Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

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Sheltering in place: unveiling how urban park characteristics shaped visiting patterns during the pandemic using machine learning by Dawon Oh, In Kwon Park

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Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning by Mingyi Kuang, Fei Fu, Fangzhou Tian, Liwei Lin, Can Du, Yuesong Zhang

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High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach by Maiken Baumberger, Bettina Haas, Sindhu Sivakumar, Marvin Ludwig, Nele Meyer, Hanna Meyer

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Simple agarose micro-confinement array and machine-learning-based classification for analyzing the patterned differentiation of mesenchymal stem cells. by Nobuyuki Tanaka, Tadahiro Yamashita, Asako Sato, Viola Vogel, Yo Tanaka

“…In addition, a machine learning based image processing software was developed (open code) to detect the differentiation patterns of the population of hMSCs automatically. …”
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Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis by Anna Zhao, Yuxiang Chen, Haoran Yang, Tingting Chen, Xianqi Rao, Ziliang Li

“… Aim: To analyse the risk factors contributing to the prevalence of periodontitis among clusters of patients with diabetes and to examine the clustering patterns of clinical blood biochemical indicators. …”
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Data-Driven Insights Into EV Charging Patterns: Machine Learning Models Reveal Key Predictors of Station Utilization in Tennessee by Seyedmehdi Khaleghian, Thanh-Nam Doan, Joe Knox, Austin Harris, Mina Sartipi

“…This study analyzes electric vehicle (EV) charging patterns and station utilization in Tennessee using machine learning (ML) techniques. …”
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Assessing the impact of climate change on flood patterns in downstream Nigeria using machine learning and geospatial techniques (2018–2024) by Desmond R. Eteh, Bunakiye R. Japheth, Charles U. Akajiaku, Ifunanya Osondu, Omabuwa O. Mene-Ejegi, Emmanuel M. Nwachukwu, Miebaka D. Oriasi, Erepamo J. Omietimi, Abiodun E. Ayo-Bali

“…This study analyzes flood patterns, identifies key environmental drivers, and predicts flood-prone areas through an integrated machine learning and geospatial analysis approach. …”
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Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns by Merve Gonca, Mehmet Birol Özel

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Assessing Smoking Determinants in Students: A Predictive Model Based on Anxiety, Distress, and Sleep Patterns by Chaudhuri Tinni, Ghosh Joyeta, Chakraborty Soma, Kumar Pankaj, Guha Banhi

“…The findings indicate that anxiety quotient, distress levels, and sleep patterns play a crucial role in determining smoking habits among students. …”
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Evaluating the impact of pattern recognition on AI skills development in universities by Dinara Kazimova, Nurgul Serikbayeva, Gulfarida Samashova, Anatoly Zatyneyko, Botagoz Sarsenbayeva

Subjects: “…pattern recognition…”
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Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states by Aayushi Tandon, Amit Awasthi, Kanhu Charan Pattnayak

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Pattern and semantic analysis to improve unsupervised techniques for opinion target identification by Khairullah khan, Ashraf Ullah, Baharum Baharudin

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Patterns of nutrients and algal biomass in an intermittent Mediterranean river under intense human activity by Gabriela Córdoba-Ariza, Rafael Muñoz-Mas, Ramon J. Batalla, Josep Mas-Pla, Sergi Sabater

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Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning by Ziqi Chen, Lingmei Qu, Qing Hao, Shuang Teng, Shuo Liu, Qin Wu, Hongtian Yi, Xianji Shen, Liang Li, Zhaonan Xu, Yanan Sun

“…Key ARGs were identified through machine learning. The diagnostic performance of candidate genes was evaluated using Receiver Operating Characteristic (ROC) analysis. …”
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Spatial pattern and driving mechanisms of dryland landscape ecological risk: Insights from an integrated geographic detector and machine learning model by Ayiturxun Shamuxi, Bo Han, Xiaobin Jin, Paruke Wusimanjiang, Abudureheman Abudukerimu, Qianli Chen, Hongtao Zhou, Min Gong

“…A novel combination of the geographic detector and machine learning was employed to identify the nonlinear driving mechanisms of ecological risk. …”
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Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models by Kim Lowell, Yuri Rzhanov

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Using Machine Learning to Identify the Dynamic Evolution Patterns of Negative Emotions in Perinatal Women: A Longitudinal Study in Southwest China by Yuan Zhang, Wenlong Li, Jian Zou, Guohui Yang, Xiaoni Zhong, Biao Xie

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Spatial Distribution Pattern of Forests in Yunnan Province in 2022: Analysis Based on Multi-Source Remote Sensing Data and Machine Learning by Guangyang Li, Hongyan Lai, Bangqian Chen, Xiong Yin, Weili Kou, Zhixiang Wu, Zongzhu Chen, Guizhen Wang

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Urban Recreational Space Heat-Emotion Distribution and Matching Pattern Based on Machine Learning: A Case Study of Guangzhou City by Wang Fuyuan, Zhang Zhiyu, Xie Yuanjing, Yang Xinyi, Sun Miao

“…Spatially, this disconnect can be categorized into three distinct types: the "polarized" pattern in the central city, the "experience enhancement" pattern in the Huadu, Panyu, Baiyun, and Conghua Districts, and the "insufficient supply" pattern in the Zengcheng, Huangpu, and Nansha Districts. …”
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