Special Issue on: "Machine Learning and the Internet of Things"
Prof. Vijay Bhaskar Semwal, NIT Jamshepur, India
Dr. Rubén González Crespo, Universidad Internacional de La Rioja, Spain
Prof. Vijender Kumar Solanki, Institute of Technology and Science, Ghaziabad, India
Dr. Tyson T. Brooks, Syracuse University, USA
Machine learning through computer systems, which propagates from network to network, is at the heart of computer intelligence. Machine learning is the key to simplifying the definition of a problem-solving platform. Basically, it is a mechanism for pattern search and building intelligence into a computer (e.g. machine) to be able to learn, implying that it will be able to do better in the future from its own experience.
This special issue aims to present machine learning research pertaining to the Internet of Things (IoT). Machines learning from IoT devices, networks and data, in particular to detect and unveil possible hidden structures and regularity patterns associated with their generation mechanism, is important. This issue will promote analysis and understanding of the nature of the machine learning data, which can be used to make predictions for future decisions and actions for computer processing. Its objective is to develop and publish efficient algorithms for designing models and analysis for machine learning prediction and to present research on how to analyse data for such applications in a way that meets demands for algorithms to be computationally efficient and at the same time robust in their performance.
The issue will carry revised and substantially extended versions of selected papers presented at 2nd International Conference on Research in Intelligent and Computing in Engineering (RICE-2017), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include, but are not limited to, the following:
- Internet of Things
- Smart cities
- Big data
- Machine learning
- Medicine, health, bioinformatics and systems biology
- Industrial and engineering applications
- Security applications
- Game playing and problem solving
- Intelligent virtual environments
- Economics, business and forecasting applications, etc.
- Distributed and parallel learning algorithms and applications
- Feature extraction and classification
- Neural networks
- Theories and models for plausible reasoning including:
- computational learning theory
- cognitive modelling
- Hybrid learning algorithms
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.
If you have any queries concerning this special issue, please email the Guest Editors at:
Vijender Kumar Solanki: firstname.lastname@example.org
Ruben Gonzalez: email@example.com
Vijay Bhasker Semwal: firstname.lastname@example.org
Manuscripts due by: 15 May, 2017
Notification to authors: 15 July, 2017
Final versions due by: 15 September, 2017