Results 11 to 20 of about 726,866 (303)

Pensamiento, Pandemia y Big Data [PDF]

, 2022
This book explores the political, social and cultural consequences of COVID-19 through a corpus of over 15.000 articles published in Spain and Latin America in the first pandemic year.

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Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data [PDF]

, 2023
Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications.
González-Crespo, Rubén, Moparthi, Nageswara Rao, Rathipriya, R., Dhamodharavadhani, S., Namasudra, Suyel   +4 more
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LightFGCNet: A Lightweight and Focusing on Global Context Information Semantic Segmentation Network for Remote Sensing Imagery

Remote Sensing, 2022
Convolutional neural networks have attracted much attention for their use in the semantic segmentation of remote sensing imagery. The effectiveness of semantic segmentation of remote sensing images is significantly influenced by contextual information ...
Yan Chen, Wenxiang Jiang, Mengyuan Wang, Menglei Kang, Thomas Weise, Xiaofeng Wang, Ming Tan, Lixiang Xu, Xinlu Li, Chen Zhang   +9 more
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Acknowledgement to Reviewers of Big Data and Cognitive Computing in 2018

Big Data and Cognitive Computing, 2019
Rigorous peer-review is the corner-stone of high-quality academic publishing [...]
Big Data and Cognitive Computing Editorial Office
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Exploring the causal pathway from ischemic stroke to atrial fibrillation: a network Mendelian randomization study

Molecular Medicine, 2020
Background and purpose Previous studies have found ischemic stroke is associated with atrial fibrillation. However, the causal association between ischemic stroke and atrial fibrillation is not clear.
Lei Hou, Mingqing Xu, Yuanyuan Yu, Xiaoru Sun, Xinhui Liu, Lu Liu, Yunxia Li, Tonghui Yuan, Wenchao Li, Hongkai Li, Fuzhong Xue   +10 more
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Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery

Remote Sensing, 2023
Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship ...
Xiaofeng Wang, Menglei Kang, Yan Chen, Wenxiang Jiang, Mengyuan Wang, Thomas Weise, Ming Tan, Lixiang Xu, Xinlu Li, Le Zou, Chen Zhang   +10 more
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Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

ISPRS International Journal of Geo-Information, 2022
Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid ...
Pranay Panjala, Murali Krishna Gumma, Hakeem Ayinde Ajeigbe, Murtala Muhammad Badamasi, Kumara Charyulu Deevi, Ramadjita Tabo   +5 more
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Spatial and Temporal Interaction Coupling of Digital Economy, New-Type Urbanization and Land Ecology and Spatial Effects Identification: A Study of the Yangtze River Delta

Land, 2023
In the digital era, the contradiction between regional urban development and land ecological protection is still prominent. Clarifying the relationship and internal interaction logic among digital economy (DE), new-type urbanization (NU), and land ...
Yuqi Zhu, Siwei Shen, Linyu Du, Jun Fu, Jian Zou, Lina Peng, Rui Ding   +6 more
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CGIAR Platform for Big Data in Agriculture - Plan of Work and Budget 2021 [PDF]

, 2021
The CGIAR Platform for Big Data in Agriculture is a cross-cutting program of the global CGIAR consortium of non-profit research institutes looking into virtually every aspect of food security spanning: genomics, breeding, agroecology, climate science ...
CGIAR Platform for Big Data in Agriculture
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Big Data, Big Knowledge: Big Data for Personalized Healthcare

IEEE Journal of Biomedical and Health Informatics, 2015
The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic.
Marco Viceconti, Peter J. Hunter, Rod D. Hose   +2 more
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