Traffic Flow Prediction Based on Fractional Seasonal Grey Model by SHEN Qinqin; ZHANG Zhijie; QI Xucun; YUE Xinyi

“…The numerical results show that the average absolute percentage of the new model has a fitting error of 8. 1260% and a prediction error of 7. 6216%, which are much better than those of the seasonal rolling GM(1, 1)model, fractional GM(1, 1) model and seasonal discrete GM(1, 1) model.…”
Get full text

A Stock Prediction Model Based on Cart and Boosting Algorithm by WANG Yu, CHEN De-yun, TANG Yuan-xin

Subjects: “…stock prediction model…”
Get full text

A novel model for malaria prediction based on ensemble algorithms. by Mengyang Wang, Hui Wang, Jiao Wang, Hongwei Liu, Rui Lu, Tongqing Duan, Xiaowen Gong, Siyuan Feng, Yuanyuan Liu, Zhuang Cui, Changping Li, Jun Ma

“…<h4>Background and objective</h4>Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. …”
Get full text

Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

Subjects: “…prediction of landslide displacement…”
Get full text

Fetal health classification with predictive algorithm by using Ensemble Model by Devi V. Sowmya, Kancharakuntla Manoj Reddy, Paleti Goutam Nayudu, Madhunala Akash

Get full text

Flight delay stacking ensemble prediction model for severe weather by SUN Yue, DING Jianli

“…Weather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the problems of low prediction accuracy and poor stability of traditional single model. …”
Get full text

Air Quality Index Prediction Using DNN-Markov Modeling by Roba Zayed, Maysam Abbod

“…DNN-Markov modeling techniques are used to predict air quality, based on environmental conditions at peak hours. …”
Get full text

A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…We employed one traditional predictive model, logistic regression, alongside six machine learning models: support vector machine (SVM), K-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), AdaBoost, and XGBoost. …”
Get full text

Evaluation of changes in prediction modelling in biomedicine using systematic reviews by Lara Lusa, Franziska Kappenberg, Gary S. Collins, Matthias Schmid, Willi Sauerbrei, Jörg Rahnenführer, on behalf of the Topic Group for High-dimensional data of the STRATOS initiative

“…Abstract The number of prediction models proposed in the biomedical literature has been growing year on year. …”
Get full text

Predictive Model for Industrial Accident: Based on a Case Study by mohsen falahati, mojtaba zokaei

“…Structural Equation Modeling (SEM) was employed to predict and determine the impact of each variable influencing  accident occurrences. . …”
Get full text

Triadic balance and network evolution in predictive models of signed networks by Hsuan-Wei Lee, Pei-Chin Lu, Hsiang-Chuan Sha, Hsini Huang

“…To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. …”
Get full text

Machine learning proteochemometric models for Cereblon glue activity predictions by Francis J. Prael, III, Jiayi Cox, Noé Sturm, Peter Kutchukian, William C. Forrester, Gregory Michaud, Jutta Blank, Lingling Shen, Raquel Rodríguez-Pérez

“…For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). …”
Get full text

Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…With the goal of addressing the difficulty of rapidly identifying the source of fire in commercial buildings, this study builds a numerical fire model based on the fire dynamics simulator (FDS) and combines it with a hybrid model to predict the location of a fire source. …”
Get full text

Modeling and prediction of foodstuff output based on ε-SVR method by DONG Mei-shuang, HE Huan, TONG Xiao-xing

“…The foodstuff output data of year 1991—2002 were applied as calibration set to develop ε-SVR model, and the prediction precision was 98.47%. The foodstuff output of year 2003 and 2004 was used as validation set, and the prediction precision by the above developed ε-SVR model were 97.5% and 95.8% for year 2003 and 2004, respectively. …”
Get full text

Research on optimization of basic rail top bending prediction model by Chunjiang Liu, Zhikui Dong, Long Ma, Xinyu Hou, Nanbing Qiao

“…The final top bending prediction model was obtained by combining the load-deflection model in the bending stage and the rebound stage. …”
Get full text

Multi-model assessment and thermodynamic prediction for oxalate-tungstate complexes by Yong Liang, Ting Pu, Zanhong Chen, Yinliang Liu, Congyu Zhang

“…The results indicate that the electrostatic model exhibits the optimal predictive performance: in the carbonate system, the predicted average relative deviation (ARD%) is less than 3.21 %; in the oxalate system, the predicted average relative deviation is less than 3.13 %; and in the molybdate/tungstate system, the predicted ARD% is 6.83 %. …”
Get full text

Prediction of air pollutant emission in Xi’an based on LEAP model by TAN Zhihai, YUAN Yubo, WANG Xuemei, LEI Qiujing, MIAO Jihong, GU Maolin, TAN Tantan

Get full text

Predictive models and WTAP targeting for idiopathic pulmonary fibrosis (IPF) by Guo-Dong Li, Juan Li, Jia-Qi Fan, Jun-Yi Li, Bin Zhao, Xiao Chen

“…This research analyzed the GSE93606 dataset of 20 non-IPF and 154 IPF patients, identifying 26 m6A regulators and developing predictive models with RF and SVM, assessed via ROC curves. …”
Get full text

Clinical prediction model for MODY type diabetes mellitus in children by D. N. Laptev, E. A. Sechko, E. M. Romanenkova, I. A. Eremina, O. B. Bezlepkina, V. A. Peterkova, N. G. Mokrysheva

Subjects: Get full text

Blood-Based Prognostic Prediction Model for Glioblastoma: Construction and Validation by Gao S, Liu Y, Kong J, Huangfu L, Yang Y, Cui H, Sun X, Shi S, Yang D

“…A Risk Score (RS) was computed from the CHPSS, and a nomogram model was constructed to predict patients’ overall survival (OS) based on the RS. …”
Get full text