Intelligent Computing in Industry Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 21717
Special Issue Editors
Interests: data mining; parallel algorithms; time series; parallel DBMS
Interests: IoT; machine learning; industrial sensor; intelligent transportation; smart city; health informatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last decade, technologies of Intelligent Computing have radically changed industrial sphere. Currently, Industry 4.0 applications deal with vast amount of various sensors installed on machines and mechanisms to collect Big Data on manufacturing processes. Such an application analyses the collected data to provide fast, cost-effective, ecology-safe, and self-monitored manufacturing. To respond adequately to the challenges above, researchers and practitioners should adapt current and develop novel methods and algorithms of Intelligent Computing. Besides this, advanced computing methods i.e. deep learning and recurrent neural networks are widely used in solving the challenging issues in massive amount of streaming data with the help of parallel computations.
This special issue aims to publish high quality articles that represent the cutting-edge research to solve challenging problems related to several domains of our society and industry. The focus of the special issue lies within the predictive maintenance of complex machines, sensor data analytics for smart manufacturing, Data Mining, Machine Learning, and Neural Networks in Industry 4.0 applications, cloud and high-performance computing to manage big industry data, and Digital twins.
Prof. Dr. Mikhail Zymbler
Dr. Sachin Kumar
Guest Editors
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Keywords
- Predictive maintenance
- Sensor data analytics
- Digital twins
- Industry 4.0
- Data Mining
- Machine Learning
- Neural Networks
- Cloud computing
- High-performance computing
- Mathematical models for streaming data analysis
- Motif discovery and anomaly detection in time series
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