Nikunj Bansal

I am a Computer Science Graduate from the University of Petroleum and Energy Studies, India, and am currently working under the guidance of Dr. Tanupriya Choudhury on various research projects. I am also grateful to be advised by Mr. Tanmay Sarkar, lecturer of Food Processing Technology at West Bengal State Council of Technical Education, India and Swaminathan P. Iyer, MD, Professor of Medicine, Dept of Lymphoma/Myeloma, University of Texas MD Anderson Cancer Center, USA

I have worked as an MLOps Engineer at Railofy. Currently working as a Data Scientist at Cognizant. Also, Building CORD.ai, a Community that enables Collaborative Open Research in Deep Learning. Also, recently acquired few professional certifications for validating my expertise as ML specialist on AWS, GCP, Databricks, & Microsoft’s Azure.

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Dataset(s)
Allergen30 Dataset
Mayank Mishra, Nikunj Bansal, Tanmay Sarkar, & Tanupriya Choudhury

Dataset on Mendeley  Dataset on RoboFlow

Publication(s)
Semantic Segmentation of PolSAR image data using Advanced Deep learning Models
Rajat Garg, Anil Kumar, Nikunj Bansal, Manish Prateek, & Shashi Kumar

Scientific Reports (nature)

We aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data.

Paper

Allergen30: Detecting food items with possible allergens using deep learning based computer vision
Mayank Mishra, Tanupriya Choudhury, Tanmay Sarkar, Nikunj Bansal, Slim Smaoui, Maksim Rebezov, Mohammad Ali Shariati, & Jose Manuel Lorenzo

Food Analytical Methods (springer)

Introducing Allergen30, a custom-made dataset with 6,000+ images of 30 commonly used food items that can trigger an allergic reaction within the human body. This work is one of the first research attempts to train a deep learning based object detection model to detect the presence of such food items from images.

Read Here  Dataset on Mendeley  Paper  Dataset on RoboFlow

Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chili Powder

Detecting Red Chilli Powder Adulteration using Machine learning Algorithms.

Idea Mining From Online Reviews Using Transformation-Based Natural Language Processing Tasks
Hussain Falih Mahdi, Lav Kumar Gupta, Tanupriya Choudhury, & Nikunj Bansal

International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT2022)

Proposed Idea Mining Framework for Extracting Ideas From Online Reviews.

Paper

IndianFood-7: Detecting Indian Food Items using deep learning based Computer Vision


International Conference on Advances and Applications of Artificial Intelligence and Machine Learning (ICAAAIML2022) (Accepted)

We are detecting Popular Indian Food Items using object detection Models on our custom dataset.

Experimentation with NMT models on low resource Indic languages
Nikunj Bansal, Goutam Datta, & Anupam Singh

IEEE International Conference on Image Information Processing (ICIIP 21)

We have applied NMT to low resources Indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it.

Paper

Articles
K-Medoids Clustering

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