Deep Learning in Mobile Information Systems
1Namal Institute, Mianwali, Pakistan
2University of Bradford, Bradford, UK
3NUCES-FAST, Lahore, Pakistan
Deep Learning in Mobile Information Systems
Description
Recent advances in technology have led to the massive penetration of mobile computing devices in today’s world. As a result, the research and development of mobile information systems and related disciplines have evolved on a wider scale. Mobile information systems now encompass a broad range of application domains in today’s world, such as healthcare, education, e-commerce, agriculture, security, and social networking.
Machine learning has evolved as a panoramic science for almost all disciplines of computer science, with intelligence being embedded at the core of information systems. Deep learning has successfully enhanced the effectiveness of mobile information systems in recent years to serve many different purposes, including object recognition, fault diagnosis, health monitoring, malware detection, and language translation. Over the last decade, mobile information systems have become more robust, autonomous, and self-organized, making tasks performed through these systems more reliable. Deep learning-based algorithms, models, and techniques, such as convolutional neural networks, probabilistic gradient algorithms, adaptive subgradient methods, and distributed deep learning over cloud methods, have been proposed, implemented, and deployed as the core decision-making engines in these systems.
This special issue invites contributions in the form of original research articles and review articles that are dedicated to exploring the applications of deep learning in relation to mobile information systems.
Potential topics include but are not limited to the following:
- Deep neural networks for visual analysis tasks through mobile information systems
- Deep neural networks for classification tasks in mobile information systems
- Deep neural networks for diagnostic tasks in mobile information systems
- Distributed deep neural networks over cloud for mobile information systems
- Computational issues of the deployment of complex neural networks for mobile clients
- Deep learning-based innovative applications of mobile information systems in different sectors, such as healthcare, agriculture, education, and security