Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Purpose: This research seeks to provide a point of view based on the creation of sustainable value to the customer of the banks in the context of total quality management and relationship marketing. It aims to develop a model to measure the value of sustainable customer peduncular under total quality management PAL (administrative leadership, involvement of employees, continuous improvement, process improvement, staff training), through the mediation of relationship marketing and objective dimensions (administrative leadership, involvement of employees, continuous improvement, improving processes , staff training), and to explore any of the variables and dimensions more influential in the creation of sustainable value to the cust

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

In the last years, a new technology called Cloud computing has been developed. Empirical and previous studies, commonly examined in business field and other domains. In this study, the significant factors that affecting the adoption of cloud computing have been examined using a frequency analysis that have been explored by the previous studies. The results showed that the most effected factors were relative advantage which followed by security and privacy, complexity, innovativeness, and external support. In this study the model of technology organization-environment was used to examine the significant factors that affecting the adoption of cloud computing.

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

إن المعتقدات الخاصة بالسحر والعرافة قديمة جدا كون الإنسان عرف الكثير منها ومارس أنواع من الأساليب السحرية منذ أقدم العصور ومن أكثر العلوم التي حرم تعليمها ونشرها هو علم السحر والسيمياء أو الكيمياء والشعبذة والحيل . ومع ذلك عرف السحر من قبل سكان بلاد وادي الرافدين ولاسيما في بابل والسحر يمثل أقدم مرحلة في تطور العقل البشري .

     Bitter substances are identified by protein receptors located on surface of taste cell membranes. Mutational polymorphism of the bitter taste receptor (TAS2R38) is a significant determinant in phenylthiocarbamide (PTC) threshold perception. This research's objectives were to find TAS2R38 polymorphisms in Iraqi people and investigate any correlations between genotype and the PTC taste sensitivity. Bitterness sensitivity was determined by assessing the capacity to differentiate and the responsiveness to a representative strip of PTC. Cheek cells samples were collected for DNA extraction, PCR amplification and genotyping. PCR was performed to amplify the short region of the TAS2R38 gene containing the initial polymorphisms of inter