Implementation of TSFS (Transposition, Substitution, Folding, and Shifting) algorithm as an encryption algorithm in database security had limitations in character set and the number of keys used. The proposed cryptosystem is based on making some enhancements on the phases of TSFS encryption algorithm by computing the determinant of the keys matrices which affects the implementation of the algorithm phases. These changes showed high security to the database against different types of security attacks by achieving both goals of confusion and diffusion.

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from

In this paper, the effect size measures was discussed, which are useful in many estimation processes for direct effect and its relation with indirect and total effects. In addition, an algorithm to calculate the suggested measure of effect size was suggested that represent the ratio of direct effect to the effect of the estimated parameter using the Regression equation of the dependent variable on the mediator variable without using the independent variable in the model. Where this an algorithm clear the possibility to use this regression equation in Mediation Analysis, where usually used the Mediator and independent variable together when the dependent variable regresses on them. Also this an algorithm to show how effect of the

The aim of this research is to construct a three-dimensional maritime transport model to transport nonhomogeneous goods (k) and different transport modes (v) from their sources (i) to their destinations (j), while limiting the optimum quantities v ijk x to be transported at the lowest possible cost v ijk c and time v ijk t using the heuristic algorithm, Transport problems have been widely studied in computer science and process research and are one of the main problems of transport problems that are usually used to reduce the cost or times of transport of goods with a number of sources and a number of destinations and by means of transport to meet the conditions of supply and demand. Transport models are a key tool in logistics an

This study aims at identifying the notion of Post-Occupancy Evaluation (POE) pertinent to the performance of three general hospitals constructed inside the Sulaimani City, tracing the relationship between the quality of the indoor environments and medical staff (doctors and nurses) satisfaction level. Using some indoor environment elements in the right way will positively influence the mood, stress level of the medical staff, and patient recovery as a result. The POE toolkits (AEDET and ASPECT) have been implemented on targeted wards at the selected hospitals. AEDET and ASPECT questionnaires were distributed among 152 medical staff to obtain their perspectives. In total, 112 valid questionnaires were received. The medica

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In this paper a nonlinear adaptive control method is presented for a pH process, which is difficult to control due to the nonlinear and uncertainties. A theoretical and experimental investigation was conducted of the dynamic behavior of neutralization process in a continuous stirred tank reactor (CSTR). The process control was implemented using different control strategies, velocity form of PI control and nonlinear adaptive control. Through simulation studies it has been shown that the estimated parameters are in good agreement with the actual values and that the proposed adaptive controller has excellent tracking and regulation performance.