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Applied Sciences
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Intelligent Computing Systems and Their Applications
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Intelligent Computing Systems and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 March 2025) | Viewed by 8496

Special Issue Editor

Prof. Dr. Yongming Li

E-Mail Website
Guest Editor
Department of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: Intelligent information processing and pattern recognition; data fusion and analysis; artificial intelligence and health informatics; artificial intelligence and mode identification; intelligent perception and human data science; biomedical signal and information processing

Special Issue Information

Dear Colleagues,

With lots of sensors applied into many fields, mass information, precise recognition, and flexibility have become integral to present intelligent computing. This poses new challenges in the advancement of intelligent computing systems, including intelligent algorithms, data processing, computing platform, and their applications. Therefore, this Special Issue intends to present new ideas and experimental results in the field of intelligent computing systems, relating to theory, algorithms, software, platforms, and their practical use.

Areas relevant to intelligent computing systems include, but are not limited to, artificial intelligence, big data analytics, complex networks, neural networks, fuzzy systems, machine learning, deep learning and real-world applications, self-organization, emerging or bio-inspired ssystems, global optimization, evolutionary algorithms meta-heuristics, fuzzy computing, and their application in various fields.

Prof. Dr. Yongming Li
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. 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

  • intelligent computing
  • artificial intelligence
  • machine learning
  • deep learning
  • intelligent system
  • computing technology and system
  • artificial intelligence
  • pattern recognition
  • machine vision
  • big data and knowledge discovery
  • applications in industry, agriculture, health, medicine, transportation, security, management, and so on
  • computer networks
  • mobile computing
  • embedded systems
  • distributed systems
  • intelligent learning systems, including algorithm/system design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

