We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Special Issue Call for Papers

Deep Learning and Intelligent System towards Smart Manufacturing

Machine learning is to design and analyze algorithms that allow computers to "learn" automatically, and allows machines to establish rules from automatically analyzing data and using them to predict unknown data. Traditional machine learning approach is difficult to meet the needs of Internet of Things (IoT) only through its outdated process starting from problem definition, appropriate information collection, and ending with model development and results verification. But however, recent scenario has dramatically changed due to the development of artificial intelligence (AI) and high-speed computing performance. Therefore, deep learning is a good example that breaks the limits of machine learning through feature engineering and gives astonishingly superior performance. It makes a number of extremely complex applications possible.

Machine learning has been applied to solve complex problems in human society for years, and the success of machine learning is because of the support of computing capabilities as well as the sensing technology. An evolution of artificial intelligence and data-driven approaches will soon cause considerable impacts to the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to publics as the artificial intelligence infiltrates into our world, and more specifically, our lives.

With the integration and extensive applications of the new generation of information technologies (such as cloud computing, IoT, big data, deep learning, AVG) in manufacturing industry, a number of countries have put forward their national advanced manufacturing development strategies, such as Industry 4.0 in Germany, Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems) in the USA, as well as Made in China 2025 and Internet Plus Manufacturing in China. Smart Manufacturing and the Smart Factory enables all information about the manufacturing process to be available when and where it is needed across entire manufacturing supply chains and product lifecycles. Smart Manufacturing is being predicted as the next Industrial Revolution or Industry 4.0. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the advances in the contextualization of data. However, without the intelligent system support, and without the support of data science technology, “smart” cannot be achieved.

Therefore, so are new emerging applications and new developments of established applications of deep learning approaches, with specific emphasis in the fields of big data, internet of things, social media data mining, web applications. Thus, this special issue aims to bring together various Smart Manufacturing and the Smart Factory research and development achievements in exploring techniques, applications, and challenges that face the evolution of artificial intelligence in the context deep learning. Interested topics include, but not limited to:

Important dates    
Submission due:31 October, 2019
Notification of final acceptance:February, 2020
Final papers:March, 2020
Publication date:determined by the Editor-in-Chief

Submission guidelines

Submissions to the special issue will be screened by the Special Issue Editors to insure that they conform to the quality standards of Enterprise Information Systems. Papers that do not pass this initial screening will be immediately returned to the authors. Reviewers will apply those standards in forming recommendations for acceptance, revision, or rejection. A maximum of two revisions will be invited. Papers should be formatted with Enterprise Information Systems style (https://www.tandfonline.com/action/authorSubmission?journalCode=teis20&page=instructions). The submission deadline is August 31, 2019. The prospective contributors should submit their papers directly to the online submission system (https://mc.manuscriptcentral.com/teis). In addition, Authors please choose the Special Issue (Deep Learning and Intelligent System towards Smart Manufacturing) in the online submission.

Mu-Yen Chen, PhD (mychen.academy@gmail.com)
Department of Information Management
National Taichung University of Science and Technology, Taiwan

Edwin David Lughofer, PhD (edwin.lughofer@jku.at)
Department of Knowledge-Based Mathematical Systems
Johannes Kepler University Linz, Austria

Erol Egrioglu, PhD (erole1977@yahoo.com)
Department of Statistics
Giresun University, Turkey


  • Methodologies, and Techniques
    • New methods for Artificial Neural Networks in combination with Deep Learning
    • New learning methods for established deep learning architectures
    • Faster and more robust methods for learning of deep models
    • Methods for non-established deep learning models
    • Complexity Reduction in and Transformation of Deep Learning Models
    • Interpretability Aspects for a better Understanding of Deep Learning Models
    • Reasoning of Input-Output Behavior of Deep Learning Models
    • Deep Learning Classifiers combined with Active Learning
    • Evolutionary–based optimization and tuning of deep learning models
    • Hybrid learning schemes
    • Incremental learning methods for self-adaptive deep models
    • Evolving techniques for deep learning systems
    • Transfer learning for deep learning systems


  • Applications
    • Big Data Analysis for Smart Manufacturing
    • Cloud and Fog Computing in Smart Manufacturing
    • Context-Awareness and Intelligent Environment Application in Smart Manufacturing
    • CPS (Cyber-Physical Systems)-based manufacturing system
    • Data-driven robotic in smart factory
    • Data-driven smart factory and smart production
    • Digital twin driven product design, manufacturing and service
    • Financial Engineering and Time Series Forecasting and Analysis in Smart Manufacturing
    • Information Security in Smart Manufacturing
    • Intelligent Human-Computer Interaction in Smart Manufacturing
    • IoT Application in Smart Manufacturing