Abstract
In today’s world, people prefer Internet applications for fulfilling their needs. One cannot give guarantee for all applications get completed and all completed are not to the level of user satisfactory. Most of the solutions exist only for major cases such as optimal response, nearby output, similar answer, failure, fraudulences. Some may be discarded by the user itself, but all applications cannot be left as that, few holds significance. At the outset, we strive to provide solutions for such significant applications to the level of user satisfactory. In this paper, a way is analysed to reprocess such applications by taking the relevance feedback based on their input and obtained output and reaches their convenience using semantic intelligence and neural networks.
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Mohan Kumar, P., Balamurugan, B. (2018). Relevance Feedback Base User Convenient Semantic Query Processing Using Neural Network. In: Panigrahi, B., Hoda, M., Sharma, V., Goel, S. (eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_3
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DOI: https://doi.org/10.1007/978-981-10-6747-1_3
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