Search

Your search keyword '"Bhattacharyya, Siddhartha"' showing total 295 results
Start Over Author "Bhattacharyya, Siddhartha" Language english

Refine your results

more »

more »

more »

more »
295 results on '"Bhattacharyya, Siddhartha"'

33. Leveraging Cofollowership Patterns on Social Media to Identify Brand Alliance Opportunities.

Author

Malhotra, Pankhuri and Bhattacharyya, Siddhartha

Subjects
CO-branding, SOCIAL media, BRANDING (Marketing), CROSS promotions, SOCIAL networks
Abstract

The use of cobranding and brand extension strategies to access new markets has been a topic of significant interest. However, surprisingly few studies have examined cross-category connections of brands using publicly available digital footprints. In this study, the authors introduce a new, scalable automated approach for identifying potential cobranding and brand extension opportunities using brand networks derived from publicly available Twitter followership data. The digital user–brand relationship, established through followership activity, is regarded as an expression of interest toward the brand. Common followership patterns between brands are then extracted to capture cointerest between those brands' audience. By utilizing the cointerest patterns, the approach aims to derive cross-category brand–brand and brand–category connections, which can serve as important measures for assessing cobranding and extensions opportunities. This article introduces a new construct, transcendence, which measures the extent to which a brand's followers overlap with those of other brands in a new category. The analysis is conducted at different points in time to help managers track shifts in brand transcendence. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

49. DQNAS: Neural Architecture Search using Reinforcement Learning

Author

Chauhan, Anshumaan, Bhattacharyya, Siddhartha, and Vadivel, S.

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem., 15 Pages, 6 Tables, 9 figures

Published
2023

50. Development of an Interactive Anatomical Three-Dimensional Eye Model

Author

Allen, Lauren K., Bhattacharyya, Siddhartha, and Wilson, Timothy D.

Abstract

The discrete anatomy of the eye's intricate oculomotor system is conceptually difficult for novice students to grasp. This is problematic given that this group of muscles represents one of the most common sites of clinical intervention in the treatment of ocular motility disorders and other eye disorders. This project was designed to develop a digital, interactive, three-dimensional (3D) model of the muscles and cranial nerves of the oculomotor system. Development of the 3D model utilized data from the Visible Human Project (VHP) dataset that was refined using multiple forms of 3D software. The model was then paired with a virtual user interface in order to create a novel 3D learning tool for the human oculomotor system. Development of the virtual eye model was done while attempting to adhere to the principles of cognitive load theory (CLT) and the reduction of extraneous load in particular. The detailed approach, digital tools employed, and the CLT guidelines are described herein.

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results