Special Issue "Big Data and Cognitive Computing: Feature Papers 2018"
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: 10 December 2018
Prof. Dr. Min Chen
Embedded and Pervasive Computing (EPIC) Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China
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Interests: big data analytics; cognitive systems; mobile cloud computing; IoT Sensing; cyber-physical systems; 5G networks; SDN; healthcare big data; emotion communications and robotics
Prof. Dr. Kai Hwang
This is a Special Issue of high quality papers (original research articles or comprehensive review papers) in open access format, by the Editorial Board Members, or those invited by the Editorial Board Members and the Editorial Office. Papers will be published, free of charge, after peer review. The scope of “Big Data and Cognitive Computing” includes, but is not limited to, the following items:
Big data, Clouds and Internet of Things (IoT)
- data storage and management
- data search and mining
- big data infrastructure and systems
- big data processing and analytics
- big data applications in science, Internet, finance, telecommunications, business, medicine, healthcare, government, transportation, industry, manufacture, etc.
- big data integrity and privacy
- big data models, algorithms, and architectures
- cloud computing and big data platform
- cloud services and big data applications
- IoT technologies for big data collections
- IoT sensing and cognitive IoT
- data-driven IoT intelligent applications
- 5G network and wireless big data
- machine learning and its applications in medicine, biology, industry, manufacturing, security, education, etc.
- deep learning
- artificial intelligence
- affect/emotion/personality/mind computing
- cognitive modeling
- cognitive informatics
- cognitive sensor-networks
- cognitive robots
- application of cognitive computing in health monitoring, intelligent control systems, bioinformatics, smart manufacturing, smart grids, image/video and signal processing, etc.
- robots and control systems
- natural language processing
- human–machine/robot interaction
Prof. Dr. Min Chen
Prof. Dr. Kai Hwang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Authors: Mahmut Taha Yazici, Shadi Basurra and Mohamed Medhat Gaber
Affiliation: School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7BD, UK
Abstract: Machine learning has traditionally been the solely performed on servers and high-performance machines. But advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gulf between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the now available tools, the opportunity has arisen to extract great value in having on-device machine learning for IoT devices. Doing ML inference on edge devices has huge potential and is still in its early stages. However, it's already more powerful than most realize. In this paper, a step forward has been taken to understand the feasibility of running Machine Learning algorithms, both training and inference, on a Raspberry Pi, an excellent system to use as the processing core of IoT devices. Three different algorithms: Random Forests, Support Vector Machine and Multi-Layer Perceptron respectively, have been tested using ten diverse data sets on the Raspberry Pi to observe their performance in terms of speed, accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in speed and more efficient in power consumption, but the Random Forest algorithm took over in accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.