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Applications of Computational Intelligence, Volume 2

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 11561

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Dr. Yue Wu

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Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; image processing and pattern recognition; theory and applications of computational intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kai Qin

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Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: machine learning; evolutionary computation; computer vision; services computing; pervasive computing
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Prof. Dr. Maoguo Gong

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Key Laboratory of Intelligent Perception and Image Understanding, Xidian University, Xi'an 710071, China
Interests: computational intelligence; evolutionary computation; neural networks; multi-objective optimization
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Prof. Dr. Qiguang Miao

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Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; machine learning; high performance calculation; big data analysis
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Special Issue Information

Dear Colleagues,

Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, over time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI.

This Special Issue invites researchers to contribute high-quality original research papers and surveys on any aspect of computational intelligence, especially papers that show the power and impact of applications of computational intelligence. The main topics for the Special Issue include, but are not limited to, the following keywords.

Dr. Yue Wu
Prof. Dr. Kai Qin
Prof. Dr. Maoguo Gong
Prof. Dr. Qiguang Miao
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

artificial intelligence
neural networks
evolutionary computation
fuzzy logic and systems
swarm intelligence
deep learning
applications of computational intelligence

Published Papers (11 papers)

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Research

22 pages, 11766 KiB

Open AccessArticle

Discrete Artificial Fish Swarm Algorithm-Based One-Off Optimization Method for Multiple Co-Existing Application Layer Multicast Routing Trees

by Ying Li, Ning Wang, Wei Zhang, Qing Liu and Feng Liu

Electronics 2024, 13(5), 894; https://doi.org/10.3390/electronics13050894 - 26 Feb 2024

Viewed by 478

Abstract

As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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19 pages, 7536 KiB

Open AccessArticle

Fast Prediction Method of Combustion Chamber Parameters Based on Artificial Neural Network

by Chenhuzhe Shao, Yue Liu, Zhedian Zhang, Fulin Lei and Jinglun Fu

Electronics 2023, 12(23), 4774; https://doi.org/10.3390/electronics12234774 - 24 Nov 2023

Cited by 1 | Viewed by 695

Abstract

Gas turbines are widely used in industry, and the combustion chamber, compressor, and turbine are known as their three important components. In the design process of the combustion chamber, computational fluid dynamics simulation takes up a lot of time. In order to accelerate the design speed of the combustion chamber, this article proposes a combustion chamber design method that combines an artificial neural network (ANN) and computational fluid dynamics (CFD). CFD results are used as raw data to establish a fast prediction model using ANN and eXtreme Gradient Boosting (XGBoost). The results show that the mean squared error (MSE) of the ANN is 0.0019, and the MSE of XGBoost is 0.0021, so the ANN’s prediction performance is slightly better. This fast prediction method combines CFD and the ANN, which can greatly shorten CFD calculation time, improve the efficiency of gas turbine combustion chamber design, and provide the possibility of achieving digital twins of gas turbine combustion chambers. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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18 pages, 4375 KiB

Open AccessArticle

Deep-Learning-Based Water Quality Monitoring and Early Warning Methods: A Case Study of Ammonia Nitrogen Prediction in Rivers

by Xianhe Wang, Mu Qiao, Ying Li, Adriano Tavares, Qian Qiao and Yanchun Liang

Electronics 2023, 12(22), 4645; https://doi.org/10.3390/electronics12224645 - 14 Nov 2023

