Abstract: A method for multi-stage processing of user queries for enhanced information retrieval. The method includes generating self-complete derived queries from a search query. The method includes extracting query entities from each derived query and mapping the query entities with a plurality of electronic documents to identify a set of relevant electronic documents. The method includes sorting the derived queries based on the number of relevant electronic documents related to each derived query to obtain a sorted sequence of derived queries and searching a result for each derived query sequentially from the set of relevant electronic documents according to the sorted sequence of derived queries, and appending the result retrieved for one derived query with a consequent derived query to obtain a final search result. The method involves breakdown of a search query into derived queries and resolve each derived query separately and sequentially to reduce complexity and computation cost.

Abstract: A method for processing one or more electronic documents for enhanced search includes defining a bounding box around each key and value of key-value pairs in a first schema file, tagging coordinates of a key corresponding to a first bounding box and coordinates of a value corresponding to a second bounding box in the first schema file. Furthermore, obtaining a first inference file, detecting coordinates of a key corresponding to a third bounding box, and determining coordinates of a fourth bounding box and the value of the first inference file that are determined by applying a normalization operation. Thereafter, extracting value encompassed by the fourth bounding box of the first inference file and automatically creating a searchable index of the first inference file with searchable key-value pairs. The method achieves an efficient and accurate clustering of data items with an accurate, meaningful, and formal objective function.

Abstract: A method and system for updating prediction model for curing process design is disclosed. The method includes updating a prediction model based on the input through a semi-supervised learning technique. The method may include receiving an input corresponding to a curing process. The method may further include generating a second set of experiments using the updated prediction model and an optimization component associated with the prediction model. The method may further include obtaining a second set of data upon performing a second set of experiments on a physical set-up. The method may further include determining an error between the predicted set of data and the second set of data. The method may further include updating the prediction model based on the second set of data when the error is out of a predefined threshold.

Abstract: A method and system for optimizing performance of Genetic Algorithm (GA) in solving scheduling problem is disclosed. The method includes receiving input constraints associated with supply and demand sides, for scheduling problem. The method include initializing set of schedules using initializer that sets initial set of solutions for GA to start optimization. The method may include generating parent population for GA. The method may include creating child population via evolution using current probabilistic parameters including crossover and mutation operators. The method may include utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate child population. The method includes determining a new population from a total population including the parent population and the child population, using custom multi-objective sorting technique. The method may further include updating probabilistic parameters of the GA during runtime using runtime adapter, when pre-determined iterations unattained.

Abstract: Disclosed is a method for identifying multi-level hierarchical relationships between data elements of a document, the method comprising receiving a plurality of sample documents each having a plurality of data elements arranged in a multi-level hierarchical data structure; classifying each of the plurality of data elements into a key entity field or a key field value based on a hierarchical relationship therebetween; identifying key entity fields, from among the classified key entity field of the plurality of data elements, having the hierarchical relationship therebetween; pairing the key entity field, with a corresponding key field value or an identified key entity field, to form a training dataset; and employing the training dataset on a neural network framework, having at least one of a textual modality or a visual modality, to identify the multi-level hierarchical relationships between the data elements of the document.

Abstract: Disclosed is a system and a method for training a neural network to detect one or more anomalies in an event data. The system comprises a processing arrangement, communicably coupled to a database configured to store the event data. Herein, the processing arrangement is configured to receive event data associated with a plurality of log events for a given time period, pre-process the received event data to generate refined event data, process the refined event data using an encoder architecture of the processing arrangement, to generate one or more event embeddings based on a first transformation model, and wherein the second encoder is configured to generate one or more contextual embeddings based on a second transformation model for each log event, and process the one or more contextual embeddings via at least one statistical technique to generate an embedding matrix to detect the one or more anomalies.

Abstract: Disclosed is a system and a method for translating an image of a structural formula of a chemical molecule into a textual identifier therefor utilizing unique tokens for each of known entities. The method comprises pre-processing the image of the structural formula to generate a standardized image; processing the standardized image using an encoder-decoder architecture, wherein an encoder generates embeddings for features in the standardized image and a decoder is implemented to associate each of the features to one of the unique tokens; recurrently processing each of the features in the standardized image for predicting corresponding unique token to generate multiple possible sequences, and dynamically calculating a correctness probability for each of the generated sequences; selecting one of the sequences with highest calculated correctness probability; and generating the textual identifier, as an output, based on the selected one of the sequences.

