T
Tirtharaj Dash
Researcher at Birla Institute of Technology and Science
Publications - 74
Citations - 564
Tirtharaj Dash is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 12, co-authored 63 publications receiving 391 citations. Previous affiliations of Tirtharaj Dash include Birla Institute of Technology & Science, Pilani - Goa & National Institute of Standards and Technology.
Papers
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Journal ArticleDOI
A study on intrusion detection using neural networks trained with evolutionary algorithms
TL;DR: Two new hybrid intrusion detection methods are reported; one is based on gravitational search (GS), and other one is a combination of GS and particle swarm optimization (GSPSO) and these two techniques have been successfully implemented to train artificial neural network (ANN).
Journal ArticleDOI
Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network
TL;DR: An automated fault diagnosis model that can diagnose multiple types of faults in the category of hard faults and soft faults is presented, which implements a feed-forward neural network trained with a hybrid metaheuristic algorithm that combines the principles of exploration and exploitation of the search space.
Book ChapterDOI
Large-Scale Assessment of Deep Relational Machines
TL;DR: Testing on datasets from the biochemical domain involving 100s of 1000s of instances; industrial-strength background predicates involving multiple hierarchies of complex definitions; and on classification and regression tasks provide substantially reliable evidence of the predictive capabilities of DRMs; along with a significant improvement in predictive performance with the incorporation of domain knowledge.
Proceedings ArticleDOI
Controlling Wall Following Robot Navigation Based on Gravitational Search and Feed Forward Neural Network
TL;DR: An attempt is made to use a new neural network training algorithm based on gravitational search (GS) and feed forward neural network (FFNN) for automatic robot navigation of wall following mobile robots.
Journal ArticleDOI
A review of some techniques for inclusion of domain-knowledge into deep neural networks
TL;DR: In this paper , the authors present a survey of ways in which existing scientific knowledge is included when constructing models with neural networks, by means of changes to: the input, the loss-function, and the architecture of deep networks.