Calls for papers

 

International Journal of Ad Hoc and Ubiquitous Computing

 

Special Issue on: "Machine Learning and Deep Learning Methods for the Applications in Ad Hoc and Ubiquitous Computing"

Guest Editors:
Prof. Chi-Hua Chen, Fuzhou University, China
Prof. Hsu-Yang Kung, National Pingtung University of Science and Technology, Taiwan
Prof. Feng-Jang Hwang, University of Technology Sydney, Australia
Dr. Lingjuan Lyu, National University of Singapore, Singapore

Ad hoc and ubiquitous computing has been widely introduced and adopted to collect data for improving the quality of decision making in various areas including engineering, manufacturing, weather monitoring, and transportation (e.g. vehicular ad-hoc networks (VANETs)). However, some arising issues, such as large volumes of data, incomplete and incompatible data sets, noise data, etc., lead to the difficulty of realizing the true value as well as exploiting the full potential. Machine learning and deep learning methods have been extensively used as a powerful tool to perform feature detection/extraction and trend estimation/forecasting in the wireless network applications. The supervised machine learning methods, for example, neural network (NN), convolutional neural network (CNN) (e.g. ResNet, DenseNet, etc.), and recurrent neural network (RNN) (e.g. long-short term memory (LSTM), gated recurrent unit (GRU)), can be exploited in the applications pertinent to prediction and classification, whereas the unsupervised machine learning methods, such as restricted Boltzmann machine (RBM), deep belief network (DBN), deep Boltzmann machine (DBM), auto-encoder (AE), and denoising auto-encoder (DAE), can be utilized for data denoising and model generalization. Furthermore, the reinforcement learning methods, including generative adversarial networks (GANs) and deep Q-networks (DQNs), are also a sophisticated tool for generative networks and discriminative networks to optimize the contesting process in a zero-sum game framework. These well-developed methods can contribute substantially to the performance enhancement of predictions and classifications in the relevant applications, but there are some research issues which require further attention from our research communities.

Subject Coverage
Suitable topics include, but are not limited, to the following:

• Novel supervised machine learning methods for the applications in ad hoc and ubiquitous computing
• Novel reinforcement learning methods for the applications in ad hoc and ubiquitous computing
• Novel optimization methods for machine learning and applications in ad hoc and ubiquitous computing
• Novel unsupervised machine learning methods for the applications in ad hoc and ubiquitous computing
• Novel federated learning methods for the applications in ad hoc and ubiquitous computing
• Novel optimization methods based on swarm intelligence for machine learning and applications in ad hoc and ubiquitous computing

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Manuscripts due by: 1 December, 2020

Notification to authors: 1 May, 2021

Final versions due by: 1 October, 2021