An Ensemble Learning Method Based on Deep Neural and Pca-Based Svm Network for Baggage Threat and Smoke Recognition

Abdelfatah Hassan Ahmed, Muaz Al Radi, Naoufel Werghi

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations

    Abstract

    Ensemble learning methods are emerging as one of the most widely used image classification and object recognition techniques. They have the advantage of achieving higher classification and recognition performance compared to single models while being easy to implement in various applications. This work proposes an ensemble-based classification network that leverages the recognition performance of both threat and smoke detection tasks. The method utilizes ensemble learning of a deep Convolutional Neural Network (CNN) combined with a Principal Component Analysis (PCA)-based Support Vector Machine (SVM) classifier. Comparisons to several single model classifiers and other state-of-the-art methods and the proposed methods were carried out. The proposed method showed superior performance in comparison for the problems of baggage X-ray imagery classification and smoke recognition.

    Original languageBritish English
    Title of host publication2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781665454742
    DOIs
    StatePublished - 2023
    Event2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 - Dubai, United Arab Emirates
    Duration: 20 Feb 202323 Feb 2023

    Publication series

    Name2023 Advances in Science and Engineering Technology International Conferences, ASET 2023

    Conference

    Conference2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
    Country/TerritoryUnited Arab Emirates
    CityDubai
    Period20/02/2323/02/23

    Keywords

    • Baggage X-ray Imagery
    • Deep learning
    • Ensemble Learning
    • Principal Component Analysis (PCA)
    • Smoke recognition
    • Support Vector Machine (SVM)

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