Employment of a Radial Basis Function Model for Predicting the Heating Load of Construction by Yuxuan Dai

“…The overall objective is to boost the precision of HL predictions and simplify the optimization process of HVAC systems. …”
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Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization by Shuai Wu, Huafeng Cai

“…To address the challenges of fluctuating power loads and inaccurate predictions by conventional methods, this paper presents a novel hybrid framework combining Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and the Improved Sparrow Search Algorithm (ISSA). …”
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Educational Data Mining: A Foundational Overview by Ilias Papadogiannis, Manolis Wallace, Georgia Karountzou

Subjects: “…educational data mining…”
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Efficient Ensemble Learning-Based Models for Plastic Hinge Length Prediction of Reinforced Concrete Shear Walls by Naser Safaeian Hamzehkolaei, Mohammad Sadegh Barkhordari

“…This study aims to develop practical machine-learning (ML) models for PHL prediction of RCSWs. For this purpose, 721 data of nonplanar and rectangular RCSWs were utilized. …”
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Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme by Ying Nie, He Bo, Weiqun Zhang, Haipeng Zhang

“…Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. …”
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Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network by Yifei Zhang, Gengxin Zhang, Dawei Wu, Qian Wang, Ebrahim Nadimi, Penghua Shi, Hongming Xu

“…Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. …”
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Prediction of Vehicle Interior Wind Noise Based on Shape Features Using the WOA-Xception Model by Yan Ma, Hongwei Yi, Long Ma, Yuwei Deng, Jifeng Wang, Yudong Wu, Yuming Peng

“…The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. …”
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A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models by Athanasios Donas, Ioannis Kordatos, Alex Alexandridis, George Galanis, Ioannis Th. Famelis

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Research on the Stability Prediction and Optimization of CNC Milling Based on Bagging–NSGAⅡ Under the Influence of Multiple Factors by Congying DENG, Qian YOU, Yang ZHAO, Lijun LIN, Guofu YIN

“…Considering these multiple influencing factors, herein, a method is proposed to predict the milling stability and determine optimal machining parameters based on a bootstrap aggregating (bagging) procedure and the non-dominated sorting genetic algorithm–Ⅱ (NSGA–Ⅱ). …”
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The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals by Yang Liu, Yang Liu, Renzhao Liang, Chengzhi Zhang

“…We employed six machine learning algorithms to predict the probability of LOS.ResultsAfter implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. …”
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Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms by Songsheng Lin, Huanting Chen, Yin Zheng, Quanji Xie, Xuehua Shen, Huichuan Lin, Shuo Lin, Yan Li

“…Four deep learning algorithms were evaluated. Each model was trained to reconstruct the SPD curves and predict the corresponding optical and chromatic parameters. …”
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Optimizing Pile Bearing Capacity Prediction Using Specific Random Forest Models Optimized by Meta-Heuristic Algorithms for Enhanced Geomechanically Applications by Nengyuan Chen

“…To achieve highly accurate predictions of Pile Bearing Capacity (PBC), the study employs a cutting-edge approach featuring Specific Random Forest (RF) prediction models, strategically enhanced with two potent meta-heuristic algorithms: the Snake Optimizer (SO) and the Equilibrium Optimizer (EO). …”
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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study by Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó

“…Abstract Background Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM. …”
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Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms by Yuyan Annie Pan, Fuliang Li, Anran Li, Zhiqiang Niu, Zhen Liu

“…This manuscript introduces a novel framework, the Physics-Guided Spatio-Temporal Graph Neural Network (PG-STGNN), specifically designed for traffic flow prediction. By integrating the principles of traffic flow physics with advanced spatio-temporal graph neural network algorithms, the framework captures complex spatio-temporal dependencies in traffic networks. …”
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Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring by Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU

“…In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things, a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring (ACB) was proposed.Firstly, the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1 000 bit, a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1 000 bit.On this basis, a method was proposed for predicting the access application volume based on the quantitative estimation, and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms, the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.…”
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Advanced Control Algorithm for Shunt Active Power Filter: Enhancing Power Quality in Autonomous Grids by Agata Bielecka

“…This paper proposes an advanced predictive control algorithm with feedback from the supply current developed for a shunt active power filter. …”
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A Probability Integral Parameter Inversion Method Integrating a Selection-Weighted Iterative Robust Genetic Algorithm by Chuang Jiang, Wei Liu, Lei Wang, Xu Zhu, Hao Tan

“…However, the use of traditional genetic algorithms (GA) for inversion prediction has problems such as poor resistance to differences, and the accuracy of inversion parameters is affected when key monitoring points are missing. …”
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A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis by Bin Li, Bo Qiao, Qianyu Zhao, Dan Yang, Rongcheng Zhu, Zhexuan Wang, Yujie Yang

Subjects: “…photosynthetic rate prediction model…”
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Modeling and Discovering Direct Causes for Predictive Models by Yizuo Chen, Amit Bhatia

“… We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

Subjects: “…Runoff prediction…”
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