Special Issue "Big Data and Artificial Intelligence for Industry 4.0"
Deadline for manuscript submissions: closed (30 July 2022) | Viewed by 19372
Interests: big data; big data analytics; NoSQL databases; data warehouse; design process; data mining; analytics; social business intelligence; opinion mining; preference queries; what-if analysis; precision farming; ontologies; machine learning; trajectory data analysis
Special Issues, Collections and Topics in MDPI journals
Interests: big data; big data analytics; NoSQL databases; social data analysis; trajectory data analysis; precision farming, business intelligence; machine learning; OLAP analysis; data warehouse; semantic web
Industry 4.0 pushes towards manufacturing automation through the digitalization of industrial processes and the adoption of smart technology such as Internet of Things (IoT) devices. Industry 4.0 solutions go beyond the application to smart factories, also covering the sectors of logistics and traceability, smart agriculture, healthcare, and others. The enormous amount of generated data poses challenges concerning the collection and management of big data, but also presents several opportunities, such as the extraction of knowledge from these data, which can drive decision-making and the continuous improvement of industrial operations and production chain processes. The distance between the research fields of electronics and data analysis is still considerable and makes it difficult to identify and seize these opportunities. The most common question that practitioners and researchers with electronic backgrounds ask is "What can I do with all this data?".
This Special Issue of Electronics aims to build a bridge between the world of electronics and data analysis by presenting state-of-the-art advancements in the adoption of big data and artificial intelligence techniques to meet the requirements of Industry 4.0. We welcome novel contributions, in every application area, of innovative methodological proposals and practical applications, as well as sophisticated review articles. The topics of interest include, but are not limited to, the following:
- Industry 4.0;
- industrial big data and AI;
- cyber-physical systems;
- smart factory;
- predictive maintenance;
- smart agriculture;
- industrial process monitoring and automation;
- digital transformation processes;
- data-driven applications
Prof. Dr. Matteo Golfarelli
Prof. Dr. Enrico Gallinucci
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. Electronics is an international peer-reviewed open access semimonthly 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 2200 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.
- Industry 4.0
- industrial big data and AI
- cyber-physical systems
- smart factory
- predictive maintenance
- smart agriculture
- industrial process monitoring and automation
- digital transformation processes
- data-driven applications