Publications

2025

1. 

   Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN

   Md Laraib Salam, Akash S Balsaraf, Gaurav Gupta, and 1 more author

   2025

   Abs DOI

   The use of neural networks and deep learning techniques in image processing has significantly advanced the field, enabling highly accurate recognition results. However, achieving high recognition rates often necessitates complex network models, which can be challenging to train and require substantial computational resources. This research presents a simplified yet effective approach to predicting both the base and exponent from images of mathematical expressions using a multi-output Convolutional Neural Network (CNN). The model is trained on 10,900 synthetically generated images containing exponent expressions, incorporating random noise, font size variations, and blur intensity to simulate real-world conditions. The proposed CNN model demonstrates robust performance with efficient training time. The experimental results indicate that the model achieves high accuracy in predicting the base and exponent values, proving the efficacy of this approach in handling noisy and varied input images.

2021

1. 

   Formation and Sticking of Air Bubbles in Water in D-Block Containers

   Gaurav Gupta, Laraib Salam, and Arunabha Adhikari

   Journal of Emerging Investigators, 2021

   Abs DOI

   Bubble formation is a common observation encountered on a daily basis. Although bubble formation takes place in all kinds of containers, containers made of d-block elements such as copper and steel present a specific phenomenon. The water bubbles stick to the walls of the container after formation and show a high mechanical and structural stability. In this study, we aimed to improve our understanding of the formation of bubbles that result from pouring water. We hypothesized that interstitial hydrogen present in the d-block metals form hydrogen bonds with the water bubbles accounting for the structural and mechanical stability. To test this, we poured water in containers of different cross-sectional areas and different materials from different heights. We also varied the temperature of water. Through these experiments, we found mathematical relations to predict the number of bubbles forming at different initial conditions and the force of H-bonding between the interstitial hydrogen and the water bubbles.