2026-10-23T14:57:41Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/124002026-10-22T11:35:27Zopenairedrivercom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

Feature Fusion Using Deep Learning Algorithms in Image Classification for Security Purposes by Random Weight NetworkKiran, Mustafa ServetSeyfi, GokhanYilmaz, MerveEsme, EnginWang, Xizhaodeep learningfeature fusionrandom weight networkX-ray securityAutomated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts features from multiple and diverse deep learning architectures and classifies them using a Random Weight Network (RWN), whose hyperparameters are optimized for maximum performance. The results show substantial improvements at each stage: while the best standalone deep learning model achieved a test accuracy of 83.55%, applying the RWN to a single feature set increased accuracy to 94.82%. Notably, the proposed feature fusion framework achieved a state-of-the-art test accuracy of 97.44%. These findings demonstrate that a modular approach combining multi-model feature fusion with an efficient classifier is a highly effective strategy for improving the accuracy and generalization capability of automated threat detection systems.Multidisciplinary Digital Publishing Institute (MDPI)2025Articlehttps://rdlopen.rawdatalibrary.net/r/gumushane/12400https://doi.org/10.3390/app151690532076-3417enApplied Sciences (Switzerland)info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/12400