Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

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

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

Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
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

Traffic flow prediction based on spatiotemporal encoder-decoder model. by Yuanming Ding, Wei Zhao, Lin Song, Chen Jiang, Yunrui Tao

“…To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. …”
Get full text

On Some Limitations of Current Machine Learning Weather Prediction Models by Massimo Bonavita

Subjects: Get full text

An Ensemble Learning Model for Short-Term Passenger Flow Prediction by Xiangping Wang, Lei Huang, Haifeng Huang, Baoyu Li, Ziyang Xia, Jing Li

“…Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. …”
Get full text

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction by Rusul L. Abduljabbar, Hussein Dia, Pei-Wei Tsai

“…This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. …”
Get full text

Reviews on Imaging-based Risk Prediction Models for Ischemic Stroke by Cui Liuping, Liu Ran, Liu Yumei, Zhou Fubo, Tao Yunlu, Xing Yingqi

“…Integrating image-based biomarkers into existing risk-prediction models may enhance risk stratification accuracy. …”
Get full text

A structure-based model for predicting serum albumin binding. by Katrina W Lexa, Elena Dolghih, Matthew P Jacobson

“…This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.…”
Get full text

Generalized Additive Model for Predicting ECBR of Stabilized Subgrades for Pavement by Alka Shah, Tejaskumar Thaker, Vipin Shukla

“…This study presents a Generalized Additive Model (GAM) to predict the effective CBR (ECBR) of soil subgrade stabilized with recycled plastic waste under repeated loading (RL) conditions. …”
Get full text

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models by Hakan Yıldırım, Festus Victor Bekun

“…In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. …”
Get full text

Molecular Modelling and Prediction of the Physicochemical Properties of Polyols in Aqueous Solution by Fontenele Maria, Dumouilla Vincent, Boit Baptiste, Dussap Claude-Gilles

“…Roquette is a producer of plant-based ingredients. Modelling, simulation, and predictive thermodynamic models are the tools that allow for the characterization of the physicochemical properties of material flows in order to optimize and control their industrial processes. …”
Get full text