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Special Issue "Computational Intelligence and Intelligent Contents (CIIC)"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Prof. Dr. Chang Choi
E-Mail Website
Guest Editor
Computer Engineering, Gachon University, Sungnam, Korea
Interests: intelligent information processing; information security; smart sensor networks
Special Issues and Collections in MDPI journals
Prof. Dr. Hoon Ko
E-Mail Website
Guest Editor
IT Research Institute, Chosun University, Gwangju, Korea
Interests: Cyber-Security; Smart-City; Human relationship based on contexts
Special Issues and Collections in MDPI journals
Prof. Dr. Xin Su
E-Mail Website
Guest Editor
College of IoT Engineering, Hohai University, Changzhou, China
Interests: mobile communication; 5G systems; edge computing/fog computing; Internet of Things applications; and sensor networks
Prof. Dr. Christian Esposito
E-Mail Website
Guest Editor
University of Salerno, Italy
Interests: Distributed Systems; Middleware; Dependability; Ubiquitous Computing and Artificial Intelligence; approaches and techniques to guarantee event deliveries in Internet-scale publish/subscribe services
Special Issues and Collections in MDPI journals

Special Issue Information

There are various mobility scenarios for users, such as along streets or within buildings, where context-related data are of key importance to offer a personalized use of ICT solutions. As a concrete example, a user approaching a specific building in a university campus would like to receive notifications of the classes being held there, or a person moving within a museum would like to be advised of the points of interest in the approaching rooms so as to plan their visit according to their interests. However, within the context of such ubiquitous and pervasive systems, it is possible to face certain problems and troubles caused by the presence of unreliable information, characterized by a certain degree of uncertainty and indecision. The position information is typically computed by a positioning system, and characterized by a certain amount of error. Such an error can determine a wrong computation of the user’s trajectory, causing wrong contextual information being used and incorrect data being provided to the user. The user may use this incorrect data to make wrong decisions. Besides, user inputs to such systems are never precise and numerical, but always vague and expressed in natural language. This demands the introduction of a certain intelligence within the system to manage subjective user inputs and untrustable contextual information. Such a needed computational intelligence may encompass fuzzy and rough set theory, evolutionary and smart computing, as well as approximate reasoning. However, these methods should not be hosted only within the cloud, but closer to the user devices, according to fog/edge computing, to device-intelligent environments dealing with a large amount of interacting nodes and/or generated volumes of data.

Computational Intelligence and Intelligent Content (CIIC) intends to bring together academic researchers and industrial practitioners to report progress in the development of Computational Intelligence and Intelligent Contents. As such, the focus of this Special Issue is related to the methods of Computational Intelligence and Intelligent Contents applied to fog/edge computing, with a focus on its applications in intelligent contents paradigms. We are soliciting contributions on (but not limited to) the following topics of interest (to be possibly extended and/or modified):

  • Computational Intelligence
  • Applied Soft Computing and Fuzzy Logic and Artificial Neural Networks
  • Intelligent Contents Security and Cyber-Security
  • Model Driven Architecture and Meta-Modeling
  • Multimedia Contents Processing and Retrieval
  • Sensors and Networks (wireless ad hoc N/W, Vehicular N/W)
  • Big Data, Intelligence Information Processing
  • Convergence / Complex Contents, Smart Learning
  • Culture Design, Universal Design, UI/UX, Interaction Design and Information Theory
  • Intelligent Contents Design Management, Methodology and Design theory
  • Intelligent Media Contents Convergence / Complex Media
  • Social media and collective intelligence
  • Social Media Big Data Analytics
  • Bio-Information Management and Security
Prof. Dr. Chang Choi
Prof. Dr. Hoon Ko
Prof. Dr. Xin Su
Prof. Dr. Christian Esposito
Guest Editor

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. 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 2200 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.

Published Papers (10 papers)

