ARTIFICIAL INTELLIGENCE-BLOCKCHAIN INTEGRATED LAND REGISTRATION VERIFICATION SYSTEM USING TREE MINFLD INTEL CLASSIFIER

Abstract

Today, ensuring the authenticity and security of land registration processes is a key challenge in property management systems.Traditional land registration systems often have problems like: fraudulent transactions, lack of transparency and inefficient verification processes as well as the ability to tamper with data.This study proposes an innovative approach which will integrate artificial intelligence (AI) and blockchain technology to deal with these challenges and enhance the verification of land registration records.The framework uses Tree Minfld Intel Classifier to analyze and classify land ownership data, which ensures that only land titles with legitimacy are properly identified.Data is fed in to be processed using Norma Lytix ScalerTrain. Normalizing and scaling the data set will thus increase the performance of our AI models.From transaction records the data set is generated. It must be split into a train-test split, which separates it into training sets and testing sets to gauge the model.After the split, the Tree Minfld Intel Classifier is trained based on processed data to identify patterns of legitimate and false land registrations.Then the trained model is used for prediction, classifying new land registration records as either accurate or questionable.Once the predictions have been made, the system interfaces with a blockchain node in order to store the verified land registration data safely.It uses smart contracts to record ownership details and the results of verification. It is impossible for anyone to modify this ledger which is tamper-proof but transparent and guaranteed against fraud.One of the essential problems with traditional land registers is that there is a possibility of unauthorized changes or disputes over ownership. To deal with this, an immutable block hash point is generated which serves as a cryptographic proof to ensure the data's authenticity and integrity.This mechanism makes any unauthorized changes in the information stored immediately detectable, thereby improving trust and safety.The whole experiment was done in a Python environment and used the transaction dataset to perform AI-based classification, integrate blockchain technology.Various python-based libraries and frameworks were employed to handle data preprocessing, model training, blockchain interactivity and evaluation of results.In conclusion, the integration of AI and blockchain system provides a completely decentralized way to verify land registrations that is resistant against fraud and transparent. This approach is especially beneficial for government agencies, business in real estate markets and legal institutions as it minimizes disputes, enhancing trust and ensuring careful dealings with property problems.With both traditional risks like omitting the details (causing fake documents to be generated by corrupt officials) and the need for a centralized validation mechanism to hand over land records, this model improves the reliability, efficiency and transparency of land register verification.The integration of Tree Minfld Intel Classifier-based AI prediction with immutable block hash points means that land records are still secure, verifiable and resistant to tampering.