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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus, the exact way by which the network will hide the information is unable to be known to anyone who does not have the weights. The second goal is to increase hiding capacity, which has been achieved by using CNN as a strategy to make decisions to determine the best areas that are redundant and, as a result, gain more size to be hidden. Furthermore, In the proposed model, CNN is concurrently trained to generate the revealing and hiding processes, and it is designed to work as a pair mainly. This model has a good strategy for the patterns of images, which assists to make decisions to determine which is the parts of the cover image should be redundant, as well as more pixels are hidden there. The CNN implementation can be done by using Keras, along with tensor flow backend. In addition, random RGB images from the "ImageNet dataset" have been used for training the proposed model (About 45000 images of size (256x256)). The proposed model has been trained by CNN using random images taken from the database of ImageNet and can work on images taken from a wide range of sources. By saving space on an image by removing redundant areas, the quantity of hidden data can be raised (improve capacity). Since the weights and model architecture are randomized, the actual method in which the network will hide the data can't be known to anyone who does not have the weights. Furthermore, additional block-shuffling is incorporated as an encryption method to improved security; also, the image enhancement methods are used to improving the output quality. From results, the proposed method has achieved high-security level, high embedding capacity. In addition, the result approves that the system achieves good results in visibility and attacks, in which the proposed method successfully tricks observer and the steganalysis program.

Cryptography and steganography are significant tools for data security. Hybrid the cryptography with Steganography can give more security by taking advantage of each technique. This work has proposed a method for improving the crypto-stego method by utilizing the proposed dictionary method to modified ciphertext then hiding modified encrypt ciphertext in the text by used the proposed modified space method. For cryptography, we have been utilized an advanced encryption standard (AES) to the encrypted message, The AES algorithm is utilized a 128bit Block Size and 256bit key size. The ciphertext characters is then replaced by the characters identified by dictionary list. The dictionary is time-dependent, where each of the equivalent words will shifting based on the time-shift equation. The modified ciphertext is then embedded into a cover text so that the attacker cannot separate them by applying cryptanalysis.  The “Modifying Spaces” method used “Spaces” to build a steganography tool that hide the secret message. The experimental results show that the proposed method has achieved high-security level when combined cryptography and steganography in such way that the ciphertext is changed to another value by a used dictionary with time sequence that makes cryptanalysis test failed to guess and identify the algorithm that been used for encryption. The stego. test shows the proposed method achieved good results in term of capacity and visibility which is approved it hard to notice. The tests also approved that the proposed methods run fast with a less computational requirement.

Temperature predicting is the utilization to forecast the condition of the temperature for an upcoming date for a given area. Temperature predictions are done by gathering quantitative data in regard to the current state of the atmosphere. In this study, a proposed hybrid method to predication the daily maximum and minimum air temperature of Baghdad city which combines standard backpropagation with simulated annealing (SA). Simulated Annealing Algorithm are used for weights optimization for recurrent multi-layer neural network system. Experimental tests had been implemented using the data of maximum and minimum air temperature for month of July of Baghdad city that got from local records of Iraqi Meteorological Organization and Seismology (IMOS) in period between 2010 to 2016. The results show that the proposed hybrid method got a high accuracy prediction results that reach nearly from real temperature records of desired year. Science, 2018, Vol. 59, No.1C, pp: 591-599 592 Keywords IntroductionArtificial neural networks (ANN) are one of the best learning methods known in the present day to solve the certain types of problems, that gives a powerful strategy to estimating vector-valued, discrete-valued and real-valued target functions. Backpropagation neural network (BPNN) is the most common method of ANN technologies. BPNN is a type of multi-layer feed forward neural network model, will be able to learn a wide range of model mapping relationship, which simulate the intelligent behavior of human brain, and is commonly used in the area of classification and pattern recognition, as well as prediction and many other various fields, this because it has strong adaptive ability. Nevertheless, the BPNN is dependent upon the error gradient descent process that the weight inevitably is categorized as local minimum points, besides slow convergence speed and may easy to cause faults for example shock. Simulated Annealing (SA) is great at global searching, and search for accuracy seems to be partial capacity Limited. SA offers a significant advantage over alternative methods is the possibility to avoid being trapped at local minimum. SA algorithm uses a random search that not only allows changes that reduce objective function, as well as several changes that raise it. The SA algorithm works effectively on a neighborhood search within solution space, acceptance probability, and inferior ways to avoid trap (i.e., local optimal solution) [ [1, 2].In this study, a temperature predicting models has been designed for predicting the minimum and maximum air temperature for month of Juley by using a combination of artificial neural network (ANN) and Simulated Annealing (SA). Minimum and maximum temperature for lead seven days were taken into account in this analysis Iraq Meteorological Organization and Seismology (IMOS) provides forecast of deviation of minimum and maximum temperature for one week and forecast of minimum and maximum temperature are very essential whenever there was a high or low temperature epoch. Statemen...