Year-2024: Recent submissions

  • Application of DL in NER in recipes 
    Singh, Ritisha; Jha, Shruti; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2024-12-01)
    Named Entity Recognition (NER) is a pivotal technique for extracting structured information from unstructured or semi-structured data. This project focuses on applying deep learning (DL) tech niques to NER for ingredient ...
  • Spoofed speech detection 
    Kushwaha, Akash; Venaik, Utkarsh; Shah, Rajiv Ratn (Advisor) (IIIT-Delhi, 2024-11-26)
    With the increasing capabilities of artificial intelligence, the risk of impersonation and deepfakes has grown, particularly in audio, where ”spoofing attacks” involve altering or faking voices to deceive systems or ...
  • Leveraging temporal pupil dynamics and deep learning for interpretable ADHD diagnosis 
    Sharma, Swati; Shukla, Jainendra (Advisor); Ray, Sonia Baloni (Advisor); Chakrabarty, Mrinmoy (Advisor) (IIIT-Delhi, 2024-12-13)
    Attention-deficit/hyperactivity disorder (ADHD) is the most common behavioral condition and the second most common chronic illness in children. This neurodevelopmental disorder characterized by persistent patterns of ...
  • Fine-grained hardness of lattice problems 
    Ali, Farhan; Bera, Debajyoti (Advisor); Kumar, Rajendra (Advisor) (IIIT-Delhi, 2024-11-27)
    A lattice is a discrete set of points that follow a repeating arrangement of points and more formally defined as a discrete additive subgroup (like Zn) of a vector space (usually Rn). The most important computational ...
  • Multiomics data integration tools for biomarkers discovery 
    Rathore, Yatin; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2024-12-13)
    Biomarkers are essential tools in modern medicine and research, enabling early disease detection, precise classification, and tailored therapeutic interventions. The integration of multi-omics data has transformed biomarker ...
  • Characterising noise in ontologies 
    Girdhar, Aditya; Goyal, Ananya; Mutharaju, Raghava (Advisor) (IIIT-Delhi, 2024-04-29)
    This thesis presents a comprehensive investigation into the nature and impact of noise on on tologies and the response of neuro-symbolic reasoners to noise. The project aims to develop a mechanism for introducing noise ...
  • Parameterized complexity of graph theoretic problems in fault-tolerant models 
    Longjam, Viviana; Majumdar, Diptapriyo (Advisor) (IIIT-Delhi, 2024-04-29)
    Fault tolerance is a crucial aspect of modern computing systems, ensuring that systems can continue to operate correctly even in the presence of faults or failures. In the case of graph theoretic problems, this translates ...
  • Parameterized complexity of graph problems with matroid constraints 
    Audrik; Majumdar, Diptapriyo (Advisor) (IIIT-Delhi, 2024-12-02)
    This report delves into the parameterized complexity of classical graph problems under matroid constraints. By exploring Independent Stable Set and various vertex-cut problems, we develop efficient algorithms leveraging ...
  • Accurate coresets 
    Ranjan, Sanskar; Shit, Supratim (Advisor) (IIIT-Delhi, 2024-12-04)
    This report shows my work where I introduced a unified framework for constructing accurate coresets in machine learning. Accurate coresets are weighted subsets of the original data that preserve the loss function of a ...
  • Counter speech ranking 
    Gupta, Karan; Akhtar, Md. Shad (Advisor) (IIIT-Delhi, 2024-11-27)
    This thesis presents the development of a novel ranking method for intent-conditioned counter speeches. The primary aim of this thesis is to analyze hate speech on online social platforms and identify the most effective ...
  • Automatic partitioning of parallel programs on heterogeneous multicore processors 
    Kansal, Rohak; Gupta, Samridh; Kumar, Vivek (Advisor) (IIIT-Delhi, 2024-11-27)
    With the advent of hybrid CPUs integrated with GPUs, achieving peak performance and energy efficiency necessitates the effective utilization of all available resources. This project explores workload partitioning strategies ...
  • Federated learning for radio environment mapping 
    Kumar, Lakshay; Sarkar, Shamik (Advisor) (IIIT-Delhi, 2024-11-28)
    My work extends previous work on proactive spatial prediction of radio environments for spectrum sharing by integrating it into a distributed Federated Learning (FL) framework. In this enhanced version of the Federated ...
  • Novel recipe generation using LLMs 
    Mehta, Saurabh; Gupta, Aditya; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2024-11-30)
    Generating novel recipes within specific cuisine constraints is a challenging task that combines creativity with cultural authenticity. This study explores the capabilities of fine-tuned large lan guage models (LLMs), ...
  • navX : online guidance platform 
    Singh, Niharika; Singh, Rajbir (Advisor) (IIIT-Delhi, 2024-11-27)
    This project presents navX, a comprehensive career guidance platform in the form of a website, designed to address the gaps in current systems by providing personalized and accessible career advice to students and parents, ...
  • Visual ocean wave analytics 
    Garg, Navyam; Sharma, Tizil; Anand, Saket (Advisor) (IIIT-Delhi, 2024-12-12)
    Accurate prediction and tracking of ocean wave dynamics especially near the beach side are important for enhancing the safety and experience of surfers. This study presents a deep learning based approach that uses computer ...
  • Chatbot for recipe videos 
    Raman, Param Kumar; Kumar, Rahul; Raj, Ankit; Adarsh (IIIT-Delhi, 2024-11-27)
    This report explores advancements and methodologies in the development and fine-tuning of large language models (LLMs) and their integration into knowledge-intensive tasks. It em phasizes the role of retrieval-augmented ...
  • Dynamic target tracking for quadrotors using liquid neural networks 
    Singal, Akanksha; Roy, Sayan Basu (Advisor) (IIIT-Delhi, 2024-12-02)
    Image Based Visual Servoing (IBVS) and autonomous navigation in robots often rely on phys ical markers for fast detection and estimation of pose and orientation. However, in real-world scenarios, the use of fiducial markers ...
  • Leveraging langchain and generative AI for PDF information retrieval 
    Himani; Singh, Pushpendra (Advisor) (IIIT-Delhi, 2024-11-25)
    One of the biggest challenges when it comes to retrieving information from diversified and complex datasets, such as PDFs, is their unstructured nature and the possibility of re ceiving irrelevant or incorrect responses. ...
  • Alteration in spin transport of a modified magnetic tunnel junction due to resonant tunneling 
    Abhisri, Arya; Ghosh, Ram Krishna (Advisor) (IIIT-Delhi, 2024-04-29)
    This study explores potential improvement in the efficiency of traditional Magnetic Tunnel Junctions (MTJ) through utilisation of an altered double MTJ with a non-magnetic metalllic spacer (tungsten) for resonant tunneling. ...
  • SLIM-SENSE: a resource efficient WiFi sensing framework towards integrated sensing and communication 
    Walecha, Aryan; Maity, Mukulika (Advisor); Bhattacharya, Arani (Advisor); Singh, Vijay Kumar (Advisor) (IIIT-Delhi, 2024-11-27)
    WiFi signals have become pivotal in sensing applications by leveraging Channel State Informa tion (CSI). WiImg, a novel system, utilizes Generative Adversarial Networks (GAN) for image inpainting to perform WiFi sensing ...

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