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Applied Sciences
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Artificial Intelligence for Complex Systems: Theory and Applications
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Artificial Intelligence for Complex Systems: Theory and 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 (20 June 2023) | Viewed by 8576

Special Issue Editors

Dr. Alexander Gegov

E-Mail Website
Guest Editor
1. School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
2. Faculty of Engineering, Technical University of Sofia, 1756 Sofia, Bulgaria
Interests: future and emerging technologies; computing; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yiannis Laouris

E-Mail Website
Guest Editor
Future Worlds Center, Nicosia, Cyprus
Interests: neuroscience; fatigue; motor neuron; Renshaw cell; learning
Dr. Endre Kadar

E-Mail Website
Guest Editor
Department of Psychology, University of Portsmouth, Portsmouth, UK
Interests: perceptual control of action in intentional contexts; application of biological strategies in robotics (e.g., chemical searching); exploratory learning in display control tasks; biased random walk analysis of motor learning tasks; mathematical models and philosophical issues in psychology; pictorial space and culture
Dr. Raheleh Jafari

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: artificial intelligence; fuzzy logic; robotics and automation; fashion design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue aims to provide a forum for presenting novel methods, approaches, techniques and algorithms for attaining a cross-fertilisation between the broad fields of complex systems and artificial intelligence.

The transdisciplinary research that the issue focuses on will expand the boundaries of our understanding by investigating the principles and processes that underline many profound problems facing society today. 

Key topics of focus for this special issue include but are not limited to: 

  • Artificial Intelligence
    • data mining and knowledge discovery
    • machine learning and machine reasoning
    • data analytics and data science
    • data driven and knowledge driven decision making
    • data-based and knowledge-based systems
  • Complex Systems
    • complex evolutionary and adaptive systems
    • emergent properties and behaviour in complex systems
    • self-organising collective systems
    • biologically and socially inspired complex systems
    • sociotechnical systems science and engineering

Dr. Alexander Gegov
Dr. Yiannis Laouris
Dr. Endre Kadar
Dr. Raheleh Jafari
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. 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

  • artificial intelligence
  • data mining and knowledge discovery
  • machine learning and machine reasoning
  • data analytics and data science
  • data driven and knowledge driven decision making
  • data-based and knowledge-based systems
  • complex systems
  • complex evolutionary and adaptive systems
  • emergent properties and behaviour in complex systems
  • self-organising collective systems
  • biologically and socially inspired complex systems
  • sociotechnical systems science and engineering

Published Papers (4 papers)

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Research

22 pages, 740 KiB  
Article
A Biologically Inspired Self-Organizing Underwater Sensor Network
by Guannan Li, Yulong Zhang, Yao Zhang, Chao Chen, Zhuoyu Wu and Yang Wang
Appl. Sci. 2023, 13(7), 4330; https://doi.org/10.3390/app13074330 - 29 Mar 2023
Viewed by 1031
Abstract
Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, [...] Read more.
Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol. Full article
(This article belongs to the Special Issue Artificial Intelligence for Complex Systems: Theory and Applications)
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19 pages, 4083 KiB  
Article
Digital Twin Architecture Evaluation for Intelligent Fish Farm Management Using Modified Analytic Hierarchy Process
by Hsun-Yu Lan, Naomi A. Ubina, Shyi-Chyi Cheng, Shih-Syun Lin and Cheng-Ting Huang
Appl. Sci. 2023, 13(1), 141; https://doi.org/10.3390/app13010141 - 22 Dec 2022
Cited by 6 | Viewed by 3570
Abstract
Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers [...] Read more.
Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers for intelligent decisions, thereby providing objective information to monitor and control factors of automatic aquaculture machines and maximize farm production. This paper analyzes the requirements of a digital transformation infrastructure consisting of five-layered digital twins using extensive literature reviews. Thus, the results help realize our goal of providing efficient management and remote monitoring of aquaculture farms. The system embeds cloud-based digital twins using machine learning and computer vision, together with sensors and artificial intelligence-based Internet of Things (AIoT) technologies, to monitor fish feeding behavior, disease, and growth. However, few discussions in the literature concerning the functionality of a cost-effective digital twin architecture for aquaculture transformation are available. Therefore, this study uses the modified analytical hierarchical analysis to define the user requirements and the strategies for deploying digital twins to achieve the goal of intelligent fish farm management. Based on the requirement analysis, the constructed prototype of the cloud-based digital twin system effectively improves the efficiency of traditional fish farm management. Full article
(This article belongs to the Special Issue Artificial Intelligence for Complex Systems: Theory and Applications)
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22 pages, 537 KiB  
Article
Property-Based Quality Measures in Ontology Modeling
by Anita Agárdi and László Kovács
Appl. Sci. 2022, 12(23), 12475; https://doi.org/10.3390/app122312475 - 06 Dec 2022
Cited by 2 | Viewed by 1379
Abstract
The development of an appropriate ontology model is usually a hard task. One of the main issues is that ontology developers usually concentrate on classes and neglect the role of properties. This paper analyzes the role of an appropriate property set in providing [...] Read more.
The development of an appropriate ontology model is usually a hard task. One of the main issues is that ontology developers usually concentrate on classes and neglect the role of properties. This paper analyzes the role of an appropriate property set in providing multi-purpose ontology models with a high level of re-usability in different areas. In this paper, novel quality metrics related to property components are introduced and a conversion method is presented to map the base ontology into models for software development. The benefits of the proposed quality metrics and the usability of the proposed conversion methods are demonstrated by examples from the field of knowledge modeling. Full article
(This article belongs to the Special Issue Artificial Intelligence for Complex Systems: Theory and Applications)
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21 pages, 3862 KiB  
Article
Classification of Valvular Regurgitation Using Echocardiography
by Imayanmosha Wahlang, Sk Mahmudul Hassan, Arnab Kumar Maji, Goutam Saha, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska
Appl. Sci. 2022, 12(20), 10461; https://doi.org/10.3390/app122010461 - 17 Oct 2022
Viewed by 1367
Abstract
Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive [...] Read more.
Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score. Full article
(This article belongs to the Special Issue Artificial Intelligence for Complex Systems: Theory and Applications)
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