Application of Deep Learning Methods for Employee Satisfaction Analysis Based on Text Data by A. A. Kazinets

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Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” by J. Wanni, C. A. Bronkhorst, D. J. Thoma

“…Abstract We respond to Schaeben et al.’s1 comment on our paper, “Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.” …”
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Learning Path Analysis of Optimizing Educational Data Mining Based on Genetic Algorithm by Wu Yanhong

“…With the extensive use of educational data mining in the field of learning analysis, identifying an effective approach to maximize learning route analysis has become a research priority. …”
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Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

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The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines by Christos Emexidis, Panagiotis Gkonis

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Transfer learning for accelerated failure time model with microarray data by Yan-Bo Pei, Zheng-Yang Yu, Jun-Shan Shen

Subjects: “…Survival analysis…”
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A software pipeline for systematizing machine learning of speech data by Jimuel Celeste, Mashrura Tasnim, Amable J. Valdés Cuervo, Enrique A. de la Cal, Eleni Stroulia

“…The reproducibility and replicability of experimental findings is an essential element of the scientific process. The machine-learning community has a long-established practice of sharing data sets so that researchers can report the performance of their models on the same data. …”
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The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning by Shen Jiang, Ningning Shi, Chang Liu

“…Abstract This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. …”
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Development of a neural network model for spatial data analysis by E. O. Yamashkina, S. A. Yamashkin, O. V. Platonova, S. M. Kovalenko

“…The paper aimed to develop and validate a neural network model for spatial data analysis. The advantage of the proposed model is the presence of a large number of degrees of freedom allowing its flexible configuration depending on the specific problem. …”
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Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis by Mohammad Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker

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TaGra: an open Python package for easily generating graphs from data tables through manifold learning by Davide Torre, Davide Chicco

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Deep learning models for the analysis of high-dimensional survival data with time-varying covariates while handling missing data by Sarah Ogutu, Mohanad Mohammed, Henry Mwambi

“…Abstract Recent advances in deep learning have expanded the potential for predictive modeling in survival analysis, particularly in high-dimensional datasets with time-varying covariates. …”
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Predicting User Purchases From Clickstream Data: A Comparative Analysis of Clickstream Data Representations and Machine Learning Models by A. Aylin Tokuc, Tamer Dag

“…Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. …”
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Flood mapping based on machine learning: a lexicometric analysis by Isabela de Oliveira Gallindo, Ana Carolyne John Munaro, Jamil Alexandre Ayach Anache, Alexandre Meira de Vasconcelos

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OpenSTARLab: open approach for spatio-temporal agent data analysis in soccer by Calvin Yeung, Kenjiro Ide, Taiga Someya, Keisuke Fujii

“…Abstract Sports analytics has become both more professional and sophisticated, driven by the growing availability of detailed performance data. This progress enables applications such as match outcome prediction, player scouting, and tactical analysis. …”
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Assisting the Learning of Clinical Reasoning by Veterinary Medical Learners with a Case Example by Gustavo Ferlini Agne, Amanda (Mandi) Nicole Carr, Roy Neville Kirkwood, Kiro Risto Petrovski

“…It emphasizes the importance of integrating client-centered care and iterative evaluation into the clinical decision-making process. Key learning strategies include facilitation in using the domains of clinical reasoning—concepts, data collection, and analysis, taking action, and reflection on encounters. …”
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Comprehensive analysis of supervised learning methods for electrical source imaging by Sarah Reynaud, Adrien Merlini, Douraied Ben Salem, François Rousseau

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Application of machine learning in diabetes prediction based on electronic health record data analysis by Yang Zihan

“…Th is research contributes to the development of machine learning in healthcare, specifically improving disease prediction models through advanced data analysis techniques.…”
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An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint by Ye Zhang, Tenglong Yu, Wenwu Wang

“…Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. …”
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Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data by Gengbo Liu, Logan Brooks, John Canty, Dan Lu, Jin Y. Jin, James Lu

“…Abstract Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). …”
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