Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. …”
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Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks by Guo Li, Hongyu Sheng

“…The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. …”
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Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks by Nader AL Theeb, Hazem Smadi, Naser Al-qaydeh

“…Furthermore, using BO algorithm for hyperparameters tuning improved the forecasting accuracy. …”
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Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…Moreover, forecast values of sweep force at the current moment help formulate the dispatch plan for the cleaning mechanism in the next moment, thus ensuring precise control of the physical model. The Improved Whale Optimization Algorithm (IWOA) is employed to optimize parameters in this blueprint, facilitating real-time scheduling by rapidly converging to the global optimum. …”
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A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

“…A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. …”
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Classification of Paddy Rice Planting Area Through Feature Selection Method Using Sentinel-1/2 Time Series Images by Shiyu Zhang, Pengao Li, Yong Xie, Wen Shao, Xueru Tian

“…Therefore, this study took Liyang City as the study area, reconstructed Sentinel-2 cloud-free time series optical images, and extracted spectral features, vegetation indexes, and other features, in combination with the polarization features of the Sentinel-1 time series radar images. The optimal feature subset was selected through the feature selection method, and machine learning algorithms were optimized for paddy rice planting area classification. …”
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Torsional Vibration Characterization of Hybrid Power Systems via Disturbance Observer and Partitioned Learning by Tao Zheng, Hui Xie, Boqiang Liang

“…In contrast, incorporating the parameter self-learning algorithm reduces the RMSE to 2.36 N·m, representing an 85.2% improvement in estimation accuracy. …”
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Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…Compared with the traditional parameter optimization approach, this paper uses the immune genetic algorithm to find the optimal hyperparameters of the model, which on the one hand has a wider choice of parameters, and on the other hand has been improved for the genetic algorithm is easy to fall into the local optimal solution, so as to improve the SOC estimation accuracy of the GRU model. …”
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Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment by Oluwatobi Adeleke, Obafemi O. Olatunji, Tien-Chien Jen, Iretioluwa Olawuyi

“…Moreover, understanding the relative importance and contribution of different waste properties to HHV prediction is critical for improving the model's predictive capability and optimizing the waste-to-energy (WTE) process. …”
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Research on early warning model of coal spontaneous combustion based on interpretability by Huimin Zhao, Xu Zhou, Jingjing Han, Yixuan Liu, Zhe Liu, Shishuo Wang

“…XGBoost, SVR, RF, LightGBM and BP models were selected as base models to establish an early warning model for CSC based on the stacking integration architecture. The grid search algorithm was utilized to optimize the model parameters, ensuring the selection of the most suitable parameter configurations. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. …”
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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. …”
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Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states by Chaochen Tang, Xueying Mo, Zhimin Ma, Zhangying Wang

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Beyond the Current Curve: A Novel Curve Warning System Considering Subsequent Curve Speed Limits by N. S. Manikandan, Ganesan Kaliyaperumal

“…Our proposed method, incorporating adjacent curve information, demonstrates significant safety improvements, particularly in reducing the RMSE error when navigating the curves on simulated autonomous vehicles.…”
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Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content by Xuguang Sun, Baoyuan Zhang, Menglei Dai, Cuijiao Jing, Kai Ma, Boyi Tang, Kejiang Li, Hongkai Dang, Limin Gu, Wenchao Zhen, Xiaohe Gu

“…The SPA algorithm also improved the inversion efficiency of LWC. …”
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Time-Variation Damping Dynamic Modeling and Updating for Cantilever Beams with Double Clearance Based on Experimental Identification by Yunhe Zhang, Fanjun Meng, Xueguang Li, Wei Song, Dashun Zhang, Faping Zhang

“…The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. …”
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A calibration procedure for simulation models of rural residential buildings using monthly energy bills: A case study in Zhejiang, China by Yuanyuan Wei, Song He, Ping Huang, Yuechen Duan, Bart Julien Dewancker, Luyao Zhou

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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

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An Enhanced Generative Adversarial Network Prediction Model Based on LSTM and Attention for Corrosion Rate in Pipelines by Pujun Long, Mi Liang, Hongjian Chen, Qin Yang

“…This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). …”
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State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach by Sesidhar Dvsr, Chandrashekhar Badachi, Chandrashekar Nagawaram, Panduranga Chary Kondoju, C. Dhanamjayulu, Innocent Kamwa

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