Samsung Electronics Co., Ltd.

Nitin Kishorbhai Tanwar

Nitesh Goyal

Deepak Sharma

Sunil Rathour

Ankit Agarwal

Choice Choudhary

Pratush Kumar Srivastava

Kamya Jaiswal

Harshit Oberoi

Vipin Khushu

Akshar Bhatnagar

Asif Anis

Sushant Vobbilisetty

A method for providing sign language is disclosed. The method includes receiving, by an electronic device, a natural language information input from at least one source for conversion into sign language. The natural language information input includes at least one sentence. The method further includes predicting, by the electronic device, an emphasis score for each word of the at least one sentence based on acoustic components. The method further includes rephrasing, by the electronic device, the at least one sentence based on the emphasis score of each of the words. The method further includes converting, by the electronic device, the at least one rephrased sentence into the sign language. The method further includes delivering, by the electronic device, the sign language.

Method and electronic device for providing sign language

A method for accidental touch prediction using machine learning (ML) classification is provided. The method includes determining, by an electronic device, a mutual data index of a sensor data using a first ML model. Further, the method includes recognizing whether the sensor data corresponds to an object touch or a non-object touch based on the mutual data index. Further, the method includes performing, by the electronic device, one of detecting that the electronic device is in a pocket mode and providing an object touch notification on a touch screen of the electronic device in response to determining that the sensor data corresponds to the object touch, and recognizing whether the sensor data corresponds to an accidental touch or a non-accidental touch using at least one second ML model based on touch data in response to determining that the sensor data corresponds to the non-object touch.

Method and electronic device for accidental touch prediction using ml classification

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Noida, India

Nitin Kishorbhai Tanwar