Cited by 1 | Viewed by 1038

Abstract

In line with rapid economic development and accelerated urbanization, the increasing discharge of wastewater and agricultural fertilizer usage has led to a gradual rise in ammonia nitrogen levels in rivers. High concentrations of ammonia nitrogen pose a significant challenge, causing eutrophication and adversely affecting the aquatic ecosystems and sustainable utilization of water resources. Traditional ammonia nitrogen detection methods suffer from limitations such as cumbersome sample handling and analysis, low sensitivity, and lack of real-time and dynamic feedback. In contrast, automated monitoring and ammonia nitrogen prediction technologies offer more efficient methods and accurate solutions. However, existing approaches still have some shortcomings, including sample processing complexity, interference issues, and the absence of real-time and dynamic information feedback. Consequently, deep learning techniques have emerged as promising methods to address these challenges. In this paper, we propose the application of a neural network model based on Long Short-Term Memory (LSTM) to analyze and model ammonia nitrogen monitoring data, enabling high-precision prediction of ammonia nitrogen indicators. Moreover, through correlation analysis between water quality parameters and ammonia nitrogen indicators, we identify a set of key feature indicators to enhance prediction efficiency and reduce costs. Experimental validation demonstrates the potential of our proposed approach to improve the accuracy, timeliness, and precision of ammonia nitrogen monitoring and prediction, which could provide support for environmental management and water resource governance. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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18 pages, 4815 KiB

Open AccessArticle

DFANet: Denoising Frequency Attention Network for Building Footprint Extraction in Very-High-Resolution Remote Sensing Images

by Lei Lu, Tongfei Liu, Fenlong Jiang, Bei Han, Peng Zhao and Guoqiang Wang

Electronics 2023, 12(22), 4592; https://doi.org/10.3390/electronics12224592 - 10 Nov 2023

Viewed by 917

Abstract

With the rapid development of very-high-resolution (VHR) remote-sensing technology, automatic identification and extraction of building footprints are significant for tracking urban development and evolution. Nevertheless, while VHR can more accurately characterize the details of buildings, it also inevitably enhances the background interference and noise information, which degrades the fine-grained detection of building footprints. In order to tackle the above issues, the attention mechanism is intensively exploited to provide a feasible solution. The attention mechanism is a computational intelligence technique inspired by the biological vision system capable of rapidly and automatically catching critical information. On the basis of the a priori frequency difference of different ground objects, we propose the denoising frequency attention network (DFANet) for building footprint extraction in VHR images. Specifically, we design the denoising frequency attention module and pyramid pooling module, which are embedded into the encoder–decoder network architecture. The denoising frequency attention module enables efficient filtering of high-frequency noises in the feature maps and enhancement of the frequency information related to buildings. In addition, the pyramid pooling module is leveraged to strengthen the adaptability and robustness of buildings at different scales. Experimental results of two commonly used real datasets demonstrate the effectiveness and superiority of the proposed method; the visualization and analysis also prove the critical role of the proposal. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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14 pages, 2336 KiB

Open AccessArticle

Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

by Jun Yang, Zilu Wu and Renbiao Wu

Electronics 2023, 12(21), 4459; https://doi.org/10.3390/electronics12214459 - 30 Oct 2023

Cited by 1 | Viewed by 841

Abstract

Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medicine. However, because of their characteristics such as short duration, low intensity, and sparse action units, this makes micro-expression spotting difficult. To address this problem and inspired by object detection methods, we propose a VoVNet-based micro-expression spotting model, driven by multi-scale features. Firstly, VoVNet is used to achieve the extraction and reuse of different scale perceptual field features to improve the feature extraction capability. Secondly, multi-scale features are extracted and fused using the Feature Pyramid Network module, incorporating optical flow features, and by realizing the interactive fusion of fine-grained feature information and semantic feature information. Finally, the model is trained and optimized on CAS(ME)² and SAMM Long Video. The experimental results show that the F1 score of the proposed model is improved by 0.1963 and 0.2441 on the two datasets compared with the baseline method, which outperforms the most popular spotting methods. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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18 pages, 3104 KiB

Open AccessArticle

PSRGAN: Perception-Design-Oriented Image Super Resolution Generative Adversarial Network

by Tao Wu, Shuo Xiong, Hui Liu, Yangyang Zhao, Haoran Tuo, Yi Li, Jiaxin Zhang and Huaizheng Liu

