Evolution, reconfiguration and low-carbon performance of green space pattern under diverse urban development scenarios: A machine learning-based simulation approach by Yujie Ren, Mengdie Zhou, Antian Zhu, Shucheng Shi, Hao Zhu, Yuzhu Chen, Shanshan Li, Tianhui Fan

“…However, as urban land-use patterns become increasingly complex and development trajectories more diverse, the relationship between green space morphology and carbon dynamics (emissions and sequestration) reveals notable heterogeneity and non-linear characteristics. …”
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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

“…We utilized a combination of 101 machine learning algorithms to analyze the TCGA-KIRC cohort and the GSE22541 KIRC patients, screening for cell death patterns closely associated with prognosis from 18 potential modes. …”
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Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter by Paval. A. Khorin, Aleksey P. Dzyuba, Aleksey V. Chernykh, Muhammad A. Butt, Svetlana N. Khonina

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Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study by Clement Twumasi, Mikail Aktas, Nicholas Santoni

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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

“…A dataset of 701 HR-ARM exams from a tertiary center, classified according to London Classification, was used to develop and test multiple machine learning (ML) algorithms. The exams were divided in a training and testing dataset with a 80/20% ratio. …”
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RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review by Md Nasim Reza, Kyu-Ho Lee, Eliezel Habineza, Samsuzzaman, Hyunjin Kyoung, Young Kyoung Choi, Gookhwan Kim, Sun-Ok Chung

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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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Audiogram Digitization Tool for Audiological Reports by Francois Charih, James R. Green

Subjects: “…Machine vision…”
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Structural Data Recognition With Graph Model Boosting by Tomo Miyazaki, Shinichiro Omachi

Subjects: “…Pattern recognition…”
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Partially restricted stacks as functions by Jared Mi, Jeffrey Xu, Jason Yang

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Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients by Bushra Mahdi Hasan, Zahraa Jasim Jaber, Ahmad Adel Habeeb

Subjects: “…Image Processing, Deep Learning, K Nearest Neighbor, Pattern Recognition, Machine Learning…”
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Spatial heterogeneity patterns along the human footprint gradient and their ecological implications: A case study in South Korea by Kyoung-Ho Kim, Minkyu Park, Taeho Park, Donghae Baek, Gyobeom Kim, Namjoo Heo, Dong Jun Chun

“…This study investigates how human footprint intensity shapes spatial heterogeneity patterns and their ecological implications in South Korea by integrating multiple heterogeneity metrics and machine learning approaches. …”
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Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990–2018) by Adriano Matousek, Tiffany H Leung, Herbert Pang

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Submarine Cable Vibration Signal Recognition Based on Sparrow Search Algorithm Optimized Support Vector Machine by GUO Jiaxing, QIAN Junxia, LIU Rui, MA Hongjuan

Subjects: “…pattern recognition…”
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Similarity Index Values in Fuzzy Logic and the Support Vector Machine Method Applied to the Identification of Changes in Movement Patterns During Biceps-Curl Weight-Lifting Exercise by André B. Peres, Tiago A. F. Almeida, Danilo A. Massini, Anderson G. Macedo, Mário C. Espada, Ricardo A. M. Robalo, Rafael Oliveira, João P. Brito, Dalton M. Pessôa Filho

“…This study presents a proposal for the use of Morisita–Horn similarity indices in modelling with machine learning methods to identify changes in positional sequence patterns during the biceps-curl weight-lifting exercise with a barbell. …”
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Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data by Jiaxin Fan, Shuai Cao, Hang Peng, Yuanjie Zhi, Shuqin Zhan, Rui Li

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An Intelligent Method for Day-Ahead Regional Load Demand Forecasting via Machine-Learning Analysis of Energy Consumption Patterns Across Daily, Weekly, and Annual Scales by Monica Borunda, Arturo Ortega Vega, Raul Garduno, Luis Conde, Manuel Adam Medina, Jeannete Ramírez Aparicio, Lorena Magallón Cacho, O. A. Jaramillo

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Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods by Fernanda Cristina Barbosa Lana, Carolina Coimbra Marinho, Bruno Barbosa Miranda de Paiva, Lucas Rocha Valle, Guilherme Fonseca do Nascimento, Leonardo Chaves Dutra da Rocha, Marcelo Carneiro, Joanna d‘Arc Lyra Batista, Fernando Anschau, Pedro Gibson Paraiso, Frederico Bartolazzi, Christiane Corrêa Rodrigues Cimini, Alexandre Vargas Schwarzbold, Danyelle Romana Alves Rios, Marcos André Gonçalves, Milena Soriano Marcolino

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Unveiling ac4C modification pattern: a prospective target for improving the response to immunotherapeutic strategies in melanoma by Jianlan Liu, Pengpeng Zhang, Chaoqin Wu, Binlin Luo, Xiaojian Cao, Jian Tang

“…We developed and confirmed an excellent acRG-related signature (acRGS) utilizing a comprehensive set of 101 algorithm combinations derived from 10 machine learning algorithms. Hereby, the acRGS, including MYO10, ZNF667, MRAS, SCO2, MAPK10, PNMA6A, KPNA2, NT5DC2, BAIAP2L2 and NDST3, delineated ac4C-associated mRNA modification patterns in melanoma. …”
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Traffic Patterns and Emergency Medical Services Prenotification Transport Estimates in Trauma Activations [Letter] by Luthfiyah S, Triwiyanto T, Ismath M

Subjects: “…emergency medical services (ems) traffic patterns and transport estimates trauma activations real-time traffic monitoring ai and machine learning in ems…”
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