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Electronics
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Advances in Intelligent Systems and Networks
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Advances in Intelligent Systems and Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 17247

Special Issue Editors

Dr. Namgi Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Republic of Korea
Interests: mobile networks; networks protocols; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyunsoo Yoon

E-Mail Website
Guest Editor
School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
Interests: AI information service; secure computing; system networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent systems and networks are advanced technologies that perceive and respond to the world around them. Intelligent systems and networks can take many forms and continually communicate and interact with their environments. The study of how systems can understand and process information emerged in the 1960s and has since grown into an important technology that is central to the industrial, social, and academic areas. The network technology has also grown exponentially due to the tremendous success of Internet services. This Special Issue focuses on advances in intelligent systems and networks. Potential topics include but are not limited to intelligent systems and networks for:

  • Advanced networks
  • Big data systems
  • Cognitive systems
  • Computational intelligence
  • Intelligent pattern recognition
  • Intelligent systems
  • Intelligent control
  • Intelligent IoT and IIoT
  • Intelligent image processing
  • Machine learning
  • Multimedia systems

Prof. Dr. Namgi Kim
Prof. Dr. Hyunsoo Yoon
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. Electronics 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

  • advanced networks
  • artificial intelligence
  • big data
  • computational intelligence
  • image processing
  • intelligent systems
  • IoT
  • multimedia systems

Related Special Issue

Published Papers (7 papers)

