Abstract: A novel eco-friendly method has been developed for the fabrication of high dense (3.3-5.2 g/cc) red mud based material blocks for shielding high energy X- and ?-rays. The red mud based material blocks with various densities were fabricated by hot compacting partially melted red mud, red mud:Bi2O3, red mud:Ba(OH)2 and red mud:Bi2O3:Ba(OH)2 samples at 1150° C., 1000° C., 1050° C. and 1000° C., respectively. This material can be used to build radiation shielding structures in medical diagnosis, radiotherapy, industrial radiography, particle accelerators, food sterilization plants, nuclear power plants, and radioactive material storage rooms, without further structural support unlike lead (concrete walls). It is economically viable and will suppress the accumulation of hazardous red mud and associated environmental pollutions.

Abstract: The present disclosure discloses method and a user intent identification system for identifying user intent from user statements. The user intent identification system receives input statement provided by a user from a Natural Language Understanding (NLU) engine. The input statement is processed to remove one or more irrelevant content. A plurality of features for each word in the processed input statement is extracted. The plurality of features comprises Parts of Speech (POS) label, dependency parse tree and word embeddings. The user intent determination system predicts class for each word in the processed input statement from a plurality of predefined classes using a neural network model. The neural network model predicts class for each word based on input vector generated for the each word based on the plurality of features. Thereafter, the user intent is identified based on class predicted for each word in processed input statement.

Abstract: A method an system for extracting factoid associated words from natural language sentences is disclosed. The method includes creating an input vector that includes a plurality of parameters for each target word in a sentence. For a target word, the plurality of parameters includes a Part of Speech (POS) vector, a word embedding, a word embedding for a head word of the target word, a dependency label, and a semantic role label. The method includes processing for each target word, the input vector through a trained neural network and assigning one or more factoid tags to each target word in the sentence. The method includes extracting text associated with factoids from the sentence based on the one or more factoid tags. The method further includes providing a response to the sentence inputted by the user based on the text associated with the factoids.

Abstract: A method an system for extracting factoid associated words from natural language sentences is disclosed. The method includes creating an input vector that includes a plurality of parameters for each target word in a sentence. For a target word, the plurality of parameters includes a Part of Speech (POS) vector, a word embedding, a word embedding for a head word of the target word, a dependency label, and a semantic role label. The method includes processing for each target word, the input vector through a trained neural network and assigning one or more factoid tags to each target word in the sentence. The method includes extracting text associated with factoids from the sentence based on the one or more factoid tags. The method further includes providing a response to the sentence inputted by the user based on the text associated with the factoids.

Abstract: The present disclosure discloses method and a user intent identification system for identifying user intent from user statements. The user intent identification system receives input statement provided by a user from a Natural Language Understanding (NLU) engine. The input statement is processed to remove one or more irrelevant content. A plurality of features for each word in the processed input statement is extracted. The plurality of features comprises Parts of Speech (POS) label, dependency parse tree and word embeddings. The user intent determination system predicts class for each word in the processed input statement from a plurality of predefined classes using a neural network model. The neural network model predicts class for each word based on input vector generated for the each word based on the plurality of features. Thereafter, the user intent is identified based on class predicted for each word in processed input statement.