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This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial;
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methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a;
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; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;
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Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the;
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Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain;
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computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors-with expertise in both the social;
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cost while increasing the information value of the data being obtained. By presenting the role of computational modeling as an integrated;
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; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;
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for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;
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Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;
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for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;
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for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science;
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Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;
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provide a better methodological framework for analysing complex data from digital learning and assessments. The term computational has been;
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; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;
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-performance computational systems biology; model inference from experimental data; multi-scale modeling and analysis methods; computational approaches;
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; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis;
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Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health;
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relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network;
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This book highlights the latest research in computational intelligence and its applications. It covers both conventional and trending;
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and Smart Systems; Track of Computer Graphics, Image Processing and Artificial Intelligence Part VI: Track of Data Driven Computational;
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is the availability of relaxometric data and simple computer program proficiency. These programs are easy to use, highly interactive and the data;
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this purpose the physically-based (process) numerical models are typically utilized. However, in data-rich cases, one may use data-driven;
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this purpose the physically-based (process) numerical models are typically utilized. However, in data-rich cases, one may use data-driven;
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This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering;
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This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering;
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Computational Methods in Cell Biology and consists of two parts: (1) data extraction and analysis to distill models and mechanisms, and (2) developing;
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