An ensemble of fine-tuned deep learning models improves facial age estimation accuracy for the 16-17 year old group to 68%, four times better than the base DEX model.
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Supervised machine learning is used to classify and prioritize incriminating digital forensic artefacts based on metadata extracted from disk images of previous cases.
citing papers explorer
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Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
An ensemble of fine-tuned deep learning models improves facial age estimation accuracy for the 16-17 year old group to 68%, four times better than the base DEX model.
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Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts
Supervised machine learning is used to classify and prioritize incriminating digital forensic artefacts based on metadata extracted from disk images of previous cases.