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 and system for designing curing processes is disclosed.

Abstract: A method for electronic processing of user queries maintaining factual consistency during processing includes receiving a user query, executing a hybrid retrieval operation, generating a context information space that includes a set of paragraph output from the paragraph retrieval operation, a set of triplet output from the triplet retrieval operation, and a set of graphs output from the graph retrieval operation. Furthermore, generating a first prompt instruction to generate an intermediate rationale as explanation output and performing an automatic fact-checking operation to verify the explanation output and re-feeding the user query along with the generated explanation output to the generative language model to derive an answer output that is succinct in one or more language parameters as compared to the generated explanation output.

Abstract: A method and system for developing a generative chemistry model based on SAFE representations is disclosed. The method includes encoding one or more chemical compounds into the SAFE representations. The method may include training an encoder-decoder transformer model based on the SAFE representations using one or more masking techniques. The encoder-decoder transformer model creates a latent space to represent encoded SAFE representations of the chemical compounds. The method may further include training a variational Autoencoder (VAE) to generate a continuous latent space by compressing the latent space of the encoder-decoder transformer model. The method may further include optimizing the continuous latent space to generate a plurality of chemical compounds with specific properties by decoding the SAFE representations of the chemical compounds.

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: The present invention discloses a system for scheduling jobs within a manufacturing environment, integrating a plurality of sensors to capture operational parameters. A processing unit, linked to the plurality of sensors, analyzes datasets to determine the operational parameters. A machine learning module coupled to the processing unit, enhances a scheduling algorithm using the operational parameters. This machine learning module includes a first predictor for estimating job processing times and forecasting operating conditions based on these parameters. A formulator adjusts the scheduling algorithm using the forecasted operating conditions for distinct time intervals. Additionally, a second predictor forecasts subsequent operating conditions based on the modified scheduling algorithm and initial forecasts. The system optimizes job scheduling, enhancing efficiency and productivity within the manufacturing environment.

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: A method and system for hypernetwork guided domain decomposition in Physics-Informed Neural Operators (PINOs). The method includes identifying a presence of at least one discontinuity in data associated with a target domain to be analyzed by a PINO. Identification of the presence is at least of a successful identification or an unsuccessful identification. The method further includes generating a plurality of sub-domains for the target domain. The plurality of sub-domains is generated uniformly upon the unsuccessful identification, and the plurality of sub-domains is generated based on a predefined discontinuity criteria upon the successful identification. The method further includes generating intra-subdomain points with respect to each of the plurality of sub-domains. The method further includes extracting a plurality of sub-domain identifiers, upon generating the intra-subdomain points, using a pre-defined feature extraction technique.

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: A method and system for multi-table question answering is provided herein. The method includes receiving a complex question from a user. The method may further include decomposing the complex question into one or more sub-questions using one or more of but not limited to coreference resolution, dependency parsing, chunking and prompt techniques. The method may further include generating code scripts for each sub-question using a Large Language Model (LLM). The method may further include executing the code scripts to retrieve relevant structured data from a plurality of tables. The method may further include generating an answer to the complex question based on the retrieved structured data.

Abstract: A method and system for multimodal response generation through a virtual agent is provided herein. The method comprises retrieving information related to an input received by the virtual agent. The virtual agent employs an Artificial Intelligence (AI) model. The method further comprises generating a response corresponding to the input based on the retrieved information. The method may further comprises generating a plurality of prompts based on user characteristics and the input. The method may further comprises modifying the response based on the plurality of prompts to generate a multimodal response.

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 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: 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.

Abstract: A multi-band communication system includes a plurality of transmit channels, where each transmit channel transmits data streams through a single sub-band, and a plurality of receive channels, where each receive channel receives data streams of a single sub-band. According to one embodiment of the invention, each transmit channel and its respective receive channel are configured with a different set of channel codes to encode and decode the data. The set of channel codes is determined by an adaptive modulation coding controller based on a condition of the link between a transmit channel and a receive channel.

Abstract: This invention discloses a method and apparatus for managing lighting systems is disclosed. The method comprises performing a training phase for a plurality of settings wherein the training is represented by statistical parameters associated with a statistical model and then performing a monitoring phase to monitor the lighting system, determining whether characteristics of the monitored lighting system correspond to the model obtained during the training phase and determining an error exists when the monitored lighting system is not within tolerance values of the statistical parameters.