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    Volume 2, Issue 1, IECE Transactions on Intelligent Systematics
    Volume 2, Issue 1, 2024
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    Quanmin Zhu
    Quanmin Zhu
    University of the West of England, United Kingdom
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    IECE Transactions on Intelligent Systematics, 2024, Volume 2, Issue 1: 14-26

    Free Access | Research Article | 27 December 2024
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    by
    IECE Author Fida Hussain Dahri 1
    IECE Author Ghulam E Mustafa Abro 2 *
    IECE Author Nisar Ahmed Dahri 3
    IECE Author Asif Ali Laghari 4
    IECE Author Zain Anwar Ali 5
    1 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
    2 Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Kingdom of Saudi Arabia
    3 Faculty of Social Sciences and Humanities, School of Education, University Technology Malaysia, Malaysia
    4 Software College, Shenyang Normal University, Shenyang 110136, China
    5 Electronic Engineering Department, Maynooth International Engineering College (MIEC), Maynooth University, Maynooth, Co. Kildare, Ireland
    * Corresponding Author: Ghulam E Mustafa Abro, [email protected]
    DOI: 10.62762/TIS.2025.613103
    Received: 17 September 2024, Accepted: 09 December 2024, Published: 27 December 2024  
    Abstract
    Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a training accuracy of 99%, a testing accuracy of 89.73%, a precision of 90.11%, a recall of 89.73%, and an F1-score of 89.79%. These findings underscore the efficacy of our methodology in tackling the intricacies of indoor scene recognition. This research substantially advances the domain of robotics and automation by establishing a more resilient and dependable framework for autonomous navigation and scene comprehension in GPS-denied settings, facilitating the development of more efficient and intelligent robotic systems.
    Graphical Abstract
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    Keywords
    indoor scene recognition
    deep convolutional neural network (CNN)
    robotics and automation autonomous navigation and GPS-Denied environments
    Funding
    This work was jointly supported by the Data and Intelligence Laboratory (D&Intel Lab), School of Computer Science and Engineering, Southeast University, China and the Robotics Control lab under the Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), King Fahd University of Petroleum and Minerals (KFUPM), Kingdom of Saudi Arabia.
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    Cite This Article
    APA Style
    Dahri, F. H., Abro, G. E. M., Dahri, N. A., Laghari, A. A., & Ali, Z. A. (2024). Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition. IECE Transactions on Intelligent Systematics, 2(1), 14–26. https://doi.org/10.62762/TIS.2025.613103
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