Accenture Global Solutions Limited

Jayashri Sridevi

Koushik M Vijayaraghavan

Aditi Kulkarni

Rajesh Nagarajan

Rajendra Prasad Tanniru

Roopalaxmi Manjunath

Srikanth Prasad

Niju Prabha

Rajendra Tanniru Prasad

Purnima Jagannathan

Luke Higgins

Shankaranand Mallapur

Sarvesh Madhusudan Damle

Balaji Venkateswaran

Xiwen Sun

Gayathri Pallail

Pareshkumar Ramchandbhai Gelot

Sudhir Hanumanthappa

Pankaj Jetley

Meiyarasu Selvam

Abhijit Avinash Kulkarni

Girish Hulkoti

Niyaz Shaffi Hameed Musthafa

Charles Grenet

Yuqing Xi

Srinivasan Kootalai Sundaram

Easwer Chinnadurai

Liang Han

Jérémy Aeck

Dhiviya Dhanasekaran

Alok Jain

Jean-Francois Depoitre

Joydeep Sarkar

Rashmi Jhawar

Prajwal Patrick Dsilva

Soumya Swarup Jena

A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data. The device may perform natural language processing on the cleansed work item data to identify synonyms for words in the cleansed work item data and may replace abbreviations in the cleansed work item data with full form text to generate final work item data. The device may identify keywords in the final work item data and may process the final work item data, the synonyms, and the keywords, with a machine learning model, to identify mappings between work items of the final work item data and to determine a confidence score for the mappings. The device may perform actions based on the mappings and the confidence score.

Utilizing machine learning and natural language processing to determine mappings between work items of various tools

A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Utilizing artificial intelligence models to manage and extract knowledge for an application or a system

A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Automated data processing and machine learning model generation

A device may receive input data identifying user stories, test case documents, event logs, and application logs associated with an application, and may perform natural language processing on the user stories and the test case documents, identified in the input data, to generate a first state diagram associated with the application. The device may process the event logs identified in the input data, with a heuristic miner model, to generate a second state diagram associated with the application, and may process the application logs identified in the input data, with a clustering model, to generate a volumetric analysis associated with the application. The device may perform post processing of the first state diagram, the second state diagram, and the volumetric analysis, to remove duplicate data and unmeaningful data and to generate modified outputs, and may perform actions based on the modified outputs.

Utilizing artificial intelligence and machine learning models to reverse engineer an application from application artifacts

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Chennai, India

Jayashri Sridevi