When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensional matrix to a one-dimensional vector matrix using the Vector Quantization (VQ) algorithm. The data is divided into training and testing. Then it is fed into 23 layers of deep neural networks (DNN) that classify satellite images. The result is 2,145,020 parameters, and the evaluation of performance measures was accuracy = 100%, loopback = 100%, and the result F1 = 100 %.
In this study, a chaotic method is proposed that generates S-boxes similar to AES S-boxes with the help of a private key belonging to
In this study, dynamic encryption techniques are explored as an image cipher method to generate S-boxes similar to AES S-boxes with the help of a private key belonging to the user and enable images to be encrypted or decrypted using S-boxes. This study consists of two stages: the dynamic generation of the S-box method and the encryption-decryption method. S-boxes should have a non-linear structure, and for this reason, K/DSA (Knutt Durstenfeld Shuffle Algorithm), which is one of the pseudo-random techniques, is used to generate S-boxes dynamically. The biggest advantage of this approach is the produ
... Show MoreDigital image is widely used in computer applications. This paper introduces a proposed method of image zooming based upon inverse slantlet transform and image scaling. Slantlet transform (SLT) is based on the principle of designing different filters for different scales.
First we apply SLT on color image, the idea of transform color image into slant, where large coefficients are mainly the signal and smaller one represent the noise. By suitably modifying these coefficients , using scaling up image by box and Bartlett filters so that the image scales up to 2X2 and then inverse slantlet transform from modifying coefficients using to the reconstructed image .
&nbs
... Show MoreEven though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show t
... Show MoreSteganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.
Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was tr
... Show MoreAs usage of internet grows in different applications around the world, many techniques were developed to guard an important data against from illegal access and modification from unauthorized users by embedding this data into visual media called host media. Audio hiding in an image is a challenge because of the large size of the audio signal. Some previous methods have been presented to reduce the data of the audio signal before embedding it in the cover image, however, these methods was at the cost of reducing the quality of the audio signal. In this paper, a Slantlet transform (SLT) based method is applied to obtain better performance in terms of audio quality. In addition, the data hiding scheme in the cover color image has been imple
... Show MoreThe aim of this study was to identify the effectiveness of using generative learning model in learning kinetic series on rings and horizontal bar in artistic gymnastics for men ,Also, the two groups were better in learning the two series of movements on the rings and horizontal bar . The experimental method was used to design two parallel groups with pretested and posttest .The sample included third graders at the College of Physical Education and Sports Sciences - University of Baghdad ,The third class (d) was chosen to represent the control group that applied the curriculum in the college, with (12) students per group. After conducting the tribal tests, the main experiment was carried out for (8) weeks at the rate of two units per week di
... Show MoreBig data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a
... Show More