Abstract: A method for applying adaptive zoom on an image is disclosed. The method includes detecting at least one input to perform zoom on the image, determining key pixels in the image, obtaining a plurality of segments by determining sizes of each of the plurality of the segments based on a number of the key pixels in each of the plurality of the segments, determining a priority order for zooming the plurality of the segments based on a density of the key pixels in each of the plurality of the segments, determining a zoom level to be applied on each of the plurality of the segments based on the density of the key pixels in each of the plurality of the segments, and adaptively zooming each of the plurality of the segments based on the zoom level and the priority order.

Abstract: 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.

Abstract: 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.

Abstract: A method for creating a toggled application icon and an electronic device therefor are provided. The method includes identifying a plurality of applications running on an electronic device, being actively toggled on a user interface of the electronic device based on a context, detecting at least one task performed by a user, detecting at least one application among the plurality of applications as at least one foreground application, detecting at least one application among the plurality of applications as at least one background application, and generating a toggled application icon. At least one of the at least one foreground application or the at least one background application is accessible through the toggled application icon based on the at least one task.