This research examines the future of television work in light of the challenges posed by artificial intelligence (AI). The study aims to explore the impact of AI on the form and content of television messages and identify areas where AI can be employed in television production. This study adopts a future-oriented exploratory approach, utilizing survey methodology. As the research focuses on foresight, the researcher gathers the opinions of AI experts and media specialists through in-depth interviews to obtain data and insights. The researcher selected 30 experts, with 15 experts in AI and 15 experts in media. The study reveals several findings, including the potential use of machine learning, deep learning, and natural language generation techniques in media work. AI aids television broadcasters in detecting fake news, generating news stories, and improving the quality of broadcasting and transmission. However, significant challenges arise when integrating AI technologies into television, such as the need for a specialized professional and programmatic workforce in the field of information technology.
Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,
... Show MoreExperimental research was carried out to investigate the effect of fire flame (high temperature) on specimens of short columns manufactured using SCC (Self compacted concrete). To simulate the real practical fire disasters, the specimens were exposed to high
temperature flame, using furnace manufactured for this purpose. The column specimens were cooled in two ways. In the first the specimens were left in the air and suddenly cooled using water, after that the specimens were loaded to study the effect of degree of
temperature, steel reinforcement ratio and cooling rate, on the load carrying capacity of the reinforced concrete column specimens. The results will be compared with behaviour of columns without burning (control specime
One of the main techniques to achieve phase behavior calculations of reservoir fluids is the equation of state. Soave - Redlich - Kwong equation of state can then be used to predict the phase behavior of the petroleum fluids by treating it as a multi-components system of pure and pseudo-components. The use of Soave – Redlich – Kwon equation of state is popular in the calculations of petroleum engineering therefore many researchers used it to perform phase behavior analysis for reservoir fluids (Wang and Orr (2000), Ertekin and Obut (2003), Hasan (2004) and Haghtalab (2011))
This paper presents a new flash model for reservoir fluids in gas – oil se
Objectives: To assess the knowledge and practice of thalassemic patients about desferal administration and
complications of iron overload.
Methodology: The present study composed of (50) thalssemic patient who are registered in center and was
performed in Ibn Al-Atheer center for congenital anemia for the period from 15/12/2006 to 1/4/2007.
Results: The result of the study showed highly significant difference at (160.05) for knowledge of thalassemic
patients and also appear highly significant difference at (P<O.O5) for practice of thalassemic patients.
Recommendations: The study recommends that there is necessity to increase the knowledge and practice of
thalassemic patient about desferal administration to minimiz