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Titlebook: Big Data – BigData 2020; 9th International Co Surya Nepal,Wenqi Cao,Liang-Jie Zhang Conference proceedings 2020 Springer Nature Switzerland

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发表于 2025-3-21 17:20:59 | 显示全部楼层 |阅读模式
期刊全称Big Data – BigData 2020
期刊简称9th International Co
影响因子2023Surya Nepal,Wenqi Cao,Liang-Jie Zhang
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Big Data – BigData 2020; 9th International Co Surya Nepal,Wenqi Cao,Liang-Jie Zhang Conference proceedings 2020 Springer Nature Switzerland
影响因子.This book constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic...The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions... .
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A Performance Prediction Model for Spark Applicationsmber of configuration parameters that are strongly related to performance. Spark performance, for a given application, can significantly vary because of input data type and size, design & implementation of algorithm, computational resources and parameter configuration. So, involvement of all these v
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Predicting the DJIA with News Headlines and Historic Data Using Hybrid Genetic Algorithm/Support Vectors. We first examine two state-of-the-art AI techniques, hybrid genetic algorithm/support vector regression and bidirectional encoder representations from transformers (BERT). After that, we proposed a new AI model that uses hybrid genetic algorithm/support vector regression and BERT to predict da
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A Web Application for Feral Cat Recognition Through Deep Learningnd detect the number of . feral cats of Australia using deep learning algorithms targeted to data captured from a set of remote sensing cameras. Feral cat recognition is an especially challenging application of deep learning since the cats are often similar and, in some cases, differ only in very sm
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Ensemble Learning for Heterogeneous Missing Data Imputationl and model-based methods. While the former brings bias to the analyses, the latter is usually designed for limited and specific use cases. To overcome the limitations of the two methods, we present a stacked ensemble framework based on the integration of the adaptive random forest algorithm, the Ja
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