Hi, I'm Nishank 👋

ISRO - Indian Space Research Organisation

Machine Learning Intern

• Designed a deep learning based equipment performance monitoring model for Antenna Control Servo System (ACSS) leveraging LSTM network to detect any degradation in performance and predict failures well in advance, thereby enabling predictive maintenance.
• Conducted trend analysis of various parameters of Antenna Control Unit (ACU) logs, employing advanced data analytics techniques.
• Co-authored and presented my research findings in a paper titled "Performance Prediction of Antenna Control Servo System Based on LSTM Network," which is accepted for presentation at IEEE Space 2024 conference, organized by ISRO and DRDO.

Tech Stack: Python, Pandas, Seaborn, scikit-learn, Tensorflow

Anand Diagnostic Laboratory - A Neuberg Associate & Neuberg Anand Reference Laboratory

Data Science Intern

• Developed an image processing model, that helps to identify the results obtained from the Typhifast ICT Card images and update it in the LIS.
• Implemented the process of segmentation, pre-processing, augmentation and prediction of results from images with an accuracy of 97%.

Tech Stack: Python, Tensorflow, OpenCV

SIFF Young Artiste

Intern

• Developed a full stack web voting application which can be used to carry out the voting of uploaded videos and can also be used to track the votes under each category and participants.
• Implemented authentication and voting dashboard for admins and ensured seamless integration with MySQL using the XAMPP server.

Tech-Stack: HTML, CSS, JS, PHP and MySQL

Performance Prediction of Antenna Control Servo System based on LSTM Network

Published and Presented in IEEE Space, Aerospace and Defence Conference 2024 (Scopus Indexed)

Research article focused on developing a predictive maintenance model through equipment performance monitoring for the Antenna Control Servo System (ACSS) using a stacked LSTM network. By employing a sliding window approach, the model processes sequential data from servo logs to predict the future current demands of the servo elevation motor during satellite passes. The study demonstrates the model's effectiveness, achieving a mean absolute error of 0.06 in predicting elevation current, thereby enabling proactive detection of equipment degradation and failures.

Optimizing Traffic Management Through Density-Driven Dynamic Traffic Signaling and Emergency Vehicle Prioritization Using Audio and Video

Published and Presented in the 7th International Conference on Innovative Computing and Communication (Scopus Indexed)

Research article focused on addressing the challenges of increasing traffic congestion and its adverse effects on daily life and emergency services. The paper presents a hybrid system that employs deep learning models for dynamic traffic signaling and real-time detection of emergency vehicles. Vehicle Detection uses Single Shot Detector (SSD) and Emergency Vehicle Detection combines audio and video inputs to confirm the presence of an emergency vehicle.

Innovating Drug Design for Alzheimer's Disease via Reinforcement Learning for Enhanced Molecular Generation

Published and Presented in the 4th International Conference on Innovations in Computational Intelligence and Computer Vision (Scopus Indexed)

Research article introducing an innovative drug design approach for Alzheimer's disease, leveraging AI techniques such as a random forest predictor, stacked GRU architecture, and reinforcement learning. The study integrates machine-learning algorithms to assess and rank compounds based on target characteristics, offering a promising avenue for efficient drug discovery in the context of Alzheimer's disease.