Adapting to change: Visitor patterns in national parks across the pandemic timeline by Dehui Christina Geng, Mingze Chen, Harry Seely, Howie W. Harshaw, Christopher Gaston, Wanli Wu, Guangyu Wang

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IMPLEMENTATION AND COMPARISON IN USING STATE PATTERN ON MAIN CHARACTER MOVEMENT (CASE STUDY : POCONG JUMP VIDEO GAME VERSION 1.0) by Sanriomi Sintaro, Deiby Tineke Salaky, Luther Alexander Latumakulita, Mahardika Inra Takaendengan, Bernard Bernard, Ade Surahman, Noorul Islam

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Cysteine pattern barcoding-based dataset filtration enhances the machine learning-assisted interpretation of Conus venom peptide therapeutics. by Rimsha Bibi, Noshaba Qasmi, Sajid Rashid

“…In this study, we conducted a comprehensive analysis of 5,985 cone snail peptides across 82 Conus species to identify unique cysteine (Cys) patterns and associated frameworks. The classification of these Cys patterns, based on conserved framework combinations, enabled the generation of species-level pattern barcodes. …”
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4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi by Javaria Manzoor Shaikh, JaeSeung Park

“…Whereas insufficient UV light and vitamin D can retard healing process, hence this experiment focuses on machine learning design in which pattern recognition and technology supports patient safety as our primary goal. …”
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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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Spatial-temporal distribution patterns change of grassland formation in Inner Mongolia since the 1980s by Anan Zhang, Jiakui Tang, Na Zhang, Xuefeng Xu, Wuhua Wang, Xiaofan Li, Maojin Li, Kaihui Li, Mengquan Wu, Shuohao Cai

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Person identification using novel local triangular binary pattern-based texture descriptor by Arti Tekade, T. Vijayan, B. Karthik, Anurag Mahajan

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Towards Automated Cadastral Map Improvement: A Clustering Approach for Error Pattern Recognition by Konstantinos Vantas, Vasiliki Mirkopoulou

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Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients by Benoit Schmauch, Vincent Cabeli, Omar Darwiche Domingues, Jean-Eudes Le Douget, Alexandra Hardy, Reda Belbahri, Charles Maussion, Alberto Romagnoni, Markus Eckstein, Florian Fuchs, Aurélie Swalduz, Sylvie Lantuejoul, Hugo Crochet, François Ghiringhelli, Valentin Derangere, Caroline Truntzer, Harvey Pass, Andre L. Moreira, Luis Chiriboga, Yuanning Zheng, Michael Ozawa, Brooke E. Howitt, Olivier Gevaert, Nicolas Girard, Elton Rexhepaj, Iris Valtingojer, Laurent Debussche, Emanuele de Rinaldis, Frank Nestle, Emmanuel Spanakis, Valeria R. Fantin, Eric Y. Durand, Marion Classe, Katharina Von Loga, Elodie Pronier, Matteo Cesaroni

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Performance Analysis of Simplification of Hyperplane Support Vector Machine by Hui Cheng, Jinglong Fang, Daquan Wang, Xingqi Wang

Subjects: “…pattern recognition…”
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Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon by Johansson Moa, Atterfors Johan, Lamm Johan

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Multi-level ecological restoration zoning in Sanmenxia City: A patterns-ecosystems-humans perspective by Guangjin Zhou, Yizhong Huan, Qijia Lou, Tao Liang, Zhiguang Shi, Xiaoman Liu, Siyuan Tao, Riqi Zhang, Jixi Gao, Yiping Zuo

“…This study proposes a multi-level ecological restoration zoning framework from a “patterns-ecosystems-humans” perspective to offer differentiated and targeted guidance. …”
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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

“…Experimental validation confirmed consistency between bioinformatics predictions and tissue-level expression patterns.ConclusionThis study delineates two anoikis-related molecular subtypes of CRSwNP and identifies AR and PTHLH as cluster-specific biomarkers. …”
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