International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Informative Article | Information Technology | India | Volume 11 Issue 6, June 2022 | Rating: 5.6 / 10

The Role of Machine Learning and Artificial Intelligence in Mobile App Development

Amit Gupta [12]

Abstract: Using machine learning (ML) and artificial intelligence (AI) in mobile application development signifies a potential move from manual coding toward a dynamic, data-driven approach. This article investigates the revolutionary potential of incorporating Machine Learning (ML) and Artificial Intelligence (AI) into mobile app development. The conventional manual coding approach gives way to a data-driven process, allowing developers to build tailored experiences, boost performance, and continuously improve mobile applications. The suggested framework provides a disciplined technique for incorporating ML/AI throughout the mobile app development lifecycle. The study initially addresses the limits of the pre-ML/AI age, defined by arduous processes, restricted customization possibilities, and static functionalities. It then offers the recommended technique, describing crucial stages such as data collecting, model creation, app integration, user engagement, and performance optimization. This framework enables real-time inference and tailored user experiences. The literature review quotes AI's transformational influence in various areas, including healthcare, banking, and entertainment, emphasizing the potential for AI-powered mobile apps to redefine user experiences and engagements. However, ethical problems, such as data privacy and algorithmic biases, must be addressed to enable AI inclusion in mobile development. The suggested methodology includes data collection, preprocessing, model creation, mobile app integration, user engagement, and feedback loops. Developers can provide seamless communication between the app and backend services by employing cloud platforms or on-device ML frameworks, allowing for real-time inference and tailored user experiences. Performance improvement and release updates are required to maintain the efficiency and relevance of ML and AI models over time.

Keywords: Machine Learning (ML), Artificial Intelligence (AI), Mobile Application Development, Data-Driven Approach, Manual Coding, User Engagement, Cloud Platforms, On-Device ML Frameworks, Model Development, Model Training, Mobile Inference, ML/AI Frameworks

Edition: Volume 11 Issue 6, June 2022,

Pages: 2006 - 2009

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