Abstract: A method and a system for predicting demand for a supply chain is disclosed. The method includes feeding input vectors to a trained Machine Learning (ML) model, for a future time-period. The input vectors include an intensity vector corresponding to an intensity of a possible disruption-event at each point of time within the future time-period and a duration vector corresponding to the duration of the possible disruption-event, and one or more extrinsic data vectors. The method further includes obtaining a demand for a target product in the future time-period from the trained ML model based on the input vectors.

Abstract: Disclosed herein is method and a system for predicting mutations in Ribonucleic acid (RNA) strains. In an embodiment, a similarity between a new viral RNA strain and reference RNA strains is determined. Further, a strain score for the new viral RNA strain is calculated based on the similarity between the new viral RNA strain and the reference RNA strains. Subsequently, mutation sites for the new viral RNA strain are identified by generating spatial nearness data corresponding to the reference RNA strains based on comparison between the strain score of the new viral RNA strain and the reference RNA strains. Finally, mutations of the new viral RNA strain are predicted by performing a generative modelling of a sequence of the new viral RNA strain with reference to the mutation sites of the new viral RNA strain.

Abstract: A system and method for selecting candidate drug compounds for a disorder through Artificial Intelligence (AI)-based drug repurposing is disclosed. The method includes extracting data including target protein-protein interaction complex corresponding to disorder from databases through Natural Language Processing (NLP) algorithm; generating semantic knowledge graph for disorder based on extracted data to identify a set of lead compounds; assigning initial rank to each of set of lead compounds based on historical clinical information and semantic knowledge graph, through predictive model; for each of set of lead compounds, determining binding affinity score through AI-based encoder-decoder model; determining molecular structure stability score based on interaction of molecular structures through deep learning model; and assigning final rank to each of set of lead compounds based on binding affinity score, molecular structure stability score, and intermediate clinical trial data.

Abstract: This disclosure relates to method and system for dynamically identifying change in customer behaviour and providing appropriate personalized recommendations. The method includes, for each of a plurality of transactions and for each of one or more customers, generating customer behaviour data based on customer transaction data and pre-defined product segmentation data; generating a state-action-reward pair based on customer transaction data and the pre-defined product segmentation data; and generating state-behaviour mapping data based on customer behaviour data and the state-action-reward pair.

Abstract: Disclosed herein is method and system for providing context based adaptive response to user interactions. A primary context of the user interactions is determined based on intents and associated named entities extracted from the user interactions. Further, secondary context of the user interactions is determined by detecting enquiry intent in user responses for queries provided for the primary context of the user interactions. Information related to primary and the secondary contexts are stored as Key Context Information (KCI) and is dynamically updated during the user interactions. Finally, context based adaptive responses are generated based on the updated KCI upon determining non-enquiry intent in subsequent user responses. The method of present disclosure maintains track of the user interactions and automatically detects changes in the context of the user interactions.

Abstract: Disclosed herein is method and system for providing context based adaptive response to user interactions. A primary context of the user interactions is determined based on intents and associated named entities extracted from the user interactions. Further, secondary context of the user interactions is determined by detecting enquiry intent in user responses for queries provided for the primary context of the user interactions. Information related to primary and the secondary contexts are stored as Key Context Information (KCI) and is dynamically updated during the user interactions. Finally, context based adaptive responses are generated based on the updated KCI upon determining non-enquiry intent in subsequent user responses. The method of present disclosure maintains track of the user interactions and automatically detects changes in the context of the user interactions.