Big data analytics and artificial intelligence in air pollution studies for the prediction of particulate matter concentration

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.

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Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

10.4018/978-1-6684-3694-3.ch021 ◽

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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A Survey on Challenges Facing Artificial Intelligence Based Pattern Recognition for Business Oriented Big Data Analytics and Solutions

Software Engineering and Algorithms - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-77442-4_20 ◽

2021 ◽

pp. 237-248

Author(s):

Ahmed Maghawry ◽

Amr Elhadidi ◽

Ahmed Alqassed ◽

Mohamed Awad ◽

Ayman Taha ◽

...

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics

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The Nature of Intelligent Analytics

Advances in Data Mining and Database Management - Intelligent Analytics With Advanced Multi-Industry Applications ◽

10.4018/978-1-7998-4963-6.ch001 ◽

2021 ◽

pp. 1-21

Author(s):

Zhaohao Sun ◽

Andrew Stranieri

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Research And Development ◽

Business Intelligence ◽

Data Analytics ◽

Data Science ◽

Big Data Analytics ◽

Business Analytics ◽

Novel Approach ◽

Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

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