Abstract: Methods, systems and computer program products for generating standardized structured data from unstructured and semi-structured images of document pages is disclosed. The embodiments include a training framework where, boundaries of one or more instances of a first and a second set-of-fields are detected, from images of document pages and tagged using unique labels. Individual fields within the set-of-fields are identified and associated with each instance and the unique labels, to generate large number of synthetically labelled documents. A neural network model is trained using the original document image and the large number of generated synthetically labelled documents. An inference framework receives as input scanned images of unstructured and semi-structured document pages. Custom object recognition module identifies different set-of-fields, and an OCR module recognizes the text from the input images. The outputs of these modules are stitched together to create a standardized structured data.

Abstract: Disclosed is a system and a method for semantic search in an enterprise. The method comprises defining entities in the enterprise, indexing content from one or more data sources of the enterprise to determine relationships between individual pieces of the content and one or more of the entities, providing an interface to allow a user to enter a search query, parsing the search query, determining relevant pieces of the content based on the parsed search query, identifying the one or more of the entities for each of the relevant pieces of content based on the relationships therebetween and configuring the interface to display each of the relevant pieces of the content under respective one or more of the entities in response to the search query.

Abstract: A method and system of predicting molecular properties is provided herein. The method includes representing a molecule as a graph and a string. The method further includes encoding the graph into a first feature representation and the string into a second feature representation, using a graph neural network and a transformer-based network, respectively. The method further includes concatenating the first feature representation obtained from the graph neural network and the second feature representation obtained from the transformer-based network to create a combined feature representation. The method further includes fusing the combined feature representation using a linear layer to obtain a synergistic combined feature representation for the molecule. The method further includes predicting one or more molecular properties for the molecule using the synergistic combined feature representation and a predictor network.

Abstract: A method and system for optimal scheduling of nursing services in a hospital is provided herein. The method and system comprises forecasting demand for the nursing services for a time interval based on historical data and external data. The method and system further comprises extracting nurses' availability data for the time interval. The method and system comprises aggregating constraints applicable to the nursing services. The method and system also comprises computing a schedule of nurses based on the constraints using at least an optimization model.

Abstract: A method and system for generating conversation flows is provided herein. The method and system comprise storing conversations between at least an agent and at least a user in logs. Further, the method and system comprise extracting topics from the logs. Also, the method and system comprise creating clusters from the extracted topics. The method and system further comprise generating the conversation flows from the clusters.

Abstract: A method and system for forecasting demand for nursing services in a hospital herein. The method and system comprises accessing external data and historical data of a hospital. The method and system further comprises combining the external data and historical data of a hospital to form a structured data aggregation. Further, the structured data aggregation is processed. The method and system further comprises forecasting for a time interval, the demand for nursing services. In addition, work drivers are an accurate workload indicator for nurse workload planning and takes into consideration patient-dependent diversified needs. Furthermore, the system comprises a productivity index that accounts for nurses down-time and administrative tasks that need to be subtracted from the time spent on clinical tasks. This is used to schedule accurately the nurse rota based on realistic hospital needs and uses historic data to predict the upcoming demand over different ranges of time-horizons.

Abstract: A method and system for augmenting a neural network is provided herein. The method comprises connecting an input layer to a pre-input layer. The method further comprises joining a hidden layer to the input layer. The method comprises linking an output layer to the hidden layer. The method further comprises connecting a layer for computing physics equations to the output layer. The neural network system further comprising, an input layer, a hidden layer connected to the input layer and an output layer joined to the hidden layer. The system further comprising a layer for computing physics equations connected to the output layer and a pre-input layer attached to the input layer.

Abstract: A method and system for training a virtual agent is provided herein. The method and system comprises storing conversations between the virtual agent and a user in logs. The method and system further comprises mining the logs to retrieve utterances. The method and system further comprises computing regression for each of the plurality of the charging time segments. The method and system further comprises providing a score to the utterances. Further, the method ranking the utterances based on the score.

Abstract: The disclosure provides a system, a method, and a computer program product for displaying a a Knowledge Graph at a client device. The method comprises receiving a sequence of selection elements in the Knowledge Graph, by a user. The method may include displaying the sequence of selected elements in a last in, first out order on selecting a UI component by the user. Also, the method may enable saving a state of display on closing display of the Knowledge Graph. Further, on reopening the display of the Knowledge Graph the saved state is rendered. The method also provides box select and panning feature for visually manipulating the Knowledge Graph. Further, in order to optimally execute the above features, nodes and edges which are the elements of a Knowledge Graph are stored in array data structures.

Abstract: The disclosure provides a system, a method, and a computer program product for displaying a sentence in a Knowledge Graph at a client device. The method comprises displaying, at the client device, at least an entity in the Knowledge Graph as a node. The method may include displaying, at the client device, at least a relation in the Knowledge Graph as an edge. The method may further include, displaying, at the client device, a sentence on selecting the edge of the Knowledge Graph, wherein the sentence is a natural language expression associated with the relation. The method also allows for visually adjusting the display density of the Knowledge Graph.