Abstract: The embodiments of present disclosure herein address unresolved problems of a file path encryption, a manual quality check and a dual bar-code verification while scanning and transferring of the answer sheets to a server via a communication network. Embodiments herein provide a system and method for ensuring a dual bar code-based authentication of answer sheets. The system and method provide a multilevel security that is achieved by a programmatic scanning and validation. A metadata tagging, and manual quality check can be carried out by an operator. The system and method provide a file transfer over a Hypertext Transfer Protocol (HTTP) network and with the help of a blow-fish algorithm, media files path is stored in an encrypted manner. The system and method restrict any third-party entity to get access to the confidential data and ensure a single user authorization from scanning, monitoring to package creation and an operator management.

Abstract: In order to predict an optimized send time for one or more end users in an email campaign, one or more times in a day are identified for sending the email campaign to the one or more end users. The email campaign may then be transmitted at one of the one or more times identified to the one or more end users.

Abstract: A system for driver risk analysis includes and/or interfaces with: a processing subsystem, a set of sensors, and a set of models and/or algorithms. Additionally or alternatively, the system can include and/or interface with any or all of: a software platform, a set of client applications, a set of user devices, and/or any other components. A method for driver risk analysis can include any or all of: collecting a set of data; analyzing the set of data to determine a set of features; analyzing the set of data to produce a set of driver risk outputs; and triggering a set of actions based on the set of driver risk outputs. Additionally or alternatively, the method can include any or all of: determining eligibility criteria associated with a set of end entities; modifying any or all of the collection of data and/or the analysis of data and/or the driver risk outputs based on the eligibility criteria; training and/or updating a set of models and/or algorithms; and/or any other processes.

Abstract: A system for driver risk analysis includes and/or interfaces with: a processing subsystem, a set of sensors, and a set of models and/or algorithms. Additionally or alternatively, the system can include and/or interface with any or all of: a software platform, a set of client applications, a set of user devices, and/or any other components. A method for driver risk analysis can include any or all of: collecting a set of data; analyzing the set of data to determine a set of features; analyzing the set of data to produce a set of driver risk outputs; and triggering a set of actions based on the set of driver risk outputs. Additionally or alternatively, the method can include any or all of: determining eligibility criteria associated with a set of end entities; modifying any or all of the collection of data and/or the analysis of data and/or the driver risk outputs based on the eligibility criteria; training and/or updating a set of models and/or algorithms; and/or any other processes.

Abstract: Embodiments are directed to providing control functionality of a display of an electronic device. The method includes determining, by the electronic device, an event and partitioning, by the electronic device, a rear side of the electronic device into dynamic tiles based on multiple parameters associated with the display, based on determining the event. The method also includes detecting, by the electronic device, a user gesture on the dynamic tile on the rear side of the electronic device and classifying, by the electronic device, the detected gesture into a gesture category. Further, the method includes determining, by the electronic device, a functionality based on the gesture on the rear side of the electronic device and the corresponding gesture category. The method includes controlling, by the electronic device, the functionality on the display of the electronic device.

Abstract: A method for risk determination of a route includes collecting a set of inputs and determining a set of risk scores. Additionally, the method can include any or all of: processing the set of inputs; organizing the set of inputs; determining a model based on the set of inputs; determining a set of risk scores; producing an outputs based on the set of risk scores; and/or any other suitable processes.

Abstract: A method for risk determination of a route includes collecting a set of inputs and determining a set of risk scores. Additionally, the method can include any or all of: processing the set of inputs; organizing the set of inputs; determining a model based on the set of inputs; determining a set of risk scores; producing an outputs based on the set of risk scores; and/or any other suitable processes.

Abstract: A device may obtain, from a collection of data sources, personal information and activity information for a group of individuals. The device may generate profile information by associating the personal information and the activity information for each individual in the group of individuals. The device may determine a set of features capable of being used to train a set of machine learning models by using one or more feature identification techniques to analyze the profile information. The set of features may include generic features and contextualized features associated with the group of individuals. The device may train the set of machine learning models using one or more subsets of features of the set of features. The device may use the set of machine learning models to process a request from a client device for a list of prospective targets for a campaign.

Abstract: A method for risk determination of a route includes collecting a set of inputs and determining a set of risk scores. Additionally, the method can include any or all of: processing the set of inputs; organizing the set of inputs; determining a model based on the set of inputs; determining a set of risk scores; producing an outputs based on the set of risk scores; and/or any other suitable processes.

Abstract: A device may obtain, from a collection of data sources, personal information and activity information for a group of individuals. The device may generate profile information by associating the personal information and the activity information for each individual in the group of individuals. The device may determine a set of features capable of being used to train a set of machine learning models by using one or more feature identification techniques to analyze the profile information. The set of features may include generic features and contextualized features associated with the group of individuals. The device may train the set of machine learning models using one or more subsets of features of the set of features. The device may use the set of machine learning models to process a request from a client device for a list of prospective targets for a campaign.