The fundamental concept behind image steganography is to conceal one image within another. To advance steganography, it may be wise to conceal multiple images within other multiple images. The major goal of our suggested strategy is to conceal a collection of related images while taking into account size equality. With the aid of a 3D-DeepCNN grounded autoencoder, we introduce a novel multi-image steganography approach in this study. Within four cover images, we attempt to encode and decode four secret images. The quantitative findings show that the suggested model shares the embedded hidden image information over every component of the cover image, without compromising image quality. The results of our model using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are (28.56, 0.928) for global secret input and (32.516, 0.973) for global cover input. The qualitative results were evaluated and give an effective performance in comparison to the current models.

Brain tumors are a pathological condition characterized by aberrant growth within the cerebral structure. The segmentation of these tumors becomes pronounced to discern their boundaries accurately amidst healthy brain tissues, owing to the variability in tumor shapes and the complexities of determining their location, size, and texture. Manual tumor segmentation, a time-consuming task, is highly susceptible to human error. In this paper, we propose a deep inception residual network for brain tumor segmentation using the UNet architecture with a pre-trained Inception ResNet V2 encoder. The Inception-Resnet block, which fuses Inception with the residual neural network, is included. With this architecture, segmentation is significantly more robust. The study is being carried out on the RSNA-MICCAI Brain Tumor Radiogenomic Classification data set, i.e., on the benchmark dataset BraTS 2020. Our network achieves dice scores of 85.7%, 91.2%, and 83.2% for enhancing tumor, whole tumor, and tumor core, respectively. The experimental findings demonstrate the effectiveness of our proposed approach compared to other methods.

Aerial imagery has emerged in numerous fields such as sustainable development, forestry, urban planning, agriculture, earth science and climate research. Extracting relevant information from satellite images, such as building detection, road extraction, and land cover classification, is crucial for decision-making. Semantic segmentation (SS),generates a dense pixel-wise segmentation map of a given satellite image where each pixel is related necessarily to a specific class. (SS) has become essential to reach the aforementioned goals. In this context, we presented an improved approach for semantic segmentation of aerial images using the pre-trained CNN VGG16 model and the modified U-Net architecture. Our results show that our proposed approach exceeds state of the art methods in accurately classifying land cover types in terms of dice coefficient and an average accuracy of 82.30% of 87.81% respectively.

In this paper, we propose a novel approach for semantic segmentation based on Deep Learning methods for Autonomous Driving. Our main idea is to develop a fully convolutional neural network (CNN) architecture for semantic segmentation based on the U-Net model. For that, we propose a model with different layers such as a convolutional layer, pooling layer, dropout layer, and fully connected layer. The experiment results obtained, using the CamVid dataset, show that the choice of the number of periods and the depth of the network have a great influence on the best results.

This paper deals with an experiment which proves that wavelet networks are capable for 3D objects modeling. To prove this, we will propose a new structure of wavelet network founded on several mother wavelets families. This new structure is in some ways similar to the classic wavelet networks but it admits some originality. Actually, wavelet network basically uses dilations and translations versions of only one mother wavelet to construct the network. The proposed structure uses several mother wavelets, in order to maximize best wavelets selection probability. An algorithm to construct this structure is presented. First, 3D object model vertices and their corresponding normal values are used to create a training set. Then, an improved Orthogonal Least Squares method version is applied to optimize wavelet selection for every mother wavelet. Some simulation results will describe the proposed wavelet network performance employing several types of Polywogs as mother wavelets.