Developing Experimental Framework of Cloud-Enabled Mobile App with ML Algorithms
Disciplines
Computer Sciences | Databases and Information Systems | OS and Networks | Systems Architecture
Abstract (300 words maximum)
This study presents an experimental framework for developing a cloud-enabled mobile application incorporating Machine Learning (ML) algorithms. The framework aims to integrate advanced ML techniques into a mobile application architecture to enable real-time data analysis and decision-making capabilities. The proposed system leverages cloud computing infrastructure for data storage and processing, facilitating seamless scalability and accessibility. Key components of the framework include the integration of ML algorithms for predictive modeling and anomaly detection, along with cloud-based storage and communication protocols. The experimental setup encompasses the design and implementation of the mobile application, incorporating features for data visualization, user interaction, and real-time alerts. Through this experimental framework, we explore the potential of cloud-enabled mobile applications in enhancing the effectiveness of ML algorithms for various applications, including water quality monitoring and environmental management.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Dr. Ahyoung Lee
Developing Experimental Framework of Cloud-Enabled Mobile App with ML Algorithms
This study presents an experimental framework for developing a cloud-enabled mobile application incorporating Machine Learning (ML) algorithms. The framework aims to integrate advanced ML techniques into a mobile application architecture to enable real-time data analysis and decision-making capabilities. The proposed system leverages cloud computing infrastructure for data storage and processing, facilitating seamless scalability and accessibility. Key components of the framework include the integration of ML algorithms for predictive modeling and anomaly detection, along with cloud-based storage and communication protocols. The experimental setup encompasses the design and implementation of the mobile application, incorporating features for data visualization, user interaction, and real-time alerts. Through this experimental framework, we explore the potential of cloud-enabled mobile applications in enhancing the effectiveness of ML algorithms for various applications, including water quality monitoring and environmental management.