Medical CodeMixed VQA (Bachelor Thesis)

Nimisha Mittal, Abdul Waheed, Muskan Goyal, Deepak Gupta, Shad Akhtar

In this research, we intend to propose a framework for code mixed visual question answering (VQA) in the medical domain. The proposed model will include answers in Hindi, English, or Codemixed (Hinglish) languages from the questions. A successful medical visual question answering (VQA) model will automatically extract the information found in medical images that will further assist in medical diagnosis. The performance of our model is at par with the experimented baselines.

BloomNet: A Robust Transformer based model for Bloom’s Learning Outcome Classification

Abdul Waheed, Muskan Goyal, Nimisha Mittal, Deepak Gupta, Ashish Khanna, Moolchand Sharma

ICNLSP 2021

Bloom taxonomy is a common paradigm for categorizing educational learning objectives into three learning levels: cognitive, affective, and psychomotor. We compare BloomNet with a diverse set of basic as well as strong baselines and we observe that our model performs better than all the experimented baselines. Further, we also test the generalization capability of BloomNet by evaluating it on different distributions which our model does not encounter during training and we observe that our model is less susceptible to distribution shift compared to the other considered models. We support our findings by performing extensive result analysis.

Intelligent Waste Management for Smart Cities

Nimisha Mittal, Priyanjali Pratap Singh, Prerna Sharma

ICIERA 2021

A smart waste collection is the center of a smart waste management system and an intelligent bin is a pivot for any step towards the development of an Integrated Platform for Waste Management. This paper presents an IoT-based smart dustbin that is capable of integrating with contemporary society as well as catering to future smart cities. The proposed implementation presents an end-to-end scalable solution for disposal as well as collection and transfer. The paper also attempts to highlight some of the prevailing hurdles in devising and achieving sustainable development plans.

Generative Adversarial Networks based PCG for games: A comprehensive study

Nimisha Mittal, Priyanjali Pratap Singh, Prerna Sharma

Deep Learning in Gaming and Animations

This survey explores the contemporary usage of GANs in the gaming domain, addresses the needed developments necessary for further progress, and is accompanied by some possible future developments using these techniques for gameplay content generation. The existing application of GANs in games is also expounded via a concise overview of existing games based on these novel approaches.

Domain Controlled Title Generation with Human Evaluation

Abdul Waheed, Muskan Goyal, Nimisha Mittal, Deepak Gupta

ICICC 2021

We study automatic title generation and present a method for generating domain-controlled titles for scientific articles. We have performed automated evaluation using ROUGE metric and human evaluation using five parameters to make a comparison between human and machine-generated titles. The titles produced were considered acceptable with higher metric ratings in contrast to the original titles. Thus we concluded that our research proposes a promising method for domain-controlled title generation.

Detection of white blood cells using optimized qGWO

Prerna Sharma, Moolchand Sharma, Divij Gupta, Nimisha Mittal

Intelligent Decision Technologies

This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.