Connect to Build

Name
Project - AI Powered NFT Platform using Cardano 1.Building AI-generated artwork through tools like Eponym 2.Storefront The dashboard provides collective information of an NFT: its owners, preview or price history. 3. Advanced Token Search 4.Supports tagging and category management for advanced search functionalities 5. Filters Facilitates easy navigation, enabling users to select items by payment method, category, listing status, and collection. 6. Creating Listings Allows users to create and maintain the collection of its NFTs, along with token information such as name, tags, description. 7.Bidding Option 7.Users can bid for NFTs listed on the platform 8.Wallet Integration(Optional) Wallets are required for storing, sending, and receiving non-fungible tokens. Ratings Provides an idea of the trending or in-demand NFTs In progress
Project - Differential Privacy on Genomic Datasets using IBM library - diffprivlib Key objective is to get familiarization on Differential privacy concepts and apply it specifically on the use case related to Health sector using IBM diffprivlib library In progress
Project - Secure messaging by redacting sensitive data Apply AI and other modern algorithmic techniques to solve this problem for secure data exchange In progress
Project - Converting Text to a Mental Map or a Diagram Apply AI and other modern algorithmic techniques to solve this problem for easy consumption of data by humans In progress
Project - Text Classification using unlabelled data and weak supervision using domain knowledge The objective is to build a supervised model with weakly labeled data. The model will consider domain information to improve the classification performance. In progress
Project - Regression Rules for Predicting Business Process Execution time Business process predictive analysis provides aims to improve operations by predicting remaining time of a partial running process instance and thus identifying delays in the process before they can occur. However, for high stake short running process instances, it is important to identify risks at the start of the process instance. We attempt to mine rule-based decisions from historical instance. The objective of this work is to evaluate existing rule induction methods and compare them with neural network based rule extraction methods. In progress