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  • Automatic Assignment of Medical Codes 
    SAMANTA, MRITYUNJAY (2021-06)
    Natural Language Processing (NLP) is one of the most challenging and rapidly growing fields in artificial intelligence. It is all about deciphering human languages and deriving meaning from them. Some of the commonly ...
  • Applications of Deep Learning: Sequence Modelling in Industrial Sound Analytics 
    CHOUDHARY, ABHISHEK (2021-06)
    In today's world of data and information, deep learning has proven to be the ace in data science applications. While we hear much of deep learning concerning computer vision, NLP (Natural Language Processing), audio analysis ...
  • Applications of Convolutional-Recurrent Neural Networks in Weather Forecasting 
    SAMANTA, ARYA (2021-06)
    In this thesis, we use convolutional long short-term memory based neural network archi- tectures to predict certain basic dynamics of weather variables. First, rainfall dynamics are studied across direct and iterative ...
  • Machine Learning in Sound Analytic 
    MOHAN, ANIKET (2021-07)
    In this project we focus on different deep Learning algorithms for noisy audio enhancement where traditional Digital signal Processing (DSP) techniques fail to enhance noisy audio clips, we also worked on the classification ...
  • Medical Report Generation Model 
    GUPTA, PAWAN (2021-07)
    After recovery, many Covid Patients have a post-corona effect, and the most common is a lung infection (Pneumonia) in treatment response of this the first things that are done are taking X-rays and creating Reports. ...
  • Privacy-Preserving Distributed learning in the Healthcare sector 
    MADAAN, HARSHIT (2021-07)
    In this thesis, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed. We use these techniques to develop binary classification models for detecting tuberculosis ...
  • Working with Small Datasets 
    SETH, JITESH (2021-07)
    Deep learning semantic segmentation algorithms can localise abnormalities or opacities from chest radiographs. However, collecting and annotating training data is expensive and requires expertise which remains a bottleneck ...
  • Application of Convolutional Recurrent Neural Network in Precipitation Forecasting 
    ABHISHEK, NAMIT (2021-08)
    In this work, we use convolutional recurrent neural network-based architectures involving ConvLSTM and ConvGRU for precipitation forecasting over the Indian region. We first compare direct and iterative approach for ...
  • Graph Neural Networks for Multivariate Time Series Forecasting 
    ., AJAY (2022-04)
    Stock market analysis is a hot topic with a very active research community working on it. Stock price prediction is of particular interest owing to the high stakes it offers. It is actually a multivariate time series ...
  • Text Analytics to Assist Financial Research 
    M P, RAHMATHULLA (2022-05)
    The amount of data is increasing in the financial domain as in every field. It is humanly impossible to analyse all these data efficiently, so we need optimised computational techniques to achieve this. In this work, We ...
  • Investigation of Mixed Layer Depth through the lens of Artificial Intelligence 
    BHASKAR, ANKIT (2022-12)
    Predicting the various oceanic parameters responsible for air-sea coupling is crucial to understanding how the climate and weather systems can affect the ecosphere. One of the most important among these oceanic parameters ...
  • Semantic Search and Question-Answering Systems 
    PARMAR, PURVA (2023-05)
    Keyword Search Systems have been in wide use since the availability of computers themselves. Keyword search returns the relevant results which contain the exact keywords used in a search query. But in today’s world of ...
  • Continual Domain Incremental Learning during Test-time 
    CHAKRABARTY, GOIRIK (2023-05)
    This thesis focuses on the problem of continual test time domain adaptation in deep learning, where a trained model needs to adapt to new and changing environments during deployment. The first contribution of this work ...
  • Meteorological Variables Nowcasting using Machine Learning and Deep Learning Techniques 
    HARAL, HRISHIKESH (2023-05)
    In this work, we address two nowcasting problem statements. The first is the PM2.5 concentration temporal nowcasting over Delhi NCR. It has been observed that the high levels of PM2.5 concentrations in Delhi, particularly ...
  • Data-driven Prediction of Crop Yield Over Germany 
    PRAMANICK, PRANTIK (2023-05)
    This research aims to create a system that predicts agricultural yields using an all-encompassing system that integrates Numerical Weather Prediction (NWP) with machine learning. We want to know if combining NWP and ML ...
  • Developing AI-Based Surveillance Software 
    PAL, DIPAYAN (2023-05)
    In this work, we developed an AI-based surveillance system to be used by businesses to improve public safety, security, and law enforcement efforts. The system has numerous potential applications, including lost item ...
  • Segmentation of Pediatric Hand Radiograph Using UNet for Bone Aging 
    CHAPKE, RASHMI (2023-05)
    The skeletal growth of children is often assessed by calculating bone age. Often developmental age differs from chronological age; hence bone aging is one of the essential steps in the clinical procedure of estimating the ...
  • Econometric Study of Credit Cycles and Sectoral Risk with NLP-based Credit Risk Index Construction 
    MONDAL, LUBDHAK (2023-05)
    This thesis proposes a study on the relationship between credit cycles and sectoral risks in the Indian context. The research will use a novel credit cycle index and a novel sectoral risk indicator based on firm-level data ...
  • Quantitative risk management and data analytics with applications to finance 
    CHANDAK, KAPIL (2023-05)
    Risk Management has important implications for many organizations, and quantifying those risks is essential. The financial impact of the extreme events which lead to these risks is huge, a part of which is discussed in ...
  • Benchmarking of Graph Autoencoder models for Gene Regulatory Network inference with prior knowledge 
    PATERIA, HARSHIT (2023-05)
    This thesis explores the benchmarking of graph auto-encoders for inferring gene regulatory networks (GRNs) using prior knowledge of known GRNs. Gene regulatory networks are crucial for understanding gene expression and ...

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