Electronics 2023, 12(21), 4420; https://doi.org/10.3390/electronics12214420 - 27 Oct 2023

Viewed by 915

Abstract

Among recent state-of-the-art realistic image super-resolution (SR) intelligent algorithms, generative adversarial networks (GANs) have achieved impressive visual performance. However, there has been the problem of unsatisfactory perception of super-scored pictures with unpleasant artifacts. To address this issue and further improve visual quality, we proposed a perception-design-oriented PSRGAN with double perception turbos for real-world SR. The first-perception turbo in the generator network has a three-level perception structure with different convolution kernel sizes, which can extract multi-scale features from four

\frac{1}{4}

size sub-images sliced by original LR image. The slice operation expands adversarial samples to four and could alleviate artifacts during GAN training. The extracted features will be eventually concatenated in later 3 × 2 upsampling processes through pixel shuffle to restore SR image with diversified delicate textures. The second-perception turbo in discriminators has cascaded perception turbo blocks (PTBs), which could further perceive multi-scale features at various spatial relationships and promote the generator to restore subtle textures driven by GAN. Compared with recent SR methods (BSRGAN, real-ESRGAN, PDM_SR, SwinIR, LDL, etc.), we conducted an extensive test with a

\times 4

upscaling factor on various datasets (OST300, 2020track1, RealSR-Canon, RealSR-Nikon, etc.). We conducted a series of experiments that show that our proposed PSRGAN based on generative adversarial networks outperforms current state-of-the-art intelligent algorithms on several evaluation metrics, including NIQE, NRQM and PI. In terms of visualization, PSRGAN generates finer and more natural textures while suppressing unpleasant artifacts and achieves significant improvements in perceptual quality. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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22 pages, 4910 KiB

Open AccessArticle

Optimal Scheduling of Emergency Materials Based on Gray Prediction Model under Uncertain Demand

by Bing Li and Qi Liu

Electronics 2023, 12(20), 4337; https://doi.org/10.3390/electronics12204337 - 19 Oct 2023

Viewed by 704

Abstract

In the context of long-term infectious disease epidemics, guaranteeing the dispatch of materials is important to emergency management. The epidemic situation is constantly changing; it is necessary to build a reasonable mechanism to dispatch emergency resources and materials to meet demand. First, to evaluate the unpredictability of demand during an epidemic, gray prediction is inserted into the proposed model, named the Multi-catalog Schedule Considering Costs and Requirements Under Uncertainty, to meet the material scheduling target. The model uses the gray prediction method based on pre-epidemic data to forecast the possible material demand when the disease appears. With the help of the forecast results, the model is able to achieve cross-regional material scheduling. The key objective of material scheduling is, of course, to reach a balance between the cost and the material support rate. In order to fulfil this important requirement, a multi-objective function, which aims to minimize costs and maximize the material support rate, is constructed. Then, an ant colony algorithm, suitable for time and region problems, is employed to provide a solution to the constructed function. Finally, the validity of the model is verified via a case study. The results show that the model can coordinate and deploy a variety of materials from multiple sources according to changes in an epidemic situation and provide reliable support in decisions regarding the dynamic dispatch of emergency materials during an epidemic period. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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20 pages, 6327 KiB

Open AccessArticle

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

by Junkai Yi and Lin Guo

Electronics 2023, 12(16), 3458; https://doi.org/10.3390/electronics12163458 - 15 Aug 2023

