Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating an emotional sentiment score without the use of labelled data. In one example, embodiments comprise receiving an input text sequence, generating an intermediate emotional sentiment score object based at least in part on the input text sequence and by utilizing an emotional sentiment machine learning model, generating an overall emotional sentiment score based at least in part on the intermediate sentiment score object and by utilizing an emotional sentiment score transformation object, and performing one or more prediction-based actions based at least in part on the overall emotional sentiment score.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating an emotional sentiment score without the use of labelled data. In one example, embodiments comprise receiving an input text sequence, generating an intermediate emotional sentiment score object based at least in part on the input text sequence and by utilizing an emotional sentiment machine learning model, generating an overall emotional sentiment score based at least in part on the intermediate sentiment score object and by utilizing an emotional sentiment score transformation object, and performing one or more prediction-based actions based at least in part on the overall emotional sentiment score.

Abstract: This disclosure relates generally to telecom inventory management, and more particularly to telecom inventory management via object recognition and localization using street-view images. In one embodiment, the method includes obtaining street-view images of a geographical area having telecom assets. The telecom assets are associated with corresponding GPS location coordinates. An object recognition model is applied to the street-view images to detect the telecom assets therein. Detecting the telecom assets includes associating the telecom assets with corresponding asset labels. A real-world location of the telecom assets is estimated in the geographical area by applying triangulation method on a set of multi-view images selected from the street-view images. The set of multi-view images are captured from a plurality of consecutive locations in vicinity of the telecom asset in the geographical area.

Abstract: This disclosure relates generally to telecom inventory management, and more particularly to telecom inventory management via object recognition and localization using street-view images. In one embodiment, the method includes obtaining street-view images of a geographical area having telecom assets. The telecom assets are associated with corresponding GPS location coordinates. An object recognition model is applied to the street-view images to detect the telecom assets therein. Detecting the telecom assets includes associating the telecom assets with corresponding asset labels. A real-world location of the telecom assets is estimated in the geographical area by applying triangulation method on a set of multi-view images selected from the street-view images. The set of multi-view images are captured from a plurality of consecutive locations in vicinity of the telecom asset in the geographical area.