Intelligent Controller Design and Fault Prediction Using Machine Learning Model by Kailash Kumar, Suyog Vinayak Pande, T. Ch. Anil Kumar, Parvesh Saini, Abhay Chaturvedi, Pundru Chandra Shaker Reddy, Krishna Bikram Shah

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Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations by Mahmoud Masoud

Subjects: “…Machine learning…”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

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A roadmap to implementing machine learning in healthcare: from concept to practice by Adam Paul Yan, Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Karim Jessa, Bren Cardiff, Lillian Sung, Lillian Sung, Lillian Sung

Subjects: “…machine learning…”
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Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning by Sangwoo Park, Osvaldo Simeone

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Quality Control of Olive Oils Using Machine Learning and Electronic Nose by Emre Ordukaya, Bekir Karlik

“…These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naïve Bayesian, K-Nearest Neighbors (k-NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. …”
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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“…**Methods:** A machine learning algorithm was developed in Scikit-learn, a Python module. …”
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Analysis Of Backscatter To Extraction Of Shoreline Using Machine Learning Methods In The Bangkalan Regency by Arifin Fahmi, Wicaksono Ashari

“…The purpose of this research is to extract the coastline by segmenting the machine learning method and find out how far the machine learning model works to distinguish the water class and the land class. …”
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Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption by Hong Cheng

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Machine learning predicting the transport mechanisms and entrainment characteristics of negative buoyant jets by Yaowen Xia, Wenfeng Gao, Qiong Li, Banglong Wu, Jia Xie, Shuting Yang

“…This study confirmed that the machine learning techniques have great potential to study the transient flow behavior of fountains.…”
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Decoding methane concentration in Alberta oil sands: A machine learning exploration by Liubov Sysoeva, Ilhem Bouderbala, Miles H. Kent, Esha Saha, B.A. Zambrano-Luna, Russell Milne, Hao Wang

“…This paper serves as a guide for building machine learning-driven models to estimate methane concentration in Alberta’s oil sands, or similar regions with methane-producing extractive industries.…”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

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Predicting dry matter intake in Pelibuey sheep using machine learning methods by Enrique Camacho-Perez, Cem Tirink, Ricardo Garcia-Herrera, Ángel T. Piñeiro-Vazquez, Fernando Casanova-Lugo, Jorge R. Canul-Solis, Antonio Leandro Chaves-Gurgel, Ceyhun Yücel, Einar Vargas-Bello-Pérez, Alfonso J. Chay-Canul

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Machine Learning-Driven Scattering Efficiency Prediction in Passive Daytime Radiative Cooling by Changmin Shi, Jiayu Zheng, Ying Wang, Chenjie Gan, Liwen Zhang, Brian W. Sheldon

Subjects: “…machine learning…”
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Predictors of depression among Chinese college students: a machine learning approach by Lin Luo, Junfeng Yuan, Chenghan Wu, Yanling Wang, Rui Zhu, Huilin Xu, Luqin Zhang, Zhongge Zhang

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Retracted: The Evaluation Model of College English Diagnostic Exercises Based on Machine Learning by Journal of Function Spaces

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Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning by Ji-Hun Ha, Yong-Hyuk Kim, Hyo-Hyuc Im, Na-Young Kim, Sangjin Sim, Yourim Yoon

“…In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.…”
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A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping by Wang Yan, Le Jiajin, Zhang Yun

“…In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results’ evaluation and multi factor quantitative judging. …”
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Machine learning algorithms can predict emotional valence across ungulate vocalizations by Romain A. Lefèvre, Ciara C.R. Sypherd, Élodie F. Briefer

“…The present study used a machine learning algorithm (eXtreme Gradient Boosting [XGBoost]) to distinguish between contact calls indicating positive (pleasant) and negative (unpleasant) emotional valence, produced in various contexts by seven species of ungulates. …”
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Retracted: Digital Art Feature Association Mining Based on the Machine Learning Algorithm by null Complexity

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