Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

“…Traditionally, empirical methods and numerical simulations have been employed to predict slope displacement. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…<h4>Objective</h4>To evaluate the effectiveness of five machine learning algorithms in predicting thickening of the basal segment of the interventricular septum and to develop a simple, yet efficient, prediction model for BSH.…”
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Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats by Xiaoting Yao, Jiaxin Li, Jiaqi Fu, Xingquan Wang, Longgang Ma, Hojjat Asadollahpour Nanaei, Ali Mujtaba Shah, Zhuangbiao Zhang, Peipei Bian, Shishuo Zhou, Ao Wang, Xihong Wang, Yu Jiang

“…Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. …”
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Comparison of Risk Assessment Models for Predicting Postpartum Venous Thromboembolism by Yonghui Xu, Sha Zhu, Ji He, XingSheng Xue, Fei Xiao

“…Methods: This retrospective study was conducted from February 2019 to February 2024. …”
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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

“…This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients. …”
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Collagen turnover biomarkers to predict outcome of patients with biliary cancer by Leonard Kaps, Muhammed A. Genc, Markus Moehler, Stephan Grabbe, Jörn M. Schattenberg, Detlef Schuppan, Rasmus Sund Pedersen, Morten A. Karsdal, Philipp Mildenberger, Annett Maderer, Nicholas Willumsen

“…The diagnostic and prognostic value of the markers was evaluated for overall survival (OS) and progression-free survival (PFS). …”
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Change in elevation predicts 100 km ultra marathon performance by Beat Knechtle, Katja Weiss, David Valero, Volker Scheer, Elias Villiger, Pantelis T. Nikolaidis, Marilia Andrade, Ivan Cuk, Robert Gajda, Thomas Rosemann, Mabliny Thuany

“…A total of 858,544 race records (732,748 from men and 125,796 from women) from 317,312 unique runners originating from 103 different countries and participating in 2,648 100-km races held in 80 different countries worldwide between 1892 and 2022 were analyzed using several descriptive, inferential and predictive methods, including a machine learning XG Boost Regression model. …”
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Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area by Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior

“…However, its measurement using traditional methods can be limited. The search for non-destructive approaches based on leaf dimensions is essential, and machine learning offers promising alternatives for accurate estimation. …”
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Assessing sepsis-induced immunosuppression to predict positive blood cultures by Enrique Hernández-Jiménez, Enrique Hernández-Jiménez, Erika P. Plata-Menchaca, Erika P. Plata-Menchaca, Damaris Berbel, Damaris Berbel, Guillem López de Egea, Guillem López de Egea, Macarena Dastis-Arias, Laura García-Tejada, Fabrizio Sbraga, Pierre Malchair, Nadia García Muñoz, Alejandra Larrad Blasco, Eva Molina Ramírez, Xose Pérez Fernández, Joan Sabater Riera, Arnau Ulsamer

“…This study proposes a novel strategy to predict positive blood cultures by assessing sepsis-induced immunosuppression status through endotoxin tolerance assessment.MethodsOptimal assay conditions have been explored and tested in sepsis-suspected patients meeting the Sepsis-3 criteria. …”
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Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC by Xinrui Shi, Yunzheng Zhao, Ke Li, Qingyu Li, Yifeng Cui, Yuhang Sui, Liang Zhao, Haonan Zhou, Yongsheng Yang, Jiajun Li, Meng Zhou, Zhaoyang Lu

“…However, reliable non-invasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice. …”
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Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies by Hamed Bin Furkan, Nabila Ayman, Md. Jamal Uddin

“…This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. …”
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The Predictive Role of StrainParameters in Predicting All-Cause Mortality in Diabetic Hypertensive Patients with Normal Left Ventricular Systolic Function in Long-Term Follow-up by Mustafa Çetin, Turhan Turan, Müjdat Aktaş, Tayyar Gökdeniz, Murat Gökhan Yerlikaya, Ezgi Kalaycıoğlu, Ender Emre, Ahmet Özderya

“…In right ventricular strain evaluation, four-chamber right ventricular strain (RV4CSL%) (26.1±5.4 vs. 20.8±6.2, p-value: 0.005) was also worse in the mortality group. …”
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Universal metrics for predicting the activity of a wide range of fuel-cell catalysts by Ganesan Elumalai, Satoshi Tominaka

“…Our results reveal that the constant-current density protocol offers a reliable and consistent assessment of catalyst performance, providing a promising alternative to existing evaluation methods. By evaluating various protocols for extracting activity metrics from the datasets of fuel-cell catalysts, we establish an optimised protocol that accounts for experimental and analytical errors in obtaining universal activity metrics. …”
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Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

“…Predictive accuracy is measured by standard error metrics (MAE, RMSE, MAPE) and the Diebold-Mariano test. …”
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Novel metabolic prognostic score for predicting survival in patients with cancer by Jinyu Shi, Chenan Liu, Xin Zheng, Yue Chen, Heyang Zhang, Tong Liu, Qi Zhang, Li Deng, Hanping Shi

“…The Kaplan–Meier method was used to evaluate the survival of patients with varying burdens of metabolic disorders. …”
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Can Phonemic Verbal Fluency Be Used to Predict Alzheimer’s Disease? by Sara García-González

“…Method: A verbal fluency task was administered to 25 patients with MCI and their respective control subjects. …”
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Multi-model assessment and thermodynamic prediction for oxalate-tungstate complexes by Yong Liang, Ting Pu, Zanhong Chen, Yinliang Liu, Congyu Zhang

“…To address the critical bottleneck of lacking fundamental thermodynamic data in the development of a new process for dissolving scheelite hydrochloric acid decomposition residues using oxalic acid, this study systematically evaluated the predictive performance of the group contribution method, the similar system linear law method, as well as the electrostatic, Fuoss, and Bjerrum theoretical models in oxalate, carbonate, molybdate, and tungstate aqueous systems. …”
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Mid-Infrared Spectroscopy for Predicting Goat Milk Coagulation Properties by Arianna Goi, Silvia Magro, Luigi Lanni, Carlo Boselli, Massimo De Marchi

“…In this study, 501 bulk goat milk samples were collected from various farms to evaluate the MCPs. Traditionally, cheesemaking aptitude is evaluated using lactodynamographic analysis, a reliable but time-consuming laboratory method. …”
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Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam by Xudong Li, Lin Sun, Xiaopei Liu, Yili Duo

“…The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. …”
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Software Defects Predictions using SQL Complexity and Naïve Bayes by Made Agus Putra Subali, I Gusti Rai Agung Sugiartha, I Made Budi Adnyana, I Putu Aditya Putra, Made Dai Subawa

“…The prediction results of this study were evaluated by considering the values of accuracy, precision, recall, and f-measure. …”
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