Abstract: The invention relates to method and system for providing visual explanations for image analytics decisions. The method includes extracting a set of local features from each of a plurality of image instances using a deep learning (DL) model; determining a feature list by aggregating the set of local features from each of the plurality of image instances; generating a two-dimensional (2D) pixel map based on the feature list; superimposing the 2D pixel map of aggregated features on each of the plurality of image instances; and providing a visual explanation for an image analytics decision on one or more of the plurality of image instances based on superimposition.

Abstract: A method for automatically generating codes to operate Machine Learning (ML) models disclosed. In some embodiments, the method includes operating, for each of a plurality of stages associated with ML model operations, at least one ML model. To operate each of the at least one ML model, the method may include selecting, for each of the at least one ML model at each of the plurality of stages, a predefined technique associated with a current stage; generating, for each of the at least one ML model at each of the plurality of stages, a code based on the selected predefined technique; generating, for each of the at least one ML model at each of the plurality of stages, a dependency configuration and an environment configuration for the generated code; and generating, for each of the at least one ML model, a pipeline configuration for execution of associated codes.

Abstract: A method for generating a temporal token file to enable access to selective multimedia content within a multimedia file is disclosed. In some embodiments, the method includes identifying a plurality of multimedia content present within the multimedia file. The method further includes generating a token file for each of the plurality of multimedia content. To generate the token file, the method includes retrieving a plurality of snippets from each of the plurality of multimedia content; and annotating each of the plurality of snippets with a textual token. The method further includes extracting a timestamp associated with each of the plurality of snippets. The method further includes generating the temporal token file associated with each of the plurality of multimedia content based on the token file and the timestamp extracted for each of the plurality of snippets.

Abstract: The invention relates to method and system for providing visual explanations for image analytics decisions. The method includes extracting a set of local features from each of a plurality of image instances using a deep learning (DL) model; determining a feature list by aggregating the set of local features from each of the plurality of image instances; generating a two-dimensional (2D) pixel map based on the feature list; superimposing the 2D pixel map of aggregated features on each of the plurality of image instances; and providing a visual explanation for an image analytics decision on one or more of the plurality of image instances based on superimposition.

Abstract: The disclosure relates to method and system recommending tuning of parameters to generate a data analytics model. The method includes identifying at a data pre-processing stage a pre-processing subset from an associated set of predefined pre-processing methods for a predefined objective. The method includes identifying at a feature selection stage a feature subset from an associated set of predefined feature selection methods for the predefined objective. The method includes identifying at a model training stage a training subset from an associated set of predefined model training methods for the predefined objective. The method further includes generating a plurality of data analytics tuples and selecting a data analytics tuple from the plurality of data analytics tuples. An output result of the data analytics tuple includes highest ranked results for the predefined objective.

Abstract: The present disclosure relates to a system (102) for assigning dynamic operation of devices in a communication network (106). The system (102) receives one or more behavioral attributes and one or more contextual attributes associated with one or more devices (232) in a communication network (106). The system (102) further determines one or more clusters (234) associated with each device from the one or more devices (232). The system (102) further determines, dynamically, one or more network slices, from a set of network slices associated with the one or more clusters (234). The system (102) further determines, dynamically, one or more analytics models associated with the one or more clusters (234). The system (102) further assigns dynamic operation of the one or more clusters (234) based on the one or more contextual attributes, the one or more network slices and the one or more analytics models.

Abstract: The present disclosure relates to system(s) and method(s) for generating an Omni-channel support platform. The method comprises integrating a multi-channel support system with a blockchain framework. Further, the method comprises generating an Omni-channel support platform based on the integration. The Omni-channel support platform comprises an Omni-channel support block for a user from a set of users. The Omni-channel support block comprises a support ledger and a support smart contract for the user. The Omni-channel support block further comprises capturing transaction data associated with the user from the multiple support channels. Further, the Omni-channel support block comprises recommending one or more resolutions to each user upon based on a support request.

Abstract: The present disclosure relates to a system (102) for assigning dynamic operation of devices in a communication network (106). The system (102) receives one or more behavioral attributes and one or more contextual attributes associated with one or more devices (232) in a communication network (106). The system (102) further determines one or more clusters (234) associated with each device from the one or more devices (232). The system (102) further determines, dynamically, one or more network slices, from a set of network slices associated with the one or more clusters (234). The system (102) further determines, dynamically, one or more analytics models associated with the one or more clusters (234). The system (102) further assigns dynamic operation of the one or more clusters (234) based on the one or more contextual attributes, the one or more network slices and the one or more analytics models.