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Titlebook: Big Data – BigData 2019; 8th International Co Keke Chen,Sangeetha Seshadri,Liang-Jie Zhang Conference proceedings 2019 Springer Nature Swit

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發(fā)表于 2025-3-23 10:54:51 | 只看該作者
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發(fā)表于 2025-3-23 17:48:58 | 只看該作者
Ion Beam Induced Gene Transfer,r proposed Segmented Parallel Dwarf Cube (SPD-Cube). Firstly, a high-dimensional cube is divided into several low-dimensional segments. Then by calculating the sub-dwarf of each low-dimensional segment, the storage space required for cube materialization is greatly reduced. Finally, combining Map/Re
13#
發(fā)表于 2025-3-23 19:26:48 | 只看該作者
Cell Damage Effects Due to Ion Implantation, practical. Previous studies conducted regarding prostate cancer diagnosis have been limited to microarray data, with limited successes. We utilized The Cancer Genome Atlas’ (TCGA) prostate cancer sequencing data to test the viability of fitting machine learning models to RNA sequencing data. A majo
14#
發(fā)表于 2025-3-24 00:54:57 | 只看該作者
Biological Effects of Ion Implantation,interactions can be applied to practical applications like targeted advertising, viral marketing, political campaigns, etc. In this paper, we propose a Multi-Feed Weighted Topic Embeddings (MFWTE) model to study user network interaction and topic diffusion patterns on Twitter. Our method extracts to
15#
發(fā)表于 2025-3-24 04:14:18 | 只看該作者
Ion Beam Mutation Breeding of Crops,aged in a notorious tax evasion practice called .. We designed and implemented this technique for the commercial taxes department, government of Telangana, India. This problem is solved in two steps. In step one, the problem is formulated as detecting fraudulent communities in a social network, wher
16#
發(fā)表于 2025-3-24 08:30:25 | 只看該作者
Cell Damage Effects Due to Ion Implantation,rs who are interested in the best routes to their destinations. This work focuses on forecasting traffic flow in major freeways in southern California using large amounts of data collected from the Caltrans Performance Measurement System. Both statistical models and machine learning models are consi
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發(fā)表于 2025-3-24 13:14:49 | 只看該作者
18#
發(fā)表于 2025-3-24 15:04:48 | 只看該作者
19#
發(fā)表于 2025-3-24 21:16:58 | 只看該作者
20#
發(fā)表于 2025-3-25 00:37:28 | 只看該作者
https://doi.org/10.1007/978-3-030-23551-2data storage; data lake; data analysis; data modeling; data management; data centers; dataflow architectur
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