Deep Learning Technology and Big Data Method for Business and Environmental Management

Dear Colleagues,

A major area of data science which is relevant to all scientific domains is big data analytics. Big data analytics is the act of going through and analyzing enormous and diverse amounts of data, which can assist organizations in making more intelligent business decisions, particularly in discovering hidden patterns, unknown correlations, and market trends. Big data analytics can make use of machine learning techniques, including deep learning.

To analyze data in real time with great accuracy and productivity, big data analytics requires new, complex algorithms based on machine learning techniques. Considering how quickly global economic markets are integrating, the economic environment is significant. Businesses, consumers, and governments are becoming increasingly aware of how events happening around the world affect their lives as well as those in their own city, state, or nation. 

This Special Issue encourages researchers using cutting-edge research methodologies based on big data and machine learning to revisit significant environmental economics and management concerns, including the application of economic theory and methods to problems that require in-depth analysis in order to improve management strategies. Many problems can be addressed, including climate change, acid rain, air pollution, urban sprawl, waste disposal, and ozone layer depletion. Both theoretical and applied contributions are encouraged for submission. To advance critical discussion, this Special Issue is especially interested in publishing empirically grounded, policy-oriented research.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Applications of big data analytics and machine learning combined with business management.
• Advances of machine-learning-based solutions in digital business environments.
• Applications of big data and machine learning in environmental management.
• Geo-spatiotemporal big data analytics for intelligence decision making in digital business.
• Novel machine-learning-based solutions in environment and energy management.
• Advances in IoT, big data, and machine learning applications in digital transformation.
• IoT, big data, and machine learning for hazard prediction and disaster response management.
• Applications of machine learning and big data analytics in the energy industry.
• Intelligent management decision-making tools in digital business environments.
• Advances of big data and machine learning in weather forecasting.
• The role of big data analytics and machine learning in managing supplier networks.
• Machine learning adaptability in big data analytics environments.
• Big data and machine learning to improve the governance in digital business.

We look forward to receiving your contributions.

Prof. Dr. Francesca Guerriero
Dr. Mohammad Shahram Moin
Dr. Reza Shahbazian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• big data
• machine learning
• deep learning
• environment
• business
• management