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The utilize of the web has made humans and companies powerless to exterior assaults. Indeed, cyber problems essentially influence information frameworks with distinctive types of malicious attacks such as spyware, virus, social engineering, etc. The Internet e-mail service, in particular, has become one of the most dependable methods of communication among people, institutions, and companies. The development of digital signatures to e-mail services has raised the e-mail security, which led to replacing the standard mailing of registered letters. Unfortunately, the process of sending and receiving e-mails has created a negative impact means on security and privacy from cybercriminals by diffusing spam and malware. As a result, e-mail hosts are constantly under attack by malicious programs that are often attached to e-mails. In this paper, the simulation model and prototype of an email traffic monitor developed and tested in order to prove the ability of our proposed method for detecting new viruses. This paper states the success possibility of this new method based on the simulation results. The results of the analysis suggest that the Cyber Security Modeling Language (CySeMoL) model has a good performance of operating system vulnerability prediction. At last, some useful suggestions in the context of the CySeMoL model are presented.

Due to the rapid evaluation in the field of communications and multimedia and the increasing use of the Internet, multimedia data security has become very urgent. of the best alternative way to achieve multimedia data security is encryption, which prevents unauthorized entities from accessing confidential data. In recent years, the chaotic system of image encryption becomes an efficient way to encrypt images due to its high security. It has certain special properties like sensitivity to initial conditions, and control parameters, pseudorandom, ergodicity, and non-convergence, etc. chaotic dynamics systems became a promising alternative to traditional encryption algorithms. In this paper, This paper presents a new algorithm for the image encryption/decryption scheme depended on a novel six-dimensional hyper-chaotic system to achieve a high level of security, the chaotic sequence generated from system employ for permutation and diffusion the original image to create an encrypted image. The performance of the algorithm has been analyzed through analyzes statistical such as Histogram Analysis, Correlation Coefficient Analysis, Information Entropy Analysis, Key Space Analysis, Key Sensitivity Analysis, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio, The experimental results show that the algorithm has good encryption performance, large keyspace equals to and the high sensitivity for small changes in secret key which makes the algorithm immune to Brute force attacks, and it can resist the statistical attacks, therefore, the presented encryption algorithm depends on a novel hyperchaotic system is more secure against the statistical and differential attacks.

The process of detect the text from the natural image is complex and difficult process because the variance by the devises that take the images and different the texts that found in images in the orientation, size and style. Given the importance the texts in images in the several of application of computer vision. In this paper dependent on the spatial natural images and on the spatial data set for the street sign that include the texts by the different size and different orientation. In this paper detected the texts in images by using robust method by using several algorithms, at the first stage making preprocessing for the image to blur the image and reduce the nose on it by Gaussian blur, second stage making processing that include canny edge detection to detect the edges and dilation, third stage applying connected component to filling all objects in image then applying stroke width transform(SWT) to detect the letter candidate and applying the system on the several images that include different types of texts.

In this paper, the fire color feature is analysis and test on a set of color spaces (RGB, HSV, YCbCr, Lab, Yiq) to account the hue component and determination the best color space to represent the properties of fire and used in fire detection to increase accuracy and reduce the detection time and false alarms,four common types of fire (wood, cork, cloth, paper or cardboard ) were used to compare them with an image containing all the basic colors and analyze them and calculating Scale factor that depending on the histogram for hues value

The security of video applications in mobile devices has become recently an important field research. Dealing with video data, which is large compared to text and image and processing it in the mobile platform is a big challenge. Generally, the efficiency of any video encryption algorithm is concerned with two criteria, the computational time required to process video data, and the memory usage according to the resources on the smartphone. The secured system in 3G devices has become a matter of importance. This paper presents an implementation of full video encryption using Elliptic Curve Cryptography (ECC) on a mobile device. The operations on ECC include doubling and addition on the finite field as the backbone for an elliptic curve. Also, mapping representation is introduced to convert every byte of plain video into a point on EC. The paper proposed multi-keys instead of using one key as usual. Also, Chaotic Key Generator (CKG) is exploited for the randomness of the multi-keys. The proposed work focuses on increasing the security with multi-keys and to get acceptable time for encryption and decryption in a limited environment like mobile. The system is implemented using Android Studio with version (3.0) and using java language, it is implemented on Android version 7.0 (Nougat) and on mobile Galaxy S8.

In this paper, we introduce and study preprocessing signal for speech emotion recognition. The aim of our work is to get pure signal which is created by sampling the signal from speaker. The discrimination between speech and music waves was achieved. A good signal is obtained by using preprocessing then it used for feature extraction. The files we used in this paper are wave-type for male, female and music have sample rate 48000, bit resolution is 16-bits and Mono channel. The Berlin dataset and RAVDESS dataset are used in this work.

In the last few years, and due to the vast widespread of social media applications, texts have become more important and get more attention. Since texts, in general, are carrying a lot of information that can be extracted and analyzed. Several researchers have done significant works in text classification. Within different scripts such as English and other western languages, several challenges and obstacles have been addressed with such a field of research. Regarding the Arabic language, the process is different from other vital languages since Arabic is considered an orthographic language that depends on the word shape. It is not easy to apply the standard text preprocessing techniques since it affects the word meaning. This paper evaluates Arabic short text classification using three standard Naïve Bayes classifiers. In our method, we classify the thesis and dissertations using their titles to perform the classification process. The collected dataset is collected from different repositories by using standard scrapping techniques. Our method classifies the document based on their titles to be placed in the desired specialization. Several preprocessing techniques have been applied, such as (punctuation removal, stop words removal, and space vectorization). For feature extraction, we adopt the TF-IDF method. We implemented three types of Naïve Bayes classifiers which are (Multinomial Naïve Bayes, Complemented Naïve Bayes, and Gaussian Naïve Bayes). The study results showed that Complemented Naïve Bayes Classifier proposed the best performance with (0.84) of accuracy for the testing phase. The results of the study are promising to be applied with different short text classifications.

The importance of branch and bound algorithm is the mathematical improvement to find the value of (X) that Maximize or minimize the objective function within a set of feasible solution, as it is reliable on the efficient evaluation of the bounds of regions or branches of the space of the research, whether they are upper or lower. In this paper, we discussed five cases with respect to branching decisions based on network solutions to calculate the nearest link with a short time. From the results, bound and branch algorithm can develop and change the obtaining solutions for the five cases under study.

The single index support vector regression model (SI-SVR) is a useful regression technique used to alleviate the problem of high-dimensionality. In this paper, we propose a robust variable selection technique for the SI-SVR model by using vital method to identify and minimize the effects of outliers in the data set. The effectiveness of the proposed robust variable selection of the SI-SVR model is explored by using various simulation examples. Furthermore, the suggested method is tested by analyzing a real data set which highlights the utility of the proposed methodology.

Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.