Year-2024 http://repository.iiitd.edu.in/xmlui/handle/123456789/1724 2026-09-02T08:39:23Z 2026-09-02T08:39:23Z Dwivedi, Shivam Grover, Anuj (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2071 2026-09-02T04:52:48Z 2024-11-22T00:00:00Z

Title: The Dot Store - a comprehensive study, UX research and in-depth implementation Authors: Dwivedi, Shivam; Grover, Anuj (Advisor) Abstract: The Dot Store, started in May 2023 as an independent project under the guidance of Dr. Anuj Grover, began as an in-depth business research endeavor involving numerous surveys and interviews to identify needs within the creator economy. This research highlighted significant opportunities, leading to the official launch of The Dot Store as a startup in September 2023. The platform was designed to empower creators by turning their product ideas into reality, han- dling all aspects from design to sales, thus addressing the challenge creators face in monetizing their content. The first creator was onboarded in October 2023, with the first sale marking the beginning of a profitable venture, achieving a max monthly revenue of Rs. 120,000/- and a max net profit of Rs. 34,000/- in August 2024. Throughout this B.Tech project, the focus was on solidifying the business fundamentals, establishing standard operating procedures, and expanding the startup further. The Dot Store operated from 532 in the IIITD Innovation and Incubation Centre at IIITD with a core team of three and an extended network of 12 volunteers till July 2024, afterwards it shifted its operations to working partially out of student startup working space in E-Cell IIITD and partially remote. Furthermore, during this BTP, the venture’s innovative approach and business potential were recognized during its progression through three stages of the television show Shark Tank India, although it did not reach the final round. This report details the development of The Dot Store, reflecting on both the challenges faced and the achievements made in creating a sustainable and empowering platform for creators of all sizes. The project exemplifies the practical application of academic and entrepreneurial principles to create a viable business solution in the modern digital landscape.

2024-11-22T00:00:00Z Aadya Darak, Sumit Jagdish (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2049 2026-08-29T22:00:48Z 2024-12-03T00:00:00Z

Title: Pipeline for end-to-end training of DL-augmented LS channel estimation Authors: Aadya; Darak, Sumit Jagdish (Advisor) Abstract: This work presents an enhanced training strategy for LSDNN (Least Squares Deep Neural Net- work) [1], a lightweight deep neural network designed to improve LS channel estimation in pilot-based OFDM systems. The approach offers two key advantages: (1) it enables LSDNN to be trained using practical data sources, such as transmitted codewords and demapper LLRs, and (2) it achieves superior Bit Error Rate (BER) performance through this end-to-end training. By removing the need for perfect CSI during training, the proposed method simplifies implementation in real-world scenarios and aligns with the goal of deploying the system on hardware such as Software Defined Radios (SDRs). Compared to our baseline model, LSDNN strikes a balance between performance and efficiency, with significantly lower computational complexity, fewer parameters, and reduced memory requirements, while delivering competitive results. The model’s ability to generalize to unseen channel models further underscores its potential for real-world applications. This work demonstrates how deep learning can complement traditional signal processing to create efficient, adapt- able, and high-performance wireless communication systems.

2024-12-03T00:00:00Z Kumari, Annu Ray, Arjun (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2047 2026-08-28T22:00:50Z 2024-12-27T00:00:00Z

Title: DL-based image analysis for CDC crossmatch Authors: Kumari, Annu; Ray, Arjun (Advisor) Abstract: Graft rejection remains a significant challenge in organ transplantation. This study proposes a deep learning (DL)-based framework to assess graft rejection risk by analyzing CDC crossmatch imaging and FISH data. The framework aims to: Develop and optimize DL algorithms for image analysis and cell viability prediction. Train the system on a comprehensive dataset to identify patterns and image features linked to graft rejection. Validate the system’s performance through rigorous testing and comparison with clinical outcomes. Integrate the system seamlessly into the clinical workflow for efficient utilization. Evaluate the clinical impact of the system on patient outcomes, organ allocation, and graft survival rates. This DL-based approach has the potential to revolutionize graft rejection risk assessment, improving accuracy, efficiency, and ultimately, patient outcomes in organ transplantation.

2024-12-27T00:00:00Z Gupta, Aryan Sethi, Tavpritesh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/2046 2026-08-28T22:00:41Z 2024-11-27T00:00:00Z

Title: AMROrbit scorecards for AIIMS dataset Authors: Gupta, Aryan; Sethi, Tavpritesh (Advisor) Abstract: This report has analyzed AMR trends utilizing a dataset from AIIMS hospitals using resistance patterns across organisms, sample types, and demography. Data was preprocessed with parallel computing that enabled handling of large datasets efficiently. The key metrics such as isolation rates, resistance trends were calculated. Resistance patterns over time have been visualized, and the shifts have been noted to be significant in altering susceptibility patterns. Another type of output used in developing these trends included scorecards to summarize such trends with an all-encompassing overview of AMR dynamics. The objective is toward well-informed decisions and more work in this regard of AMR.

2024-11-27T00:00:00Z