http://repository.iiitd.edu.in/xmlui/handle/123456789/928 2026-04-10T22:38:17Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1614 Cuisine fusion Gupta, Arsh; Makkar, Kshitij; Bagler, Ganesh (Advisor) 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 companies and professional chefs create new recipes. The aim of the study is to explore computational techniques which can be utilised in order to convert a recipe which belongs to a cuisine (Source cuisine) to another cuisine (Target cuisine) by changing one ingredient from the Original recipe. For ease of understanding we will call the starting recipe from the Source cuisine as Original recipe and the final recipe which belongs to the Target cuisine as the Transformed recipe. There are two major tasks that need to be done in order to change a recipe from one cuisine to another computationally. (1) Swap each ingredient of the Original recipe with the ingredients present in the database and, (2) Classify the recipe based on its ingredients and check which cuisine does the recipe belong to. The Dataset used mainly comprises of labeled corpus of Yummly Dataset recipes. We make use of different Machine Learning, natural Language Processing and Deep Learning techniques to achieve the aim of the study. In recent years, Travel and Tourism has flourished and different ethnicities have started to live in the same countries. Some people feel the need of fusion cuisines. Some people also love to cook their own food and customize the recipes to their need. These types of computational models and studies in the field of Computational Gastronomy are not only research fields, and can also be used to create new recipes and innovation in the food industry. 2023-11-29T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1613 Relaxed concurrent counting bloom filters Mishra, Dhruv; Bajaj, Kshitij; Chatterjee, Bapi (Advisor) Counting Bloom filters are highly space-efficient with a small probability of false positives. Concurrent access to a Counting bloom filter can greatly increase its insertion and query throughput. 2023-11-29T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1601 Personal health record app reward system Singh, Yash Vardhan; Sethi, Tavpritesh (Advisor) The goal of this project is to include a strong incentive system into the Personal Health Record (PHR) Android application in order to improve user engagement and participation. The principal aim is to incentivize users to engage in a range of app-related tasks, including forming new habits, accomplishing daily targets, and submitting physical health assessments. After completing these tasks, users receive points from the integrated reward system, which promotes a sense of advancement and accomplishment. Users get access to different levels by accumulating points inside the tiered reward system. An increasing level is shown by every 100 points, giving the program a gamified feel. These levels serve as a concrete indicator of the users' dedication to health-related activities in addition to denoting their proficiency. The reward system's ability to give customers choice in how they use their accrued points is one of its main benefits. Redeeming points for different advantages, such as buying coupons, improves the app's total value proposition. By allowing users to communicate with one another within the app, the reward system also encourages social engagement and builds a sense of community and support. The incentive system that has been put in place successfully makes the PHR app a dynamic and interesting platform that motivates users to take charge of their health and develop healthy behaviors. By using Google Firebase, the infrastructure is dependable and expandable, which paves the way for further app innovations and improvements. 2023-11-29T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1600 Understanding ASR using speechbrain Arora, Satyam; Shah, Rajiv Ratn (Advisor) Automatic Speech Recognition has been a prominent sector in Computer Science Research for decades, generating thousands of research papers in recent years. It is a complex and evolving field, having intersections with ML, NLP, DL and other prominent AI sectors. Being a Complex (involving many steps), Diverse (lots of ways to implement each step, also differing according to the final task) and Computationally heavy field, it had a relatively smaller practitioner base. With the revolution in the Chip Industry, the problem of computation has been solved. The only problem remains in reducing the complexity so that even amateur computer professionals can start their journey on ASR and increase their depth gradually. SpeechBrain, released in 2019, is the exact solution to that problem. It is an all-in-one and user-friendly toolkit that can be used to learn and develop state-of-the-art speech systems aimed at different Speech-related problems. In this report, I have included chapters that are necessary for having a basic understanding of ASR, a basic knowledge of SpeechBrain Repository, and finally, how I have worked in and around this Repository, changed architectures & developed an interactive Web Application capable of Text Generation & Automatic Speech Recognition using SpeechBrain. 2023-11-28T00:00:00Z