Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data

Dear Colleagues,

Over the past decade, the rise of machine learning (ML) and deep learning (DL) evolved in various areas of life, especially in medicine, cyber security, finance, and education. In machine learning, the development of ensemble methods has gained significant attention from the scientific community. Machine learning ensemble methods combine multiple learning algorithms to obtain a better predictive performance than could be obtained from any of the constituent learning algorithms alone.

On the other hand, deep learning methods have actively been extended to other parts of machine learning, including reinforcement learning and transfer/meta-learning. At the same time, standard deep learning methods, such as convolutional neural networks (CNNs), have also been extensively studied and applied to diverse industrial fields. The training of these networks depends upon the data. The sensing data could be collected through sensors, images, actuators, virtual learning environments (for education), and IoT devices.

The aim of this Special Issue is to examine the latest theoretical and practical applications of deep and ensemble learning in various fields. Furthermore, we seek to contribute to the demonstration of new algorithms and application domains of deep learning to solve problems in various research areas. We will promote research and development of deep learning for multimodal data by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

Dr. Kamran Shaukat
Dr. Suhuai Luo
Guest Editors

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