Special Issue "Intelligent Computing for Engineering Applications"

Submission Deadline: 31 May 2021
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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


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.

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

Published Papers
  • 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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  • 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:419       Downloads:333        Download PDF