Enhanced genetic algorithm for indoor low-illumination stereo matching energy function optimization by Zhang Hongjin, Wei Hui

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Numerical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm by Ying-Chun CHANG, Min-Chie CHIU, Meng-Ru WU

“…With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.…”
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RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 by Wenjie Tang, Yangjun Deng, Xu Luo

“…We propose an enhanced chip surface defect detection algorithm based on an improved version of YOLOv8, termed RST-YOLOv8. …”
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Research on Improved Equivalent Diagonal Strut Model for Masonry-Infilled RC Frame with Flexible Connection by Guang Yang, Erfeng Zhao, Xiaoya Li, Emad Norouzzadeh Tochaei, Kan Kan, Wei Zhang

“…Employed with inversion analysis theory, the parameter in the proposed model was estimated through artificial fish swarm algorithm. …”
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A zenith wet delay improved model in China based on GPT3 and random forest by Shaoni Chen, Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Guangsheng Liu

“…To more accurately capture and predict complicated variations in ZWD, this paper developed the CRZWD model by a combination of the GPT3 model and random forests (RF) algorithm using 5-year atmospheric profiles from 70 radiosonde (RS) stations across China. …”
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Precise Rail Transit Sleeper Positioning Technology Based on Improved YOLOv8n Model by YAN Xiaoxia, GUO Jianqin, ZHAI Huchao, LIU Yutao, LI Junxin, WANG Liangxian

“…Through optimization of loss function and structural constraints on sleeper counting, a sleeper object detection model YOLOv8n_SC is proposed based on the improved YOLOv8n model. …”
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HAD-YOLO: An Accurate and Effective Weed Detection Model Based on Improved YOLOV5 Network by Long Deng, Zhonghua Miao, Xueguan Zhao, Shuo Yang, Yuanyuan Gao, Changyuan Zhai, Chunjiang Zhao

“…Therefore, this study, focusing on three common types of weeds in the field—<i>Amaranthus retroflexus</i>, <i>Eleusine indica</i>, and <i>Chenopodium</i>—proposes the HAD-YOLO model. With the purpose of improving the model’s capacity to extract features and making it more lightweight, this algorithm employs the HGNetV2 as its backbone network. …”
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Improvement in positional accuracy of neural-network predicted hydration sites of proteins by incorporating atomic details of water-protein interactions and site-searching algorith... by Kochi Sato, Masayoshi Nakasako

“…Here, we report the improvements in prediction accuracy by the reorganized CNN together with the details in the architecture, training data, and peak search algorithm.…”
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Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods by Bingkun Luo, Peter J. Minnett, Chong Jia

“…This study aimed to assess the potential to improve the accuracy of satellite-based <i>SST_skin</i> retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). …”
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Low pressure PEM electrolyzer system modeling with heat loss representation by Asmaa A. Ghany, Mohamed Mahmoud Samy

“…These systems function efficiently at reduced pressures, leading to lower cost of operation and improved safety. A mathematical model is developed for the PEM electrolyzer to enhance the prediction of the system's behavior and output parameters, accompanied by a brief description of the assumption to simplify the model. …”
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Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin, Yifan Chen

“…To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. …”
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‌Rockburst intensity grading prediction based on the LOF-ENN-KNN model‌ by Haoran Ge, Jiyong Zhang, Congbo Ma, Kai Hui, Yicong Li, Ziniu Wu

“…Through the comparison and exploration of different sampling methods and different combinations of LOF algorithm, single resampling technology (SMOTE, ADASYN) or simple technology superposition (LOF-SMOTE, LOF-ADASYN) is easy to introduce over-fitting or negative coupling effect, while LOF-ENN-KNN significantly improves the robustness and generalization ability of the model through modular design. …”
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Numerical Modeling on the Damage Behavior of Concrete Subjected to Abrasive Waterjet Cutting by Xueqin Hu, Chao Chen, Gang Wang, Jenisha Singh

“…In this study, a numerical framework based on a coupled Smoothed Particle Hydrodynamics (SPH)–Finite Element Method (FEM) algorithm incorporating the Riedel–Hiermaier–Thoma (RHT) constitutive model is proposed to investigate the damage mechanism of concrete subjected to abrasive waterjet. …”
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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. …”
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Novel CP model and CP-assisted meta-heuristic algorithm for flexible job shop scheduling with preventive maintenance by Lixin Zhao, Leilei Meng, Weiyao Cheng, Yaping Ren, Biao Zhang, Hongyan Sang

“…Finally, the experimental evaluation on benchmark instances validates the capability of the CP model and CVNSQ-CP. Specifically, compared with existing mathematical models, the proposed CP model proves 3 new optimal solutions and improves 11 current best-known solutions for FJSP-FPM, and it improves 13 current best-known solutions for FJSP-PPM. …”
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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

“…Focaler inner IoU is incorporated to improve bounding box matching and localization, thereby accelerating model convergence. …”
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FOX-TSA hybrid algorithm: Advancing for superior predictive accuracy in tourism-driven multi-layer perceptron models by Sirwan A. Aula, Tarik A. Rashid

“…Results indicate that the hybrid FOX-TSA achieves superior predictive accuracy (94.64 %), faster convergence speed (reducing iterations by 25 %), and improved F1-score (94.63 %) on the test dataset. These findings underline the potential of the hybrid FOX-TSA algorithm to advance predictive modelling in the tourism sector and other domains requiring complex data handling.…”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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Auxiliary Model-Based Multiple Innovation Recursive Algorithm on Nonlinear Systems utilizing KeyTerm Separation Technique by Fang Qiu, Yan Ji

“…For further improving the parameter estimation accuracy, an auxiliary model-based multi-innovation extended least-squares algorithm is presented by using the multi-innovation identification theory. …”
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An efficient approach for mathematical modeling and parameter estimation of PEM fuel based on Young’s double-slit experiment algorithm by Basma S. Alqadi, Deema Mohammed Alsekait, Mohamed F. Issa, Essam H. Houssein, Fatma H. Ismail, Mokhtar Said, Nour Mostafa, Fahmi Elsayed

“…Abstract This paper introduces a novel optimization algorithm, Young’s double-slit experiment algorithm (YSDE), for accurately estimating the unknown parameters of Proton Exchange Membrane Fuel Cell (PEMFC) models. …”
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