Critical analysis of Big Data challenges and analytical methods
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In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.About:
This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Big Data for Qualitative Research
TL;DR: Big Data for Qualitative Research as discussed by the authors explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis.
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The ethical balance of using smart information systems for promoting the United Nations' sustainable development goals
TL;DR: In this article, the authors explore how technology can be used to address the SDGs and in particular Smart Information Systems (SIS), the technologies that build on big data analytics, typically facilitated by AI techniques such as machine learning.
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Deep convolutional neural network–based image classification for COVID-19 diagnosis
TL;DR: In this paper, the authors used convolutional neural network (CNN) models such as ResNeXt, Channel Boosted CNN, DenseNet, AlexNet, and VGG 16 to diagnose COVID-19 pandemic in an effective way.
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Sustainability through the Digitalization of Industrial Machines : Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors
TL;DR: Increasing the fuel efficiency of industrial machines through digitalization can enable the transport and logistics sector to overcome challenges such as low productivity growth and increasing CO2 emissions as discussed by the authors. But, it is not suitable for all machines.
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The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review
TL;DR: In this article, the authors explored the relevant factors for BIS adoption using a systematic literature review (SLR) and an expert-ranking survey of 63 studies that were published in Scopus and WoS databases from 2011 to 2020.
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