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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A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection by N. K. Sreeja

“…This paper proposes a novel instance-based classification algorithm called Weighted Pattern Matching based Classification (PMC+) for classifying imbalanced data. …”
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A Model for Detecting <i>Xanthomonas campestris</i> Using Machine Learning Techniques Enhanced by Optimization Algorithms by Daniel-David Leal-Lara, Julio Barón-Velandia, Lina-María Molina-Parra, Ana-Carolina Cabrera-Blandón

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Evaluation of the Proposed Hand Vein Authentication System using Machine Learning by rajaa ahmed, Ziyad Tariq Mustafa Al-Ta’i

“…The features are extracted through PCA Net for acquiring the most distinctive attributes of hand veins. The different machine learning algorithms used in this evaluation for classification of the extracted features include SVM, Logistic Regression, and Naive Bayes. …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…PurposeTo develop effective machine learning models that analyze pattern visual evoked potentials (PVEPs) to predict the stabilized visual acuity (VA) of patients with treated ocular trauma.MethodsThis experiment included 260 patients (220 males, average age 42.54 years) with unilateral ocular trauma. …”
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Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases by Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Tianming Yao, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani

“…Accordingly, the aim of our study was to test the hypothesis that machine learning algorithms can distinguish stool microbiota patterns—and their responses to fiber—across diseases with previously reported overlapping dysbiotic microbiota profiles. …”
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Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis by Ana Rocha, Cristina Costeira, Raul Barbosa, Florbela Gonçalves, Miguel Castelo-Branco, Joaquim Viana, Margarida Gaudêncio, Filipa Ventura

“…Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. …”
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Muses or Stereotypes? Identifying Historical Patterns of Sexism in a Corpus of Brazilian Lyrics by Janaina Nogueira de Souza Lopes, Vitória Pereira Firmino, Valéria Quadros dos Reis

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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. …”
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An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999–2018 by Qun Tang, Yong Wang, Yan Luo

“…This study aimed to construct a machine learning (ML) algorithm that can accurately and transparently establish correlations between demographic variables, dietary habits, and ASCVD. …”
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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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Towards Automated Cadastral Map Improvement: A Clustering Approach for Error Pattern Recognition by Konstantinos Vantas, Vasiliki Mirkopoulou

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Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

“…As a powerful data mining tool, neural networks are a suitable method for discovering hidden patterns in the information of diabetic patients. In this study, in order to discover hidden patterns and diagnose diabetes, a particle swarm intelligence algorithm has been used along with a neural network to increase the accuracy of diabetes diagnosis. …”
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A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5 by Fatima Es-sabery, Ibrahim Es-sabery, Junaid Qadir, Beatriz Sainz-de-Abajo, Begonya Garcia-Zapirain

“…Many researchers prefer using machine and deep learning techniques for this analysis. …”
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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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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…Abstract Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. …”
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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

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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

“…Immune cell infiltration was analysed using the CIBERSORT algorithm. CRSwNP samples were classified via consensus clustering. …”
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3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology by Chongyang YAO, Yongxin CHOU, Zhiwei LIANG, Haiping YANG, Jicheng LIU, Dongmei LIN

“…On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. …”
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Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET by Cristian Pérez-Granados, Karl-L. Schuchmann

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