Abstract: According to embodiments illustrated herein, there is provided a system for predicting a health condition of a first patient. The system includes a document processor configured to extract one or more headings from one or more medical records of the first patient based on one or more predefined rules. The document processor is further configured to extract one or more words from one or more phrases written under each of the extracted one or more headings, wherein the one or more phrases correspond to documentation of the observation of the first patient by a medical attender. The system further includes one or more processors configured to predict the health condition of the first patient based on a count of the one or more words in historical medical records and the one or more medical records.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier.

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain.

Abstract: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.

Abstract: Method, system, and apparatus for automatic stance classification. Propositions can be collected that are relevant to a query. A classifier can classify the stance of each proposition based on whether the proposition supports the query, opposes the query, or is neutral with respect to the query in order to thereafter provide substantive data for decision making based on and extracted from the query. The stance can be classified based on, for example, an SVM-SC (SVM Based Stance Classification) approach and/or an NN-SC (Neural Network Stance Classification Approach).

Abstract: A system provides an end-to-end solution for invoice processing which includes reading invoices (both pdfs and images), extracting key relevant information from the face of invoices, organizing the relevant information in a structured template as a key-value pair, and comparing invoices based on the similarities between different invoice fields to identify potential duplicate invoices.

Abstract: The disclosed embodiments illustrate methods of data processing for text classification of a target domain. The method includes generating a plurality of clusters from a plurality of first text segments corresponding to a plurality of source domains, based on an association of the plurality of first text segments with a plurality of categories. The method further includes computing a similarity score of each of a plurality of second text segments corresponding to the target domain for each of the plurality of clusters. The method further includes identifying a pre-specified count of clusters from the plurality of clusters, based on the computed similarity score. Further, the method includes training a first classifier by utilizing first text segments in the identified pre-specified count of clusters, wherein the trained first classifier is utilized to automatically classify the plurality of second text segments into categories associated with the identified pre-specified count of clusters.

Abstract: The disclosed embodiments illustrate methods and systems for predicting requirements of a user for resources. The method includes transforming a message posted by the user into a first message vector. The method further includes categorizing one or more first message vectors into one or more categories. The method further includes transforming each of the categorized first message vectors into one or more second message vectors using a wavelet transform technique. The method further includes determining, for each of the categorized first message vectors, a first score based on at least a probability distribution of one or more coefficients associated with each associated feature. The method further includes selecting a predefined number of features based on at least the first score. The method further includes training one or more classifiers on the selected predefined number of features to identify at least the one or more needs of the user.

Abstract: Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: A method and a system for response evaluation of users from electronic documents are disclosed. In an embodiment, one or more questions and a first response pertaining to each of the one or more questions are extracted from one or more first electronic documents. Further, a second response pertaining to each of the one or more extracted questions and metadata are extracted from one or more second electronic documents. For the second response pertaining to each of the one or more extracted questions, a score is determined based on one or more similarity measures that correspond to a category of each of the one or more extracted questions. Thereafter, the response evaluation is rendered on a user interface displayed on a display screen. The response evaluation comprises at least the determined score for the second response pertaining to each of the one or more extracted questions.

Abstract: A method and system for performing semantic analysis for electronic communication using a lexicon is provided. A neural network model is trained with a plurality of annotated text strings, the annotations comprising characteristic tuples that indicate characteristics for the text strings. An unannotated text string is received that comprises a plurality of words from a user. A characteristic matrix for the received text string is generated using a lexicon. The determined characteristic matrix is input into the trained neural network. And a characteristic tuple that indicates a characteristic for the received text string is received as output from the trained neural network.

Abstract: The disclosed embodiments illustrate methods data processing for real-time text analysis. The method includes receiving text content from a plurality of user-computing devices, wherein the text content comprises at least a current text segment and a previous text segment. The method further includes extracting one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment. The method further includes categorizing the current text segment into a predetermined category of one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier. Further, the method includes predicting a likelihood of evolution of an attribute in the text content, based on the predetermined category associated with the current text segment.

Abstract: A method, non-transitory computer readable medium and apparatus for transforming a knowledge base into a machine readable format for an automated system are disclosed. For example, the method includes clustering two or more documents of a plurality of documents in the knowledge base that are similar based upon a domain specific shingling function, identifying a pattern from each document of the plurality of documents that are clustered, wherein the pattern comprises a sequence of steps, aggregating the pattern of the each document of the plurality of documents that are clustered into a trie data-structure that is machine readable, receiving a request and applying the trie data-structure to provide a solution to the request.

Abstract: Method, system, and apparatus for automatic stance classification. Propositions can be collected that are relevant to a query. A classifier can classify the stance of each proposition based on whether the proposition supports the query, opposes the query, or is neutral with respect to the query in order to thereafter provide substantive data for decision making based on and extracted from the query. The stance can be classified based on, for example, an SVM-SC (SVM Based Stance Classification) approach and/or an NN-SC (Neural Network Stance Classification Approach).

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain.

Abstract: The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier.

Abstract: The disclosed embodiments illustrate method and system for medical data processing for generating personalized advisory information. The method includes receiving a request comprising a generic clinical care pathway report and medical data of a patient from a user-computing device. Further, the medical data comprises one or more factors associated with a patient profile and a population segment of the patient. The method further includes extracting one or more influence factors and one or more population segments to generate one or more knowledge bases. The method further includes comparing the one or more factors with the extracted one or more influence factors and the one or more population profiles. The method further includes generating a personalized advisory information. The method further includes rendering the generated personalized advisory information on an interactive user interface of the user-computing device over the communication network for selection by a medical practitioner.

Abstract: A method and a system for response evaluation of users from electronic documents are disclosed. In an embodiment, one or more questions and a first response pertaining to each of the one or more questions are extracted from one or more first electronic documents. Further, a second response pertaining to each of the one or more extracted questions and metadata are extracted from one or more second electronic documents. For the second response pertaining to each of the one or more extracted questions, a score is determined based on one or more similarity measures that correspond to a category of each of the one or more extracted questions. Thereafter, the response evaluation is rendered on a user interface displayed on a display screen. The response evaluation comprises at least the determined score for the second response pertaining to each of the one or more extracted questions.