of Reverse Logistics Networks for Hazardous Waste Incorporating Health, Safety, and Environmental Management: Insights from Large Cruise Ship Construction by Huilin Li, Jiaqi Yang, Wei Zhang

“…Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. …”
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Optimization Configuration of Electric–Hydrogen Hybrid Energy Storage System Considering Power Grid Voltage Stability by Yunfei Xu, Yiqiong He, Hongyang Liu, Heran Kang, Jie Chen, Wei Yue, Wencong Xiao, Zhenning Pan

“…This model, applied to the IEEE-33 standard test system, utilizes the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to optimize the capacity and location of the electric–hydrogen hybrid energy storage system. …”
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Active Magnetic Bearing Rotor Model Updating Using Resonance and MAC Error by Yuanping Xu, Jin Zhou, Long Di, Chen Zhao, Qintao Guo

“…Modelling error is minimized by applying a numerical optimization Nelder-Mead simplex algorithm to properly adjust FE model parameters. …”
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An Inter-Regional Lateral Transshipment Model to Massive Relief Supplies with Deprivation Costs by Shuanglin Li, Na Zhang, Jin Qin

“…A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…Experimental results on the real-world Chicago Park District Beach water IoE dataset validate the effectiveness of our proposed approach, showing a 1.4% improvement in accuracy over the traditional voting ensemble model and a 93.3% reduction in training time as well as prediction time. …”
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The financial impact of human resources configuration: A quantitative analysis based on modified single candidate optimizer by Zhuozhuo Zhang, Jun Lu, Qi Wang

“…This paper presents an innovative HR optimization framework that uses a modified metaheuristic model, called the Modified Single Candidate Optimizer (MSCO) algorithm to resolve this task. …”
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BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION by YE Ling, JIANG HongKang, ZOU YuQing, CHEN HuaPeng, WANG LiCheng

“…The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.…”
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Research on autonomous driving scenario modeling and application based on environmental perception data by Ming Cao, Wufeng Duan, Changqing Huo, Song Qiu, Mingchun Liu

“…Results indicated that the optimized autonomous driving algorithm significantly enhances vehicle performance in similar scenarios within highly realistic simulation scenarios, thereby improving the security of algorithm optimization and validation. …”
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Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron by Hossein Moayedi, Azfarizal Mukhtar, Serhan Alshammari, Mohamed Boujelbene, Isam Elbadawi, Quynh T Thi, Mojtaba Mirzaei

“…A series of parametric studies are performed to improve the effectiveness of the employed models. …”
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A Novel Separable Model and Decomposition Method for Sensor Locational Decision Problem by Linfeng Yang, Jie Li, Jin Ye, Zhigang Zhao

“…An improved interior point method based on optimal centering parameter is employed to solve the NLP subproblem. …”
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Two-grid mixed finite element method combined with the BDF2-θ for a two-dimensional nonlinear fractional pseudo-hyperbolic wave equation by Yan Wang, Yining Yang, Nian Wang, Hong Li, Yang Liu

“…In this article, a fast two-grid mixed finite element (T-GMFE) algorithm based on a time second-order discrete scheme with parameter θ is considered to numerically solve a class of two-dimensional nonlinear fractional pseudo-hyperbolic wave models. …”
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Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model by Jin Zhu, Shuowen Yang, Shuyan Li, Nan Zhou, Yi Shen, Jincheng Xing, Lixin Xu, Zhichao Hong, Yifei Yang

“…This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. …”
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ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages by Yan Mo, Shaowei Bai, Wei Chen

“…Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. …”
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Menstrual cycle inspired latent diffusion model for image augmentation in energy production by Gamal M. Mahmoud, Mostafa Elbaz, Wael Said, Amira A. Elsonbaty

“…First, a menstrual cycle-inspired metaheuristic algorithm is integrated to improve generated images’ pixel integrity and structural coherence. …”
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