Accenture Global Solutions Limited

Ankur Garg

Mathangi Sandilya

Srikanth G Rao

Tarun Singhal

Anshul Gupta

Bolaka Mukherjee

Sowmya Rasipuram

Vipin Anilkumar Bhasin

Jayaprakash Nooji Shekar

Akshay Tutika

Sankar Ghosh

Shailesh Chaphekar

Deepa Dharmit Shah

Karishma Khanna

Vipin Bhasin

Srivani Mareddy

Pallav Chourey

Anagha Avinash Ghate

Suraj Khaitan

Kumar Vaibhav

Raja Sekhar Velagapudi

Rahel James Kale

Issac Abraham Alummoottil

Omkar Sudhakar Deorukhkar

Meeta Lal Budhrani

Veera Raghavan Valayaputhur

Rani Gopalakrishnan

Richa Sahrawat Malik

A video translation system that generates an output video in a target language which includes a translated/output audio track that runs in synchrony with the video content of a received input video in a source language and further displays translated subtitles corresponding to the translated audio track is disclosed. Upon receiving the input video, the domain of the input video can be identified. A translation engine and a transcription engine are selected based on the domain and the pair of languages corresponding to the input video and the output video. The output audio track is generated using the translation engine and merged with a manipulated video, which runs in synchrony with the output audio track to generate the output video. The transcription engine generates subtitles translated from the source language to the target language for the output video.

Video translation platform

A plurality of personalized news feeds are generated from input feeds including digital content items based on a dynamic taxonomy data structure. Entities are extracted from the input feeds and relationship strengths are obtained for the extracted entities and the digital content items. The dynamic taxonomy data structure is updated with the extracted entities and entries for the digital content news items are included at the corresponding branches based on the relationship strengths. Attributes are obtained for the entities and those entities corresponding to the trending topics are identified. Personalized news feeds are generated including the digital content items listed under the entities. Digital content items are added or removed from the digital content feeds based on one or more entity attributes.

Dynamic taxonomy builder and smart feed compiler

Examples for the baggage weight prediction system are provided. The system may create a baggage prediction model in response to a carrier baggage weight prediction requirement requesting a total baggage weight to be carried in a future transportation operation by a carrier. The baggage prediction model may be used to determine a predicted baggage to be carried by a plurality of future passengers associated with a future transportation operation. The system may determine a predicted weight for the predicted baggage. The system may sort the predicted baggage into checked baggage and a carry-on baggage. The system may determine a carry-on baggage weight associated with the carry-on baggage based on the baggage prediction model. The system may determine a baggage weight ratio associated with the predicted baggage. The system may generate a baggage prediction result for performing a baggage management action.

Baggage weight prediction

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Gurgaon, India

Ankur Garg