Accenture Global Solutions Limited

Araghya Biswas

Rajul Agarwal

Abhilash Manu

Shringar Kashyap

Rohini Minunath Nagare

Gaurav Tiwari

A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Target recognition and verification using image processing techniques and/or artifical intelligence

A device receives image data that depicts an individual, identifies the individual by using a target recognition technique to analyze the image data, and identifies human attributes of the individual. The device determines a risk level that represents a likelihood of the individual being or becoming involved in a harmful act. The risk level may be determined by using the human attributes as part of a machine-learning-driven emotional analysis of the individual. The device provides a message, selected based on whether the risk level satisfies at least one of the one or more threshold risk levels, to another device, to cause the other device to perform actions that are indicative of the risk level being associated with a low amount of risk, or other actions indicative of the risk level being associated with a high amount of risk and that are associated with assisting in prevention of the harmful act.

Performance of an emotional analysis of a target using techniques driven by artificial intelligence

An image-based product classification and recommender system employs a machine learning (ML) model for analyzing images for providing relevant recommendations to the users. An input image received from a user device is analyzed by the model for extraction of the image features that correspond to various attributes of a product in the image. A first subset of the image features is initially extracted and then applied to the input image to extract a next set of image features. The output from the model is then used for identifying products that match the user-selected product in the input image. The image-based product classification and recommender system also categorizes products in received images based on product attributes identified from the received images.

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Kolkata, India

Araghya Biswas