Browsing by Author "Muralikrishnan, Madhuvanti"

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    Final Product Report - Team WannaBe
    Pujari, Akash; Ellaboina, Venkatesh; Peiris, Vibhavi; Pundir, Prachi; Muralikrishnan, Madhuvanti; Veeramachaneni, Nihitha (2022-12-09)
    Do you wish to express yourself artistically? We believe that becoming an engineer, doctor, or lawyer is relatively simple, yet they argue that artists are born, not produced. Here is an AI-powered program that can help you become a singer or a painter! You may have seen other efforts aimed at certain fields like healthcare, hardware design, and so on. We stopped at nothing in the project to investigate if AI can assist us with modest everyday routine activities. We investigated the use of AI/ML technologies to benefit our everyday lives. We have devised strategies to clarify your voice (transform your bathroom singing to studio-recorded audio quality) and reduce background noise from home-recorded meetings (COVID effect!). It may assist you in transforming your sloppy artwork into a gorgeous painting. Of course, this isn't only for artists; it can be used as a 'filter' for Instagram or Snapchat, and it may be quite useful for police agencies when producing drawings from scribbles. The trick is training the model on a large dataset and utilizing a Generative Adversarial Network (the greatest invention in AI), which results in mapping and producing pictures with a high match score based on the training dataset. We have developed a web application that has two segments : i) Wanna be a Painter - This segment allows users to upload grayscale images/sketches or hand drawings to convert them into beautiful paintings. ii) Wanna be a Singer - This segment allows users to upload audio files into the web application and download the augmented audio with crisp and clear voice. Gitlab link - https://code.vt.edu/wannabe
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    Team 2 for End Users
    Paidiparthy, Manoj Prabhakar; Ramanujan, Ramaraja; Teegalapally, Akshita; Muralikrishnan, Madhuvanti; Balar, Romil Khimraj; Juvekar, Shaunak; Murali, Vivek (Virginia Tech, 2023-01-11)
    A huge collection of Electronic Theses and Dissertations (ETDs) has valuable information. However, accessing the information from these documents has proven to be challenging as the process is mostly manual. We propose to build a unique Information Retrieval System that will support searching, ranking, browsing, and recommendations for a large collection of ETDs. The system indexes the digital objects related to the ETD, like documents, chapters, etc. The user can then query the indexed objects through a carefully designed web interface. The web interface provides users with utilities to sort, filter, and query specific fields. We have incorporated machine learning models to support semantic search. To enhance user engagement, we provide the user with a list of recommended documents based on the user's actions and topics of interest. A total of 57,130 documents and 21,537 chapters were indexed. The system was tested by the Fall 2022 CS 5604 class, which had 28 members, and was found to fulfill most of the goals set out at the beginning of the semester.