The research aims to identify the effect of the training program that is based on integrating futuristic thinking skills with classroom interaction patterns on mathematics teachers in order to provide their students with creative solution skills. The research sample consisted of 31teachers (15 teachers for the experimental group and 16 for the control groups). The researcher developed a measure for the academic self-efficacy consisting of (39) items. Its validity, reliability, coefficient of difficulty and discriminatory power were estimated. To analyze the findings, the researcher adopted the Mann-Whitney (U) test and the effect size, and the findings were as follows: There is a statistically significant difference at the significance level of (0.05) between the average scores of mathematics teachers of the experimental group and the control group on the academic self-efficacy scale.
The vast majority of EC applications are the web-based deployed in 3-tire Server-Client environment, the data within such application often resides within several heterogeneous data sources. Building a single application that can access each data sources can be a matter of challenging; this paper concerns with developing a software program that runs transparently against heterogeneous environment for an EC-application.
Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network
... Show MoreThere are large numbers of weakness in the generated keys of security algorithms. This paper includes a new algorithm to generate key of 5120 bits for a new proposed cryptography algorithm for 10 rounds that combine neural networks and chaos theory (1D logistic map). Two methods of neural networks (NN) are employed as Adaline and Hopfield and the results are combined through several sequential operation. Carefully integrating high quality random number generators from neural networks and chaos theory to obtain suitable key for randomness and complexity.
The aim of the current study is to identify the level of goal conflict with twelfth-grade students in South Sharqiah/ Sultanate of Oman according to gender and specialization. The study used the descriptive method. A scale of (28) items was developed and divided into six dimensions: time pressure, goal achievement, limit of power, limit of budget, incompatible strategies, and unclear task. To validate the scale, it was piloted (40) students. The scale was administered to a sample of (402) students (209) males in the Governorate of South Sharqiah. The results showed that the conflict level was high in “unclear task”, and an average conflict level in “limit of power”. Other dimensions (goal achievement, time pressure, limit of powe
... Show MoreMany cryptosystems and security techniques use substitution boxes to ensure the data’s secure communication. A new technique is presented for generating a robust S-box to fulfill security requirements. The AES algorithm represents a block cipher cryptographic algorithm. It was selected by the National Institute of Science and Technology as the optimal cryptographic algorithm in 2011. Through the study of the properties of original S-BOX, this algorithm has been subjected to a number of attacks (linear, differential, statistical, and interpolation), and original S-BOX has been static, which makes the attack strong and shows a weakness in the algorithm. It is necessary to make this algorithm more efficient and powerful through
... Show MoreAn intelligent software defined network (ISDN) based on an intelligent controller can manage and control the network in a remarkable way. In this article, a methodology is proposed to estimate the packet flow at the sensing plane in the software defined network-Internet of Things based on a partial recurrent spike neural network (PRSNN) congestion controller, to predict the next step ahead of packet flow and thus, reduce the congestion that may occur. That is, the proposed model (spike ISDN-IoT) is enhanced with a congestion controller. This controller works as a proactive controller in the proposed model. In addition, we propose another intelligent clustering controller based on an artificial neural network, which operates as a reactive co
... Show MoreSpeech encryption approaches are used to prevent eavesdropping, tracking, and other security concerns in speech communication. In this paper, a new cryptography algorithm is proposed to encrypt digital speech files. Initially, the digital speech files are rearranged as a cubic model with six sides to scatter speech data. Furthermore, each side is encrypted by random keys that are created by using two chaotic maps (Hénon and Gingerbread chaotic maps). Encryption for each side of the cube is achieved, using the based map vector that is generated randomly by using a simple random function. Map vector that consists of six bits, each bit refers to one of the specific chaotic maps that generate a random key to encrypt each face of the cube. R
... Show MoreIn this paper an accurate Indian handwritten digits recognition system is
proposed. The system used three proposed method for extracting the most effecting
features to represent the characteristic of each digit. Discrete Wavelet Transform
(DWT) at level one and Fast Cosine Transform (FCT) is used for features extraction
from the thinned image. Besides that, the system used a standard database which is
ADBase database for evaluation. The extracted features were classified with KNearest
Neighbor (KNN) classifier based on cityblock distance function and the
experimental results show that the proposed system achieved 98.2% recognition
rate.