News are considered the most press arts that supply the target audiences with daily information and events happened inside and outside society since it is formed by depending on its resources which have a deep relation with formal corporations to gain their satisfaction in order to support their authority and spreading their domination by using mass media in editing their viewpoints and achieving wide acceptance among public opinion. In the field of technological development and changing in the fields of politics, society, culture, economics etc. inside Iraqi society and democratic transition help to convert news agenda from independent variation to a fellow variation while in the past the variation of mass media was the independent one that has a great impact on audience agenda. It finds out the following results:
1- Security file agenda occupies the first place from the rest of other files that news content is included in the newspapers samples and obtain a repetition of (48) with percentage amount of (18.18%)
2- The study discovers the limits of variations and plurality of sources and the relation of this plurality in building news agenda. The newspapers under study depend on formal resources from which they get information by 336 repetitions with a percentage amount of 72.73%.
The study ends with the following conclusions:
1- There is a cooperative interest in building news agenda mechanisms in the Iraqi newspapers under study. All of them agree on agenda of services and security issues and differ in building their news agenda for economic, political, social, religious, and administrative corruption.
2- They adopted formal sources in bias for building news agenda and this is in turn reflected in news-editorial sources in the newspapers under study.
During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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