This project presents a comprehensive framework to address class imbalance in the PTB-XL electrocardiogram (ECG) dataset by integrating diffusion models with state-of-the-art deep learning architectures, including 1D-CNN-LSTM ...

Recent advancements in text-to-speech (TTS) technology have made synthetic speech sound more natural and expressive. However, creating audiobook narration that accurately captures emotions and maintains context over multiple ...

Effective multi-robot search and rescue hinges on rapid exploration, robust situational awareness, and judicious use of scarce bandwidth. This paper introduces a hybrid coordination architecture that combines centralised ...

Medical information from various sources has to be integrated and compiled together to provide a better understanding of a patient’s health. Mappings between different standards and domain ontologies are constructed to ...

In response to the escalating demand for the generation of novel and diverse cooking recipes, this research in Natural Language Processing introduces a new tool—Ratatouille. The tool utilizes various Deep Learning models, ...

The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intel- ligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, ...

This BTech project aims to explore the use of two pivotal concepts in machinelearning: coresets and federated learning. Leveraging insights from the AIsummer school hosted by the esteemed advisors in these domains, this ...

Traditional cooking recipes follow a structured format that can be effectively modelled by analysing and accurately representing the rules and semantics of different sections within the recipe text. In this paper, we propose ...

With growing diversity in personal food preference and regional cuisine style, personalized information systems that can transform a recipe into any selected regional cuisine style that a user might prefer would help food ...

In multi-robot systems, maintaining joint action consistency is crucial, especially when individual robots have inconsistent beliefs about the world. This thesis explores two methods to address this challenge. The first ...