Intelligent Identification and Prediction of Roof Deterioration Areas Based on Measurements While Drilling by Jing Wu, Zhi-Qiang Zhao, Xiao-He Wang, Yi-Qing Wang, Xiao-Xiang Wei, Zhi-Qiang You

“…Based on these findings, this paper proposes a deep learning algorithm that employs Long Short-Term Memory (LSTM) recurrent neural networks for classification prediction, along with a random forest algorithm for regression prediction, aimed at the intelligent identification and prediction of roof deterioration zones. …”
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The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression by Chunxiao Zhang, Junjie Yue

“…The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. …”
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Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction by Sheng Cang, Achuan Wang

“…In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. …”
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Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion by Junping Yao, Tianle Sun

“…In this paper, a rainfall runoff pollution prediction method based on grey neural network algorithm is proposed in consideration of the current situation that the accuracy of research results related to rainfall runoff pollution prediction needs to be improved. …”
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A Prediction Model Based on the Long Electrode Source for Fault Anomaly in Tunnel by Daiming Hu, Hao Liu, Xiaodong Yang, Mingxin Yue

“…The resistivity method has been widely used to predict the water-bearing structure of tunnels. The traditional resistivity uses the point electrode (PE) source in the tunnel to excite the electric field. …”
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Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging by Yunjuan Yan, Weixiong Zha, Jungang Shi, Liping Yan

“…Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified.…”
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Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model by Dongge Cui, Chuanqu Zhu, Qingfeng Li, Qiyun Huang, Qi Luo

“…The results show that both the GM (1, 1) and BP neural network models can predict accurate results. The prediction optimized by the particle swarm algorithm is more accurate and has more substantial applicability. …”
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Machine learning for workpiece mass prediction using real and synthetic acoustic data by D. S. Whittaker, J. Gregório, T. F. Byrne

“…Abstract We apply a feedforward neural network using supervised learning to sound recordings obtained without specialised equipment as workpieces undergo a simple manufacturing process to predict their mass. We also report a simple technique to seed synthetic from real data for training and testing the algorithm. …”
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Prediction of Bus Arrival Time Based on Gated Recurrent Unit Neural Networks by LU Juntian;SUN Ling;SHI Quan

“…In order to increase the public transportation usage and the reasonability of the bus schedule by the management department, a novel prediction model of bus arrival time is proposed. This predicting model based on gated recurrent unit(GRU) neural network, analyzed the big data of historical GPS data about floating vehicle and considers the influence of different routes, bus station location, different drivers, weather conditions, time distribution and other factors. …”
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Predicting Missing Links Based on Power-Law Distribution and Information Transmission Efficiency by Yi Wang, Jun Ma

“…Through experiments on multiple real-world networks, the algorithm demonstrates high prediction accuracy and achieves impressive results when evaluated using standard metrics. …”
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EVALUATION OF MACHINE LEARNING MODELS FOR BORON PREDICTION IN ANDISOL SOILS OF NARIÑO-COLOMBIA by David Álvarez Sánchez, Xilena López Estrella, Eduar Manso Ordoñez, Laura López Rivera, Jeison Rodriguez Valenzuela

“…To explore the application of ML tools for the prediction of Boron levels in Andisols soils of Nariño has been explored and identifying the most efficient algorithm. …”
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Risk factors and bone mineral density in predicting the risk of fracture in postmenopausal women by O. A. Nikitinskaya, N. V. Toroptsova, N. V. Demin

“…The Russian model FRAX®, an algorithm for estimating the 10-year absolute risk of fractures, which is based on the identification of risk factors that increase fracture risk, was proposed in 2012 to detect people at high risk for fracture.Objective: to estimate the sensitivity and specificity of the Russian model FRAX® versus dual energy X-ray absorptiometry (DEXA) for predicting high fracture risk.Patients and methods. …”
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Machine learning-based e-commerce platform repurchase customer prediction model. by Cheng-Ju Liu, Tien-Shou Huang, Ping-Tsan Ho, Ping-Tsan Ho, Jui-Chan Huang, Ching-Tang Hsieh

“…After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. …”
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Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model by Chuansheng Zhang, Minglai Yang

“…Based on the commonly recommended PSO-ELM model for ET₀ prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…This study provides a reliable and efficient machine learning method for ship resistance prediction.…”
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Integrating Multi-Layer Perceptron Regression with Innovative Optimization for Accurate Building Cooling Load Prediction by Subhiya Zeynalli, Baharak Eslami

“…This study explores the application of a machine learning model called Multi-Layer Perceptron Regression (MLPR) for building cooling demand prediction. Through a hybridization technique with two cutting-edge optimization algorithms, the Brown Bear Optimization Algorithm (BBOA) and the Non-Monopolize Search Algorithm (NMSA), it sets out to explore its optimization potential. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
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Patients with metabolic syndrome and premature atrial contractions: predicting the atrial fbrillation onset by A. I. Olesin, I. V. Konstantinova, V. S. Ivanov

“…Aim. To develop an algorithm for the prediction of the atrial fbrillation onset in patients with metabolic syndrome (MS) and premature atrial contractions (PACs) in a prospective study. …”
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Intelligent prediction for face straightness based on sensor data and human operation information by SUN Yan, FU Xiang, WANG Ranfeng, JIA Yifan, ZHANG Zhixing

“…A machine learning classification algorithm was employed to establish a prediction model for the face straightness of the current mining cycle. …”
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A network security situation prediction method based on hidden Markov model by Xiong ZHAN, Hao GUO, Peng ZHANG, Shu MAO

“…Aiming at the dynamic and real-time characteristics of network security situation prediction,a network security situation prediction model based on hidden Markov model（HMM）was designed.According to the HMM,the situation in t+1 from t time can be calculated.Meanwhile,the maximum entropy algorithm was combinated with HMM to improve the accuracy rate.The analysis and simulation experiments show that the method has the characteristics of autonomous learning and active defense,and it also balance the speed and accuracy.…”
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