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Mathematics
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Recent Advances in Computational Intelligence Methodologies for Industries
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Recent Advances in Computational Intelligence Methodologies for Industries

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10505

Special Issue Editors

Dr. Jérôme Mendes

E-Mail Website
Guest Editor
Computational Intelligence Group, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
Interests: fuzzy systems; evolving systems; intelligent control; failure detection
Special Issues, Collections and Topics in MDPI journals
Dr. Alireza Mousavi

E-Mail Website
Co-Guest Editor
Department of Mechanical and Aerospace Engineering, Systems Engineering Research Group, Brunel University, London UB8 3PH, UK
Interests: applied control and automation; system engineering; autonomous systems and robotics; data analytics; machine learning; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Manuel Soares Moura

E-Mail Website
Co-Guest Editor
Institute of Systems and Robotics (ISR-UC), and Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Coimbra, Portugal
Interests: energy efficiency; renewable energy; energy storage; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. António J. Baptista

E-Mail Website
Co-Guest Editor
Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), Porto, Portugal
Interests: product and system development; complex system descriptive modelling; multi-dimensional and agnostic performance framework design; sustainability and circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Morad Danishvar

E-Mail Website
Co-Guest Editor
Department of Mechanical and Aerospace Engineering, Systems Engineering Research Group, Brunel University, London, UK
Interests: data analytics, machine learning and AI; SCADA; digital manufacturing; industry 4.0; decision support systems; simulation and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In all industries, more and more equipment, machines, and tools have several in-built sensors and smart materials that give them the ability to hear, think, learn, and solve problems with minor human intervention. In this context, using advanced systems that comprise several sensors and intelligent materials, the self-diagnosis, self-awareness, and self-healing control capabilities will be strongly present across various industries, with robustness, efficiency, sustainability, resilience, and autonomy capabilities.

Advanced computational intelligence methodologies have attracted huge academic and industry interest in areas from statistics and mathematics to machine learning, resulting in new levels of real applications in several industries, including but not limited to industrial monitorization and control, sustainable industry, internet of things (IoT), smart grids, electrical power and energy systems, robotic, bioengineering, and so on.

This multidisciplinary Special Issue intends to disseminate the recent developments and applications on the topics of advanced computational intelligence methodologies across various industries.

The following application topics are welcome (but are not limited to):

  • Intelligent control and identification;
  • Decision-making systems;
  • KPI monitoring and control;
  • Self-diagnosis, self-awareness, and self-healing control applications;
  • Smart manufacturing;
  • Sustainable industry (performance analysis, lean methodologies, etc.);
  • Smart grids;
  • Power and energy systems;
  • Internet of things (IoT);
  • Cyber-physical systems;
  • Biomedical engineering.

Dr. Jérôme Mendes
Dr. Alireza Mousavi
Dr. Pedro Manuel Soares Moura
Dr. António J. Baptista
Dr. Morad Danishvar
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. Mathematics 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 2600 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

  • fuzzy systems and neural networks
  • deep learning and transfer learning
  • reinforcement learning
  • evolutionary algorithms
  • modern control (adaptive, distributed, predictive, networked, robust, etc.)
  • intelligent and AI-based control and monitoring
  • soft computing algorithms
  • evolving/iterative/self-organizing design
  • decision-making systems
  • soft sensing

Published Papers (5 papers)

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Research

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17 pages, 4602 KiB  
Article
Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare
by Njud S. Alharbi, Hadi Jahanshahi, Qijia Yao, Stelios Bekiros and Irene Moroz
Mathematics 2023, 11(18), 3942; https://doi.org/10.3390/math11183942 - 17 Sep 2023
Cited by 3 | Viewed by 939
Abstract
In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the [...] Read more.
In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Methodologies for Industries)
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23 pages, 544 KiB  
Article
A Binary Black Widow Optimization Algorithm for Addressing the Cell Formation Problem Involving Alternative Routes and Machine Reliability
by Paulo Figueroa-Torrez, Orlando Durán, Broderick Crawford and Felipe Cisternas-Caneo
Mathematics 2023, 11(16), 3475; https://doi.org/10.3390/math11163475 - 11 Aug 2023
Cited by 2 | Viewed by 774
Abstract
The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with [...] Read more.
The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size (Pop) set to 100, and the number of iterations (Maxiter) defined as 75. Procreating Rate (PR) is set at 0.8, Cannibalism Rate (CR) is set at 0.4, and the Mutation Rate (PM) is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature’s best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Methodologies for Industries)
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20 pages, 2019 KiB  
Article
Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation
by Jorge Ribeiro, Pedro Andrade, Manuel Carvalho, Catarina Silva, Bernardete Ribeiro and Licínio Roque
Mathematics 2022, 10(9), 1604; https://doi.org/10.3390/math10091604 - 09 May 2022
Cited by 5 | Viewed by 1889
Abstract
Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of [...] Read more.
Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of Playful Probing approach to obtain insights that allow understanding of how to design for interaction with ML algorithms, (2) the integration of a Reinforcement Learning (RL) agent for Human–AI collaboration in maintenance planning and (3) the visualisation of CBM indicators. Using a design science research approach, we designed a Playful Probe protocol and materials, and evaluated results by running a participatory design workshop. Our main contribution is to show how to elicit ideas for integration of maintenance planning practices with ML estimation tools and the RL agent. Through a participatory design workshop with participants’ observation, in which they played with CBM artefacts, Playful Probes favour the elicitation of user interaction requirements with the RL planning agent to aid the planner to obtain a reliable maintenance plan and turn possible to understand how to represent CBM indicators and visualise them through a trajectory prediction. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Methodologies for Industries)
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Review

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22 pages, 991 KiB  
Review
A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs
by Bárbara de Matos, Rodrigo Salles, Jérôme Mendes, Joana R. Gouveia, António J. Baptista and Pedro Moura
Mathematics 2023, 11(1), 173; https://doi.org/10.3390/math11010173 - 29 Dec 2022
Cited by 4 | Viewed by 1875
Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment [...] Read more.
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Methodologies for Industries)
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19 pages, 518 KiB  
Review
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration
by Jorge Pereira, Jérôme Mendes, Jorge S. S. Júnior, Carlos Viegas and João Ruivo Paulo
Mathematics 2022, 10(3), 300; https://doi.org/10.3390/math10030300 - 19 Jan 2022
Cited by 21 | Viewed by 3667
Abstract
Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that [...] Read more.
Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence Methodologies for Industries)
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