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Big Data and Deep Learning in E-Health
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Big Data and Deep Learning in E-Health

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 2190

Special Issue Editors

Prof. Dr. Abir Hussain

E-Mail Website
Guest Editor
Department of Computer Science, Liverpool John Moores University, Liverpool L3 5UA, UK
Interests: machine learning; data science; ehealth; image processing; data analysis; signal forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dhiya Al-Jumeily OBE

E-Mail Website
Guest Editor
Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: AI-based clinical decision making; medical knowledge engineering; patient safety; human–machine interaction; wearable and intelligent devices and instruments; AI for addressing united nations sustainable development goals; eSystem engineering; air and water pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 14th International Conference on the Developments on eSystems Engineering (DeSE2021) will continue the success of the previous DeSE conferences. The main theme of DeSE2021 is: Sensors, eSystem Development, AI, and Industry 4.0.

DeSE2021 will provide a leading forum for disseminating the latest results in eSystem Development, AI, Sensors, and Industry 4.0. Currently a high level of interest is being generated through the development of a wide end varied range of eSystems. There are many high profile projects, all around the world, seeking to transfer many services and facilities into state-of-the-art electronic technologies. Authors of the selected papers from DeSE 2021 are invited to submit the extended versions of their original papers and contributions regarding the following topics that related to the application in sensors:

  • Advanced robotics;
  • Nanomaterials and energy;
  • Sensors, sensors applications, and sensors platforms;
  • Internet of Everything and its applications;
  • AI and its applications;
  • Biomedical intelligence, image processing, and medical imaging and clinical data analysis;
  • Bio-informatics, health informatics, and bio-computing;
  • Computational intelligence;
  • Decision support systems;
  • Genetic algorithms;
  • Novel data processing and analytics, tools, and systems;
  • Big Data systems, mining and management, tools and applications;
  • Machine Learning, web-based decision making;
  • Deep learning methods and techniques;
  • General session: eSystems engineering.

Other submissions, that are not the extended versions of DeSE2021 conference papers, are also welcome.

Prof. Dr. Abir Hussain
Prof. Dr. Dhiya Al-Jumeily
Guest Editors

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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

22 pages, 3456 KiB  
Article
3D Object Recognition Using Fast Overlapped Block Processing Technique
by Basheera M. Mahmmod, Sadiq H. Abdulhussain, Marwah Abdulrazzaq Naser, Muntadher Alsabah, Abir Hussain and Dhiya Al-Jumeily
Sensors 2022, 22(23), 9209; https://doi.org/10.3390/s22239209 - 26 Nov 2022
Cited by 5 | Viewed by 1421
Abstract
Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, [...] Read more.
Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments. Full article
(This article belongs to the Special Issue Big Data and Deep Learning in E-Health)
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