Conduent Business Services, LLC

Arun Rajkumar

Narayanan Unny

Simarjot Kaur

Nupur Labh

Saurabh Srivastava

Sriranjani Ramakrishnan

Shourya Roy

Himanshu Sharad Bhatt

Koyel Mukherjee

Theja Tulabandhula

Sharanya Eswaran

Asmita Metrewar

Satish Prasad Rath

Shisagnee Banerjee

Anuj Mahajan

Jayati Bandyopadhyay

Sakshi Agarwal

Tuhin Bhattacharya

James Macurak

Saurav Bhattacharya

Rishabh Singh

Neha Singh

Kowshik Chilamkurthi

Sahil Manchanda

Prateek Yadav

Poorvi Agarwal

Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.

Method and system for forecasting in sparse data streams via dense data streams

A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.

Trained pattern analyzer for roll out decisions

Systems, methods, and processing nodes determine and perform preventive maintenance on a transport vehicle in a transportation system. The method includes extracting features for previous incidents that have occurred to a plurality of transport vehicles in the transportation system. The method also includes determining a criticality of types of incidents based on the features extracted. The method includes predicting, based on the criticality of types of incidents and the features extracted, details of at least one future incident for a first transport vehicle from the plurality of transport vehicles. The details include a predicted type of the at least one future incident, a predicted time of the at least one future incident, and a predicted criticality of the at least one future incident. Additionally, the method includes performing a prescriptive action for the first transport vehicle to mitigate the at least one future incident in the first transport vehicle.

Modelling operational conditions to predict life expectancy and faults of vehicle components in a fleet

System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.

Personalizing application interfaces based on usage

Methods and systems for personalized trust recommendation based on a dynamic trust model. In an example embodiment, a trust framework can be derived from a set of use-case specific factors. Invasive data and non-invasive data can be collected from a user (or a group of users) based on activity data and profile data associated with the user. A dynamic trust profile can be created (or learned) based on the invasive data and the non-invasive data collected from the user. A recommended level of trustworthiness can be then provided to the user respect to a particular situation and/or entity (e.g. other users) within the trust framework based on the dynamic trust profile of the user and which is personalized for the user.

Method and system for dynamic trust model for personalized recommendation system in shared and non-shared economy

The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving real-time input data comprising labeled instances of the source domain and unlabeled instances of the target domain from a computing device. The method further includes determining source specific representation corresponding to the source domain and a common representation shared between the source domain and the target domain. Based on a positive contribution from the source specific representation and the common representation, the labeled instances of the source domain are classified. The method further includes training a generalized classifier based on a positive contribution from the common representation. The method further includes automatically performing text classification on the unlabeled instances of the target domain based on the trained generalized classifier. The result of the text classification is rendered at a user interface of the computing device.

Method and system for text classification based on learning of transferable feature representations from a source domain

The disclosed embodiments illustrate method for displaying targeted multimedia items to a ridesharing group. The method includes transmitting a plurality of multimedia items to a computing device installed in a vehicle. The method further includes receiving at least one feedback provided by a plurality of users on each of the transmitted plurality of multimedia items. The method includes determining an effectiveness score for each of the transmitted plurality of multimedia items based on the corresponding at least one feedback provided by the plurality of users. The method includes selecting a plurality of targeted multimedia items from the plurality of multimedia items in real-time based on the determined effectiveness score and a pre-specified constraint. The method includes transmitting the selected plurality of targeted multimedia items in a prioritized order to the computing device. The transmitted plurality of targeted multimedia items is displayed to the plurality of users traveling in the vehicle.

Method and system for displaying targeted multimedia items to a ridesharing group

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Chennai, India

Arun Rajkumar