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Strategies For Social And Educational Inclusion In Visually Impaired People

We used two database for the experiments, BOSSBase 1.01 and BOSSBase 1.01 + BOWS 2. The BOSSBase 1.01 database contains 10,000 pairs of images divided into 4,000 pairs for training, 1,000 pairs for validation, and 5,000 for testing. The partition of the BOSSBase 1.01 database was based on the works by Xu, Wu & Shi , Ye, Ni & Yi and Zhang et al. . This chapter encompasses the current situation due to the SARS-CoV-2 pandemic accelerating students’ migration to virtual platforms.

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The obtained accuracy and loss curves of CNN Xu-Net in WOW with 0.4bpp BOSSBase 1.01 are presented in Fig. 6, Ye-Net in WOW 0.4bpp with BOSSBase 1.01 + BOWS 2 in Fig. 7, and Yedroudj-Net in S-UNIWARD 0.4bpp with BOSSBase 1.01 are presented in Fig.

Table 10

A VGG19 classification model was used in parts c, d, and e, to predict COVID-19 positive and negative cases. For differentiation with the other VGG19 model, we use the name VGG19 Covid. In variation c, the datasets passed through the classification without lung segmentation. This network proposed by Ye, Ni & Yi , it uses an SRM filter bank for steganographic noise extraction. The feature extraction stage consists of eight convolutional layers, a TLU activation function after the first layer, and TanH for the others. The classification stage has one fully connected layer and Softmax activation function.

It is important to note that the convolution kernels’ size was set to 5 × 5 and to achieve that, some of the filters were padded with zero. The mentioned improvements in convergence refer to the lower number of epochs and iterations needed to train the CNNs, which means training in less time. The training process duration can not be compared because they depend on other factors like hardware specifications.

Table 3

Reinel Tabares-Soto conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. For comparison purposes, all CNNs were implemented as presented in the original papers. This allowed to establish a baseline along with results reported in literature. Figure 5 shows the training curves for Xu-Net without the strategy. Hyperbolic Tangent activation function is commonly used in neural networks. It provides nonlinear activation while being a smooth differentiable function.

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  • This stage is located after the global average pooling layer and is the same in all architectures.
  • Furthermore, Table 5 shows the results of part e in which segmentation masks were inverted and applied to images.
  • Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images.
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