Year-2024

 

Recent Submissions

  • Development of a continuous heart rate variability (HRV) monitoring system 
    Megha; Deb, Sujay (Advisor) (IIIT-Delhi, 2024-11-27)
    Heart Rate Variability is a widely studied physiological marker that reflects the autonomic regulation of the heart. HRV represents the autonomic nervous system’s control over the heart and is crucial for cardiovascular ...
  • Knowledge graph creation using LLM 
    Narnoli, Bhavya; Shukla, Jainendra (Advisor) (IIIT-Delhi, 2024-12-09)
    Knowledge Graphs are essential for structuring data into machine-interpretable formats, enabling ac- curate information retrieval, improved decision-making, and understanding of complex relationships. While KGs have seen ...
  • The Dot Store - a comprehensive study, UX research and in-depth implementation 
    Dwivedi, Shivam; Grover, Anuj (Advisor) (IIIT-Delhi, 2024-11-22)
    The Dot Store, started in May 2023 as an independent project under the guidance of Dr. Anuj Grover, began as an in-depth business research endeavor involving numerous surveys and interviews to identify needs within the ...
  • Pipeline for end-to-end training of DL-augmented LS channel estimation 
    Aadya; Darak, Sumit Jagdish (Advisor) (IIIT-Delhi, 2024-12-03)
    This work presents an enhanced training strategy for LSDNN (Least Squares Deep Neural Net- work) [1], a lightweight deep neural network designed to improve LS channel estimation in pilot-based OFDM systems. The approach ...
  • DL-based image analysis for CDC crossmatch 
    Kumari, Annu; Ray, Arjun (Advisor) (IIIT-Delhi, 2024-12-27)
    Graft rejection remains a significant challenge in organ transplantation. This study proposes a deep learning (DL)-based framework to assess graft rejection risk by analyzing CDC crossmatch imaging and FISH data. The ...
  • AMROrbit scorecards for AIIMS dataset 
    Gupta, Aryan; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2024-11-27)
    This report has analyzed AMR trends utilizing a dataset from AIIMS hospitals using resistance patterns across organisms, sample types, and demography. Data was preprocessed with parallel computing that enabled handling of ...
  • User-aware emotion journaling 
    Goel, Kanishk; Singh, Pushpendra (Advisor) (IIIT-Delhi, 2024-11-27)
    This report introduces a personalized journaling framework designed to enhance emotional awareness and therapeutic interventions. The framework integrates pre-journal assessments using the Emotion Reactivity Scale (ERS) ...
  • Tangible AR in learning interaction design 
    Jaiswal, Tejas; Srivastav, Aditi; Kumar, Kartik; Srivastava, Anmol (Advisor) (IIIT-Delhi, 2024-11-27)
    This research investigates the integration of Tangible Augmented Reality (Tangible AR) with shape-changing interfaces to enhance interaction design education. By combining physical manipulation with digital augmentation, ...
  • Traffic violation trends : machine learning driven predictive model & data visualization 
    Kaushik, Sarvagya; Sharma, Kunal; Biyani, Pravesh (Advisor) (IIIT-Delhi, 2024-11-27)
    Our project presents an interactive dashboard designed for analyzing and visualizing traffic violations across Jharkhand. Built using Streamlit, the dashboard incorporates geospatial data and interactive filters to offer ...
  • Exploring perceptions and realities of quitting smoking in India 
    Saumya; Singh, Pushpendra (Advisor) (IIIT-Delhi, 2024-11-27)
    Tobacco consumption, the leading preventable cause of death globally, claims 8 million lives annually and reduces life expectancy by 15 years. India, the second-largest producer and consumer of tobacco, faces a public ...
  • Computational gastronomy: ML and DL predictors for Umami peptides 
    Singh, Pavit; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2024-04-22)
    Umami is an important widely-used taste component of food seasoning. Umami peptides are spe- cific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are ...
  • A machine learning-based approach for predicting a recipe’s cooking time from its ingredients and direction 
    Reddy, Sreekar; Thappa, Deepak; Kaushal, Divyansh; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2024-11-28)
    This paper focuses on predicting cooking times for recipes based on advanced natural language processing techniques and machine learning models. With the increase in the number of online recipe datasets, there is a massive ...