Abstract: A method of optimizing training of a machine learning model is disclosed. The method may include receiving an input training data using an optimization device. The input training data may include a plurality of training data samples. Further, a set of relevant training data samples from the input training data may be identified. The method may use the optimization device to select a suitable type and configuration of a machine learning model from a plurality of types and configurations of machine learning models for processing the set of relevant training data samples. Furthermore, the method may validate the set of relevant training data samples.

Abstract: A method and system for extraction of text from a video for performing selective searching of text in the video is disclosed. The disclosure provides a text extraction device which receives a plurality of frames of a video. The frames may include at least one set of interrelated frames comprising a common text. A reference frame is identified comprising a reference pattern of a text in the frames. The reference pattern is matched with a pattern associated with a text within each of the frames. In order to do so, one or more interrelated frames are identified from the frames based on a pattern match. A set of interrelated frames is obtained comprising a text having a pattern matching the reference pattern. A relevant frame from the set of interrelated frames is selected based on text quality criteria and the common text is extracted from the relevant frame.

Abstract: The present invention relates to a process for production of high yield of hydrogen by carrying out the dry reforming of the dry gas generated from the process itself by utilizing the same catalyst for cracking and producing high yield of light olefins such as ethylene, propylene and butylenes from residue feedstocks.

Abstract: A method of selecting samples to represent a cluster is disclosed. The method may include receiving one or more clusters by an optimization device. Each of the one or more clusters may include a plurality of samples. The method may determine a count of number of samples to be selected from each of the one or more clusters and may generate an array-based distance matrix for each of the one or more clusters. The method may sort the plurality of samples of the cluster based on a degree of variability of the plurality of samples in the cluster. The sorting may be performed using the array-based distance matrix for each of the one or more clusters. Further, the method may select the determined count of number of samples from the sorted plurality of samples of each of the plurality of clusters to represent the cluster.

Abstract: Method (400) and system for clustering data samples is disclosed. The method (400) may include receiving (402) a plurality of batches, each of the plurality of batches including a plurality of samples, and creating (404) a set of clusters from a first batch of the plurality of batches, using a clustering technique. Each cluster of the set of clusters may include one or more samples. The one or more samples are determined based on variability of the one or more samples and cluster attributes associated with each cluster. The method may further include reconfiguring (406) the set of clusters that includes at least one of: repopulating (400A) the existing set of clusters with samples from remaining batches of the plurality of batches; and adding (400B) a new set of clusters to the existing set of clusters and populating the new set of clusters with samples from the remaining batches.

Abstract: A method for detecting point anomaly in a text data is disclosed. The method may include tokenizing text input comprising a plurality of text entities into a plurality of tokens, annotating, by the point anomaly detection device, the plurality of tokens, based on one or more annotation parameters. One or more annotation parameters comprise a part-of-speech, a sentiment polarity, a negation statement, and domain rules. The method further includes identifying from one or more annotated tokens, at least one of: one or more anomaly subject tokens, one or more anomaly type tokens, and one or more action type tokens from plurality of tokens. The method further includes generating inferences based on the identified one or more anomaly type tokens, the one or more action type tokens from the plurality of tokens, and the one or more annotation parameters.

Abstract: This disclosure relates to a method and system for extracting information from contents of an input file. The method may include identifying text data from the input file, receiving a text input from a user for identifying relevant text entities from the plurality of text entities, and automatically generating a search pattern corresponding to the text input. The method may further include determining a pattern associated with each of the plurality of text entities, and mapping the search pattern corresponding to the text input with patterns associated with the plurality of text entities. The method may further include identifying one or more matching patterns from the patterns associated with the plurality of text entities based on the mapping, and extracting, from the plurality of text entities, relevant text entities corresponding to the one or more matching patterns.

Abstract: In an embodiment, a method of performing summarization of text is disclosed. The method may include receiving an input text including a plurality of paragraphs and a user-query including one or more tokens. The method may further include segregating the input text into the plurality of paragraphs, creating a plurality of paragraph-vectors representative of the plurality of paragraphs, and clustering the plurality of paragraph-vectors to generate one or more clusters of paragraph-vectors. The method may further include determining a relevant cluster of paragraph-vectors from the one or more clusters of paragraph-vectors, based on a degree of similarity of each cluster of paragraph-vectors with the user-query. The relevant cluster of paragraph-vectors is representative of a set of relevant paragraphs from the input text. The set of relevant paragraphs corresponding to the relevant cluster of paragraph-vectors may be outputted.

Abstract: Disclosed herein is system and method for examining relevancy of documents. The system, based on request from the user extracts documents from one or more data sources. The system then obtains from the user, user intention information and user queries. The system then analyses each document with respect to user intention information in order to determine a relevancy level of each document. The relevancy level is indicated in the form of a ranking score. The system ranks and displays the documents to the user in the order of their scores. The system also highlights important excerpts from the documents and provides one or more responses to the one or more user queries submitted by the user for each and every document. Based on the received responses, user provides feedback for further training the system, thereby achieving better accuracy.

