Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU by M. Fathima Begum, Subhashini Narayan

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A Simple Computational Approach to Predict Long-Term Hourly Electric Consumption by Eugene Pinsky, Etienne Meunier, Pierre Moreau, Tanvi Sharma

“…By exploiting the patterns in past data points, we could forecast long-term consumption with a computationally simple algorithm. Our approach is simple to interpret. It incorporates the seasonality of past consumption and can predict power consumption for any time scale. …”
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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…MethodsTo address these issues, Multi-Way Improved Whale Optimization Algorithm (MWIWOA) was proposed to optimize the SVM-based prediction model for the internal corrosion rate of long-distance submarine pipelines. …”
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Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

“…The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. …”
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Novel hybrid model to improve the monthly streamflow prediction: Integrating ANN and PSO by Baydaa Abdul Kareem, Salah L. Zubaidi

“…Precise streamflow forecasting is crucial when designing water resource planning and management, predicting flooding, and reducing flood threats. This study invented a novel approach for the monthly water streamflow of the Tigris River in Amarah City, Iraq, by integrating an artificial neural network (ANN) with the particle swarm optimisation algorithm (PSO), depending on data preprocessing. …”
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An optimal multi-disease prediction framework using hybrid machine learning techniques by Aditya Gupta, Amritpal Singh

“…The proposed work leverages genetic algorithm-based recursive feature elimination and AdaBoost to predict two prominent life-style diseases. …”
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A hybrid model for predicting response to risperidone after first-episode psychosis by Giovany Oliveira Costa, Vanessa K. Ota, Matheus Rodrigues Luiz, Joice Santos Rosa, Gabriela Xavier, Jessica Honorato-Mauer, Marcos L. Santoro, Carolina Muniz Carvalho, Daniel A. Cavalcante, Amanda V.G. Bugiga, Rodrigo A. Bressan, Gerome Breen, Ary Gadelha, Cristiano Noto, Diego R. Mazzotti, Sintia I. Belangero

“…The hybrid model, which included duration of untreated psychosis, Clinical Global Impression-Severity (CGI-S) scale scores, age, cannabis use, and 406 SNVs, showed the best performance (balanced accuracy: 72.9% [CI 0.62-0.84], RF algorithm). Conclusion: A hybrid model including clinical and genetic predictors can enhance prediction of response to antipsychotic treatment.…”
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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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