Smart Technology for Smart Response During Pandemic Times

2022 ◽  
pp. 261-278
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Smart City ◽  
Critical Role ◽  
Communication Strategy ◽  
Smart Technology ◽  
Information And Communication ◽  
Remote Learning

The formal response to COVID-19 through ICT is presented with a focus on testing COVID-19, ICTs and tracking COVID-19, ICTs and COVID-19 treatment, and policies and strategies. The chapter highlights the critical role of ICTs and e-government for technologies to fight coronavirus. It covers delivery of remote learning, ICT trends, artificial intelligence (AI), and big data in fighting the pandemic, in addition to social media application for awareness of citizens such as emergencies, protection, and pandemic news. The notion of developing an information and communication strategy for redesigning smart city transformation in a pandemic is highlighted.

scholarly journals The role of big data in smart city

2016 ◽  
Vol 36 (5) ◽  
pp. 748-758 ◽  
Author(s):  
Ibrahim Abaker Targio Hashem ◽  
Victor Chang ◽  
Nor Badrul Anuar ◽  
Kayode Adewole ◽  
Ibrar Yaqoob ◽  
...  
Keyword(s):  
Big Data ◽  
Smart City

Role of Machine Learning in Big Data Peregrination

2021 ◽  
pp. 235-276
Author(s):  
Aradhana Behura ◽  
Sanjaya Kumar Panda
Keyword(s):  
Machine Learning ◽  
Big Data

Distributed Machine Learning in Big Data Era for Smart City

2017 ◽  
pp. 151-177
Author(s):  
Yuan. Zuo ◽  
Yulei. Wu ◽  
Geyong. Min ◽  
Chengqiang. Huang ◽  
Xing. Zhang
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Smart City ◽  
Distributed Machine Learning

scholarly journals The Role of Big Data and Machine Learning in COVID-19

2020 ◽  
Vol 11 (2sup1) ◽  
pp. 01-20
Author(s):  
Mustafa Abdel-Karim Ababneh ◽  
◽  
Aayat Amin Al-Jarrah ◽  
Damla Karagozlu ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data

scholarly journals Analyzing Large Microbiome Datasets Using Machine Learning and Big Data

2021 ◽  
Vol 1 (3) ◽  
pp. 138-165
Author(s):  
Thomas Krause ◽  
Jyotsna Talreja Wassan ◽  
Paul Mc Kevitt ◽  
Haiying Wang ◽  
Huiru Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Data Sets ◽  
Raw Data ◽  
Public And Private ◽  
Rumen Microbiome

Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.

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