Abstract: In an embodiment, a method of performing summarization of text is disclosed. The method may include receiving an input text including a plurality of paragraphs and a user-query including one or more tokens. The method may further include segregating the input text into the plurality of paragraphs, creating a plurality of paragraph-vectors representative of the plurality of paragraphs, and clustering the plurality of paragraph-vectors to generate one or more clusters of paragraph-vectors. The method may further include determining a relevant cluster of paragraph-vectors from the one or more clusters of paragraph-vectors, based on a degree of similarity of each cluster of paragraph-vectors with the user-query. The relevant cluster of paragraph-vectors is representative of a set of relevant paragraphs from the input text. The set of relevant paragraphs corresponding to the relevant cluster of paragraph-vectors may be outputted.

Abstract: A method and device for identifying machine learning models for detecting entities is disclosed. The method includes identifying a first entity from within data. A machine learning model trained to identify the first entity is absent in a plurality of machine learning models. The method may include extracting a first set of entity attributes associated with the first entity and matching the first set of entity attributes with each of a plurality of second set of entity attributes. The method may further comprises identifying a second entity from the set of second entities based on the matching. Similarity between a second set of entity attributes associated with the second entity and the first set of entity attributes is above a similarity threshold. The method may include retraining a machine learning model associated with the second entity to identify the first entity based on the first set of entity attributes.

Abstract: A method and a device for creating and training machine learning models is disclosed. In an embodiment, a method for training a machine learning model for identifying entities from data includes creating a first plurality of clusters from a first plurality of data samples in a first dataset and a second plurality of clusters from a second plurality of data samples in a second dataset. The method further includes determining a rank for each of the first plurality of clusters and a rank for each of the second plurality of clusters. The method includes retraining the machine learning model using at least one of the first plurality of clusters weighted based on the rank determined for each of the first plurality of clusters and at least one of the second plurality of clusters weighted based on the rank determined for each of the second plurality of clusters.

Abstract: This disclosure relates to system and method for optical character recognition. In one embodiment, the method comprises providing an image data to a plurality of customized machine learning algorithms or various customized neural networks, configured to recognize a set of pre-defined characters. The method comprises presenting one or more suggestions for the character to the user in response to negative character recognition, and training a customized machine learning algorithm corresponding to the character if one of the suggestions is identified by the user. If the suggestions are rejected by the user, the method comprises prompting the user to identify the character and determining presence of the character in the set of pre-defined characters. The method further comprises training a customized machine learning algorithm corresponding to the character if the character is present, or dynamically creating a customized machine learning algorithm corresponding to the character if the character is not present.

Abstract: This disclosure relates to system and method for optical character recognition. In one embodiment, the method comprises providing an image data to a plurality of customized machine learning algorithms or various customized neural networks, configured to recognize a set of pre-defined characters. The method comprises presenting one or more suggestions for the character to the user in response to negative character recognition, and training a customized machine learning algorithm corresponding to the character if one of the suggestions is identified by the user. If the suggestions are rejected by the user, the method comprises prompting the user to identify the character and determining presence of the character in the set of pre-defined characters. The method further comprises training a customized machine learning algorithm corresponding to the character if the character is present, or dynamically creating a customized machine learning algorithm corresponding to the character if the character is not present.

Abstract: An organic light emitting display device includes a plurality of pixels in a display area; a data driver configured to supply a data signal to the pixels; and a data converter configured to output a correction image data utilized in generation of the data signal, and the data converter is configured to generate a stress data corresponding to an input image data, to accumulate and store at least a portion of the stress data in a compressed state, and to generate the correction image data obtained by correcting the input image data according to the accumulated stress data.

Abstract: An organic light emitting display device includes a plurality of pixels in a display area; a data driver configured to supply a data signal to the pixels; and a data converter configured to output a correction image data utilized in generation of the data signal, and the data converter is configured to generate a stress data corresponding to an input image data, to accumulate and store at least a portion of the stress data in a compressed state, and to generate the correction image data obtained by correcting the input image data according to the accumulated stress data.

Abstract: A light emitting device comprising a first electrode, a second electrode, an emissive material between the first and second electrodes and a mixing agent separate from the emissive material is disclosed. In certain examples, the mixing agent may be effective to increase a fraction of excitons formed as a singlet state.

Abstract: A light emitting device comprising a first electrode, a second electrode, an emissive material between the first and second electrodes and a mixing agent separate from the emissive material is disclosed. In certain examples, the mixing agent may be effective to increase a fraction of excitons formed as a singlet state.

Abstract: A photovoltaic cell and devices using the photovoltaic cell are provided. In certain examples, the photovoltaic cell may include a first material disposed on a first electrode and effective to generate an exciton upon absorption of electromagnetic energy. In some examples, the photovoltaic cell may also include a second material electrically coupled to the first electrode and separated from the first material, the second material effective to receive the generated exciton from the first material. In other examples, the photovoltaic cell may also include a second electrode electrically coupled to the second material and electrically coupled to the first electrode. Solar panels and power systems using the photovoltaic cell are also disclosed.