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IEEE/ICACT20260079 Slide.20        [Big Slide]       [YouTube]	Oral Presentation
	In this work, we proposed a hybrid multimodal deep learning architecture for Android forensic analysis that effectively combines CNNs, LSTMs, and autoencoders to analyze heterogeneous forensic artifacts. The experimental results demonstrate that the proposed framework achieves high detection accuracy under real-world conditions while maintaining a false positive rate below 4%. In addition to its strong performance, the model is optimized for mobile deployment, making it suitable for practical and scalable Android forensic applications.

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