Glaucoma Detection in Patients using Machine Learning Algorithms

Abstract

Land registry systems play a vital role in establishing and verifying land ownership, which is necessary for assuring the protection of ownership rights. The existing land registry systems, however, have a variety of problems, including a lack of transparency, fraud, and inefficiency, which can lead to conflicts, bogus claims, and a lack of confidence in the land registry system. In order to get around these issues and provide a more trustworthy, open, and effective land registry system, this research offers a blockchain-based AI-enabled land register system. Blockchain technology can be used to offer a tamperproof record of all land ownership transactions, eliminating the need for middlemen and reducing the risk of corruption. AI can automate the verification and processing of land ownership transactions as well as offer predictive modeling to find and prevent incorrect claims, increasing the overall efficacy and integrity of the land registration system. This paper explores the potential of a blockchain-based AI-enabled land registry system, weighs its benefits and drawbacks, and makes recommendations for additional research and potential areas of application. The proposed solution may produce a more transparent and trustworthy land registration system, which would benefit both people and the economy as a whole. The proposed approach exhibits remarkable advancements, boasting over a 650% increase in scalability. Not only does it offer the most accurate models, with LightGBM and XGBoost achieving an impressive 98% accuracy, but it also outperforms other models in terms of precision, recall, and F1-score. LightGBM stands out as the top-performing model, with 95% precision, 97% recall, and 96% F1-score. In terms of log loss, the model also performs exceptionally well, with LightGBM displaying a log loss score of less than 0.1. This suggests that the model’s predictions are highly accurate. The results highlight the proposed approach’s effectiveness and superior performance across a range of evaluation metrics.