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Research

Article
A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network
Sensors 2021, 21(8), 2644; https://doi.org/10.3390/s21082644 - 09 Apr 2021
Cited by 1 | Viewed by 495
Abstract
A vital and challenging task in computer vision is 3D Object Classification and Retrieval, with many practical applications such as an intelligent robot, autonomous driving, multimedia contents processing and retrieval, and augmented/mixed reality. Various deep learning methods were introduced for solving classification and [...] Read more.
A vital and challenging task in computer vision is 3D Object Classification and Retrieval, with many practical applications such as an intelligent robot, autonomous driving, multimedia contents processing and retrieval, and augmented/mixed reality. Various deep learning methods were introduced for solving classification and retrieval problems of 3D objects. Almost all view-based methods use many views to handle spatial loss, although they perform the best among current techniques such as View-based, Voxelization, and Point Cloud methods. Many views make network structure more complicated due to the parallel Convolutional Neural Network (CNN). We propose a novel method that combines a Global Point Signature Plus with a Deep Wide Residual Network, namely GPSP-DWRN, in this paper. Global Point Signature Plus (GPSPlus) is a novel descriptor because it can capture more shape information of the 3D object for a single view. First, an original 3D model was converted into a colored one by applying GPSPlus. Then, a 32 × 32 × 3 matrix stored the obtained 2D projection of this color 3D model. This matrix was the input data of a Deep Residual Network, which used a single CNN structure. We evaluated the GPSP-DWRN for a retrieval task using the Shapnetcore55 dataset, while using two well-known datasets—ModelNet10 and ModelNet40 for a classification task. Based on our experimental results, our framework performed better than the state-of-the-art methods. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Analysis of Learning Influence of Training Data Selected by Distribution Consistency
Sensors 2021, 21(4), 1045; https://doi.org/10.3390/s21041045 - 04 Feb 2021
Viewed by 464
Abstract
This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we [...] Read more.
This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Deepint.net: A Rapid Deployment Platform for Smart Territories
Sensors 2021, 21(1), 236; https://doi.org/10.3390/s21010236 - 01 Jan 2021
Cited by 6 | Viewed by 1041
Abstract
This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use [...] Read more.
This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles
Sensors 2021, 21(1), 202; https://doi.org/10.3390/s21010202 - 30 Dec 2020
Cited by 1 | Viewed by 611
Abstract
Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the [...] Read more.
Driver-centered infotainment and telematics services are provided for intelligent vehicles that improve driver convenience. Driver-centered services are performed after identification, and a biometrics system using bio-signals is applied. The electrocardiogram (ECG) signal acquired in the driving environment needs to be normalized because the intensity of noise is strong because the driver’s motion artifact is included. Existing time, frequency, and phase normalization methods have a problem of distorting P, QRS Complexes, and T waves, which are morphological features of an ECG, or normalizing to signals containing noise. In this paper, we propose an adaptive threshold filter-based driver identification system to solve the problem of distortion of the ECG morphological features when normalized and the motion artifact noise of the ECG that causes the identification performance deterioration in the driving environment. The experimental results show that the proposed method improved the average similarity compared to the results without normalization. The identification performance was also improved compared to the results before normalization. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks
Sensors 2021, 21(1), 199; https://doi.org/10.3390/s21010199 - 30 Dec 2020
Viewed by 735
Abstract
The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. [...] Read more.
The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics
Sensors 2020, 20(24), 7130; https://doi.org/10.3390/s20247130 - 12 Dec 2020
Cited by 1 | Viewed by 462
Abstract
Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of [...] Read more.
Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
New Service Virtualisation Approach to Generate the Categorical Fields in the Service Response
Sensors 2020, 20(23), 6776; https://doi.org/10.3390/s20236776 - 27 Nov 2020
Cited by 1 | Viewed by 352
Abstract
Software services communicate with different requisite services over the computer network to accomplish their tasks. The requisite services may not be readily available to test a specific service. Thus, service virtualisation has been proposed as an industry solution to ensure availability of the [...] Read more.
Software services communicate with different requisite services over the computer network to accomplish their tasks. The requisite services may not be readily available to test a specific service. Thus, service virtualisation has been proposed as an industry solution to ensure availability of the interactive behaviour of the requisite services. However, the existing techniques of virtualisation cannot satisfy the required accuracy or time constraints to keep up with the competitive business world. These constraints sacrifices quality and testing coverage, thereby delaying the delivery of software. We proposed a novel technique to improve the accuracy of the existing service virtualisation solutions without sacrificing time. This method generates the service response and predicts categorical fields in virtualised responses, extending existing research with lower complexity and higher accuracy. The proposed service virtualisation approach uses conditional entropy to identify the fields that can be used to drive the value of each categorical field based on the historical messages. Then, it uses joint probability distribution to find the best values for the categorical fields. The experimental evaluation illustrates that the proposed approach can generate responses with the required fields and accurate values for categorical fields over four data sets with stateful nature. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization
Sensors 2020, 20(16), 4641; https://doi.org/10.3390/s20164641 - 18 Aug 2020
Viewed by 723
Abstract
Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed [...] Read more.
Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network
Sensors 2020, 20(12), 3491; https://doi.org/10.3390/s20123491 - 20 Jun 2020
Cited by 6 | Viewed by 1082
Abstract
Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown [...] Read more.
Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy. However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself. In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals. First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position. We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis. These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data. Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset. Experimental results show that the proposed method achieves a classification accuracy of 99.11%, 99.74%, and 99.73% in the binary classification of valence and arousal, and, in four-class classification, respectively. We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN. We then verify the best performing shape of both the kernel and input data experimentally. We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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Article
Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia
Sensors 2020, 20(7), 1978; https://doi.org/10.3390/s20071978 - 01 Apr 2020
Cited by 6 | Viewed by 1037
Abstract
Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are [...] Read more.
Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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