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Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II

Submission Deadline: 31 December 2024 View: 390 Submit to Special Issue

Guest Editors

Dr. Danial Jahed Armaghani, University of Technology Sydney, Australia
Dr. Xuzhen He, University of Technology Sydney, Australia
Prof. Pijush Samui, National Institute of Technology Patna, India
Dr. Pouyan Fakharian, Semnan University, Iran
Prof. Jian Zhou, Central South University, China

Summary

In the last two decades, the topic of computational intelligence (CI) has undergone several definitions. Adaptation and self-organization algorithms and implementations that permit or facilitate appropriate behaviours (intelligent behaviour) in complex and dynamic settings are included in the notion of CI. One or more properties of reason, such as generalisation, discovery, association, and abstraction, are said to be present in this computer paradigm, which demonstrates a capacity to adapt to new conditions and learn from them. Many of the issues we face today in the area of engineering are becoming more complicated because of the prevalence of amorphous structures and behaviours, as well as large-scale, low dependability, and a scarcity of shared or comprehensive information. This intricacy necessitated that the scope of CI is widened to highlight adaptability.

 

In order to operate a system similar to human thinking, CI relies on three primary components: artificial neural networks, fuzzy logic, and evolutionary computation, both of which employ machine learning theories to cope with uncertainty. Hybrid CI models have shown a greater performance and application level in numerous fields of engineering than conventional CI models, which had serious limitations such time-consuming human participation and a lack of resilience. Metaheuristic algorithms may be utilised to improve base model hyper-parameters (CI models), adding extra value to frequently used base intelligence approaches.

 

This Special Issue focuses on the creation of unique hybrid intelligence strategies for handling regression, classification, and time series challenges. We invite scholars to submit original research papers that will help to promote ongoing research on the use of emerging CI and hybrid CI systems to assess and solve complex engineering challenges. In addition, state-of-the-art research reports, reviews, and critical evaluations of CI and hybrid CI systems are most welcome.


Keywords

Fuzzy and neuro-fuzzy Systems
Support vector machines-based systems
Genetic algorithm and genetic programming
Deep learning-based techniques
Time series systems
Hybrid artificial neural network systems
Evolutionary algorithms
Theory-guided CI systems
Metaheuristic and optimization algorithms

Published Papers


  • Open Access

    ARTICLE

    Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

    Duc-Dam Nguyen, Nguyen Viet Tiep, Quynh-Anh Thi Bui, Hiep Van Le, Indra Prakash, Romulus Costache, Manish Pandey, Binh Thai Pham
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.056576
    (This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
    Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >

  • Open Access

    ARTICLE

    LoRa Sense: Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

    Bhanu Pratap Reddy Bhavanam, Prashanth Ragam
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052355
    (This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
    Abstract The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantly to the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development. This study provides valuable insights into optimizing wireless communication, paving the way for a more connected and productive future in the mining industry. The IoT revolution is advancing across industries, but harsh geometric environments, including open-pit mines, pose unique challenges for reliable communication. The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency… More >

  • Open Access

    ARTICLE

    Optimizing Bearing Fault Detection: CNN-LSTM with Attentive TabNet for Electric Motor Systems

    Alaa U. Khawaja, Ahmad Shaf, Faisal Al Thobiani, Tariq Ali, Muhammad Irfan, Aqib Rehman Pirzada, Unza Shahkeel
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257
    (This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
    Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

  • Open Access

    ARTICLE

    Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

    Danial Jahed Armaghani, Zida Liu, Hadi Khabbaz, Hadi Fattahi, Diyuan Li, Mohammad Afrazi
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210
    (This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
    Abstract Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s)… More >

  • Open Access

    ARTICLE

    Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

    Shengdong Cheng, Juncheng Gao, Hongning Qi
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 871-892, 2024, DOI:10.32604/cmes.2024.052830
    (This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
    Abstract Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments… More >

    Graphic Abstract

    Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

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