In this study, the researcher aims to analyze the content of the physics textbook for the 3rd intermediate grade according to the criteria for designing and producing infographics, and the research community consists of the content of the physics textbook for the 3rd intermediate grade intermediate grade for the academic year 2021-2022. The researcher adopted the analysis instruments with a number of the criteria for designing and producing infographics. The results revealed randomness in the percentage of the criteria included in the content of the physics textbook for the 3rd intermediate grade, and they are not compatible with the proposed criteria by the experts also.

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

The aim of the current research is to analyze the content of the two science books (chemistry units) for the two grades ( first and second intermediate ) according to environmental ethics, as the research community and its sample of the two science books (chemistry units) for the two grades ( first and second intermediate ), approved by the Iraqi Ministry of Education for the academic year ( 2021-2022 ), and the two researchers adopted the descriptive ( analytical ) approach, and the researchers built a standard for environmental ethics, and the validity of the tool was confirmed by presenting it to a group of experts, and then the researchers analyzed the two books in light of the standard prepared based on the implicit explicit idea, and

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c