A Method to Improve Computational Efficiency of Productivity Evaluation with Rectangular Coalbed Methane Reservoir by Li Chen, Sun Lichun, Sun Hansen, Feng Ruyong, Wang Cunwu, Zhang Fang

“…At present, there is no method to quickly evaluate the productivity of finite conductivity fracture model in rectangular coalbed methane reservoir. …”
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Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients by Gaimei Wang, Cendi Lu, Owusu Mensah Solomon, Yujia Gu, Yijing Ling, Fanchi Xu, Yumin Tao, Yehong Wei

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Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. …”
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Transparent and reliable construction cost prediction using advanced machine learning and explainable AI by Lifei Chen, Changyong Xu, Wei Hong Lim, Abhishek Sharma, Sew Sun Tiang, Kim Soon Chong, El-Sayed M. El-kenawy, Amel Ali Alhussan, Marwa M. Eid, Doaa Sami Khafaga

“…Ten machine learning models, including Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbor Regression, and advanced ensemble methods such as XGBoost, CatBoost, and HistGradient Boosting, were evaluated on the RSMeans dataset. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Overall, the results indicate that ensemble methods generally provide robust classification performance for innovation prediction tasks. …”
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Evaluating Diverse Meta-modeling Approaches for Predicting Performance Characteristics of a Twin Air Intake Based on Experimental Data by Human AMIRI, U. C. Kucuk, O. Kucukoglu, Y. F. Kuscu, O. V. Ozdemır

“…The results reveal that the Random Forest Regression (RFR) model outperforms all other models across all evaluated metrics, demonstrating its superior effectiveness in predicting the performance characteristics of the twin air intake system.…”
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Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men by Krittaka Soha, Sadiporn Phuthomdee, Thanapat Srichai, Lanchakorn Kittiratanawasin, Win Min Han, Sirinya Teeraananchai

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An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer—A Multicentric Study by Simona Parisi, Francesco Saverio Lucido, Federico Maria Mongardini, Roberto Ruggiero, Francesca Fisone, Salvatore Tolone, Antonio Santoriello, Francesco Iovino, Domenico Parmeggiani, David Vagni, Loredana Cerbara, Ludovico Docimo, Claudio Gambardella

“…Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. …”
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A Moroccan soil spectral library use framework for improving soil property prediction: Evaluating a geostatistical approach by Tadesse Gashaw Asrat, Timo Breure, Ruben Sakrabani, Ron Corstanje, Kirsty L. Hassall, Abdellah Hamma, Fassil Kebede, Stephan M. Haefele

“…Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. …”
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Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India by Devraj Dhakal, Kanwarpreet Singh, Kennedy C. Onyelowe, Steven Alejandro Salazar Cazco, Abhishek Sharma, Nassir Alarifi, Fakhrul Islam, Randeep, Krishna Prakash Arunachalam, Youssef M. Youssef

“…These advancements highlight the capability of the SBSP method to enhance susceptibility predictions and reduce overestimation in areas of high vulnerability. …”
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Evaluating the radiosensitivity of the oral microbiome to predict radiation-induced mucositis in head and neck cancer patients: A prospective trial by Andreas R. Thomsen, Elsa Beatriz Monroy Ordonez, Michael Henke, Benedikt Luka, Jörg Sahlmann, Henning Schäfer, Vivek Verma, Nadine Schlueter, Anca-Ligia Grosu, Tanja Sprave

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Accuracy of Predictive Equations for Metabolizable Energy Compared to Energy Content of Foods for Dogs and Cats Estimated by In Vivo Methods in Brazil by Pedro Henrique Marchi, Andressa Rodrigues Amaral, Leonardo de Andrade Príncipe, Larissa Wünsche Risolia, Mariana Fragoso Rentas, Ana Beatriz Fasolai, Rafael Vessecchi Amorim Zafalon, Gabriela Luiza Fagundes Finardi, Juliana Toloi Jeremias, Raquel Silveira Pedreira, Júlio Cesar de Carvalho Balieiro, Thiago Henrique Annibale Vendramini

“…The data were analyzed using SAS, and Student’s <i>t</i> test was used with a significance of 5%. For all predictive equations, there was a non-conformity between their results and those obtained by in vivo methods. …”
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Predictive modeling of the mechanical behavior of 3D-printed polylactic acid/wood composite: Comparison of GEP and ANN methods by Abhijit Bhowmik, Raman Kumar, Ranganathaswamy M. K., Y. Karun Kumar, Priyaranjan Samal, Abinash Mahapatro, Abdulaziz N. Alhazaa, Valentin Romanovski, A. Johnson Santhosh

“…This study introduces a novel approach for predicting the mechanical properties of 3D-printed polylactic acid wood composites using gene expression programming (GEP) and artificial neural networks (ANN) modeling methods. …”
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Predictive Modeling of Water Level in the San Juan River Using Hybrid Neural Networks Integrated with Kalman Smoothing Methods by Jackson B. Renteria-Mena, Eduardo Giraldo

“…The results show that the combination of neural networks with smoothing filters, especially the RTS filter and smoothed Kalman filter, provided the most accurate predictions, outperforming traditional methods. This research has important implications for water resource management and flood prevention in vulnerable areas such as Chocó. …”
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Optimal Control Strategy and Evaluation Framework for Frequency Response of Combined Wind–Storage Systems by Jie Hao, Huiping Zheng, Xueting Cheng, Yuxiang Li, Liming Bo, Juan Wei

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EVALUATION OF MONOCYTE TO HIGH-DENSITY LIPOPROTEIN CHOLESTEROL RATIO AS A PREDICTIVE MARKER OF DIABETES MELLITUS SEVERITY IN OLDER PATIENTS by Gülru Ulugerger Avcı, Derya Argun, Günay Can, Mehmet Fatih Kılıçlı

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Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. …”
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Evaluating Different Methods for the Estimation of Bare Soil Surface Reflectance Using Multispectral Satellite Image Time Series by E. Sofikiti, V. Tsironis, K. Karantzalos

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Comparison and Application of Pore Pressure Prediction Methods for Carbonate Formations: A Case Study in Luzhou Block, Sichuan Basin by Wenzhe Li, Pingya Luo, Yatian Li, Jinghong Zhou, Xihui Hu, Qiutong Wang, Yiguo He, Yi Zhang

“…To address this issue, this study systematically investigates and compares three classical pore pressure prediction approaches—namely, the equivalent depth method, the Eaton method, and the effective stress method—within the geological context of the Luzhou Block. …”
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A Comparative Study of Applying Active-Set and Interior Point Methods in MPC for Controlling Nonlinear pH Process by Syam Syafiie, Chia Yaw Kwan

“…A comparative study of Model Predictive Control (MPC) using active-set method and interior point methods is proposed as a control technique for highly non-linear pH process. …”
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