Research Publication
My passion for research began in college, where I continuously engaged in exploration and discovery. Published my first paper 'An architecture of machine translation from speech to sign language based on machine learning'. Driven by fashion of generative model, published second paper on 'Application of LSTM model for western music composition', which received significant recognition. Also contributed to the field of medical AI with a paper on 'Pneumonia detection using Meta Learning'.
ICEB - 2018
International Conference on Economic and Business management and business analytics
This platform helps to put a spotlight on your research and present it to an international community of peers or would like to catch up on the latest trends in the field. It provides unique opportunity to have your paper published in prestigious indexed journals as well as to get inspiration for your next research project. It helps in staying up to date on the latest developments in the field is a crucial element of lifelong learning.
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Presented my paper titled 'Machine Translation Architecture from Speech to Sign Language' along with other authors.
NeurIPS - 2021
International Conference on Neural Information Processing Systems
Participated in prestigious NeurIPS International conference on artificial intelligence. The experience was enriched in latest cutting-edge ongoing research and highlighting the interesting topics to grow in few years in the field of machine learning. Engaged in various presentations, tutorials, talks, and workshops helped to get deeper understanding of the subject fulfilling my curiosity.
Website: 2025 Conference
My Paper
This paper explores the possibility of music generation specially focusing on the use of Long Short-Term Memory (LSTM) model. It uses music composition from J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin. Successfully validated the AI generated music as human composed by the 30 volunteers.
Pneumonia Detection using Ensemble Transfer Learning
The paper addresses the health threat posed by the lung infection called pneumonia on the children all around the world. The author of the paper make comparison between dep learning approach sch as training single deep models from scratch, ensemble learning, transfer learning, and meta-learning to classify pneumonia from chest X-ray images.
This research paper introduces a machine learning-based architecture designed to bridge the communication gap between deaf individuals who use sign language and those unfamiliar with it. The second module employs a machine learning algorithm to translate the text into sign language.