Browsing by Type "conference object"

Now showing 1 - 7 of 7
  • Publication
    Deep Learning Techniques for Colorectal Cancer Detection: Convolutional Neural Networks vs Vision Transformers
    (2024)
    Sari, Meriem
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    Moussaoui, Abdelouahab
    ;
    Hadid, Abdenour 
    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
  • Publication
    Deploying model obfuscation: towards the privacy of decision-making models on shared platforms
    (2024)
    Sadhukhan, Payel
    ;
    Chakraborty, Tanujit 
    ;
    Sengupta, Kausik
    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.
      24  2
  • Publication
    Driver's Facial Expression Recognition Using Global Context Vision Transformer
    (2023)
    Saadi, Ibtissam
    ;
    Cunningham, Douglas W
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    Abdelmalik, Taleb-Ahmed
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    Hadid, Abdenour 
    ;
    El Hillali, Yassin
    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
  • Publication
    Greywater reuse in the water-scarce Gulf Region
    (2019)
    Paleologos E.K.
    ;
    Elhakeem M.
    ;
    El Amrousi M.
    ;
    Canal Forgues, Eric 
    ;
    Saba M
    Treated wastewater has emerged in many countries as a component of the water stream and a way to supplement, primarily, landscape and agricultural irrigation. Several European and Asian states have, in addition, promoted the use of greywater in the interior of buildings. Regulations for greywater reuse are, by and large, not in place and quality standards for different types of application are in evolution. At the same time constructed wetlands as stand-Alone or as part of the wastewater treatment system have shown promise as a way to improve wastewater effluent, while upgrading ecosystem and aesthetic aspects of a site. Gulf countries, such as the United Arab Emirates (UAE) are faced with natural water scarcity, exorbitant water demands, beyond their renewable resources and desalination capacities, overloaded wastewater treatment systems that have resulted in releases of untreated wastewater in the marine environment, and increasing populations and expanded economic activities that would further accentuate existing water problems. The current article discusses these issues and, given the public reluctance in the UAE to accept interior greywater reuse, it focuses on the applicability of constructed wetlands in the Gulf region and their potential to enhance irrigation streams and landscape appeal.
      175  79
  • Publication
    Knowing the class distinguishing abilities of the features, to build better decision-making models
    (2024)
    Sadhukhan, Payel
    ;
    Sengupta, Kausik
    ;
    Palit, Sarbani
    ;
    Chakraborty, Tanujit 
    Explainability allows end-users to have a transparent and humane reckoning of an ML scheme's capability and utility. ML model's modus opernadi can be explained via the features which trained it. To this end, we found no work explaining the features' importance based on their class-distinguishing abilities. In a given dataset, a feature is not equally good at distinguishing between the data points' possible categorizations (or classes). This work explains the features based on their class or category-distinguishing capabilities. We estimate the variables' class-distinguishing capabilities (scores) for pair-wise class combinations, utilize them in a missing feature context, and propose a novel decision-making protocol. A key novelty of this work lies in the refusal to render a decision option when the missing feature (of the test point) has a high class-distinguishing potential for the likely classes. Two real-world datasets are used empirically to validate the explainability of our scheme.
      14  1