Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…Subsequently, six machine learning (ML) models were developed and compared to identify the optimal model. …”
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Optimizing oil production forecasts in Iranian oil fields: a comprehensive analysis using ensemble learning techniques by Mohammad Ghodsi, Pouya Vaziri, Mahdi Kanaani, Behnam Sedaee

“…The inclusion of advanced optimization strategies, such as Genetic Algorithm (GA), Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO), ensures that each base model reaches its highest potential performance. …”
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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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Research on the Location-Routing Optimization of International Freight Trains Considering the Implementation of Blockchain by Zhichao Hong, Hao Shen, Wenjie Sun, Jin Zhang, Hongbin Liang, Gang Zhao

“…In this case, 43 stations in the Indo-China Peninsula are selected as origin stations, and two Chinese stations are designated terminal stations. An improved NSGA-II algorithm (ANSGAII-OD) is proposed to resolve the location-routing optimization model. …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…In all experiments, XGBoost performed best individually, while SVM was worst. The ensemble models outperformed individuals, with the GridSearchCV ensemble achieving 97.3% accuracy, an improvement exceeding the existing literature’s models by 2.3%. …”
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Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials by Jiahao Ye, Lirong Xie, Lan Ma, Yifan Bian, Chuanshi Cui

“…Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multi-objective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudo-inverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). …”
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Fire spread simulations using Cell2Fire on synthetic and real landscapes by Minho Kim, Cristobal Pais, Marta C. Gonzalez

“…In response, we used two optimization methods to improve the simulation’s accuracy. …”
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Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection by R. Hari Prasada Rao, Agam Das Goswami

“…Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet −101 deep learning approach that effectively improves the performance of the model. …”
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Multiobjective Optimization of Stress-Release Boot of Solid Rocket Motor under Vertical Storage Based on RBF Model by Qiuwen Miao, Huihui Zhang, Zhibin Shen, Weiyong Zhou

“…To optimize a SRM with star and finocyl grain, the RBF (radial basis functions) model that satisfies the accuracy requirements was established based on parametric modeling technology and the OPLHS (Optimal Latin Hypercube Sampling) method. …”
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Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia by Yi Yang, Yao Dong, Yanhua Chen, Caihong Li

“…In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. …”
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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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Optimization Research on Magnetic Interference Parameter Identification and Compensation for AUV Platforms by Haodong Wen, Guohua Zhou, Kena Wu, Xinkai Hu, Liezheng Tang, Shuai Xia

“…To further improve training performance, a stacking ensemble learning (STACKING) model is introduced, with L-SHADE and BPNN as base learners and Convolutional Neural Network (CNN) as the meta-learner, integrating the advantages of both algorithms for optimization. …”
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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…A dataset comprising 63 observations from a quarry mine in Malaysia is employed, with influential parameters normalized and utilized for model development. Consequently, we integrate optimization algorithms (GOA and GWO) with MLP to fine-tune the model’s parameters and improve prediction accuracy. …”
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Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System by Nu Wen, Ying Zhou, Yang Wang, Ye Zheng, Yong Fan, Yang Liu, Yankun Wang, Minmin Li

“…To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. …”
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Design and optimization of an open personalized human-computer interaction system for New Year Painting based on the learner's model by Zaozao Guo, Muhamad Firdaus Ramli, Wenpeng Zhang

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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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Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks by R. Kanmani, S. Mary Praveena

“…To address these limitations, this work proposes a novel bio-inspired Termite Colony Optimization-based Multi-Agent System (TCO-MAS) integrated with an LSTM model for predictive adaptability. …”
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Strength prediction and optimization for microwave sintering of large-dimension lithium hydride ceramics: GA-BP-ANN modeling by Hongzhou Yan, Huayan Chen, Wenyan Zhang, Maobing Shuai, Bin Huang

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Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams by Zhengze Huang, Yuqi Bai, Hengyu Liu, Yun Lin

“…This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. …”
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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. …”
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