Wipro Limited

Vinutha Bangalore Narayanamurthy

Manjunath Ramachandra Iyer

Sibsambhu Kar

Arindam Chatterjee

Chandrashekar Bangalore Nagaraj

Sriram Chaudhury

Sibsambu Kar

A method and a system of selective encryption of a test dataset is disclosed. In an embodiment, the method may include determining a relevancy grade associated with each of a plurality of datapoints within a test dataset by comparing the test dataset with a common heat map, wherein the common heat map is generated using a plurality of training datasets. The method may further include calculating, based on the relevancy grade, an encryption level associated with each of the plurality of datapoints. The method may further include selectively encrypting at least one datapoint from the plurality of datapoints based on the encryption level associated with each of the plurality of datapoints. The at least one data point is rendered to a user after being decrypted.

Method and system for selectively encrypting dataset

The disclosure relates to method and system for training an artificial neural network (ANN) based image classifier using class-specific relevant features. The method includes receiving the ANN based image classifier, training image dataset, and various features of the training image dataset. The method further includes determining a relative relevance value of each of the features corresponding to each of the classes based on the ANN based image classifier, segregating co-occurring features from the features for each of the classes based on the training image dataset and the ANN based image classifier, identifying an imbalance in the class-specific relevant features for each of the classes based on the relative relevance value of each of the features corresponding to each of the classes, and updating the ANN based image classifier based on the imbalance in the class-specific relevant features and the co-occurring features for each of the classes.

Method and system for training artificial neural network based image classifier using class-specific relevant features

A method and a system of determining a relation between two or more entities in a text document is disclosed. In an embodiment, the method may include receiving training text data annotated with two or more entities, and creating one or more n-grams based on the training text data. The method may further include generating a Convolutional Neural Network (CNN) model using the one or more n-grams, and creating an entity vector using at least one of a word embedding and a numeric embedding based on the training text data. The method may further include generating a relation-entity model using the CNN model and the entity vector.

Method and device for determining a relation between two or more entities

The present disclosure relates to a method and a system for generating sentiment-based summaries for a user review. In an embodiment, a text analyzer receives a block of text indicating a user review. The text analyzer may generate one or more vectors for the plurality of words. Further, a relation is identified among the one or more vectors. A model is trained to identify a relation among the one or more vectors. Using the relation between the one or more vectors, a sentiment associated with the block of text is determined. Thereafter, one or more keywords from the block of text contributing to the determined sentiment is are identified and are classified into categories according to the sentiment contributed by the one or more words. Thereafter, the summary is generated for each category using the corresponding one or more words.

Method and system for generating review summaries

The present disclosure relates to a method and a system for generating optimized response to user input. The system may receive a user input indicative of a data required by the user. The system identifies one or more keywords based on the user input. The system determines user expertise level based on search graphs generated using the one or more keywords. The system retrieves a plurality of responses relevant to the data based on the one or more keywords. The system assigns a value to each of the plurality of responses based on the user expertise level. The system identifies a base response in one or more responses having the value greater than a threshold value. Finally, the system collates content of the one or more responses excluding the base response with content of the base response, in a pre-defined sequential order, for generating the optimized response to user input.

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Bengaluru, India

Vinutha Bangalore Narayanamurthy