The research is aimed at investigating how the New York Times framed the war against ISIS in its news coverage and which news sources it adopted while reporting on this war.
The research could be classified under descriptive researches. The survey methodology has been adopted and the content analysis has been used. The research sample consists of all the news stories the New York Times have published about the war against ISIS from 10/17/2016 to 4/16/2017 according to the comprehensive sampling method. The number of news stories that were analyzed was (155) news story. The research tool was (coding scheme).
The research has reached the following conclusions:
1. In its news coverage of the war against ISIS, the New York Times focused on highlighting the US role in the war, amplifying it and presenting it as major and pivotal.
2. The newspaper did not pay any attention to counter-frames, that is, the frames that contradict the U.S. participation in the war against ISIS, as it did not publish any news story that present opinions opposing the participation of the U.S. in this war.
3. The newspaper framed the war against ISIS within the war on terror frame, by indicating the continuity of the threat posed by ISIS to international security and peace.
4. The newspaper preferred the adoption of official news sources at the expense of informal news sources.
5. The research revealed the newspaper s bias to American official news sources, and to news sources that have good relationships with American government at the expense of news sources that do not have such relationships.
6. A drop in the use of anonymous sources has been observed that agrees with the results of some recently conducted researches.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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