Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

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Study on influencing factors of age-adjusted Charlson comorbidity index in patients with Alzheimer's disease based on machine learning model by Jian Ding, Jian Ding, Zheng Long, Yiming Liu, Min Wang

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Generating high-precision farmland irrigation pattern maps using remotely sensed ecological indices and machine learning algorithms by Yuqi Liu, Yang Wang, Yanling Liao, Renkuan Liao, Jirka Šimůnek

“…To address this, we utilized the Google Earth Engine (GEE) platform, integrating environmental covariates and machine learning algorithms, to generate distribution maps of irrigation patterns (micro-irrigation and surface irrigation) at a 30-meter resolution for the Turpan-Hami Basin. …”
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Machine learning-based monitoring of land cover and reclamation plantations on coal-mined landscape using Sentinel 2 data by Mayank Pandey, Alka Mishra, Singam L. Swamy, James T. Anderson, Tarun Kumar Thakur

“…This study examines the reclamation of coal mine overburdens through reforestation, using high-resolution Sentinel 2 satellite data classified by various Machine Learning (ML) algorithms. Support Vector Machine has been identified as a more accurate and effective ML algorithm compared to Random Forest and Maximum Likelihood Classifier in delineating land use and vegetation classes, particularly forests, and in distinguishing reclamation plantations into three age classes: young (4 ± 3 years), middle-aged (10 ± 2 years), and mature (15 ± 2 years). …”
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A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM) by A. V. L. N. Sujith, Naila Iqbal Qureshi, Venkata Harshavardhan Reddy Dornadula, Abinash Rath, Kolla Bhanu Prakash, Sitesh Kumar Singh

“…The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. …”
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Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan by Anas Mohamed Abaker Babai, Olugbenga Ajayi Ehinola, Omer.I.M. Fadul Abul Gebbayin, Mohammed Abdalla Elsharif Ibrahim

Subjects: “…Machine learning…”
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Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosisResearch in context by Caihong Ning, Hui Ouyang, Jie Xiao, Di Wu, Zefang Sun, Baiqi Liu, Dingcheng Shen, Xiaoyue Hong, Chiayan Lin, Jiarong Li, Lu Chen, Shuai Zhu, Xinying Li, Fada Xia, Gengwen Huang

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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

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Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

Subjects: “…machine learning…”
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Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

Subjects: “…Machine learning…”
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Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation by Nagalakshmi R, Surbhi Bhatia Khan, Ananthoju Vijay kumar, Mahesh T R, Mohammad Alojail, Saurabh Raj Sangwan, Mo Saraee

“…This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. …”
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Vapor pressure deficit (VPD) downscaling based on multi-source remote sensing, in-situ observation, and machine learning in China by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…New hydrological insights for the region: Machine learning-based downscaling methods offer a potential solution to enhance the accuracy of VPD spatiotemporal distribution. …”
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Comparative Analysis of pKa Predictions for Arsonic Acids Using Density Functional Theory-Based and Machine Learning Approaches by Miroslava Nedyalkova, Diana Heredia, Joaquín Barroso-Flores, Marco Lattuada

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Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

“…We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. …”
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Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction by Gregor Kohls, Erik M. Elster, Peter Tino, Graeme Fairchild, Christina Stadler, Arne Popma, Christine M. Freitag, Stephane A. De Brito, Kerstin Konrad, Ruth Pauli

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A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study by Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

“…Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. …”
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Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors by Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata

“…In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. …”
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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients by Yulin Lai, Peiyuan Huang

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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer by Xiaolong Liu, Pengxian Tao, He Su, Yulan Li

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A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia by Dionicio Neira-Rodado, Juan Camilo Paz-Roa, John Willmer Escobar, Miguel Ángel Ortiz-Barrios

“…The proposed spatiotemporal approach integrates machine learning, signal analysis, and statistical features, capturing geographical, temporal, and event-specific factors. …”
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