http://repository.iiitd.edu.in/xmlui/handle/123456789/1 CSE 2026-09-06T00:43:56Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2102 Title: Generative genomics Authors: Ali, Syed Yasser; Sethi, Tavpritesh (Advisor) Abstract: 1. Generative genomics leverages computational models, particularly transformer-based architectures, to create synthetic genomic data. This study explores their application through a pipeline for gene expression analysis, synthetic data generation, and dataset augmentation, enabling the identification of genes previously missed due to low sample sizes. We compared a transformer-based model, ReALTabFormer, with a statistical model, Gaussian Mixture Model (GMM), for synthetic data generation. 2. The pipeline, implemented in three modular steps—Data Downloader, Synthetic Data Generation, and Data Analysis—supports independent execution or seamless integration using a single GSE_ID. It downloads gene expression data from platforms (GPL10558, GPL570, GPL6480), generates augmented datasets with GMM, and performs enrichment analysis on both original and augmented data. Additionally, an interactive tool was developed to process 384 embeddings derived from abstracts of 15,471 GSE IDs. The tool enables term- or disease-specific searches, retrieves related GSE IDs, visualizes relationships using 3D PCA plots, and generates cosine similarity heatmaps. These functionalities were extended into a fully functional website that integrates data downloading, synthetic data generation, and analysis, though it is not currently hosted. 3. A Retrieval-Augmented Generation (RAG) system was prototyped for Tuberculosis, Ovarian Cancer, and Breast Cancer, integrating KEGG genes, KEGG drugs, and TDT data and capturing genes using NER from Abstracts of Papers available on Pubmed. This system uses both JSON query based approach and Knowledge Graph-based approaches, powered by Llama 3.1 (70B). Current efforts are focused on refining the RAG system only for Tuberculosis to optimize its methodology then further extend it. This research demonstrates the potential of transformer models in synthetic data generation, addressing challenges of scalability and data quality, and advancing functional genomics and disease research. 2024-11-26T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2101 Title: Magical interaction in XR Authors: Kumar, Prince; Kumar, Ayush; Ansari, Mohammad Areeb; Srivastava, Anmol (Advisor) Abstract: This project introduces a novel Magical Interaction in XR, enabling users to create floating platforms in real-time using intuitive hand gestures. These platforms act as sequential stepping stones, blending walking and flying for creative navigation in virtual environments. Features like teleportation and object manipulation enhance adaptability, making it more immersive while ensuring user engagement. By integrating magical elements into XR interaction, this system redefines locomotion, making virtual experiences more interactive, engaging, and intuitive for users. 2024-11-27T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2100 Title: Deterministic coreset project Authors: Srivastava, Atharv; Shit, Supratim (Advisor) 2024-11-27T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2099 Title: Ethics of artificial intelligence surveillance Authors: Babu, Ishwar; Mehroliya, Om; Kumar, Manohar (Advisor) Abstract: This BTech project critically examines the ethical dimensions of Artificial Intelligence (AI) surveillance technologies in contemporary governance and policing systems. AI surveillance, en compassing tools such as facial recognition, predictive policing, and data analytics, has emerged as a double-edged sword—promising advancements in crime prevention and resource allocation while posing significant risks to individual rights, privacy, and societal equity. The study investigates the ethical challenges associated with AI surveillance, including algo rithmic bias, lack of transparency, accountability dilemmas, and socio-economic disparities rein forced by these technologies. Drawing on international case studies such as the Chicago Police Department’s Strategic Subject List and the deployment of facial recognition systems in India, the research highlights how historical and structural biases are perpetuated by predictive algo rithms. It also considers global regulatory approaches, contrasting the stringent data protection frameworks of the European Union with the extensive yet controversial surveillance practices in China. To address these challenges, the project evaluates existing ethical guidelines and proposes a balanced framework for AI surveillance that prioritizes fairness, accountability, and respect for human rights. By incorporating community engagement, transparency mechanisms, and robust oversight, the framework aims to mitigate the ethical pitfalls of AI surveillance while enabling its potential for societal benefit. This report underscores the urgent need for interdisciplinary solutions and adaptive governance to navigate the complex intersection of technological innovation and ethical responsibility in AI-driven surveillance. It advocates for a proactive and inclusive approach to safeguard civil liberties in the age of digital transformation. 2024-11-27T00:00:00Z