Machine learning technique for the identification of two-phase (oil-water) flow patterns through pipelines by Daniel Yesid Uribe-Tarazona, Carlos Mauricio Ruiz-Diaz, Octavio Andrés González-Estrada

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The potential of machine learning in classifying relapse and non-relapse in children with clubfoot based on movement patterns by Lianne Grin, Sieglinde Bogaert, Saskia Wijnands, Arnold Besselaar, Marieke van der Steen, Jesse Davis, Benedicte Vanwanseele

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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

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Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types by Yingxin Shang, Kaishan Song, Zhidan Wen, Fengfa Lai, Ge Liu, Hui Tao, Xiangfei Yu

“…Soil erosion, a major source of CDOM received by lakes, transports terrestrial organic matter to water bodies, altering sources, bioavailability and molecular complexity of CDOM significantly. Yet, the spatial patterns of CDOM in lakes from different soil erosion regions are still unknown. …”
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Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact by Enrique Mondragon-Estrada, Jane W. Newburger, Steven R. DePalma, Martina Brueckner, John Cleveland, Wendy K. Chung, Bruce D. Gelb, Elizabeth Goldmuntz, Donald J. Hagler, Jr., Hao Huang, Patrick McQuillen, Thomas A. Miller, Ashok Panigrahy, George A. Porter, Jr., Amy E. Roberts, Caitlin K. Rollins, Mark W. Russell, Martin Tristani-Firouzi, P. Ellen Grant, Kiho Im, Sarah U. Morton

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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. …”
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Direct photo-patterning of halide perovskites toward machine-learning-assisted erasable photonic cryptography by Yingjie Zhao, Mengru Zhang, Zhaokai Wang, Haoran Li, Yi Hao, Yu Chen, Lei Jiang, Yuchen Wu, Shuang-Quan Zang, Yanlin Song

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A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns by Rajamanickam Yuvaraj, Shivam Chadha, A. Amalin Prince, M. Murugappan, Md. Sakib Bin Islam, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury

“…The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. …”
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Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis by Jingyang Wang, Haiyang Bian, Jiangshan Tan, Jun Zhu, Lulu Wang, Wei Xu, Lei Wei, Xuegong Zhang, Yanmin Yang

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AI-assisted Pattern Generator for Garment Design by Michael Danner, Elena Brake, Gabriela Kosel, Yordan Kyosev, Katharina Rose, Matthias Rätsch, Holger Cebulla

Subjects: “…ai-assisted pattern construction…”
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Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis by Caroline L Alves, Loriz Francisco Sallum, Francisco Aparecido Rodrigues, Thaise G L de O Toutain, Patrícia Maria de Carvalho Aguiar, Michael Moeckel

“…This study aims to analyze electroencephalography (EEG) connectivity patterns to better understand PD progression and stage of the disease using machine learning. …”
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INSTITUTE OF SCIENCE OF MACHINES AND METALLURGY DVO RAN INCREASING OF EXACTNESS OF POROUS MODELS IN CASTING BY INVESTMENT PATTERNS by I. G. Sapchenko, S. G. Zhilin, O. N. Komarov

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Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools by Markus Netzer, Yannic Palenga, Philipp Goennheimer, Juergen Fleischer

“…In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

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A Pattern‐Based Machine Learning Model for Imputing Missing Records in Coastal Wind Observation Networks by Nan‐Jing Wu, Tai‐Wen Hsu, Ting‐Chieh Lin

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Discovering the key symptoms for identifying patterns in functional dyspepsia patients: A doctor's decision and machine learning by Da-Eun Yoon, Heeyoung Moon, In-Seon Lee, Younbyoung Chae

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Novel delayed binary time-series pattern based machine learning techniques for stock market forecasting by Zeqiye Zhan, Song-Kyoo Kim

“…This study proposes an innovative machine learning technique for stock market forecasting that leverages delayed binary time-series patterns to enhance prediction accuracy. …”
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Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times by Mikel Barrena-Herrán, Itziar Modrego-Monforte, Olatz Grijalba

“…The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. …”
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Spatiotemporal estimation of ambient forest phytoncides: Unveiling patterns through geospatial-based machine learning approach by Aji Kusumaning Asri, Hao-Ting Chang, Chia-Pin Yu, Wan-Yu Liu, Yinq-Rong Chern, Rui-Hao Xie, Shih-Chun Candice Lung, Kai Hsien Chi, Yu-Cheng Chen, Sen-Sung Cheng, Gary Adamkiewicz, John D. Spengler, Chih-Da Wu

“…The robustness of these models was confirmed through extensive validation. Spatial pattern analysis revealed that variations in these biogenic compound concentrations were linked to meteorological conditions and vegetation types. …”
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Unsupervised machine learning identifies opioid taper reversal patterns in a longitudinal cohort (2008-2018). by Monika Ray, Joshua J Fenton, Patrick S Romano

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