After the information revolution that occurred in the Western world, and the developments in all fields, especially in the field of education and e-learning, from an integrated system based on the effective employment of information and communication technology in the teaching and learning processes through an environment rich in computer and Internet applications, the community and the learner were able to access information sources and learning at any time and place, in a way that achieves mutual interaction between the elements of the system and the surrounding environment. After the occurrence of the phenomenon of Covid 19, it led to a major interruption in all educational systems that had never happened before, and the disrupt

Image processing applications are currently spreading rapidly in industrial agriculture. The process of sorting agricultural fruits according to their color comes first among many studies conducted in industrial agriculture. Therefore, it is necessary to conduct a study by developing an agricultural crop separator with a low economic cost, however automatically works to increase the effectiveness and efficiency in sorting agricultural crops. In this study, colored pepper fruits were sorted using a Pixy2 camera on the basis of algorithm image analysis, and by using a TCS3200 color sensor on the basis of analyzing the outer surface of the pepper fruits, thus This separation process is done by specifying the pepper according to the color of it

The aim of this study was to identify the effectiveness of using generative learning model in learning kinetic series on rings and horizontal bar in artistic gymnastics for men ,Also, the two groups were better in learning the two series of movements on the rings and horizontal bar . The experimental method was used to design two parallel groups with pretested and posttest .The sample included third graders at the College of Physical Education and Sports Sciences - University of Baghdad ,The third class (d) was chosen to represent the control group that applied the curriculum in the college, with (12) students per group. After conducting the tribal tests, the main experiment was carried out for (8) weeks at the rate of two units per week di

In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

Background: Machine learning relies on a hybrid of analytics, including regression analyses. There have been no attempts to deploy a sinusoidal transformation of data to enhance linear regression models.
Objectives: We aim to optimize linear models by implementing sinusoidal transformation to minimize the sum of squared error.
Methods: We implemented non-Bayesian statistics using SPSS and MatLab. We used Excel to generate 30 trials of linear regression models, and each has 1,000 observations. We utilized SPSS linear regression, Wilcoxon signed-rank test, and Cronbach’s alpha statistics to evaluate the performance of the optimization model. Results: The sinusoidal

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.

Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.

In this paper, a method for hiding cipher text in an image file is introduced . The
proposed method is to hide the cipher text message in the frequency domain of the image.
This method contained two phases: the first is embedding phase and the second is extraction
phase. In the embedding phase the image is transformed from time domain to frequency
domain using discrete wavelet decomposition technique (Haar). The text message encrypted
using RSA algorithm; then Least Significant Bit (LSB) algorithm used to hide secret message
in high frequency. The proposed method is tested in different images and showed success in
hiding information according to the Peak Signal to Noise Ratio (PSNR) measure of the the
original ima

One of the primary problems in internet is security, mostly when computer utilization is increasing in all social and business areas. So, the secret communications through public and private channels are the major goal of researchers. Information hiding is one of methods to obtain a security communication medium and protecting the data during transmission.
This research offers in a new method using two levels to hide, the first level is hiding by embedding and addition but the second level is hiding by injection. The first level embeds a secret message in one bit in the LSB in the FFT and the addition of one kashida. Subtraction of two random images (STRI) is RNG to find positions for hiding within the text. The second level is the in