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- PublicationDriver's Facial Expression Recognition Using Global Context Vision Transformer(2023)Driver's facial expression recognition plays a critical role in enhancing driver safety, comfort, and overall driving experience by proactively mitigating potential road risks. While most existing works in this domain rely on CNN - based approaches, this paper proposes a novel method for driver facial expression recognition using Global Context Vision Transformer (DFER-GCViT). With its inherent capabilities of transformer-based architectures and global context modeling, the proposed method handles challenges commonly encountered in real-world driving scenarios, including occlusions, head pose variations, and illumination conditions. Our method consists of three modules: preprocessing for face detection and data augmentation, facial feature extraction of local and global features, and expression classification using a modified GC-ViT classifier. To evaluate the performance of DFER-GCViT, extensive experiments are conducted on two benchmarking datasets namely the KMU-FED driver facial expression dataset and FER2013 general facial expression dataset. The experimental results demonstrate the superiority of DFER-GCViT in accurately recognizing driver's facial expressions, achieving an average accuracy of 98.27 % on the KMU-FED dataset and 73.78% on the FER2013 dataset, outperforming several state-of-the-art methods on these two benchmarking datasets.
Scopus© Citations 1 4 - PublicationDeep Learning Techniques for Colorectal Cancer Detection: Convolutional Neural Networks vs Vision Transformers(2024)Colorectal cancer (CRC) is one of the most common cancers among humans, its diagnosis is made through the visual analysis of tissue samples by pathologists; artificial intelligence (AI) can automate this analysis based on histological images generated from different tissue samples. In this paper we aim to enhance this digital pathology process by proposing two deep learning (DL) based methods that are extremely accurate and reliable despite several limitations. Our first method is based on Convolutional Neural Networks (CNN) in order to classify different classes of tissues into cancerous and non-cancerous cells based on histological images. Our second method is based on Vision Transformers and also classifies images into cancerous and non cancerous cells. Due to the sensitivity of the problem, the performance of our work will be estimated using accuracy, precision, recall and F -score metrics since they ensure more credibility to the classification results; our models have been tested and evaluated with a dataset collected from LC25000 database containing 10000 images of cancerous and non-cancerous tissues, our models achieved promising results with an overall accuracy of 99.84 % and 98.95 % respectively with precision= 100%, recall= 100% and Fl-score= 100%, we observed that both of our models overcame several state-of-the-art results.
9 - PublicationDeploying model obfuscation: towards the privacy of decision-making models on shared platforms(2024)The automation of the industrial paradigms characterizes the era of Industry 4.0. The implementation nuances involve data and model sharing among allies and partners working on the same domain. Privacy and security of data and models are fundamental necessities that must be satisfied for this protocol's proper functioning. To this end, we propose a conceptual and algorithmic framework of a model obfuscation scheme. It is built upon the extant data obfuscation paradigm. The future work lies with the implementation and establishment of its viability. This research is expected to develop into deployable model obfuscation technique which practitioners from the industrial domain can adopt.
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