Advanced KPI framework for IVF pregnancy prediction models in IVF protocols by Sergei Sergeev, Iuliia Diakova

“…Leveraging recurrent neural networks, our model demonstrates high accuracy in predicting the likelihood of clinical pregnancy within specific IVF treatment cycles (AUC = 0.68–0.86; test accuracy = 0.78, F1 score = 0.71, sensitivity = 0.62; specificity = 0.86) comparable to time-lapse system but with a simpler approach. …”
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Thermodynamic Modeling and Phase Prediction for Binary System Dinitrogen Monoxide and Propane by Alireza Bozorgian, Behrouz Raei

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Application of Improved EEMD-NNBR Coupling Model in Annual Runoff Prediction by ZHENG Fangfang, WANG Wensheng, ZHANG Lanting

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Predictive model of malignancy probability in pulmonary nodules based on multicenter data by Yuyan Huang, Yong Chen, Fang He, Li Jiang

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Improving model-free prediction of chaotic dynamics by purifying the incomplete input by Hongfang Tan, Lufa Shi, Shengjun Wang, Shi-Xian Qu

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Research on a complaint prediction model utilizing joint neural networks by Xiaoliang MA, Ying LIU, Jie GAO

“…By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and their combined modeling methods.The Logistic regression model identified five major influencing factors, predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors, achieving a prediction accuracy of 90.6%.On this basis, a combined model was constructed with an accuracy rate of up to 92.8%.After practical application in a provincial telecom operator, the repeat complaint rate decreased by 3.2%, demonstrating a significant impact.Strong support is provided for improving the service quality of telecom operators and reducing repeat complaints, which is of great significance for the development of the telecom industry in China.…”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. …”
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3D modeling to predict vascular involvement in resectable pancreatic adenocarcinoma by Sguinzi R, Vidal J, Poroes F, Bartolucci DA, Litchinko A, Gossin E, Fingerhut A, Toso C, Buhler L, Egger B

“…The pre-operative computed tomography (CT) images were processed by segmentation with 3D reconstruction and printed as 3D models. Two radiologists specialized in pancreatic imaging and two pancreatic surgeons blindly and independently analyzed the pre-operative CT scans and 3D models using a defined checklist. …”
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Unplanned cesarean sections in advanced maternal age: A predictive model by Joyce Veenstra, Zoë Cohen, Fleurisca J. Korteweg, David P. van derHam, Simone M. Kuppens, Janna A. Kroese, Brenda B. Hermsen, Marije M. Kamphuis, Joost W. Vanhommerig, Maria G. vanPampus

“…Subsequently, a predictive model and risk scores were constructed to predict unplanned cesarean section. …”
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Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR by FENG Yu, WU Yunxing, GU Wenjing, PANG Qiong, GU Yanchang, CHEN Siyu

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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

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Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures by Hui Wang, Haoqi Tan, Tian Qu, Jiupeng Zhang

“…The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.…”
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Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections by Cecilia Bonazzetti, Ettore Rocchi, Alice Toschi, Nicolas Riccardo Derus, Claudia Sala, Renato Pascale, Matteo Rinaldi, Caterina Campoli, Zeno Adrien Igor Pasquini, Beatrice Tazza, Armando Amicucci, Milo Gatti, Simone Ambretti, Pierluigi Viale, Gastone Castellani, Maddalena Giannella

“…Abstract Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). …”
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Predictive Modeling of Tool Life in Turning Using ANN-Taguchi Hybridization by Shrishail Sollapur B., Shubham R. Suryawanshi, Mitali Mhatre S., Dipak K. Dond, Ganesh Chate, Abhijit Bhowmik

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Predictive modeling of 3D reconstruction trajectories for potential programmable materials by Yechun Jin, Jie Li, Qi Yu, Panghua Tian

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An interpretable and adaptable data-driven model for performance prediction in thermal plants by G. Prokhorskii, M. Preißinger, S. Rudra, E. Eder

“…To safely operate complex industrial systems such as thermal power plants, establishing reliable monitoring tools is paramount for better understanding the underlying processes. Data-driven models are a useful aid for monitoring and control of thermal power plants, but they require an effective feature selection to allow for an accurate, computationally efficient, and interpretable model. …”
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Application of machine learning models in predictive maintenance of Francis hydraulic turbines by Júlio César Silva de Souza, Oswaldo Honorato Júnior, Geraldo Lúcio Tiago Filho, Otávio Augusto Salgado Carpinteiro, Hailton Silveira Domingues Biancardine Júnior, Ivan Felipe Silva dos Santos

Subjects: “…Machine Learning Model, Multilayer Perceptron (MLP)…”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…A surrogate decision tree developed from random forests predictions reached an area under the curve of 0.929.CONCLUSION: Random forest models effectively predicted acute oral toxicity, particularly when addressing class imbalance through cost-sensitive learning and resampling. leveraging explainable artificial intelligence techniques, including permutation feature importance, surrogate decision tree analysis and local interpretable model-agnostic explanations, this study identified key molecular descriptors driving toxicity. …”
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Development and evaluation of predictive models for pregnancy risk in UK dairy cows by Matthew Barden, Robert Hyde, Martin Green, Andrew Bradley, Edna Can, Rachel Clifton, Katharine Lewis, Al Manning, Luke O'Grady

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Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods by Ling Zhu, Shan He, Wanting Zheng, Yuanyuan Tong, Feng Yang

“…To bridge this gap, we propose an ensemble prediction model enhanced with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve interpretability and clinical utility. …”
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