To predict the intent of Deutsche Telekom customers using Python with pytorch and Seq2seq, NLP Deep Learning techniques.

There are lot of data of users who create expense report in their respective organisation and employees who try to do fraud will be caught using this deep learning analysis

To predict the expense category for suspicious spends using Python with tensorflow keras, Deep learning, NLP techniques.

Prediction of temperature of brake disc is important to save computational time which would be very high when people perform mechanical simulations

Brake may failif different brake disc are used for the conditions which are not suitable for those particular types of brake disc. So clusterning and classification technique are used to identify which brake disc would be suitable for which kind of conditions

To predict surrounding temperature of brake disc using Python and Excel with Linear Regression, scatter diagram techniques.

Managing identity lifecycle due to multiple identities for same userTo identify all potential identities of User

To personalize experience by inbuilt chatbotTo recommend articles using information retrieval

User shadow accesses and bypassing authenticationsClassification of network traffic accesses

Computational time is high for failure prediction using 3D CFDTo predict if brake disc sustains the environment conditions or not

Computational time is high in identifying invoices manually which are duplicateTo identify duplicate invoices and help client in business spend management

CAD repair is manual and monotonous taskTo predict if defect is simple or complex and then automate repair process for simple defects

To personalize experience of Deutsche Telekom customersTo recommend contracts