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This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. [ABSTRACT FROM AUTHOR]

COVID-19 and other neighbouring diseases spread widely, resulting in a global epidemic that was impossible to manage and control. While numerous measures have been put in place to detect an infected person and protect uninfected areas from contracting these contagious diseases, the spread of diseases like COVID-19 continues to be rapid. As of the time of writing this paper, the number of affected people has continued to rise, and there is no clear indication of the number of people who are infected but have gone undiscovered and are spreading the infections. That is why, in order to combat the threat of contiguous disease spread, this research presented an upgraded autonomous socio-contact tracing system on a mobile platform. As a result, a generic system development process was used to create a system that allows an infected person who has been tested positive to track their electromagnetic ID card in order to determine their exact location and the risk of spreading contagious diseases. by an autonomous smart assistant that assists in describing symptoms. As a result, the system is equipped with notifications alerts for the stages of social group identification, processing, and control in order to avoid the spread of contagious diseases. The development of this system is critical for controlling epidemic diseases that are spreading over the world (particularly COVID-19) and posing a threat to people's lives. Furthermore, it contributes to a greater understanding of the seriousness of epidemic diseases and how to avoid them. [ABSTRACT FROM AUTHOR]

With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient. [ABSTRACT FROM AUTHOR]

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique. [ABSTRACT FROM AUTHOR]

Smartphones and Mobile App are acceptable as learning tool, especially related to history which control the present and build the future; particularly the study of history is the practical application of the Qur'an and the Sunnah. The history subject, specifically on the stories of the prophets contains many lessons and wisdoms give values that are transformed into the child's mind and translated into his/her behavior. In Saudi Arabia elementary schools, history is firstly introduced to fourth grade that contains the stories of the prophets, may not get the strong impact of the story in soul and memory when they only read it from the book with simple pictures of the story places, thus they might not enjoy and get the required values from those precious stories. For the teachers, it is hard and difficult to ensure that students imagined and understood the story in the right way. Hence, in order to facilitate the learning of these topics, the objective of this paper is to develop a mobile application with visual-learning style to help in improving children imagination of the stories with 3D characters\items using augmented reality technology. The methodology involves analysis, design, implementation and testing. The result of this project is an education tool that helps the teachers in their teaching environment and improves the students learning process. Therefore, the result of this project has contributed to the educational for young children in Saudi Arabia; towards the development of knowledge society to achieving Saudi Arabia Vision 2030. [ABSTRACT FROM AUTHOR]

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches. [ABSTRACT FROM AUTHOR]

Tangible User Interface (TUI) is an interface that allows user to interact with digital information through physical environment. We have developed an interactive system serves the educational field within the geographical domain. The system allows children to discover and learn about countries from different continents using tangible objects. In our project we selected so far six countries that represent six continents in the world. The selected countries are: Saudi Arabia, Egypt, United States of America, France, Brazil, and Australia. With the system, children are required to manipulate tangible objects. Their task is to select a landmark from a country and placing it on the table top. The system will recognize the object and provides users with feedback. The system involves the recognition of six elements for each country: (capital, flag, climate, currency, population and industries). This application has been evaluated in a field study with children 9-11 years of age. The initial results are promising and show that such an interactive system can support interaction and collaboration among young children, as well as enriches the learning process and makes it more enjoyable. [ABSTRACT FROM AUTHOR]

Dyslexia is a problem that an individual has since birth; it poses difficulties for the rest of their life, similar to other learning disabilities. Spelling, writing, reading and in certain instances speech can all be undermined by the language processing disorder. Idleness or a lack of intellect are not associated with dyslexia. Furthermore, sight problems do not cause dyslexia. Rather, data is evaluated and understood by the brain in alternative ways, making dyslexia a neurological ailment afflicting both children and grown-ups. This paper seeks to devise a puzzle game application based on eye tracking, which will assist with focusing attention, as well as a chatbot that can motivate users. This should prove beneficial to individuals with dyslexia, parents of dyslexics, or experts such as reading professionals, instructors and teachers who are assisting dyslexics. Based on the best current understanding of how to assist dyslexics, we meticulously assessed every application prior to its inclusion. [ABSTRACT FROM AUTHOR]

The traditional learning process used in schools is outdated. Students nowadays spend most of their time using technologies for entertainment and to communicate with their friends. Technology can also be used to present opportunities in teaching difficult subjects, such as mathematics, in a better environment. This project aims to improve the mathematical skills of addition and subtraction in young children aged 6 to 8 by combining education and entertainment in a simple math learning game. Using ideas from Jerome Bruner's theory, and Bloom's Taxonomy of cognitive goals, this application will help children learn and interact effectively with the world around them. This project presents the design and development of a game-based learning application that improves the mathematical skills of young children. Specifically, our target-users for this application are primary school students who are learning addition and subtraction. This paper provides discussion and guidance on how to evaluate the impact of Mathematics Game-Base Learning Application. The overall results of the experimental study indicate a positive effect of Mathematics Game-Base Learning Application on the learning process. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles • Presenting Modified Harmony Search Algorithm for optimizing Micro-grid • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named "modified harmony search (MHS) algorithm". Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods. [ABSTRACT FROM AUTHOR]