scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

2021 ◽  
pp. 1-24
Author(s):  
Amine Belhadi ◽  
Sachin S. Kamble ◽  
Angappa Gunasekaran ◽  
Karim Zkik ◽  
Dileep Kumar M. ◽  
...  
Keyword(s):  
Big Data ◽  
Six Sigma ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Lean Six Sigma ◽  
Chemical Company ◽  
Green Performance

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

What Can Fitness Apps Teach Us About Group Privacy?

2021 ◽  
pp. 1-30
Author(s):  
Miriam J. Metzger ◽  
Jennifer Jiyoung Suh ◽  
Scott Reid ◽  
Amr El Abbadi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Individual Privacy ◽  
Recent Advances

This chapter begins with a case study of Strava, a fitness app that inadvertently exposed sensitive military information even while protecting individual users' information privacy. The case study is analyzed as an example of how recent advances in algorithmic group inference technologies threaten privacy, both for individuals and for groups. It then argues that while individual privacy from big data analytics is well understood, group privacy is not. Results of an experiment to better understand group privacy are presented. Findings show that group and individual privacy are psychologically distinct and uniquely affect people's evaluations, use, and tolerance for a fictitious fitness app. The chapter concludes with a discussion of group-inference technologies ethics and offers recommendations for fitness app designers.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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