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Intelligent Computing for Engineering Applications

Submission Deadline: 31 May 2021 (closed) View: 277

Guest Editors

Dr. Prasenjit Chatterjee, MCKV Institute of Engineering, India
Dr. Dragan Pamučar, University of Defence in Belgrade, Serbia
Dr. Çağlar Karamaşa, Anatolian University, Turkey

Summary

Sustainable computing is a rapidly expanding research area spanning over all fields of engineering. With the exponential growth of digital technologies, impacts of climate change and increasing socio-environmental pressures are the major drivers for the strategic changes to any industry. Development and innovations in data collection and computation process greatly indicate that an industrial organisation which operates in a sustainable manner, ultimately leads to the knowledge to amend the entire decision making system and refine the organizational goals. This special issue aims to collect high quality research papers on smart sustainable and intelligent computing models and their applications in solving real time engineering and management problems to provide a forum for the state of the art developments. It aims to cover advanced and multidisciplinary researches on sustainable smart computing. 

The theme of the special issue broadly focuses on innovations in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries. Papers include the list of topics that spans a wide range of topics in smart intelligent systems and computing domains including but not limited to association rule learning, big data analytics, classification tree analysis, computational intelligence and algorithms for sustainability, combinatorial optimization, convolutional neural networks, cloud computing, computational intelligence, fuzzy computing, granular computing, genetic algorithms, data mining and exploration, knowledge-based systems, logistic regression, machine learning, mathematical optimization, multiple-criteria decision-making, operations research and optimization, support vector machines, swarm intelligence, quantum computing to name a few in domains of agriculture, system sciences, logistics, supply chain, manufacturing, healthcare, bioinformatics, power, energy and environmental engineering to name a few.

Keywords

Sustainable computing, big data analytics, fuzzy computing, multiple-criteria decision-making, machine learning, engineering applications

Published Papers

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  • Open Access

    ARTICLE

    A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

    Yuhua Li, Zhiqiang He, Junxia Ma, Zhifeng Zhang, Wangwei Zhang, Prasenjit Chatterjee, Dragan Pamucar
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 239-262, 2022, DOI:10.32604/cmes.2022.016287
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract The current deep convolution features based on retrieval methods cannot fully use the characteristics of the salient image regions. Also, they cannot effectively suppress the background noises, so it is a challenging task to retrieve objects in cluttered scenarios. To solve the problem, we propose a new image retrieval method that employs a novel feature aggregation approach with an attention mechanism and utilizes a combination of local and global features. The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism. After… More >

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    2045

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    1259

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    1

  • Open Access

    ARTICLE

    Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

    Marina Davidović, Tatjana Kuzmić, Dejan Vasić, Valentin Wich, Ansgar Brunn, Vladimir Bulatović
    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 207-226, 2021, DOI:10.32604/cmes.2021.016071
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually. This paper presents a novel method for the detection of road defects. The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology. The defects were categorized in three major groups with the following geometric primitives: points, lines and polygons. The method suggests the detection of point objects from matched point clouds, panoramic images and ortho photos. Defects were mapped as point, line or polygon… More >

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    2703

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    2281

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    1

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    1

  • Open Access

    ARTICLE

    Quantile Version of Mathai-Haubold Entropy of Order Statistics

    Ibrahim M. Almanjahie, Javid Gani Dar, Amer Ibrahim Al-Omari, Aijaz Mir
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 907-925, 2021, DOI:10.32604/cmes.2021.014896
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract Many researchers measure the uncertainty of a random variable using quantile-based entropy techniques. These techniques are useful in engineering applications and have some exceptional characteristics than their distribution function method. Considering order statistics, the key focus of this article is to propose new quantile-based Mathai-Haubold entropy and investigate its characteristics. The divergence measure of the Mathai-Haubold is also considered and some of its properties are established. Further, based on order statistics, we propose the residual entropy of the quantile-based Mathai-Haubold and some of its property results are proved. The performance of the proposed quantile-based Mathai-Haubold More >

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    2418

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    1622

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    1

  • Open Access

    ARTICLE

    High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes

    Luciano Pereira da Silva, Bruno Benato Rutyna, Aline Roberta Santos Righi, Marcio Augusto Villela Pinto
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 699-715, 2021, DOI:10.32604/cmes.2021.014239
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract In this article, we improve the order of precision of the two-dimensional Poisson equation by combining extrapolation techniques with high order schemes. The high order solutions obtained traditionally generate non-sparse matrices and the calculation time is very high. We can obtain sparse matrices by applying compact schemes. In this article, we compare compact and exponential finite difference schemes of fourth order. The numerical solutions are calculated in quadruple precision (Real * 16 or extended precision) in FORTRAN language, and iteratively obtained until reaching the round-off error magnitude around 1.0E −32. This procedure is performed to ensure More >

