Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

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Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor by Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang, Weiya Hao

“…This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. …”
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Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends by Vivek Singh, Gaurav Tiwari, Amarendra Singh, Rajeeb Samanta, Atul Kumar Srivastava, Deewan Singh Bisht, Ashish Routray, Sushil Kumar, Shivaji Singh Patel, Abhishek Lodh

“…Notably, research since 2019 highlights a shift toward machine‐based algorithms aimed at improving intensity predictions. …”
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Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics by Md Abu Sufian, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, Boumediene Hamzi

Subjects: “…cardiovascular risk prediction…”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

Subjects: “…prediction of monthly sediment concentration…”
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RETRACTED: Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm by Longjun Guo, Juan Wang, Jiaqi Teng, Yukun Chen

“…As the development of deep learning DL-based bone age prediction methods have achieved great success. However, it also faces the issue of huge computation overhead in deep features learning. …”
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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. …”
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Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning. by Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato

“…In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. …”
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Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data by Indrastanti Ratna Widiasari, Rissal Efendi

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Enhanced clustering techniques for lithofacies and electrofacies identification in post-salt carbonate and siliciclastic reservoirs: a case study from the campos basin, Brazil by Abel Carrasquilla, Herson Rocha

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Automation system for finding parking places by О. Napadii, N. Tsopa, Ye. Batrak

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Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello, Agostino Marengo

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A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making by Michal Pruski, Simone Willis, Kathleen Withers

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Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

“…In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. …”
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Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…However, studies focusing the spatial prediction of geophysical data remain limited. In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. …”
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Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning by Lucijano Berus, Jernej Hernavs, David Potocnik, Kristijan Sket, Mirko Ficko

“…Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. …”
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Prediction-Based Distributed State Estimation Control for IoT-Enabled Train Positioning Systems by L. Ponnarasi, P. B. Pankajavalli, Y. Lim, R. Sakthivel, Sultan Alfarhood, Mejdl Safran

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Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China by Zhefan Li, Zhaokang Zhou, Zhenhua Liu, Jiahe Si, Jiaming Ou

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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

“…However, precise and continuous spectral signatures, important for large-area crop growth monitoring and early prediction of yield production with cutting-edge algorithms, can be only provided via hyperspectral imaging. …”
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