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Intelligent Systems and Applications of Data Science and Internet of Things Techniques

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 11139

Special Issue Editors

Prof. Dr. Liang-Bi Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
Special Issues, Collections and Topics in MDPI journals
Dr. Tian-Hsiang Huang

E-Mail Website
Guest Editor
Center for Big Data Research, Kaohsiung Medical University, Kaohsiung, Taiwan
Interests: computational statistics; cloud computing; information retrieval; big data analytics; machine learning; Artificial Intelligence (AI)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Applied Sciences on the subject of “Intelligent Systems and Applications of Data Science and Internet of Things Techniques.” Recently, data science (DS) and the Internet of Things (IoT) are primary emerging techniques which are adopted in intelligent systems and applications. Furthermore, thanks to the latest results of research on artificial intelligence (AI), the current systems and applications can count on the intelligence of their operating procedures. In other words, the ongoing development of intelligent systems and applications promotes or improves existing systems and applications by adopting and integrating emerging techniques (such as big data analysis, AI, and IoT). The result is more convenience and benefits for our work and life. This Special Issue will deal with advanced data science, AI, and IoT techniques for intelligent systems and applications. The topics of interest for publication include, but are not limited to:

  • AIoT-based systems applications;
  • AIoT-based systems designs;
  • Artificial intelligence over Internet of Things (AIoT);
  • Autonomous systems;
  • Big/smart data analysis and applications;
  • Computational intelligence;
  • Deep learning-based intelligent systems;
  • Edge and fog computing;
  • Expert systems;
  • Fuzzy systems;
  • Healthcare;
  • Industry 4.0;
  • Intelligent cyberphysical systems;
  • Intelligent networking technology;
  • Intelligent privacy, security, and trust;
  • Intelligent/smart IoT;
  • Machine learning-based intelligent systems;

Prof. Dr. Liang-Bi Chen
Dr. Tian-Hsiang Huang
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. Applied Sciences 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 2400 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

  • artificial intelligence (AI)
  • artificial intelligence over Internet of Things (AIoT)
  • big data analysis
  • data science
  • deep learning
  • expert systems
  • intelligent computing
  • intelligent systems
  • Internet of Things (IoT)
  • machine learning

Published Papers (3 papers)

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Research

15 pages, 1248 KiB  
Article
A Connectivity-Based Clustering Scheme for Intelligent Vehicles
by Zahid Khan, Anis Koubaa, Sangsha Fang, Mi Young Lee and Khan Muhammad
Appl. Sci. 2021, 11(5), 2413; https://doi.org/10.3390/app11052413 - 9 Mar 2021
Cited by 19 | Viewed by 2240
Abstract
The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing [...] Read more.
The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks. Full article
(This article belongs to the Special Issue Intelligent Systems and Applications of Data Science and Internet of Things Techniques)
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14 pages, 913 KiB  
Article
Choice between Surgery and Conservative Treatment for Patients with Lumbar Spinal Stenosis: Predicting Results through Data Mining Technology
by Li-Ping Tseng, Yu-Cheng Pei, Yen-Sheng Chen, Tung-Hsu Hou and Yang-Kun Ou
Appl. Sci. 2020, 10(18), 6406; https://doi.org/10.3390/app10186406 - 14 Sep 2020
Cited by 1 | Viewed by 3599
Abstract
Currently, patients with lumbar spinal stenosis (LSS) have two treatment options: nonoperative conservative treatment and surgical treatment. Because surgery is invasive, patients often prefer conservative treatment as their first choice to avoid risks from surgery. However, the effectiveness of nonoperative conservative treatment for [...] Read more.
Currently, patients with lumbar spinal stenosis (LSS) have two treatment options: nonoperative conservative treatment and surgical treatment. Because surgery is invasive, patients often prefer conservative treatment as their first choice to avoid risks from surgery. However, the effectiveness of nonoperative conservative treatment for patients with LSS may be lower than expected because of individual differences. Rules to determine whether patients with LSS should undergo surgical treatment merits exploration. In addition, without a decision-making system to assist patients undergoing conservative treatment to decide whether to undergo surgical treatment, medical professionals may encounter difficulty in providing the best treatment advice. This study collected medical record data and magnetic resonance imaging diagnostic data from patients with LSS, analyzed and consolidated the data through data mining techniques, identified crucial factors and rules affecting the final outcome the patients with LSS who opted for conservative treatment and ultimately underwent surgical treatment, and, finally, established an effective prediction model. This study applied logistic regression (LGR) and decision tree algorithms to extract the crucial features and combined them with back propagation neural networks (BPNN) and support vector machines (SVM) to establish the prediction model. The crucial features obtained are as follows: reduction of the intervertebral disc height, age, blood pressure difference, leg pain, gender, etc. Among the models predicting whether patients with LSS ultimately underwent surgical treatment, the model combining LGR and the decision tree for feature selection with a BPNN has a testing accuracy rate of 94.87%, sensitivity of 0.9, specificity of 1, and area under the receiver operating characteristic curve of 0.952. Adopting these data mining techniques to predict whether patients with LSS who opted for conservative treatment ultimately underwent surgical treatment may assist medical professionals in reaching a treatment decision and provide clearer treatment. This may effectively mitigate disease progression, aid the goals of precision medicine, and ultimately enhance the quality of health care. Full article
(This article belongs to the Special Issue Intelligent Systems and Applications of Data Science and Internet of Things Techniques)
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17 pages, 6178 KiB  
Article
Automatic Chinese Font Generation System Reflecting Emotions Based on Generative Adversarial Network
by Lu Chen, Feifei Lee, Hanqing Chen, Wei Yao, Jiawei Cai and Qiu Chen
Appl. Sci. 2020, 10(17), 5976; https://doi.org/10.3390/app10175976 - 28 Aug 2020
Cited by 4 | Viewed by 3777
Abstract
Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and [...] Read more.
Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model. Full article
(This article belongs to the Special Issue Intelligent Systems and Applications of Data Science and Internet of Things Techniques)
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