The paper present design of a control structure that enables integration of a Kinematic neural controller for trajectory tracking of a nonholonomic differential two wheeled mobile robot, then  proposes a Kinematic neural controller to direct a National Instrument mobile robot (NI Mobile Robot). The controller is to make the actual velocity of the wheeled mobile robot close the required velocity by guarantees that the trajectory tracking mean squire error converges at minimum tracking error. The proposed tracking control system consists of two layers; The first layer is a multi-layer perceptron neural network system that controls the mobile robot to track the required path , The second layer is an optimization layer ,which is impleme

In this paper, we will show that the Modified SP iteration can be used to approximate fixed point of contraction mappings under certain condition. Also, we show that this iteration method is faster than Mann, Ishikawa, Noor, SP, CR, Karahan iteration methods. Furthermore, by using the same condition, we shown that the Picard S- iteration method converges faster than Modified SP iteration and hence also faster than all Mann, Ishikawa, Noor, SP, CR, Karahan iteration methods. Finally, a data dependence result is proven for fixed point of contraction mappings with the help of the Modified SP iteration process.

This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with

    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe

Clustering algorithms have recently gained attention in the related literature since
they can help current intrusion detection systems in several aspects. This paper
proposes genetic algorithm (GA) based clustering, serving to distinguish patterns
incoming from network traffic packets into normal and attack. Two GA based
clustering models for solving intrusion detection problem are introduced. The first
model coined as handles numeric features of the network packet, whereas
the second one coined as concerns all features of the network packet.
Moreover, a new mutation operator directed for binary and symbolic features is
proposed. The basic concept of proposed mutation operator depends on the most
frequent value

    Cryptography can be thought of as a toolbox, where potential attackers gain access to various computing resources and technologies to try to compute key values. In modern cryptography, the strength of the encryption algorithm is only determined by the size of the key. Therefore, our goal is to create a strong key value that has a minimum bit length that will be useful in light encryption. Using elliptic curve cryptography (ECC) with Rubik's cube and image density, the image colors are combined and distorted, and by using the Chaotic Logistics Map and Image Density with a secret key, the Rubik's cubes for the image are encrypted, obtaining a secure image against attacks. ECC itself is a powerful algorithm that generates a pair of p

 Fluoroscopic images are a field of medical images that depends on the quality of image for correct diagnosis; the main trouble is the de-nosing and how to keep the poise between degradation of noisy image, from one side, and edge and fine details preservation, from the other side, especially when fluoroscopic images contain black and white type noise with high density. The previous filters could usually handle low/medium black and white type noise densities, that expense edge, =fine details preservation and fail with high density of noise that corrupts the images. Therefore, this paper proposed a new Multi-Line algorithm that deals with high-corrupted image with high density of black and white type noise. The experiments achieved i