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Electronics
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Recent Advances and Applications of Computational Intelligence
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Recent Advances and Applications of Computational Intelligence

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2197

Special Issue Editor

Dr. Tsungjung Hsieh

E-Mail Website
Guest Editor
Center for General Education, National Formosa University, Yunlin 632, Taiwan
Interests: engineering optimization; system reliability; data science; computational intelligence; decision and simulation

Special Issue Information

Dear Colleagues,

Computational intelligence is a computing paradigm based on bionics and language, which has shown diverse extensions in theory, design, application, and development. From the basic technical development of the three pillars of technology: neural network, fuzzy system, and evolutionary computing, to the growth of data science and deep learning in recent years, the applications of computational intelligence achieve success in various fields, including environmental sustainability, industrial manufacturing, explainable AI, biological information, financial engineering, medical services, and intelligent machinery, etc.

This Special Issue invites researchers and professional practitioners to contribute high-quality original research papers and technical developments on various aspects of computational intelligence. The submitted papers hope to include technical development and application fields. The main topics of the Special Issue include but are not limited to the following keywords.

Dr. Tsungjung Hsieh
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 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 network
  • evolutionary computation
  • fuzzy logic and methods
  • swarm intelligence
  • optimization method
  • decision support systems

Published Papers (3 papers)

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Research

20 pages, 12138 KiB  
Article
Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm
by Kuei-Hsiang Chao and Thi Thanh Truc Bau
Electronics 2024, 13(7), 1207; https://doi.org/10.3390/electronics13071207 - 25 Mar 2024
Viewed by 457
Abstract
In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA [...] Read more.
In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Computational Intelligence)
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17 pages, 1384 KiB  
Article
Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning
by Junkai Yi and Yongbo Tian
Electronics 2024, 13(5), 973; https://doi.org/10.3390/electronics13050973 - 03 Mar 2024
Viewed by 797
Abstract
Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this [...] Read more.
Insider threats are one of the most costly and difficult types of attacks to detect due to the fact that insiders have the right to access an organization’s network systems and understand its structure and security procedures, making it difficult to detect this type of behavior through traditional behavioral auditing. This paper proposes a method to leverage unsupervised outlier scores to enhance supervised insider threat detection by integrating the advantages of supervised and unsupervised learning methods and using multiple unsupervised outlier mining algorithms to extract from the underlying data useful representations, thereby enhancing the predictive power of supervised classifiers on the enhanced feature space. This novel approach provides superior performance, and our method provides better predictive power compared to other excellent abnormal detection methods. Using only 20% of the computing budget, our method achieved an accuracy of 86.12%. Compared with other anomaly detection methods, the accuracy increased by up to 12.5% under the same computing budget. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Computational Intelligence)
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15 pages, 2667 KiB  
Article
Single-Instruction-Multiple-Data Instruction-Set-Based Heat Ranking Optimization for Massive Network Flow
by Lingling Tan, Yongyue Wang, Junkai Yi and Fei Yang
Electronics 2023, 12(24), 5026; https://doi.org/10.3390/electronics12245026 - 16 Dec 2023
Viewed by 581
Abstract
In order to cope with the massive scale of traffic and reduce the memory overhead of traffic statistics, the traffic statistics method based on the Sketch algorithm has become a research hotspot for traffic statistics. This paper studies the problem of the top-k [...] Read more.
In order to cope with the massive scale of traffic and reduce the memory overhead of traffic statistics, the traffic statistics method based on the Sketch algorithm has become a research hotspot for traffic statistics. This paper studies the problem of the top-k flow statistics based on the Sketch algorithm and proposes a method to estimate the flow heat from massive network traffic using the Sketch algorithm and identify the kth flow with the highest heat by using a bitonic sort algorithm. In view of the performance difficulties of applying multiple hash functions in the implementation of the Sketch algorithm, the Single-Instruction-Multiple-Data (SIMD) instruction set is adopted to improve the performance of the Sketch algorithm so that SIMD instructions can process multiple fragments of data in a single step, implement multiple hash operations at the same time, compare and sort multiple flow tables at the same time. Thus, the throughput of the execution task is improved. Firstly, the elements of data flow are described and stored in the form of vectors, while the construction, analysis, and operation of data vectors are realized by SIMD instructions. Secondly, the multi-hash operation is simplified into a single vector operation, which reduces the CPU computing resource consumption of the Sketch algorithm. At the same time, the SIMD instruction set is used to optimize the parallel comparison operation of the flow table in a bitonic sort algorithm. Finally, the SIMD instruction set is used to optimize the functions in the Sketch algorithm and top-k sorting algorithm program, and the optimized code is tested and analyzed. The experimental results show that the time consumed by the advanced vector extensions (AVX)-instructions-optimized version has a significant reduction compared to the original version. When the length of KEY is 96 bytes, the instructions consumed by multiple hash functions account for less in the entire Sketch algorithm, and the time consumed by the optimized version of AVX is about 67.2% of that in the original version. As the length of KEY gradually increases to 256 bytes, the time consumed by the optimized version of AVX decreases to 53.8% of the original version. The simulation results show that the AVX optimization algorithm is effective in improving the measurement efficiency of network flow. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Computational Intelligence)
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