@inproceedings{7d0659b7517e4c6e983d1309fcc909fb,
title = "An Ensemble Learning Method Based on Deep Neural and Pca-Based Svm Network for Baggage Threat and Smoke Recognition",
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.",
keywords = "Baggage X-ray Imagery, Deep learning, Ensemble Learning, Principal Component Analysis (PCA), Smoke recognition, Support Vector Machine (SVM)",
author = "Ahmed, {Abdelfatah Hassan} and Radi, {Muaz Al} and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 ; Conference date: 20-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1109/ASET56582.2023.10180635",
language = "British English",
series = "2023 Advances in Science and Engineering Technology International Conferences, ASET 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 Advances in Science and Engineering Technology International Conferences, ASET 2023",
address = "United States",
}