Cited by 2 | Viewed by 916

Abstract

The Industrial Internet of Things (IIoT) is used in various industries to achieve industrial automation and intelligence. Therefore, it is important to assess the network security situation of the IIoT. The existing network situation assessment methods do not take into account the particularity of the IIoT’s network security requirements and cannot achieve accurate assessment. In addition, IIoT transmits a lot of heterogeneous data, which is subject to cyber attacks, and existing classification methods cannot effectively deal with unbalanced data. To solve the above problems, this paper first considers the special network security requirements of the IIoT, and proposes a quantitative evaluation method of network security based on the Analytic Hierarchy Process (AHP). Then, the average under-/oversampling (AUOS) method is proposed to solve the problem of unbalance of network attack data. Finally, an IIoT network security situation assessment classifier based on the eXtreme Gradient Boosting (XGBoost) is constructed. Experiments show that the situation assessment method proposed in this paper can more accurately characterize the network security state of the IIoT. The AUOS method can achieve data balance without generating too much data, and does not burden the training of the model. The classifier constructed in this paper is superior to the traditional classification algorithm. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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17 pages, 6411 KiB

Open AccessArticle

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

by Fangchun Dong, Jingbing Li, Uzair Aslam Bhatti, Jing Liu, Yen-Wei Chen and Dekai Li

Electronics 2023, 12(16), 3444; https://doi.org/10.3390/electronics12163444 - 15 Aug 2023

Cited by 6 | Viewed by 1271

Abstract

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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17 pages, 9267 KiB

Open AccessArticle

Hybrid Encrypted Watermarking Algorithm for Medical Images Based on DCT and Improved DarkNet53

by Dekai Li, Jingbing Li, Uzair Aslam Bhatti, Saqib Ali Nawaz, Jing Liu, Yen-Wei Chen and Lei Cao

Electronics 2023, 12(7), 1554; https://doi.org/10.3390/electronics12071554 - 25 Mar 2023

Cited by 15 | Viewed by 1317

Abstract

To solve the problem of robustness of encrypted medical image watermarking algorithms, a zero watermarking algorithm based on the discrete cosine transform (DCT) and an improved DarkNet53 convolutional neural network is proposed. The algorithm targets medical images in the encrypted domain. In this algorithm, DCT is performed on the encrypted medical image to extract 32-bit features as feature 1. DarkNet53, a pre-trained network, was chosen for migration learning for the network model. The network uses a fully connected layer and a regression layer instead of the original Softmax layer and classification layer, changing the original classification network into a regression network with an output of 128. With these transformations, 128-bit features can be extracted from encrypted medical images by this network, and then DCT is performed to extract 32-bit features as feature 2. The fusion of features 1 and 2 can effectively improve the robustness of the algorithm. The experimental results show that the algorithm can accurately distinguish different encrypted medical images and can effectively restore the original information from the encrypted watermarked information under traditional and geometric attacks. Compared with other algorithms, the proposed method demonstrates better robustness and invisibility. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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16 pages, 2123 KiB

Open AccessArticle

Modeling Noncommutative Composition of Relations for Knowledge Graph Embedding

by Chao Xiang, Cong Fu, Deng Cai and Xiaofei He

Electronics 2023, 12(6), 1348; https://doi.org/10.3390/electronics12061348 - 12 Mar 2023

Viewed by 1172

Abstract

Knowledge Graph Embedding (KGE) is a powerful way to express Knowledge Graphs (KGs), which can help machines learn patterns hidden in the KGs. Relation patterns are useful hidden patterns, and they usually assist machines to predict unseen facts. Many existing KGE approaches can model some common relation patterns like symmetry/antisymmetry, inversion, and commutative composition patterns. However, most of them are weak in modeling noncommutative composition patterns. It means these approaches can not distinguish a lot of composite relations like “father’s mother” and “mother’s father”. In this work, we propose a new KGE method called QuatRotatScalE (QRSE) to overcome this weakness, since it utilizes rotation and scaling transformations of quaternions to design the relation embedding. Specifically, we embed the relations and entities into a quaternion vector space under the difference norm KGE framework. Since the multiplication of quaternions does not satisfy the commutative law, QRSE can model noncommutative composition patterns naturally. The experimental results on the synthetic dataset also support that QRSE has this ability. In addition, the experimental results on real-world datasets show that QRSE reaches state-of-the-art in link prediction problem. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, Volume 2)

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