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Data Science and Internet of Everything (IoE)
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Data Science and Internet of Everything (IoE)

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 March 2019) | Viewed by 31584

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
Prof. Dr. Hissam Tawfik

E-Mail Website
Guest Editor
School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS16 5LF, UK
Interests: eHealth; IoT; Time-series prediction; predictive analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Panos Liatsis

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: Image processing, pattern recognition, machine learning, sensor systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 11th International Conference on Developments in eSystems Engineering will continue the success of the previous DeSE conferences. The main theme of DeSE2018 is Sensors, Data Science and Internet of Everything (IoE).

DeSE2018 will be held 2–5 September, 2018, in Cambridge, England, UK. It will provide a leading forum for disseminating the latest results in eSystem Development, Data Science and Big Data Research, IoT Development, and Applications, Big Data, Smart City, Smart Health, Smart Living and Smart Home, Health networking, Learning Analytics, Business Intelligence, Cloud Computing. Authors of the selected papers from DeSE 2018 are invited to submit the extended versions of their original papers and contributions regarding the following topics that related to the application in Sensors:

  • Internet of Everything and its applications
  • Advanced in Applications of AI
  • Biomedical intelligence and clinical data analysis
  • Bio-informatics, health informatics and bio-computing
  • Computational intelligence
  • Data mining, machine learning and expert systems
  • Image processing and medical imaging
  • Big data systems, big data algorithms, mining and management, toold and allpications
  • Deep learning methods and techniques

Dr. Abir Jaafar Hussain
Prof. Dhiya Al-Jumeily
Prof. Hissam Tawfik
Prof. Panos Liatsis
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.

Keywords

  • Sensors
  • Big data
  • Machine learning
  • Data science applications
  • Advanced AI

Published Papers (6 papers)

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Research

18 pages, 5319 KiB  
Article
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases
by Iam Palatnik de Sousa, Marley Maria Bernardes Rebuzzi Vellasco and Eduardo Costa da Silva
Sensors 2019, 19(13), 2969; https://0-doi-org.brum.beds.ac.uk/10.3390/s19132969 - 05 Jul 2019
Cited by 66 | Viewed by 5976
Abstract
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue [...] Read more.
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the “locally-interpretable model-agnostic explanations” methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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21 pages, 2507 KiB  
Article
Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis
by Aguinaldo Bezerra, Ivanovitch Silva, Luiz Affonso Guedes, Diego Silva, Gustavo Leitão and Kaku Saito
Sensors 2019, 19(12), 2772; https://0-doi-org.brum.beds.ac.uk/10.3390/s19122772 - 20 Jun 2019
Cited by 18 | Viewed by 5442
Abstract
Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a [...] Read more.
Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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10 pages, 1362 KiB  
Article
A First Implementation of Underwater Communications in Raw Water Using the 433 MHz Frequency Combined with a Bowtie Antenna
by Samuel Ryecroft, Andrew Shaw, Paul Fergus, Patryk Kot, Khalid Hashim, Adam Moody and Laura Conway
Sensors 2019, 19(8), 1813; https://0-doi-org.brum.beds.ac.uk/10.3390/s19081813 - 16 Apr 2019
Cited by 73 | Viewed by 4465
Abstract
In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water [...] Read more.
In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed. However, Underwater wireless communications rely mainly on alternative communication methods such as optical and acoustic, with radio frequencies being an under-exploited method. This research presents real world results conducted in the Leeds and Liverpool Canal for the novel use of the 433 MHz radio frequency combined with a bowtie antenna in underwater communications in raw water, achieving distances of 7 m at 1.2 kbps and 5 m at 25 kbps. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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16 pages, 4534 KiB  
Article
Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images
by Haya Alaskar, Abir Hussain, Nourah Al-Aseem, Panos Liatsis and Dhiya Al-Jumeily
Sensors 2019, 19(6), 1265; https://0-doi-org.brum.beds.ac.uk/10.3390/s19061265 - 13 Mar 2019
Cited by 125 | Viewed by 6625
Abstract
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated [...] Read more.
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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14 pages, 1713 KiB  
Article
Design and Application of Toxic and Harmful Gas Monitoring System in Fire Fighting
by Yufeng Fan, Xiaodong Zhu, Hulin Sui, Haotai Sun and Zhongming Wang
Sensors 2019, 19(2), 369; https://0-doi-org.brum.beds.ac.uk/10.3390/s19020369 - 17 Jan 2019
Cited by 13 | Viewed by 4475
Abstract
In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and [...] Read more.
In recent years, fire accidents in petrochemical plant areas and dangerous goods storage ports in China have shown a trend of frequent occurrence. Toxic and harmful gases are diffused in the scenes of these accidents, which causes great difficulties for fire fighting and rescue operations of fire fighting forces, and consequently, casualties of firefighters often occur. In order to ensure the safety of firefighters in such places, this paper designs a monitoring system of toxic and harmful gases specially used in fire fighting and rescue sites of fire forces, and establishes the transmission network, monitoring terminal and data processing software of the monitoring system of toxic and harmful gases, establishing the danger model of the monitoring area of toxic and harmful gas-monitoring terminal, and the danger model of fire fighters’ working area, fusing the field toxic and harmful gas data, terminal positioning data, and field environmental data, designing the data structure of the input data set and the network structure of the RNN cyclic neural network model, and realizing the dynamic early warning of toxic and harmful gases on site. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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18 pages, 3074 KiB  
Article
Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase
by Bumjoon Jo and Sungwon Jung
Sensors 2018, 18(9), 3032; https://0-doi-org.brum.beds.ac.uk/10.3390/s18093032 - 10 Sep 2018
Cited by 10 | Viewed by 3563
Abstract
With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. [...] Read more.
With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. As the index of HBase only supports a one-dimensional row keys, the spatial data is commonly enumerated using linearization techniques. However, the linearization techniques cannot completely guarantee the spatial proximity of data. Therefore, several studies have attempted to reduce false positives in spatial query processing by implementing a multi-dimensional indexing layer. In this paper, we propose a hierarchical indexing structure called a quadrant-based minimum bounding rectangle (QbMBR) tree for effective spatial query processing in HBase. In our method, spatial objects are grouped more precisely by using QbMBR and are indexed based on QbMBR. The QbMBR tree not only provides more selective query processing, but also reduces the storage space required for indexing. Based on the QbMBR tree index, two query-processing algorithms for range query and kNN query are also proposed in this paper. The algorithms significantly reduce query execution times by prefetching the necessary index nodes into memory while traversing the QbMBR tree. Experimental analysis demonstrates that our method significantly outperforms existing methods. Full article
(This article belongs to the Special Issue Data Science and Internet of Everything (IoE))
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