Indian epics have not been analyzed computationally to the extent that Greek epics have. In this paper, we show how interesting insights can be derived from the ancient epic Mahabharata by applying a variety of analytical techniques based on a combination of natural language processing, sentiment/emotion analysis and social network analysis methods. One of our key findings is the pattern of sig...

Survival analysis, often called time-to-event analysis, is a set of methods focused on failure time prediction. Such methods are developed in a variety of research domains like medicine, sociology, economics or engineering, and are used to analyze different types of failures. For example, in business applications the failure may denote bankruptcy or loan default. One of the most important chara...

A system for the automatic categorization of the results of crystallization experiments generated by robotic screening is presented. Images from robotically generated crystallization screens are taken at preset time intervals and analyzed by the computer program Crystal Experiment Evaluation Program (CEEP). This program attempts to automatically categorize the individual crystal experiments int...

In this survey paper we start with a discussion how functionals of finite type can be used for the proof-theoretic extraction of numerical data (e.g. effective uniform bounds and rates of convergence) from non-constructive proofs in numerical analysis. We focus on the case where the extractability of polynomial bounds is guaranteed. This leads to the concept of hereditarily polynomial bounded a...

Phylogenetics is the study of relationship among species or genes with the combination of molecular biology and mathematics. Most of the present phylogenetic analysis softwares and algorithms have limitations of low accuracy, restricting assumptions on size of the dataset, high time complexity, complex results which are difficult to interpret and several others which inhibits their widespread u...

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...

This paper presents an efficient optimization procedure to find the optimal shapes of double curvature  arch  dams  considering  fluid–structure  interaction  subject  to  earthquake  loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element  analysis  dur...