OnDevice Information Extraction from SMS using Hybrid Hierarchical Classification. Cluttering of SMS inbox is one of the serious problems that users today face in the digital world where every online login, transaction, along with promotions generate multiple SMS. This problem not only prevents users from searching and navigating messages efficiently but often results in users missing out the relevant information associated with the corresponding SMS like offer codes, payment reminders etc. In this paper, we propose a unique architecture to organize and extract the appropriate information from SMS and further display it in an intuitive template. In the proposed architecture, we use a Hybrid Hierarchical Long Short Term Memory (LSTM)Convolutional Neural Network (CNN) to categorize SMS into multiple classes followed by a set of entity parsers used to extract the relevant information from the classified message. The architecture using its preprocessing techniques not only takes into account the enormous variations observed in SMS data but also makes it efficient for its ondevice (mobile phone) functionalities in terms of inference timing and size.

Mobile UserActivity Prediction Utilizing LSTM Recurrent Neural Network The demand for mobile services is increasing exponentially and mobile telecommunication operators are facing new challenges such as bandwidth choking and resource allocation issues consequently. One of the most promising methods for overcoming these challenges is using Big Data Analytics on Call Detail Record (CDR). In this paper, a LongShort Term Memory neural network is utilized for user activity prediction for the first time. The real CDR data is preprocessed and used for training the neural network. The accuracy of this prediction is evaluated in normal values and anomalies. The proposed system can predict user activity accurately using a limited amount of training data and its performance in anomalous behaviors is promising. The networks ability to be generalized is also evaluated with the crossdataset test. The neural network is trained with one cells data and predict another cells activity properly. The proposed system is a step forward toward designing a practical and efficient CDR anomaly prediction system.