A Multistep Prediction of Hydropower Station Inflow Based on Bagging-LSTM Model by Lulu Wang, Hanmei Peng, Mao Tan, Rui Pan

“…Under the joint influence of soil, upstream inflow, and precipitation, the inflow is often characterized by time lag, nonlinearity, and uncertainty and then results in the difficulty of accurate multistep prediction of inflow. To address the coupling relationship between inflow and the related factors, this paper proposes a long short-term memory deep learning model based on the Bagging algorithm (Bagging-LSTM) to predict the inflows of future 3 h, 12 h, and 24 h, respectively. …”
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Research on Temperature Prediction of EMU Transformer Based onMultiple Nonlinear Regression by YAN Youjun

“…At present, the use of EMU data is mainly based on quantitative observation and qualitative analysis, lacking of accurate state prediction. Therefore, this paper proposes a prediction algorithm based on multiple nonlinear regression to establish a transformer temperature prediction model. …”
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Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data by Peiyuan Li, Yin Yu, Daning Huang, Zhi‐Hua Wang, Ashish Sharma

“…In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). …”
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A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles by Shuai Sun, Jun Zhang, Jun Bi, Yongxing Wang

“…The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods. …”
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Hybridisation of artificial neural network with particle swarm optimisation for water level prediction by Sarah J. Mohammed, Salah L. Zubaidi

“… Accurate water level (WL) prediction is essential for the efficient management of various water resource projects. …”
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Prediction of magnetohydrodynamics fluid flow with viscous dissipation through artificial neural network by Sewli Chatterjee

“…The Levenberg–Marquardt algorithm to predict the Nusselt Number (NN) and Skin Friction Coefficients (SFCs). …”
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Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning by FENG Xin, LIU Yanju, TONG Hongfu, QIAN Shuni

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LEO satellite Internet resource allocation strategy based on terminal traffic prediction by SHEN Fei, LYU Chengcheng, ZHANG Jiaxuan, RUAN Xiaoting

“…An improved LSTM-ARIMA algorithm was proposed with real datasets by the strategy to accurately predict the data traffic generated in the ground area over a certain period of time in the future. …”
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Predicting Energy Consumption in Building Heating Systems Using Model Identification Methods by Qu Minglu, Du Shanghe, Zhang Xinlin, Yu Zhen, Li Huai

“…During deployment, the predicted <italic>R</italic>² value and total energy consumption deviation were 0.87 and 5.18%, respectively. …”
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Kinetic rate constant prediction supports the conformational selection mechanism of protein binding. by Iain H Moal, Paul A Bates

“…The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. …”
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A dynamic service migration strategy based on mobility prediction in edge computing by Lanlan Rui, Shuyun Wang, Zhili Wang, Ao Xiong, Huiyong Liu

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Energy Consumption Prediction of Building Heating Systems Based on Model Identification Methods by 曲明璐, 杜尚赫, 张欣林, 于 震, 李 怀

“…During deployment, the predicted R2 value and total energy consumption error were 0.87 and 5.18%, respectively. …”
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Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper by Héliton Pandorfi, Alan C. Bezerra, Roberto T. Atarassi, Frederico M. C. Vieira, José A. D. Barbosa Filho, Cristiane Guiselini

“…ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. …”
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Prognosis method to predict small-sized breast cancer affected by fibrocystic disease by S. A. Velichko, E. M. Slonimskaya, I. G. Frolova, D. G. Bukharin, A. V. Doroshenko

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A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs. by Zhichao Miao, Eric Westhof

“…Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. …”
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Meteorological and satellite-based data for drought prediction using data-driven model by ALI H. AHMED SULIMAN

“…The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. …”
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Dynamic feature selection for silicon content prediction in blast furnace using BOSVRRFE by Junyi Duan

“…Abstract Accurate prediction of silicon content in blast furnace ironmaking is essential for optimizing furnace temperature control and production efficiency. …”
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Singular Spectrum Analysis With Conditional Predictions for Real‐Time State Estimation and Forecasting by H. Reed Ogrosky, Samuel N. Stechmann, Nan Chen, Andrew J. Majda

“…A modified version of the traditional SSA algorithm, referred to as SSA with conditional predictions (SSA‐CP), is presented to address these issues. …”
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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. …”
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Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness by Luca Marchetti, Emilio Siena, Mario Lauria, Denise Maffione, Nicola Pacchiani, Corrado Priami, Duccio Medini

“…Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building from previous studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. …”
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