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    1494

  • Open Access

    ARTICLE

    Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection

    Ruikun Li, Yun Li, Wen He, Lirong Chen, Jianchao Luo
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 381-398, 2021, DOI:10.32604/cmes.2021.016264
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract Anomaly detection is an important method for intrusion detection. In recent years, unsupervised methods have been widely researched because they do not require labeling. For example, a nonlinear autoencoder can use reconstruction errors to attain the discrimination threshold. This method is not effective when the model complexity is high or the data contains noise. The method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal… More >

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    2209

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    1368

  • Open Access

    ARTICLE

    A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm

    Yunliang Huo, Ji Xiong, Zhixing Guo, Qianbing You, Yi Peng
    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 359-379, 2021, DOI:10.32604/cmes.2021.015314
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract Cloud manufacturing is a new manufacturing model with crowd-sourcing characteristics, where a cloud alliance composed of multiple enterprises, completes tasks that a single enterprise cannot accomplish by itself. However, compared with heterogeneous cloud tasks, there are relatively few studies on cloud alliance formation for homogeneous tasks. To bridge this gap, a novel method is presented in this paper. First, a homogeneous cloud task distribution model under cloud environment was constructed, where services description, selection and combination were modeled. An improved leapfrog algorithm for cloud task distribution (ILA-CTD) was designed to solve the proposed model. Different More >

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    2152

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    1368

  • Open Access

    ARTICLE

    Control Charts for the Shape Parameter of Power Function Distribution under Different Classical Estimators

    Azam Zaka, Ahmad Saeed Akhter, Riffat Jabeen, Aamir Sanaullah
    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1201-1223, 2021, DOI:10.32604/cmes.2021.014477
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract In practice, the control charts for monitoring of process mean are based on the normality assumption. But the performance of the control charts is seriously affected if the process of quality characteristics departs from normality. For such situations, we have modified the already existing control charts such as Shewhart control chart, exponentially weighted moving average (EWMA) control chart and hybrid exponentially weighted moving average (HEWMA) control chart by assuming that the distribution of underlying process follows Power function distribution (PFD). By considering the situation that the parameters of PFD are unknown, we estimate them by More >

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    2950

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    1745

  • Open Access

    ARTICLE

    Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

    Annan Jiang, Xinping Guo, Shuai Zheng, Mengfei Xu
    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock.… More >

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    2540

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    1918

  • Open Access

    ARTICLE

    Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures

    Muhammad Riaz, Harish Garg, Hafiz Muhammad Athar Farid, Ronnason Chinram
    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 771-800, 2021, DOI:10.32604/cmes.2021.014174
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract This paper aims to introduce the novel concept of the bipolar picture fuzzy set (BPFS) as a hybrid structure of bipolar fuzzy set (BFS) and picture fuzzy set (PFS). BPFS is a new kind of fuzzy sets to deal with bipolarity (both positive and negative aspects) to each membership degree (belonging-ness), neutral membership (not decided), and non-membership degree (refusal). In this article, some basic properties of bipolar picture fuzzy sets (BPFSs) and their fundamental operations are introduced. The score function, accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy More >

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    3708

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    2366

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    2

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    10

  • Open Access

    ARTICLE

    Power Aggregation Operators and Similarity Measures Based on Improved Intuitionistic Hesitant Fuzzy Sets and their Applications to Multiple Attribute Decision Making

    Tahir Mahmood, Wajid Ali, Zeeshan Ali, Ronnason Chinram
    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1165-1187, 2021, DOI:10.32604/cmes.2021.014393
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract Intuitionistic hesitant fuzzy set (IHFS) is a mixture of two separated notions called intuitionistic fuzzy set (IFS) and hesitant fuzzy set (HFS), as an important technique to cope with uncertain and awkward information in realistic decision issues. IHFS contains the grades of truth and falsity in the form of the subset of the unit interval. The notion of IHFS was defined by many scholars with different conditions, which contain several weaknesses. Here, keeping in view the problems of already defined IHFSs, we will define IHFS in another way so that it becomes compatible with other… More >

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    3191

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    2286

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    2

  • Open Access

    ARTICLE

    Spherical Linear Diophantine Fuzzy Sets with Modeling Uncertainties in MCDM

    Muhammad Riaz, Masooma Raza Hashmi, Dragan Pamucar, Yuming Chu
    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1125-1164, 2021, DOI:10.32604/cmes.2021.013699
    （This article belongs to the Special Issue: Intelligent Computing for Engineering Applications)
    Abstract The existing concepts of picture fuzzy sets (PFS), spherical fuzzy sets (SFSs), T-spherical fuzzy sets (T-SFSs) and neutrosophic sets (NSs) have numerous applications in decision-making problems, but they have various strict limitations for their satisfaction, dissatisfaction, abstain or refusal grades. To relax these strict constraints, we introduce the concept of spherical linear Diophantine fuzzy sets (SLDFSs) with the inclusion of reference or control parameters. A SLDFS with parameterizations process is very helpful for modeling uncertainties in the multi-criteria decision making (MCDM) process. SLDFSs can classify a physical system with the help of reference parameters. We… More >

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    2542

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