Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m², while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m². …”
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TRIPOD statement: a preliminary pre-post analysis of reporting and methods of prediction models by Gary S Collins, Karel G M Moons, Lotty Hooft, Ewout W Steyerberg, Pauline Heus, Merel van Diepen, Friedo W Dekker, Amir H Zamanipoor Najafabadi, Chava L Ramspek, Wilco C Peul

“…Objectives To assess the difference in completeness of reporting and methodological conduct of published prediction models before and after publication of the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.Methods In the seven general medicine journals with the highest impact factor, we compared the completeness of the reporting and the quality of the methodology of prediction model studies published between 2012 and 2014 (pre-TRIPOD) with studies published between 2016 and 2017 (post-TRIPOD). …”
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Optimizing Genomic Selection Methods to Improve Prediction Accuracy of Sugarcane Single-Stalk Weight by Zihao Wang, Chengcai Xia, Yanjie Lu, Qi Liu, Meiling Zou, Fenggang Zan, Zhiqiang Xia

“…After rigorous filtering of Hyper_seq_FD sequencing data from 409 sugarcane samples, we identified 31,204 high-quality single-nucleotide polymorphisms (SNPs) evenly distributed across all 32 chromosomes, providing a comprehensive representation of the sugarcane genome. In this study, we evaluated the predictive performance of various genomic selection (GS) methods for single-stalk weight in the 299 orthogonal population, with the male parent being GZ_73-204 and the female parent being GZ_P72-1210, and in the 108 reciprocal population, with the male parent being GZ_P72-1210 and the female parent being GZ_73-204. …”
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Evaluation of the nutrient digestibility at each age in dogs diet by in vitro and in vivo methods by Kyeongho Jeon, Jihwan Lee, Minho Song, Kihyun Kim, Minseok Jo, Seyeon Chang, Dongcheol Song, Sehyun Park, Hyuck Kim, Hyeun Bum Kim, Jinho Cho

“…The objective of this study was to evaluate in vitro predictions of digestibility at each age (puppy, adult, and senior) in dogs of dry matter (DM), organic matter (OM), crude protein (CP), gross energy (GE), crude fiber (CF), and ether extract (EE) using dog diets. …”
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METHODS OF EVALUATION OF STATE INNOVATIVE SERVICES’ EFFICIENCY IN COMMUNICATIONS SECTOR OF TATARSTAN REPUBLIC by N. S. Aksyonova

“…Methods: graphic, economic-mathematical, trends construction method and predicting method. …”
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Research progress on shale gas productivity evaluation: concepts, methods and future directions by ZHU Suyang, PENG Zhen, DI Yunting, PENG Xiaolong, LIU Dongchen, GUAN Wenjie

“…These advancements aim to avoid the limitations in existing productivity evolution methods and support the development of robust and rational shale gas productivity evaluation models and methods, providing theoretical guidance for accurate well productivity prediction, production stabilization, and efficient resource development.…”
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Evaluating Clonal Expansion of HIV-Infected Cells: Optimization of PCR Strategies to Predict Clonality. by Sarah B Laskey, Christopher W Pohlmeyer, Katherine M Bruner, Robert F Siliciano

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Navigating the unstructured by evaluating alphafold's efficacy in predicting missing residues and structural disorder in proteins. by Sen Zheng

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Predicting congenital syphilis cases: A performance evaluation of different machine learning models. by Igor Vitor Teixeira, Morgana Thalita da Silva Leite, Flávio Leandro de Morais Melo, Élisson da Silva Rocha, Sara Sadok, Ana Sofia Pessoa da Costa Carrarine, Marília Santana, Cristina Pinheiro Rodrigues, Ana Maria de Lima Oliveira, Keduly Vieira Gadelha, Cleber Matos de Morais, Judith Kelner, Patricia Takako Endo

“…<h4>Objective</h4>The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment.…”
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Extended sample size calculations for evaluation of prediction models using a threshold for classification by Rebecca Whittle, Joie Ensor, Lucinda Archer, Gary S. Collins, Paula Dhiman, Alastair Denniston, Joseph Alderman, Amardeep Legha, Maarten van Smeden, Karel G. Moons, Jean-Baptiste Cazier, Richard D. Riley, Kym I. E Snell

“…We have reported closed-form solutions to estimate the sample size required to target sufficiently precise estimates of accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and an iterative method to estimate the sample size required to target a sufficiently precise estimate of the F1-score, in an external evaluation study of a prediction model with a binary outcome. …”
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Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning by Qingyun Wan, Kai Liu, Yuyang Bo, Xiya Yuan, Mufeng Li, Xiaoqiu Wang, Chuang Chen, Lanying Liu, Wenzhong Wu

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Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods by Yuhan Jiang, Xu Wang, Li Li, Yifan Wang, Xuelin Wang, Yingxue Zou

“…Seven mainstream machine learning methods were then employed to construct predictive models, evaluated for reliability and robustness through tenfold cross-validation and sensitivity analysis. …”
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A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps by Kunsong Zhao, Zhou Xu, Meng Yan, Lei Xue, Wei Li, Gemma Catolino

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Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types by Jurgita Gammall, Alvina G. Lai

“…Different machine learning methods achieved similar performance with DeepSurv model slightly underperforming compared to other methods. …”
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A fundamental study for predicting the urban street noise by use of the image method approach by Mitsuo OHTA, Yasuo MITANI

“…This paper describes a unified statistical method of structural prediction of an arbitrarily fluctuating street noise in the general urban noise environment, especially from a fundamental viewpoint. …”
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Prediction Method for Anisotropy and Fracture Parameters of Reservoirs with Vertical Fractures Based on Acoustic Logging by MENG Jikun, QI Qiaomu, LI Linxin, LI Xiaobin, ZHENG Qinren

“…To this end, this paper proposes a method, which can realize the prediction of fracture parameters and quantitative detection of gas-filled fractures in reservoirs with vertical fractures only by using conventional logging data (P-wave velocity, fast/slow S-wave velocity and density). …”
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A new method to predict average flow velocity and conveyance capacity of meandering streams by Cristopher Alexander Gamboa-Monge, Ana Maria Ferreira-da-Silva, Laura Segura-Serrano, Isabel Guzmán-Arias

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Research on the Bearing Remaining Useful Life Prediction Method Based on Optimized BiLSTM by Yi Zou, Wenlei Sun, Tiantian Xu, Bingkai Wang

“…The predictive performance of the remaining useful life (RUL) estimation model for bearings is of utmost importance, and the setting method of the bearing degradation threshold is crucial for detecting its early degradation point, as it significantly affects the performance of the RUL prediction model. …”
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Displacement-Sensing Method Based on Residual Scaling for One-Shot MMF Specklegram Prediction by Bohao Shen, Jianzhi Li

“…Regression evaluation metrics demonstrate that the proposed model has a significant improvement over other displacement-sensing methods based on MMF specklegrams, with prediction errors approximately three times lower than ResNet. …”
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A Method to Assess Localized Impact of Better Floodplain Topography on Flood Risk Prediction by Guy J.-P. Schumann, Konstantinos M. Andreadis

“…We propose an analysis based on a targeted observation algorithm to evaluate potential data acquisition subregions in terms of their impact on the prediction of flood risk over the entire study area. …”
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