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Results for “"Nuzath Tabassum Arthi"”

4 results

AI-Powered Deepfake Detection Using CNN and Vision Transformer Architectures

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M. Sheikh, Urmi Kirtonia, Nuzath Tabassum Arthi, Md Al-Imran

Year: 2025Citations: 3

The increasing use of artificial intelligence-generated deepfakes creates major challenges in maintaining digital authenticity. Four AI-based models, consisting of three CNNs and one Vision Transformer, were evaluated using large face image datasets. Data preprocessing and augmentation techniques im...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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DeFaX: A Cross-Attention Fusion Framework for Robust and Explainable Deepfake Detection

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Md Al-Imran, Md Sifatullah Sheikh, Urmi Kirtonia, Nuzath Tabassum Arthi et al.

Journal: IEEE AccessYear: 2025Citations: 1

The increasing realism of GAN-generated facial forgeries has intensified the need for reliable deepfake detection systems capable of operating under diverse generative conditions. Traditional detectors often struggle to simultaneously capture global semantic structures and fine-grained local artifac...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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Comparative Study of Pre-Trained Deep Learning Models for Brain Tumor MRI Image Classification with K-Fold Cross Validation and Ensemble

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Sajid Mahmud, Ankon Saha, Nuzath Tabassum Arthi, Musharrat Khan et al.

Year: 2025

Manual review of brain MRI alone struggles to deliver timely, reliable tumor diagnosis. We study transfer-learning baselines and a simple ensemble for four-class MRI classification (glioma, meningioma, pituitary, no tumor). Using the BRISC 2025 T1-weighted corpus (6,000 images across axial, coronal,...

Life SciencesNeuroscienceNeurology
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Transfer Learning for Brain Tumor MRI Image Classification: A Comparative Study of Custom Deep Learning Models with K-Fold Cross Validation and Ensemble

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Ankon Saha, Sajid Mahmud, Nuzath Tabassum Arthi, Musharrat Khan et al.

Year: 2025

Manual review of brain MRI image alone struggles for timely and reliable tumor diagnosis. We tested a custom ensemble framework for four-class MRI image classification (glioma, meningioma, pituitary, no tumor). On the BRISC 2025 T1-weighted corpus, we do a uniform preprocessing: grayscale and denois...

Life SciencesNeuroscienceNeurology
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