Uncertainties in predicting the medium-term future occurrence of grapevine budburst in Europe: the role of phenological models and climate scenarios by Sergi Costafreda-Aumedes, Luisa Leolini, Christoph Menz, Chenyao Yang, Giovanni Argenti, Camilla Dibari, Marco Bindi, Helder Fraga, Joao Santos, Daniel Molitor, J. Arturo Torres-Matallana, Marko Simeunović, Marco Moriondo

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Predicting Freeway Pavement Construction Cost Using a Back-Propagation Neural Network: a Case Study in Henan, China by Jie He, Zhiguo Qi, Wen Hang, Chihang Zhao, Mark King

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Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review by Eugene Yee Hing Tang, Jacob Brain, Serena Sabatini, Eduwin Pakpahan, Louise Robinson, Maha Alshahrani, Aliya Naheed, Mario Siervo, Blossom Christa Maree Stephan

“…However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. …”
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Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

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Hybrid Deep Learning Models for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga, Vikash Jugoo

“…This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. …”
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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…The correlation between shoe size and the dependent variable was weaker (r = 0.823), and the inclusion of shoe size in the model negatively impacted the prediction accuracy. …”
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Hyperparameter optimization of machine learning models for predicting actual evapotranspiration by Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo

“…Consequently, AET data is limited, prompting the use of meteorological and soil features for prediction. This study develops and evaluates machine learning models for AET prediction based on two input combinations. …”
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Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…Open public remote sensing datasets were used to develop machine learning prediction models (Random Forest, XGBoost, Keras models, etc.) and time series forecasting models (LSTM, CNN, etc.) for predicting and forecasting groundwater sheet recharge, respectively. …”
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Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

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Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
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Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models by Hakan Yıldırım, Festus Victor Bekun

“…In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. …”
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Statistical models for predicting the number of under-five mortality in Nepal. by Madhav Kumar Bhusal, Shankar Prasad Khanal

“…<h4>Objective</h4>This study aimed to develop a suitable statistical model using the associated factors to predict the number of under-five mortality a mother in Nepal encountered throughout her lifetime.…”
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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

“…This study aimed to validate the accuracy of currently used risk assessment models (RAMs) for predicting postpartum VTE. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

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Macroeconomic models for predicting indirect impacts of disasters: A review by Tinger Zhu, Charalampos Avraam, Jack W. Baker

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Benchmarking machine learning models for predicting lithium ion migration by Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

“…With LiTraj, we demonstrate that classical ML models and graph neural networks (GNNs) for structure-to-property prediction of percolation and migration barriers can distinguish between “fast” and “poor” ionic conductors. …”
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Explainable models for predicting crab weight based on genetic programming by Tao Shi, Lingcheng Meng, Limiao Deng, Juan Li

“…Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. More importantly, the generated models can provide potential interpretability, which is particularly valuable for domain experts in fisheries management and ecological research.…”
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PREDICTING THE SHELF LIFE OF SUNFLOWER MEAL USING KINETIC MODELS by T. Matveeva, V. Papchenko, P. Petik, N. Staroselska, V. Khareba, O. Khareba

“…The study proposes a method for predicting the oxidative stability of sunflower meal during long-term storage using the Arrhenius model, which describes the dependence of the reaction rate on temperature. …”
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Predicting avalanche danger in northern Norway using statistical models by K.-U. Eiselt, R. G. Graversen, R. G. Graversen

“…The binary-case RF model exhibits a much higher overall accuracy (76 %) than the four-level case RF model (57 %), which is due to the latter model often misclassifying ADL 1 as ADL 2 and ADL 4 as ADL 3. …”
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Comparative Study of Deep Learning Models for Predicting Stock Prices by Van-Thang Duong, Duc-Tuan-Anh Nguyen, Thi-Thu-Hang Pham, Van-Hau Nguyen, Van-Quoc Anh Le

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