Abstract: The present disclosure describes a method, apparatus, and computer readable medium for Surface Mount Technology (SMT). The system comprising a Data Integration (DI) platform configured to collate data from one or more units in an assembly line, an Artificial Intelligence (AI) platform configured to process the collated data, using one or more machine learning techniques, to generate predictive and preventive analysis for the one or more units present in the assembly line. The system further disclose a Digital Twin Simulation (DTS) platform configured to simulate an exact replica of all the units present in the assembly line, provide visual representation, allow the one or more operators in the assembly line to take at least one action and provide the at least one action taken by the one or more operators in the assembly line as feedback signal to AI platform to improve prediction rate of said system.

Abstract: The present disclosure describes a method, apparatus, and computer readable medium for providing predictive and preventive analysis relating to fitness of elements in an assembly line, prior to final product assembling. According to the present disclosure, when the elements in assembly lines pass the testing performed by the element testing units, the test data of the elements is processed by the system to predict whether the elements when assembled, results in a fit product. When the final product is predicted to be unfit, the system detects non-reliable element(s) and provide explanation/feedback for the failure of the final product.

Abstract: The present disclosure describes a method, system, and computer readable medium for facilitating predictive maintenance of testing machine using a combination of deep learning. The method comprises performing receiving plurality of tested data of a plurality of products being tested by the testing machine. The method further comprises applying a predictive model, having predictive model parameters, upon the plurality of tested data to predict a plurality of future test data corresponding to the plurality of products. The method further comprises determine a deviation between the plurality of tested data and the plurality of future test data, wherein the deviation indicates fault in the testing machine. The method further comprises determine a fault level of the testing machine by comparing the deviation with a predefined threshold and determining, during run-time, the maintenance required for the testing machine based on the fault level.

Abstract: The present disclosure describes a method, apparatus, and computer readable medium for anonymizing medical records using a combination of deep learning and smart templatization. The method comprises performing tokenization on an input medical record comprising one or more sentences to generate tokenized data and generating one or more templatized sentences by performing templatization on the tokenized data, where performing the templatization comprises replacing one or more known patterns in the tokenized data with predefined patterns. The method further comprises identifying one or more PHI sentences from the templatized sentences using a trained classifier, each PHI sentence may comprise one or more PHI. The method further comprises identifying the PHI in the medical record by processing the identified PHI sentences using a trained model and generating an anonymized medical record by anonymizing the identified PHI in the input medical record.

Abstract: The present disclosure describes a method, system, and a computer readable medium for extracting tabular data present in a document. The method comprises detecting presence of at least one table in the document using a deep learning based model and a statistical method. The method further comprises identifying a type of the table based on determining a count of horizontal and vertical lines, presence of outer borders, and presence of row-column intersections in the table. The type of the table comprises a bordered table, a partially bordered table, or a borderless table. The method further comprises processing the detected table, depending on its type, to identify one or more cells present in the table. The method further comprises generating an output file by extracting the tabular data present in the table, where the extracting comprises performing optical character recognition on the identified one or more cells.