The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

Due to the lack of practical usage of the learned subject, traditional learning is becoming an obstacle in front of further knowledge gaining. Classical learning methods are out-dated for the modernized world that required more productive, collaborative, and more curious individuals. Therefore, a new learning method is essential to provide knowledge and skills to pursue world development without falling behind. The purpose of this study is to find the positive effects of using Phenomenon-Based Learning (PhenoBL) as a studying approach in teaching ICT skills to students and its impact on giving the motivation and improving ICT skills for students at primary schools of Sulaimani city in Iraq. The key method is to teach ICTs through other classes with a universal as PhenoBL, the cross-curricular approach which is built into the classes. Then, a survey is established to uncover the reason behind making students learn about the ICT from places other than school and shortage in keeping those skills in student's mind for a longer time. Our results showed that using Phenomenon-based learning students' scores are improved by more than 10% which makes using this method significantly effective. Furthermore, using phenomenon-based learning allows the student to keep and maintain gained skills for longer periods of time.

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Recently, there has been an increasing attention towards the use machine learning platforms notably Amazon Machine learning and Microsoft Azure Machine learning (ML) to undertake sentiment analysis. The present experimental study compared Amazon ML against Microsoft Azure ML as platforms for performing sentiment analysis. The evaluation was done d using the evaluation metrics: accuracy, reliability, precision, F-score and Recall. Data was sourced from Twitter a microblogging platform. The sentiment analytics model was created based on Logistic regression analysis algorithms. Results confirmed that Microsoft Azure ML is more accurate and precise than Amazon ML as platforms for Sentiment Analysis and that Azure could be used reliably, accurately and securely to build Sentiment Analysis models for social media sites.

Domain Name System (DNS) is one of few services on the Internet which is allowed through every security barrier. It mostly depends on the User Datagram Protocol (UDP) as the transport protocol, which is a connectionless protocol with no built-in authentication mechanism. On top of that, DNS responses are substantially larger than their corresponding requests. These two key features made DNS a fabulous attacking tool for cybercriminals to reflect and amplify a huge volume of requests to consume their victim's resources. Recent incidents revealed how harsh DNS could be when it is abused with great complexity by attackers. Moreover, these events had proven that any defense mechanism with single point deployment couldn’t accurately and efficiently overcome an attack volume with high dynamicity. In this paper, we proposed the Efficient Distributed-based Defense Scheme (EDDS) to overcome the shortcomings of a centralized-based defense mechanism. By using an authentication message exchange, which is a Challenge-Handshake Authentication Protocol (CHAP)-based authentication mechanism. It is deployed on multiple nodes to determine the legitimacy of the DNS request. Moreover, it significantly reduces the impact of the amplification factor for the fake DNS requests without having any side effects on legitimate ones. Then, a Stateful Packet Inspection (SPI)-based packet filtering is proposed to distinguish legitimate requests from fake ones by considering the results of the authentication procedure. Both authentication-message exchange and SPI-based filtering are introduced to provide detection accuracy without reducing the quality of service for legitimate users. As the simulation results show, the proposed mechanism can efficiently and accurately detect, isolate, and discard the bogus traffic with minimal overhead on the system.