The Decentralized Application for Image Classification

Abstract

The Ethereum platform's decentralised application for data prediction and analysis seeks to fundamentally alter how users interact with their data. This programme offers a safe and user- friendly solution for data administration and analysis by using the power of blockchain technology, smart contracts, and machine learning. Users have the ability to upload their data to the Ethereum blockchain, where it is secured against unauthorised access and secrecy by employing a proprietary encryption technique. The security of user data is ensured throughout the analysis process by this encryption technology. Users may get insightful information from their encrypted data by integrating a machine learning model. In order to protect the privacy of user data, the machine learning model is trained using encrypted data that is kept on the InterPlanetary File System (IPFS). The user is given access to the analysis results in the form of thorough reports, allowing them to base their choices on the study's predictions and findings. The cross-platform architecture used to construct the user interface gives consumers a fluid and simple experience. It makes it simple for users to input their data, request analyses, and examine the outcomes in an intuitive style. The interface is designed to be more user-friendly and accessible and to accommodate users with different degrees of technical skill. The programme delivers improved data security, scalability, and efficiency in managing massive amounts of data because to its decentralised structure. Users may trust the integrity of their data and the accuracy of the analytical findings by getting rid of centralised middlemen and taking use of the openness and immutability of the blockchain. Overall, the Ethereum platform's decentralised application for data analysis and prediction empowers users with safe data management, precise forecasts, and a user-friendly interface, creating new opportunities for data-driven decision-making.