IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v45y2023i2d10.1007_s10878-023-00988-w.html
   My bibliography  Save this article

RETRACTED ARTICLE: AHI: a hybrid machine learning model for complex industrial information systems

Author

Listed:
  • Mustafa Musa Jaber

    (Dijlah University College
    Al-Turath University College)

  • Mohammed Hassan Ali

    (Imam Ja’afar Al-Sadiq University)

  • Sura Khalil Abd

    (Dijlah University College)

  • Mustafa Mohammed Jassim

    (Al-Farahidi University)

  • Ahmed Alkhayyat

    (The Islamic University)

  • Ezzulddin Hasan Kadhim

    (Al-Mustaqbal University College)

  • Ahmed Rashid Alkhuwaylidee

    (Mazaya University College)

  • Shahad Alyousif

    (Dijlah University College
    Gulf University)

Abstract

A summary of the numerous hybrid machine learning (HML) patterns is provided in this paper, which covers the complete ML lifecycle from model construction to data preparation to training to deployment to ongoing management. As a resource for the primary decision and control of production systems, industrial information systems (IIS) is a major research field in industrial systems management. Industrial and manufacturing methods are being inundated with massive amounts of data due to the increasing use of industrial information systems (IIS). Data management in networked industrial systems is examined in this paper. We recommend hybrid machine learning (HML) patterns for these customers as a stop-gap measure on the road to the cloud. To overcome the missing data problem, we propose using hybrid machine learning (HML) to solve this issue. This challenge has been given a more comprehensive range of possible solutions thanks to advances in machine learning technology. Here, a complex industrial information system based on a hybrid machine learning model (CIIS-HMLM) is proposed to address recovering the sensor’s lost data that failed. Nonlinear data modeling using an intelligent algorithm is discussed in detail. In addition, this presents a method for processing data to ensure uninterrupted service for consumers using HML. We classify many research difficulties related to the effective design and proper implementation of CIIS-HMLM. As a wrap-up, we provide a few ideas for further research on this topic.

Suggested Citation

  • Mustafa Musa Jaber & Mohammed Hassan Ali & Sura Khalil Abd & Mustafa Mohammed Jassim & Ahmed Alkhayyat & Ezzulddin Hasan Kadhim & Ahmed Rashid Alkhuwaylidee & Shahad Alyousif, 2023. "RETRACTED ARTICLE: AHI: a hybrid machine learning model for complex industrial information systems," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-22, March.
  • Handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-00988-w
    DOI: 10.1007/s10878-023-00988-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-023-00988-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10878-023-00988-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    2. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    3. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    4. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    5. Francesco Pelella & Luca Viscito & Federico Magnea & Alessandro Zanella & Stanislao Patalano & Alfonso William Mauro & Nicola Bianco, 2023. "Comparison between Physics-Based Approaches and Neural Networks for the Energy Consumption Optimization of an Automotive Production Industrial Process," Energies, MDPI, vol. 16(19), pages 1-22, September.
    6. Jiachao Peng & Hanfei Chen & Lei Jia & Shuke Fu & Jiali Tian, 2023. "Impact of Digital Industrialization on the Energy Industry Supply Chain: Evidence from the Natural Gas Industry in China," Energies, MDPI, vol. 16(4), pages 1-32, February.
    7. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Danfeng Zhang & Xin Wang & Liang Zhao & Huaqing Xie & Chen Guo & Feizhou Qian & Hui Dong & Yun Hu, 2023. "Numerical Investigation on Heat Transfer and Flow Resistance Characteristics of Superheater in Hydrocracking Heat Recovery Steam Generator," Energies, MDPI, vol. 16(17), pages 1-15, August.
    9. Jun Dong & A-Ru-Han Bao & Yao Liu & Xi-Hao Dou & Dong-Ran Liu & Gui-Yuan Xue, 2022. "Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    10. Alexandra I. Khalyasmaa & Alina I. Stepanova & Stanislav A. Eroshenko & Pavel V. Matrenin, 2023. "Review of the Digital Twin Technology Applications for Electrical Equipment Lifecycle Management," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    11. Gian Marco Paldino & Fabrizio De Caro & Jacopo De Stefani & Alfredo Vaccaro & Domenico Villacci & Gianluca Bontempi, 2022. "A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines," Energies, MDPI, vol. 15(6), pages 1-17, March.
    12. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T., 2022. "Atikokan Digital Twin: Machine learning in a biomass energy system," Applied Energy, Elsevier, vol. 310(C).
    13. Jayant Kalagnanam & Dzung T. Phan & Pavankumar Murali & Lam M. Nguyen & Nianjun Zhou & Dharmashankar Subramanian & Raju Pavuluri & Xiang Ma & Crystal Lui & Giovane Cesar da Silva, 2022. "AI-Based Real-Time Site-Wide Optimization for Process Manufacturing," Interfaces, INFORMS, vol. 52(4), pages 363-378, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:45:y:2023:i:2:d:10.1007_s10878-023-00988-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.