Browsing Year-2023 by Title

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  • Network inference in sparse datasets 
    Pandey, Harsh; Kumar, Vibhor (Advisor) (IIIT-Delhi, 2023-11-29)
    This research addresses the challenge of network inference in large-scale, sparse datasets. Inspired by the Gini coefficient, we introduce a novel methodology leveraging machine learning, including the GENIE3 algorithm and ...
  • Optimizing load balancing for edge applications 
    Temura, Arjun; Shah, Rinku (Advisor) (IIIT-Delhi, 2023-11-29)
    Hybrid clouds have gained popularity in recent years due to their ability to run applications across the on-premises data-center/edge and public clouds. The presence of edge cloud has significantly decreased latency and ...
  • Personal health record app 
    kumar, Rohit; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2023-12-11)
    The Indian government introduced the National Digital Health Mission (NDHM) with the aim of enhancing healthcare accessibility through the digitization and streamlining of patient data. Our proposal involves the development ...
  • Predicting prediction of probiotic species in the gut using statistical and machine learning 
    Sharma, Aditya Aryan; Harshal; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2023-11-29)
  • Prediction of disease genes using transcription factor binding pattern 
    Vimal, Anand; kumar, Vibhor (Advisor); chandra, Omkar (Advisor) (IIIT-Delhi, 2023-10-29)
    This study presents a novel approach leveraging machine learning techniques to analyze epigenomic and transcription factor binding patterns for the identification of key genes, including non-coding genes, associated with ...
  • Prediction of microbial colonization in human gut and oral microbiomes 
    Mithun; Ghosh, Tarini Shankar (Advisor) (IIIT-Delhi, 2023-11-29)
    This study seeks to forecast microbial colonization in the human gut and oral microbiomes by examining various factors, including diet, host genetics, and environmental influences. Through the utilization of sophisticated ...
  • RNN based molecule generation for developing novel therapeutics for neurodegenerative diseases 
    Sibin; Vaishnavi; Murugan, Arul (Advisor) (IIIT-Delhi, 2023-11-29)
    Neurodegenerative diseases pose a significant global health challenge, necessitating innovative approaches for drug discovery. This study explores the application of recurrent neural networks (RNNs) in the generation of ...
  • Unit test generation using LLMs: a comparative performance analysis of autogeneration tools 
    Bhatia, Shreya; Jalote, Pankaj (Advisor) (IIIT-Delhi, 2023-11-29)
    Generating unit tests is a crucial undertaking in software development, demanding substantial time and e↵ort from programmers. The advent of Large Language Models (LLMs) introduces a novel avenue for unit test script ...

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