Paper Title
Predictive Analysis of Cohesiveness in Multivariate Sequences Using Recurrent Neural Networks

Abstract
This study is a revised analysis of team effectiveness prediction based on a probability of social sensitivity described as avoluntary inclination of the members to adhere to group general etiquette, practices and norms. Rather than focusing on individual performance metrics like skills and job experience, this study formulates the arbitrary organizational composition of members in a team as a sequence problem. The proposed measure of collective intelligence is modeled through input-to-state and state-to-state transitions in neural networks based on the concept of memory cells to learn the vocabulary of social cohesiveness in variable length sequences. By extending the type of dataset to unconventional time-series, like members in a team engaged in problem-solving activities, this study hopes to achieve results at par with derived survey findings in applied disciplines (socio-psychology). Keywords - Sequence modeling, prediction, memory-cell