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Research

19 pages, 6821 KiB  
Article
Interactivity Recognition Graph Neural Network (IR-GNN) Model for Improving Human–Object Interaction Detection
by Jiali Zhang, Zuriahati Mohd Yunos and Habibollah Haron
Electronics 2023, 12(2), 470; https://doi.org/10.3390/electronics12020470 - 16 Jan 2023
Cited by 2 | Viewed by 1717
Abstract
Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction [...] Read more.
Human–object interaction (HOI) detection is important for promoting the development of many fields such as human–computer interactions, service robotics, and video security surveillance. A high percentage of human–object pairs with invalid interactions are discovered in the object detection phase of conventional human–object interaction detection algorithms, resulting in inaccurate interaction detection. To recognize invalid human–object interaction pairs, this paper proposes a model structure, the interactivity recognition graph neural network (IR-GNN) model, which can directly infer the probability of human–object interactions from a graph model architecture. The model consists of three modules: The first one is the human posture feature module, which uses key points of the human body to construct relative spatial pose features and further facilitates the discrimination of human–object interactivity through human pose information. Second, a human–object interactivity graph module is proposed. The spatial relationship of human–object distance is used as the initialization weight of edges, and the graph is updated by combining the message passing of attention mechanism so that edges with interacting node pairs obtain higher weights. Thirdly, the classification module is proposed; by finally using a fully connected neural network, the interactivity of human–object pairs is binarily classified. These three modules work in collaboration to enable the effective inference of interactive possibilities. On the datasets HICO-DET and V-COCO, comparative and ablation experiments are carried out. It has been proved that our technology can improve the detection of human–object interactions. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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18 pages, 26150 KiB  
Article
A Multilayered Audio Signal Encryption Approach for Secure Voice Communication
by Hanaa A. Abdallah and Souham Meshoul
Electronics 2023, 12(1), 2; https://doi.org/10.3390/electronics12010002 - 20 Dec 2022
Cited by 8 | Viewed by 2311
Abstract
In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems [...] Read more.
In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the cryptosystems rely on the values of audio samples, which allows for saving time while increasing their resistance to hackers and environments with a noisy background. The main feature of the proposed approach is to consider three levels of encryption namely fusion, substitution, and permutation where various combinations are considered. The resulting cryptosystems are compared to the one-dimensional logistic map-based encryption techniques and other state-of-the-art methods. The performance of the suggested cryptosystems is evaluated by the use of the histogram, structural similarity index, signal-to-noise ratio (SNR), log-likelihood ratio, spectrum distortion, and correlation coefficient in simulated testing. A comparative analysis in relation to the encryption of logistic maps is given. This research demonstrates that increasing the level of encryption results in increased security. It is obvious that the proposed salting-based encryption method and the multilayer DCT/DST cryptosystem offer better levels of security as they attain the lowest SNR values, −25 dB and −2.5 dB, respectively. In terms of the used evaluation metrics, the proposed multilayer cryptosystem achieved the best results in discrete cosine transform and discrete sine transform, demonstrating a very promising performance. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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10 pages, 7613 KiB  
Article
Efficient Vision-Based Face Image Manipulation Identification Framework Based on Deep Learning
by Minh Dang
Electronics 2022, 11(22), 3773; https://doi.org/10.3390/electronics11223773 - 17 Nov 2022
Cited by 1 | Viewed by 2060
Abstract
Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise [...] Read more.
Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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15 pages, 5214 KiB  
Article
Design of Chained Document HTML Generation Technique Based on Blockchain for Trusted Document Communication
by Hyun-Cheon Hwang and Woo-Je Kim
Electronics 2022, 11(7), 1006; https://doi.org/10.3390/electronics11071006 - 24 Mar 2022
Cited by 7 | Viewed by 2069
Abstract
Digital document communication between an enterprise and a customer is becoming a primary form of communication rather than the traditional physical document communication. A PDF document, the most popular document format, provides an identical document layout regardless of OS or device and has [...] Read more.
Digital document communication between an enterprise and a customer is becoming a primary form of communication rather than the traditional physical document communication. A PDF document, the most popular document format, provides an identical document layout regardless of OS or device and has a content integrity verification feature with a digital signature. However, it has a bad user experience, such as low readability on a mobile device. On the other hand, an HTML document has a weakness in verifying the content integrity even though it is the primary document format and provides a good user experience on mobile devices. There are certified document services using blockchain technology, but it is still vulnerable to verifying content integrity. Furthermore, research on the document HTML has proposed the trusted document generation technique by HTML conformance and digital signature; however, this research does not provide content delivery verification, and there is a file size overhead. In this paper, we have developed the chained document HTML by defining HTML conformance, digital signature, and blockchain technology. First, the chained document HTML has to embed all resources and does not allow loading content on-demand. Second, the file is signed by a digital signature, and the signature value is added in the file header. Lastly, the metadata to verify the content integrity is inserted in a blockchain node. We have created the chained document HTML generation and verification experiment environment by Ethereum and Python. We have confirmed that the chained document HTML provides content and delivery integrity verification in the research. We expect the chained document HTML will be widely used in document communication between an enterprise and a customer, especially if the document has sensitive personal information that might have a legal dispute. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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20 pages, 653 KiB  
Article
Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search
by Emad Mabrouk, Yara Raslan and Abdel-Rahman Hedar
Electronics 2022, 11(7), 982; https://doi.org/10.3390/electronics11070982 - 22 Mar 2022
Cited by 2 | Viewed by 2015
Abstract
The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many [...] Read more.
The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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16 pages, 3426 KiB  
Article
Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing
by Seokju Oh, Donghyun Kim, Chaegyu Lee and Jongpil Jeong
Electronics 2022, 11(6), 899; https://doi.org/10.3390/electronics11060899 - 14 Mar 2022
Viewed by 2093
Abstract
Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, [...] Read more.
Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, and communicates with heterogeneous devices, is important to industrial sites and is now indispensable. To ensure the fairness and quality of the IIoT with limited network resources, the network connection of the IIoT needs to be constructed more intelligently. Many studies are being conducted on the efficient use of the resources that are imposed on IIoT devices. Therefore, in this paper, we propose a collaboration optimization method for heterogeneous devices that is based on cloud–fog–edge architecture. First, this paper proposes a knowledge distillation-based algorithm that can collaborate on cloud–fog–edge computing on the basis of distributed control. Second, to compensate for the shortcomings of knowledge distillation, we propose a framework for combining a soft-label-based alarm level. Finally, the method that is proposed in this paper was verified through several experiments, and it is shown that this method can effectively shorten the response time and solve the problems of existing IIoT networks, and that it can be efficiently applied to heterogeneous devices. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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15 pages, 10941 KiB  
Article
Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process
by Eunnuri Cho, Tai-Woo Chang and Gyusun Hwang
Electronics 2022, 11(3), 477; https://doi.org/10.3390/electronics11030477 - 06 Feb 2022
Cited by 10 | Viewed by 3287
Abstract
The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in the manufacturing process can play a major [...] Read more.
The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in the manufacturing process can play a major role in preventing unexpected time and economic losses. However, the extraction of information about the manufacturing process is limited when there are missing values in the data and a data imbalance set. In this study, we improve the quality classification performance by solving the problem of missing values and data imbalances that can occur in the manufacturing process. This study proceeds with data cleansing, data substitution, data scaling, a data balancing model methodology, and evaluation. Five data balancing methods and a generative adversarial network (GAN) were used to proceed with data imbalance processing. The proposed schemes achieved an F1 score that was 0.5 higher than the F1 score of previous studies that used the same data. The data preprocessing combination proposed in this study is intended to be used to solve the problem of missing values and imbalances that occur in the manufacturing process. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks)
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