The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

         In this paper a hybrid system was designed for securing transformed or stored text messages(Arabic and english) by embedding the message in a colored image as a cover file depending on LSB (Least Significant Bit) algorithm in a dispersed way and employing Hill data encryption algorithm for encrypt message before being hidden, A key of 3x3 was used for encryption with inverse for decryption, The system scores a good result for PSNR rate      ( 75-86) that differentiates according to  length of message and image resolution

         In this paper a hybrid system was designed for securing transformed or stored text messages(Arabic and english) by embedding the message in a colored image as a cover file depending on LSB (Least Significant Bit) algorithm in a dispersed way and employing Hill data encryption algorithm for encrypt message before being hidden, A key of 3x3 was used for encryption with inverse for decryption, The system scores a good result for PSNR rate      ( 75-86) that differentiates according to  length of message and image resolution.

Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the te

Circular data (circular sightings) are periodic data and are measured on the unit's circle by radian or grades. They are fundamentally different from those linear data compatible with the mathematical representation of the usual linear regression model due to their cyclical nature. Circular data originate in a wide variety of fields of scientific, medical, economic and social life. One of the most important statistical methods that represents this data, and there are several methods of estimating angular regression, including teachers and non-educationalists, so the letter included the use of three models of angular regression, two of which are teaching models and one of which is a model of educators. ) (DM) (MLE) and circular shrinkage mod

Nowad ays, with the development of internet communication that provides many facilities to the user leads in turn to growing unauthorized access. As a result, intrusion detection system (IDS) becomes necessary to provide a high level of security for huge amount of information transferred in the network to protect them from threats. One of the main challenges for IDS is the high dimensionality of the feature space and how the relevant features to distinguish the normal network traffic from attack network are selected. In this paper, multi-objective evolutionary algorithm with decomposition (MOEA/D) and MOEA/D with the injection of a proposed local search operator are adopted to solve the Multi-objective optimization (MOO) followed by Naï

The main objective of this paper is to study the behavior of Non-Prismatic Reinforced Concrete (NPRC) beams with and without rectangular openings either when exposed to fire or not. The experimental program involves casting and testing 9 NPRC beams divided into 3 main groups. These groups were categorized according to heating temperature (ambient temperature, 400°C, and 700°C), with each group containing 3 NPRC beams (solid beams and beams with 6 and 8 trapezoidal openings). For beams with similar geometry, increasing the burning temperature results in their deterioration as reflected in their increasing mid-span deflection throughout the fire exposure period and their residual deflection after cooling. Meanwhile, the existing ope

The main objective of this paper is to study the behavior of Non-Prismatic Reinforced Concrete (NPRC) beams with and without rectangular openings either when exposed to fire or not. The experimental program involves casting and testing 9 NPRC beams divided into 3 main groups. These groups were categorized according to heating temperature (ambient temperature, 400°C, and 700°C), with each group containing 3 NPRC beams (solid beams and beams with 6 and 8 trapezoidal openings). For beams with similar geometry, increasing the burning temperature results in their deterioration as reflected in their increasing mid-span deflection throughout the fire exposure period and their residual deflection after cooling. Meanwhile, the existing ope