In this study an illustrated key for the identification of the Iraqi species of the genus Chalcophorella Kerr. is given with information about general distribution for each species. Information is also given about period of collection, plants on which the specimens were collected. The real host plant if they are available and known are also given referring to the previous works.

The aim of the research is to identify the imaginative thinking skills included in the content of the chemistry textbook for fifth-grade students in biological sciences at the preparatory stage, which is approved by the Ministry of Education, General Directorate of Curricula in the academic year 2020-2021. To achieve the objective of the study, the researcher depends on the previous studies in the field and content of imaginative thinking skills for preparing a list of skills that includes (3) essential skills and (11) sub-skills and (28) items related to all skills. The researcher adopted the analytical descriptive approach because of its appropriation of the goals of the research. Then the researcher analyzed the content of the book de

This research aimed to evaluate the level of readiness to teach science in the light of the information, media, and technology skills among undergraduate students at the Faculty of Edu-cation, King Khalid University. To achieve this goal, a descriptive and analytical approach was used. A list of readiness to teach science was prepared in the light of Information, media, and technology skills, and in the light of this list, a cognitive test, observation sheet, and attitude scale were prepared to assess readiness to teach science in its three aspects, cognitive, behavioral, and emotional. The sample of the research consisted of (42) students enrolled in field training courses at the sixth, seventh and eighth levels. Research tools were app

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Deepfake is a type of artificial intelligence used to create convincing images, audio, and video hoaxes and it concerns celebrities and everyone because they are easy to manufacture. Deepfake are hard to recognize by people and current approaches, especially high-quality ones. As a defense against Deepfake techniques, various methods to detect Deepfake in images have been suggested. Most of them had limitations, like only working with one face in an image. The face has to be facing forward, with both eyes and the mouth open, depending on what part of the face they worked on. Other than that, a few focus on the impact of pre-processing steps on the detection accuracy of the models. This paper introduces a framework design focused on this asp

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff