Publications & Research
My academic contributions and research papers
Machine Learning Approach for Clay Bricks Forecasting
Published in Materials Today: Proceedings, 2022
Abstract
This research paper presents a novel machine learning approach for forecasting the production and demand of clay bricks in the construction industry. The study employs various predictive models and compares their performance to identify the most accurate forecasting method. Results indicate that the proposed approach significantly improves prediction accuracy compared to traditional methods, potentially leading to better resource allocation and reduced waste in the construction sector.
Research Interests
Researching optimal architectural patterns for integrating AI capabilities into enterprise systems, with a focus on scalability, maintainability, and performance optimization.
Exploring advanced patterns for cloud-native architectures, including multi-cloud strategies, serverless computing, and event-driven systems for maximum business agility.
Investigating applications of machine learning for predictive analytics in various industries, with particular interest in forecasting models and their practical implementation.
Studying the ethical implications of artificial intelligence and developing architectural frameworks for responsible AI development, deployment, and governance in enterprise environments.