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National Union Catalog
Includes entries for maps and atlases
THE INDIAN LISTENER
The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service,Bombay ,started on 22 December, 1935 and was the successor to the Indian Radio Times in english, which was published beginning in July 16 of 1927. From 22 August ,1937 onwards, it was published by All India Radio,New Delhi.In 1950,it was turned into a weekly journal. Later,The Indian listener became "Akashvani" in January 5, 1958. It was made a fortnightly again on July 1,1983. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes,who writes them,take part in them and produce them alo...
AKASHVANI
"Akashvani" (English) is a programme journal of ALL INDIA RADIO, it was formerly known as The Indian Listener. It used to serve the listener as a bradshaw of broadcasting ,and give listener the useful information in an interesting manner about programmes, who writes them, take part in them and produce them along with photographs of performing artists. It also contains the information of major changes in the policy and service of the organisation. The Indian Listener (fortnightly programme journal of AIR in English) published by The Indian State Broadcasting Service, Bombay, started on 22 December, 1935 and was the successor to the Indian Radio Times in English, which was published beginning ...
Deep Learning Applications and Intelligent Decision Making in Engineering
Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
