Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Let  be a non-trivial simple graph. A dominating set in a graph is a set of vertices such that every vertex not in the set is adjacent to at least one vertex in the set. A subset  is a minimum neighborhood dominating set if  is a dominating set and if for every  holds. The minimum cardinality of the minimum neighborhood dominating set of a graph  is called as minimum neighborhood dominating number and it is denoted by  . A minimum neighborhood dominating set is a dominating set where the intersection of the neighborhoods of all vertices in the set is as small as possible, (i.e., ). The minimum neighborhood dominating number, denoted by , is the minimum cardinality of a minimum neighborhood dominating set. In other words, it is the

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

The main purpose of this work is to introduce some types of fuzzy convergence sequences of operators defined on a standard fuzzy normed space (SFN-spaces) and investigate some properties and relationships between these concepts. Firstly, the definition of weak fuzzy convergence sequence in terms of fuzzy bounded linear functional is given. Then the notions of weakly and strongly fuzzy convergence sequences of operators  are introduced and essential theorems related to these concepts are proved. In particular, if ( ) is a strongly fuzzy convergent sequence with a limit  where linear operator from complete standard fuzzy normed space  into a standard fuzzy normed space  then  belongs to the set of all fuzzy bounded linear operators

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things,

Teachers should have acceptable ratios of multiple intelligences. Knowing their most dominate intelligence, teachers will be capable of understanding and considering the multiple intelligences of their students. Accordingly, they will be able to use Gardner’s theory as an effective approach of teaching for all kinds of learners and not limited to a specific type of students especially those who have high ratios of verbal-linguistic and logic-mathematical intelligences

تهدف هذه الدراسة إلى معرفة الفرق في التحصيل الدراسي في الكيمياء والاتجاه نحو العلوم بين طلاب التخصص العلمي وطلاب التخصص الصناعي الصف الأول كلية التربية ابن الهيثم، كذلك تهدف الدراسة إلى معرفة العلاقة بين التحصيل الدراسي في الكيمياء ودرجات اختبار نهاية الفصل في الكيمياء، والمعدل التراكمي بعد أخذ المقرر الدراسي والاتجاه نحو العلوم بالنسبة لطلاب التخصص العلمي وطلاب التخصص الصناعي.

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The research examines the awareness of the components of responsible citizenship among primary schoolgirls in Jeddah using a descriptive approach. A test was then prepared, which consists of loyalty, affiliation to homeland, conscious citizenship based on the knowledge of rights, the fulfillment of duties, family roles in education and society revival, homeland and preservation of the components and gains, participation in achievements, and of national heritage. To maintain the validity and reliability of the study, 28-item test of multiple-choice and short essays type was applied to a random sample of (303) female students in the (fourth-fifth-sixth) primary school class in Jeddah. Results have shown that the level of awareness of schoo