Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques

DISCOVERY OF NODE JAMMING ATTACKES IN MOBILE WIRELESS NETWORKS PROBABILISTIC APPROACH

Most examples that lead to communication and deception indicate our plans to maximize the use of low-frequency statistics and statistics and reduce network connections. The current process may be the result of most of the trade in public, not to be enjoyed using the fonts installed in the cell phone. Our approach provides an opportunity to communicate in all communications and communications. In comparison to other methods that use monitoring, our method is consistent with using the number, reduces the connection and decreases the correct rate. In addition, our approach provides access to communication and access, but overall monitoring is relevant to social mediation. In an environment where the GS does not work, the fault can use home use. See sites, machines, and methods that make a lot of mistakes in the site environment. The situation may not only depend on the nut and the environment. Our approach is to simply build traffic surveillance and it is very important in connection with network vision. Most internet skills take place in the text. Finally, we have a high failure line to use our road