14 pages, 831 KiB  
Article
Optimizing Container Placement in Data Centers by Deep Reinforcement Learning
by Hyeonjeong Kim and Cheolhoon Lee
Appl. Sci. 2025, 15(10), 5720; https://doi.org/10.3390/app15105720 - 20 May 2025
Viewed by 214
Abstract
As our society becomes increasingly digitized, the demand for computing power provided by data centers continues to grow; consequently, operating costs are increasing exponentially. Data centers supply virtualized servers to customers, primarily in the form of lightweight containers. Since the number of containers [...] Read more.
As our society becomes increasingly digitized, the demand for computing power provided by data centers continues to grow; consequently, operating costs are increasing exponentially. Data centers supply virtualized servers to customers, primarily in the form of lightweight containers. Since the number of containers to be allocated is fixed, they should be optimally placed on physical servers to minimize the number of required servers and reduce costs. However, current data center operations do not prioritize reducing the number of physical servers through optimized container placement. Instead, containers are distributed across existing servers primarily to maintain stability. Therefore, costs associated with servers, auxiliary facilities, and electricity consumption have increased. To address this issue, we propose an optimization method that ensures economic efficiency without compromising system stability. Specifically, we utilize deep reinforcement learning (DRL), which has been widely applied in various fields, to optimize container placement. Our approach outperforms traditional heuristic algorithms and offers the additional advantage of handling fixed-size inputs, enabling flexible operation regardless of the number of containers. Using DRL in container placement has further reduced the number of servers and operating costs while enhancing overall system flexibility. Full article
(This article belongs to the Special Issue Intelligent Computing Systems and Their Applications)
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25 pages, 2046 KiB  
Article
Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification
by İbrahim Berkan Aydilek, Arzu Uslu and Cengiz Kına
Appl. Sci. 2025, 15(10), 5435; https://doi.org/10.3390/app15105435 - 13 May 2025
Viewed by 258
Abstract
Classification of diseases is of great importance for early diagnosis and effective treatment processes. However, etiological factors of some common diseases complicate the classification process. Therefore, classification of health datasets by processing them with artificial neural networks can play an important role in [...] Read more.
Classification of diseases is of great importance for early diagnosis and effective treatment processes. However, etiological factors of some common diseases complicate the classification process. Therefore, classification of health datasets by processing them with artificial neural networks can play an important role in the diagnosis and follow-up of diseases. In this study, disease classification performance was examined by using Extreme Learning Machine (ELM), one of the machine learning methods, and an opposition-based WSO algorithm with a random opposite-based learning strategy is proposed. Common health datasets: Breast, Bupa, Dermatology, Diabetes, Hepatitis, Lymphography, Parkinsons, SAheart, SPECTF, Vertebral, and WDBC are used in the experimental studies. Performance evaluation was made by accuracy, precision, sensitivity, specificity, and F1 score metrics. The proposed IWSO-based ELM model has demonstrated better classification success compared to the ALO, DA, PSO, GWO, WSO, OWSO metaheuristics, and LightGBM, XGBoost, SVM, Neural Network (MLP), CNN machine and deep learning methods. In the Wilcoxon test, it was determined that IWSO was p < 0.05 when compared to other algorithms. In the Friedman test, it was determined that IWSO was first in the ranking of success compared to other algorithms. The results reveal that the IWSO approach developed with ELM is an effective method for the accurate diagnosis of common diseases. Full article
(This article belongs to the Special Issue Intelligent Computing Systems and Their Applications)
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26 pages, 8628 KiB  
Article
Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-Based Knowledge Discovery Modules
by Gheorghe Grigoras, Răzvan Gârbea and Bogdan-Constantin Neagu
Appl. Sci. 2024, 14(18), 8228; https://doi.org/10.3390/app14188228 - 12 Sep 2024
Cited by 1 | Viewed by 2950
Abstract
The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower plants are more complex to build up than other power plants, the operation [...] Read more.
The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower plants are more complex to build up than other power plants, the operation regimes and maintenance activities become essential for the hydropower companies to optimize their performance, such that including the data-driven approaches in the decision-making process represents a challenge. In this paper, a comprehensive and multi-task framework integrated into a Knowledge Discovery module based on Data Mining to support the decisions of the operators from the control rooms and facilitate the transition from the classical to smart Supervisory Control and Data Acquisition (SCADA) system in hydropower plants has been designed, developed, and tested. It integrates tasks related to detecting the outliers through advanced statistical procedures, identifying the operating regimes through the patterns associated with typical operating profiles, and developing strategies for loading the generation units that consider the number of operating hours and minimize the water amount used to satisfy the power required by the system. The proposed framework has been tested using the SCADA system’s database of a hydropower plant belonging to the Romanian HydroPower Company. The framework can offer the operators from the control room comparative information for a time horizon longer than one year. The tests demonstrated the utility of a Knowledge Discovery module to ensure the transition toward smart SCADA systems that will help the decision-makers improve the management of the hydropower plants. Full article
(This article belongs to the Special Issue Intelligent Computing Systems and Their Applications)
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17 pages, 660 KiB  
Article
Digitalization of Management Processes in Small and Medium-Sized Enterprises—An Overview of Low-Code and No-Code Platforms
by Roman Domański, Hubert Wojciechowski, Jacek Lewandowicz and Łukasz Hadaś
Appl. Sci. 2023, 13(24), 13078; https://doi.org/10.3390/app132413078 - 7 Dec 2023
Cited by 6 | Viewed by 3650
Abstract
The permanent digitization of management processes entails, among other things, a need for the automation of the process of making certain business decisions. The aim of the article is to review and evaluate low-code/no-code platforms used, for instance, in small and medium-sized enterprises, [...] Read more.
The permanent digitization of management processes entails, among other things, a need for the automation of the process of making certain business decisions. The aim of the article is to review and evaluate low-code/no-code platforms used, for instance, in small and medium-sized enterprises, available on the Polish IT market. Using a systematic literature review, an assessment of the scale of the discussed issue, involving the number of publications, detailed topics covered, etc., is provided in the theoretical part of the study. During our research, using grey incidence analysis, a ranking of low-code/no-code platforms is created based on the characteristics that they offer. The article highlights the benefits of using new technologies in the form of low-code/no-code platforms in the management of smaller organizations. Full article
(This article belongs to the Special Issue Intelligent Computing Systems and Their Applications)
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