Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review

Asmetha Jeyarani, R.; Senthilkumar, Radha

doi:10.1016/j.rasd.2023.102228

Cited by 12 publications

(1 citation statement)

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“…3.4.5. VGG16 model VGG16 [34] is a classic deep learning model that gained prominence for its simplicity and effectiveness. It consists of 16 layers, including 13 convolutional layers and 3 fully connected layers.…”

Section: Inceptionv3 Modelmentioning

confidence: 99%

Signature Verification Based on Deep Learning

Salama

2023

Alexandria Journal of Science and Technology

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Signature verification is considered one of the main features in determining the person identity. Our proposed framework emphasizes the potential of Deep Learning Models (DLMs) in revolutionizing signature verification techniques and underscores the need for continuous exploration and advancement in the realm of automated signature authentication. Therefore, five pre-trained DLMs, ResNet50, DenseNet121, MobileNetV3, InceptionV3, and VGG16, based on four different datasets, CEDAR, BH-Sig260 Bengali, BHSig260 Hindi, and ICDAR 2011(Dutch),are introduced in this paper to verify the person identity. Furthermore, data augmentation techniques are applied to overcome dataset limitations and increase the framework's performance. Additionally, transfer learning and finetuning techniques are performed to reduce computational time and memory usage. It is observed that the InceptionV3 DLM based on the ICDAR 2011 (Dutch) achieved the best performance of 100% accuracy, 100% AUC and 100% sensitivity. While, CEDAR Dataset achieves performance with an accuracy of 99.76%, an AUC of 99.94%, sensitivity of 99.76%, precision of 99.76%, an F1-score of 99.71%, score, and a computational time of 13.627s.

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Section: Inceptionv3 Modelmentioning

confidence: 99%