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Browsing by Subject "Neural computers"

Browsing by Subject "Neural computers"

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  • Automatic recognition of captchas using neural networks 
    Geagea, Marybel (Notre Dame University-Louaize, 2019-05)
    The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a test used to differentiate between humans and machines. In this thesis, we have implemented a deep neural network architecture to automatically recognize CAPTCHAs. The main idea behind this thesis is to test the reliability of test-based CAPTCHAs in differentiating between humans and machines. We utilized convolutional neural networks instead of the conventional strategies of CAPTCHA breaking which, typically, use segmentation techniques. Our model is comprised of 8 layers to learn textbased images; ...
  • Plant leaf classification using dual path convolutional neural networks 
    Rizk, Sara (Notre Dame University-Louaize, 2019-05)
    Taxon identification is highly needed for a wide variety of research including ecology, agronomy and medicine. As of 1970, classification of plants was introduced into computer vision techniques. Most research conducted in this area focuses on leaves due to their availability as well as their ability to discretize. The most common features researchers base their work on are shape, texture and venation. This research study proposes a dual path, dual feature model for plant leaf identification. We weigh our research on shape and venation features. Sobel operators are used for primary and secondary ...
  • Temporal difference method with hebbian plasticity rule as a learning algorithm in networks of chaotic spiking neurons 
    Aoun, Mario A. (Notre Dame University-Louaize., 2007)
    The aim of this thesis is to deluge the latest studies in chaotic dynamics and their relevance in neural computing, as also to inspect a new learning algorithm for a network of chaotic spiking neurons as it is recently proposed by Nigel Crook et al. [1]. The thesis will tackle the latest research in the field of information processing and chaotic neural networks, and will contribute to the recent work of Nigel Crook et al. [1] by finding a suitable learning algorithm for chaotic neurons. The learning algorithm based on biological realism will be implemented in a network of chaotic spiking ...