Computational Estimation of Football Player Wages by Yaldo L., Shamir L.

“…Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). …”
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ML-Based Materials Evaluation in 3D Printing by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Jakub Kopowski

“…Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment of material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts of data from previous printing processes to predict the performance of different materials (including those used in multi-material printing) under different conditions. …”
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Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions by M. O. Otun

“…We discuss various AI/ML algorithms, including supervised learning, deep learning, and reinforcement learning, and their applications in ligand-based and structure-based drug design. …”
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Mitochondria-related genes as prognostic signature of endometrial cancer and the effect of MACC1 on tumor cells. by Xuefen Lin, Jianfeng Zheng, Yanhong Li, Linying Liu, Qinying Liu, Jie Lin, Yang Sun

“…The predictive performance, immune features, and anti-tumor response of the mitochondrial signature were evaluated through multiple algorithms. …”
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Artificial Intelligence Applications in Pediatric Craniofacial Surgery by Lucas M. Harrison, Ragan L. Edison, Rami R. Hallac

“…Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. AI-driven algorithms assist in preoperative planning by identifying anatomical abnormalities, predicting surgical outcomes, and guiding personalized treatment strategies. …”
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Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer by Tan Z, Chen X, Fu S, Huang Y, Li H, Gong C, Lv D, Yang C, Wang J, Ding M, Wang H

“…Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes.Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. …”
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A Novel Multiobjective Optimization Approach for EV Charging and Vehicle-to-Grid Scheduling Strategy by Muhammad Aurangzeb, Yifei Wang, Sheeraz Iqbal, Md Shafiullah, Sultan Alghamdi, Zahid Ullah

“…The ring seal search (RSS) algorithm ensures the optimum compatibility of the EV charging and discharging profiles revolving around multiple objectives, such as cost of charging, peak load demand reduction, and grid stability. …”
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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…The results show that, compared with second-order response surface (RS), radial basis function (RBF), traditional ELM, and other optimized ELM, a LWOA-ELM model has higher prediction accuracy. After updating, the FRF data and frequencies have a significant match to the experimental model. …”
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Building accurate numerical models by Sharapov Dmitry

“…Key strategies such as algorithm optimization, parallel computing, and efficient data management are essential for maximizing computational resources. …”
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Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models by O. Drofa

“…The results of coupling Pochva to the numerical weather prediction limited-area model Bolam are presented.</p>…”
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Using machine learning to simultaneously quantify multiple cognitive components of episodic memory by Soroush Mirjalili, Audrey Duarte

“…Using a machine learning algorithm known as “transfer learning”, we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. …”
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Unsupervised detection of semantic correlations in big data by Santiago Acevedo, Alex Rodriguez, Alessandro Laio

“…This recognition enables, for instance, the prediction of missing parts of an image or text based on their context. …”
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RELIABILITY ANALYSIS OF REACTION FORCE DEVELOPED IN THE LUBRICATED REVOLUTE JOINT FOR A SLIDER-CRANK SYSTEM INCLUDING JOINT WITH CLEARANCE AND LUBRICATION by ZHAO Kuan, XUE He, CHEN JianJun, QIAO XinZhou

“…The system dynamic model was set up based on Newton-Euler method,The prediction accurary of Support Vector Machine Regression is difficult to reach the target accurary because the selection of parameters isn’t accurate. …”
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A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics by Fatéma Zahra Benchara, Mohamed Youssfi

“…The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. …”
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Pruning Bayesian networks for computationally tractable multi-model calibration by Nicolas Gratius, Mario Bergés, Burcu Akinci

“…In this work, we propose a graph pruning algorithm to reduce computational cost while minimizing the loss in calibration ability by incorporating domain-driven metrics for selection purposes. …”
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Simulation of the Performance of a Centrifugal Chiller by Luo Yi, Zhang Libiao, Hang Bingbing, Gong Chengcheng

“…The simulation software successfully predicts various parameters, such as chilled water temperatures, cooling water temperatures, and flow rates of the centrifugal chiller under different loads.…”
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. …”
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Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction by Ander Biguri, Subhadip Mukherjee, Xuzhi Zhao, Xi Liu, Xinyi Wang, Rui Yang, Yi Du, Yahui Peng, Mikael Brudfors, Mark Graham, Hyungon Ryu, Oliver Kutter, Andreas Hauptmann, Mustafa Al-Rubaye, Miika T. Nieminen, Mikael A. K. Brix, Austin Yunker, Rajkumar Kettimuthu, John C. Roeske, Sasidhar Alavala, Subrahmanyam Gorthi, Carola-Bibiane Schonlieb

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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Eligible subjects were matched on a 1∶1 propensity score according to sex, age, and years of education and were divided into MCI group and normal cognition group, with 158 cases in each group. …”
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Stroke aftercare in Germany: findings from an online survey in the outpatient setting of a neurovascular network by John-Ih Lee, Robin Jansen, Jan F. Cornelius, Hubert Schelzig, Bernd Turowski, Rüdiger J. Seitz, Til Menge, Philipp Albrecht, Philipp Albrecht, Sebastian Jander, Jan Sobesky, Hans-Jürgen von Giesen, Marcel Dihné, Lars Wojtecki, Lars Wojtecki, Tristan Kölsche, Sajjad Muhammad, Tobias Ruck, Sven G. Meuth, Michael Gliem

“…The evidence-based acute treatment of stroke patients in Germany is carried out according to standardized algorithms in more than 300 certified stroke units, and its quality is repeatedly assured by the German Stroke Society (DSG) and others. …”
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