Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

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Color-Based Lifetime Estimation of LEDs Using Spectral Power Distribution Prediction Through Analytical and Machine Learning Models by J. Lokesh, Savitha G. Kini, M. G. Mahesha, Anjan N. Padmasali

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Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models by S. Bjerregaard-Michelsen, L.Ø. Poulsen, A. Bjerrum, M. Bøgsted, C. Vesteghem

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A Deep Learning Model Leveraging Time-Series System Call Data to Detect Malware Attacks in Virtual Machines by A. Alfred Raja Melvin, Jaspher W. Kathrine, Andrew Jeyabose, D. Cenitta

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Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions by Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng

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Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers by Vijoleta Vrhovac, Marko Orošnjak, Kristina Ristić, Nemanja Sremčev, Mitar Jocanović, Jelena Spajić, Nebojša Brkljač

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Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data by Yilin Chen, Xueqin Huang, Lu Tian

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Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to Neoadjuvant Therapy for Rectal Cancer by Li S, Li Z, Li S, Jiang P, Han H, Zhang Y, Lu Y

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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

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Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)? by Sara Cotelli Kureth, Elana Summers

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Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study by Jalal Hatem Hussein Bayati, Abid Mahboob, Laiba Amin, Muhammad Waheed Rasheed, Abdu Alameri

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Application of Machine Learning Technologies for Managing Multifactor Threats in an Integrated Model of Cognitive Security Center at Defense Industry Enterprise by Pavel Panilov, Tatiana Tsibizova

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Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs by Xieling Chen, Haoran Xie, Di Zou, Gary Cheng, Xiaohui Tao, Fu Lee Wang

“…This study investigates the application of machine learning and BERT models to identify topic categories in helpful online course reviews and uncover factors that influence the overall satisfaction of learners in massive open online courses (MOOCs). …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

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Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients by Gaimei Wang, Cendi Lu, Owusu Mensah Solomon, Yujia Gu, Yijing Ling, Fanchi Xu, Yumin Tao, Yehong Wei

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Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children by Gizachew Mulu Setegn, Belayneh Endalamaw Dejene

“…This research aims to enhance machine learning models by integrating Explainable AI (XAI) methods to more accurately predict the level of dietary diversity in Ethiopian preschool children. …”
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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

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Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma by Yaomin Li, Pei Ouyang, Zongliao Zheng, Jiapeng Deng, Aishun Guo, Weiwei Wang, Yawei Liu, Yuping Peng, Yankai Liao, Xiran Wang, Hai Wang, Zhaojun Wang, Zhitai Mo, Jianming Weng, Haiyan Xv, Xiaoxia Zheng, Junlu Liu, Yajuan Wang, Yongfu Cao, Guanglong Huang, Xian Zhang, Songtao Qi

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Machine learning-driven model for predicting knowledge, attitudes, and practices regarding medication safety among residents in Hubei, China by Chao Mei, Chao Mei, San-Lan Wu, San-Lan Wu, Tao Zhou, Tao Zhou, Yong-Ning Lv, Yong-Ning Lv, Yu Zhang, Yu Zhang, Chen Shi, Chen Shi, Wei-Jing Gong, Wei-Jing Gong

“…Univariate and multivariate Logistic regression analyses, along with machine learning (ML) techniques, were applied to identify risk factors associated with medication safety KAP.ResultsOut of 1,065 distributed questionnaires, 1,042 were valid (91.8% response rate). …”
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Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning by Zhang ZL, Chen KJ, Chen H, Zhu MM, Gu JJ, Jiang LS, Zheng L, Zhou SG

“…Using univariate logistic regression and multivariate logistic regression to analyze the risk factors associated with the occurrence of FOC, we constructed a FOC risk predictive model via ten different machine learning methods and evaluated the predictive performance of the model.Results: Our study indicated that educational level, history of adverse pregnancy outcomes, history of cesarean section, planned pregnancy, assisted reproduction, income, payment, SAS scores, and age are independent risk factors for FOC. …”
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