Accenture Global Solutions Limited

Vipin Anilkumar Bhasin

Bolaka Mukherjee

Mathangi Sandilya

Srikanth G Rao

Anshul Gupta

Tarun Singhal

Ankur Garg

Akshay Tutika

Manish Phukan

Swati Dubey

Biju Subrahmanian

Mamta Kanad Bhagwat

T Siva Rama Sarma

Meeta Lal Budhrani

Pallav Chourey

J Nooji Shekar

Richa Sahrawat Malik

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

A system for providing cognition assistance including a contextual memory trainer, which receives preprocessed data including facial data, scene data, and activity data related to a video in association with temporal data and geographical location data of the camera that captured the video, where the scene data, the activity data, the geographical location data, and the temporal data collectively define spatiotemporal data. The trainer identifies an unknown aspect in the preprocessed data based on historical data and determines a predefined priority factor therefor. The priority factor includes one of a frequency of occurrence within a set period and relative proximity of the unknown aspect to the camera, a known face, place, or scene. The unknown aspect is prioritized for annotation based on a value of the priority factor exceeding a predefined threshold value, based on which facial data is associated with the spatiotemporal data to provide contextual annotated data.

Cognition assistance

A device may receive video data associated with a video, and may identify image data associated with the video data and that corresponds to a frame of the video. The device may process the image data, with an image analysis model, to determine an image compliance score associated with a policy and with context of the image data, and may identify text data associated with the video data. The device may process the text data, with a text analysis model, to determine a text compliance score associated with the policy and with context of the text data, and may determine, based on the image compliance score and the text compliance score, an aggregate compliance score associated with the frame. The device may determine whether the aggregate compliance score satisfies a threshold associated with the policy, and may perform actions based on whether the aggregate compliance score satisfies the threshold.

Utilizing machine learning models to identify context of content for policy compliance determination

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Pune, India

Vipin Anilkumar Bhasin