Special Issue "Intelligent Computing for Engineering Applications"

Submission Deadline: 31 May 2021 (closed)
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
  • Quantile Version of Mathai-Haubold Entropy of Order Statistics
  • 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 entropy is investigated by simulation… More
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  • High Order of Accuracy for Poisson Equation Obtained by Grouping of Repeated Richardson Extrapolation with Fourth Order Schemes
  • 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 that there is no… More
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  • Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection
  • 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 data. However, compressed features may… More
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  • A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm
  • 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 from the current alternatives, the… More
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  • Control Charts for the Shape Parameter of Power Function Distribution under Different Classical Estimators
  • 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 using three classical estimation methods,… More
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  • Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm
  • 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. The calculation process of parameters… More
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  • Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures
  • 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 numbers (BPFNs). Additionally, the concept… More
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  • Spherical Linear Diophantine Fuzzy Sets with Modeling Uncertainties in MCDM
  • 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 discuss various real-life applications of… More
  •   Views:671       Downloads:555        Download PDF