Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data by Gen Long, Sarintip Tantanee, Korakod Nusit, Pitikhate Sooraksa

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Fine-Pruning: A biologically inspired algorithm for personalization of machine learning models by Joseph Bingham, Saman Zonouz, Dvir Aran

Subjects: “…machine learning…”
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Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions by Khaled Chahine

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Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study by Zihan Li, Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu

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Predicting Deep Venous Thrombosis Using Artificial Intelligence: A Clinical Data Approach by Aurelian-Dumitrache Anghele, Virginia Marina, Liliana Dragomir, Cosmina Alina Moscu, Mihaela Anghele, Catalin Anghel

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Demonstration of accurate ID-VG characteristics modeling in SiC mosfets using separated artificial neural networks with small training dataset by Manopat Chankla, Bang-Ren Chen, Shivendra Kumar Singh, Yogesh Singh Chauhan, Wen-Jay Lee, Nan-Yow Chen, Songphol Kanjanachuchai, Tian-Li Wu

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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring by Yiannis Kiouvrekis, Theodor Panagiotakopoulos, Efthymia Nousi, Ioannis Filippopoulos, Agapi Ploussi, Ellas Spyratou, Efstathios P. Efstathopoulos

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A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort by Yun C. Lin, Daniel Mallia, Andrea O. Clark-Sevilla, Adam Catto, Alisa Leshchenko, Qi Yan, David M. Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi

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Opinion Mining and Perceptions of Self-Determination in Videoconference Classrooms Using Machine Learning and Multidimensional Sentiment Frameworks by Bosede Iyiade Edwards

Subjects: “…Azure Machine Learning…”
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MLOps Components, Tools, Process, and Metrics: A Systematic Literature Review by Adrian P. Wozniak, Mateusz Milczarek, Joanna Wozniak

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Machine learning based disruption prediction using long short-term memory in KSTAR by Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam

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Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study by Somayeh Ghiasi Hafezi, Atena Ghasemabadi, Negar Soleimani, Maryam Allahyari, Mina Moradi, Amin Mansoori, Rana Kolahi Ahari, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

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BMLFTS: A Novel Approach to Process and Track of Food Quality Data Using Blockchain and Machine Learning by Ajay B. Gadicha, Vijay B. Gadicha, Mayur S. Burange

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Predicting Diabetes Risk Using Machine Learning: A Comparative Study on the Yazd Health Study (YaHS) by Fateme Sefid, Nazanin Norouzi-Ghahjavarestani, Malihe Soleymani-Tabasi, Jamal Zarepour-Ahmadabadi, Ghasem Azamirad, Mohamah yahya Vahidi Mehrjardi, Masoud Mirzaei, Seyed Mehdi Kalantar

Subjects: “…machine learning…”
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Machine learning for classifying affective valence from fMRI: a systematic review and meta-analysis by Charith Chitraranjan, Ruwan Dayananda, Dakshitha Suriyaaratchie, Nuwan Abeynayake, Svetlana Shinkareva

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Insights on risk score development: Considerations for early-stage hepatocellular carcinoma models by Zhanna Zhang, Gongqiang Wu

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Boosting any learning algorithm with Statistically Enhanced Learning by Florian Felice, Christophe Ley, Stéphane P. A. Bordas, Andreas Groll

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Risk prediction model for waitlist mortality in patients with left ventricular assist devices by Anjan Tibrewala, MD, MS, Duc Thinh Pham, MD, Mo Hu, MS, Lucia C. Petito, PhD, Jonathan D. Rich, MD, Finn Gustafsson, MD, PhD, Theo M.M.H. de By, MBA, PhD, Kevin Veen, MD, PhD, Donald M. Lloyd-Jones, MD, ScM, Sanjiv J. Shah, MD

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Integrated neural network and AspenPlus model for entrained flow gasification kinetics investigation by Balaban Dario, Lubura Jelena, Kojić Predrag

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MACHINE LEARNING MODELS FOR PERFORMANCE PREDICTION OF YOUNG PLAYERS IN FOOTBALL ACADEMIES – A REVIEW by Armand Florin Bertea, Cezar Honceriu

Subjects: “…machine learning…”
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