Table 14. Weights of MOORA Ratio, MOORA reference point, MOOSRA and COPRAS methods.

Image quality is relevant to the performance of computer vision applications. The interference of rain streaks often greatly depreciates the visual effect of images. It is a traditional and critical vision challenge to remove rain streaks from rainy images. In this paper, we introduce a deep connectionist screen blend model for single-image rain removal research. The novel deep structure is mainly

In reaction systems, preimages and nth ancestors are sets of reactants leading to the production of a target set of products in either 1 or n steps, respectively. Many computational problems on preimages and ancestors, such as finding all minimum-cardinality nth ancestors, computing their size or counting them, are intractable. In this paper, we characterize all nth ancestors using a Boolean formula

The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations

Three-way concept analysis in incomplete contexts lays the theory dealing with the data in incomplete contexts, especially three kinds of partially known formal concepts including SE-ISI formal concept, ISE-SI formal concept and ISE-ISI formal concept. Generally speaking, not every attribute is essential in an incomplete context since the purpose of research is different. Thus, we propose four kinds

In the world of information retrieval, recognizing hand-written digits stands as an interesting application of machine learning (deep learning). Though this is already a matured field, a way to recognize digits using an effective optimization using soft computing technique is a challenging task. Training such a system with larger data often fails due to higher computation and storage. In this paper

This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The fuzzy sets can be chosen manually or determined by a metaheuristic optimization method (particle swarm optimization

Alzheimer’s disease (AD) and cognitive impairment due to aging are the recently prevailing diseases among aged inhabitants due to an increase in the aging population. Several demographic characters, structural and functional neuroimaging investigations, cardio-vascular studies, neuropsychiatric symptoms, cognitive performances, and biomarkers in cerebrospinal fluids are the various predictors for AD

This paper proposes a hybrid algorithm-based control model for enhancing the low voltage ride through (LVRT) capability of the doubly fed induction generator (DFIG) system. The hybrid algorithm is the combined execution of both modified elephant herding algorithm (MEHA) and random forest (RF) algorithm, and hence the technique is named as MEHRFA. The purpose of the proposed control technique ensures

Security-by-contract is a paradigm proposed for the secure installation, usage, and monitoring of apps into mobile devices, with the aim of establishing, controlling, and, if necessary, enforcing security-critical behaviors. In this paper, we extend this paradigm with new functionalities allowing for a quantitative estimation of such behaviors, in order to reveal in real time the more and more challenging

Overlap and grouping functions, as two kinds of particular binary aggregation functions, have been continuously discussed in the literature for their vast preponderance in some real applications. Meanwhile, after Hu introduced the notion of three-way decision spaces, the decision evaluation functions in three-way decision spaces constructed from the so-called semi-decision evaluation functions have

Training optimization plays a vital role in the development of convolution neural network (CNN). CNNs are hard to train because of the presence of multiple local minima. The optimization problem for a CNN is non-convex, hence, has multiple local minima. If any of the chosen hyper-parameters are not appropriate, it will end up at bad local minima, which leads to poor performance. Hence, proper optimization

Advances in information and communication technology have created innovator technologies such as cloud computing, Internet of Things, big data analysis and artificial intelligence. These technologies have penetrated production systems and converted them smart. However, this transformation did not only affect production systems, but also differentiated supplier selection processes. In the supplier selection

This paper investigates an elliptic entropy of uncertain random variables and its application in the area of portfolio selection. We first define the elliptic entropy to characterize the uncertainty of uncertain random variables and give some mathematical properties of the elliptic entropy. Then we derive a computational formula to calculate the elliptic entropy of function of uncertain random variables

In order to realize high-precision sensor sensing system for unmanned vehicle, a method based on D–S (Dempster–Shafer) evidence theory was presented. D–S evidence theory is a method of evidence processing. However, because of the limitation of multiplication rule, it cannot deal with the evidence of high conflict caused by the failure of a sensor in the system. In the existing D–S theory framework

Metaheuristics have proven their efficiency in treating complex optimization problems. Generally, they produce good results quite close to optimal despite some weaknesses such as premature convergence and stagnation in the local optima. However, some techniques are used to improve the obtained results, one of them is the adoption of chaos theory. Including chaotic sequences in metaheuristics has proven

We define the concept of Cesàro summability method in intuitionistic fuzzy normed spaces and prove a related Tauberian theorem. Also, we define slowly oscillating sequences in intuitionistic fuzzy normed spaces, prove related theorems and show that Cesàro summability of slowly oscillating sequences implies ordinary convergence in intuitionistic fuzzy normed spaces. Finally, we give an analogue of classical

The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions

The affiliation of the author Shih-Yu Li.

Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in

A novel uncertainty representation framework is introduced based on the inter-linkage between the inherent fuzziness and the agent’s confusion in its representation. The measure of fuzziness and this confusion is considered to be directly related to the lack of distinction between membership and non-membership grades. We term the proposed structure as confidence fuzzy set (CFS). It is further generalized

The time-varying delay makes the state of a chaotic system more complex, which has a great influence on stability analysis and synchronization. To solve this problem, we utilize the method of adaptive intermittent control and the theory of Lyapunov stability to realize the synchronization of chaotic systems with time-varying delay. Firstly, we propose the control strategy to achieve the asymptotic

An information system (IS) is an important model in the field of artificial intelligence. A fuzzy information system (FIS) may be regarded as an IS under fuzzy environment. This paper obtains some results on a FIS. Operators on a FIS are first researched. Then, relationships between FISs are discussed from two aspects of dependence and separability, and dependence between FISs is characterized by the

Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is

Effect algebras form an algebraic formalization of the logic of quantum mechanics. For lattice effect algebras E , we investigate a natural implication and prove that the implication reduct of E is term equivalent to E . Then, we present a simple axiom system in Gentzen style in order to axiomatize the logic induced by lattice effect algebras. For effect algebras which need not be lattice-ordered,

The main idea of this work is the exploration of granular structures by applying the hybrid models of fuzzy soft sets and fuzzy soft graphs to discuss the indiscernibility partition of set of universe. The information granulation is examined by applying fuzzy soft theory, and the corresponding behavior of granules is reviewed. This article proposes a novel technique of formation of granular structures

One of the challenges in rollout of 5G is utilizing the radio communication which is not accessible by today’s radio system. To achieve a high data rate, it is necessary to make 5G networks compatible with advanced waveform. In this correspondence, we discussed advanced waveform technique non-orthogonal multiple access (NOMA) and filter bank multicarrier (FBMC) for 5G network. The design of advanced

The imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities’ inhabitants. Considering that social media generate and/or open real-time entry points to vast amounts of data pertinent to well-being and quality of life, such as citizens’

In this paper, a hybrid scheme based on recurrent neural networks for approximate fuzzy coefficients (parameters) of fuzzy linear and polynomial regression models with fuzzy output and crisp inputs is presented. Here, a neural network is first constructed based on some concepts of convex optimization and stability theory. The suggested neural network model guarantees to find the approximate parameters

This paper aims to provide a fuzzy solution for fuzzy singular differential equations (FSDEs) in which the coefficient matrices and/or initial conditions are considered as fuzzy matrices and/or numbers. In addition, the fuzzy derivative is in the sense of the granular derivative. To achieve the aim, some new concepts such as the rank and index of fuzzy matrices, and granular inverse of a nonsingular

In this paper, we analyze the population diversity of grammatical evolution (GE) on multiple levels of genetic information: chromosome diversity, expression diversity, and output diversity. Thereby, we use a tree-similarity metric from tree-based GP literature to determine similarity of expression trees generated in GE. The similarity of outputs is determined via their correlation. We track the pairwise

This work highlights the importance of fuzzy-wavelet denoise and fuzzy-wavelet compress in modeling the municipal residential water consumption estimation. To begin, fuzzy logic is used with different rules, membership criteria and fuzzy set. Based on accuracy of the developed model, optimum number of rules and best membership function were selected. To improve the accuracy of the single fuzzy model

In a hyper structure \((X,\star )\), \(x\star y\) is a non-empty subset of X. For a state s, \(s(x\star y)\) need not be well defined. In this paper, by defining \(s^*(x\star y)=sup\{s(z)\mid z\in x\star y\}\), we introduce notions of sup-Bosbach states, state-morphisms and sup-Riečan states on a bounded hyper EQ-algebra and discuss the related properties. The states on bounded hyper EQ-algebras are

Three-way decisions (3WDs) with decision-theoretic rough sets (DTRSs) are a new method for solving problems of risky decision. In DTRSs, it is crucial to the determination of the loss function. Pythagorean fuzzy (PF) sets are a more powerful mathematical tool than intuitionistic fuzzy sets for dealing with uncertainty and inaccuracy. Although the researchers have introduced Pythagorean fuzzy numbers

As in terrestrial facilities, safety is one of the most important issue in ships. Vessels navigating in many parts of the world face many different, tough and dangerous navigational risks. In this context, twenty-three fundamental risks which are frequently encountered in ship navigation were considered in this study and examined by an FMEA-based TOPSIS approach under single valued neutrosophic sets

The quick and accurate estimation of the state of health (SOH) of Li-ion battery is a technical difficulty in battery management system research. For the low accuracy of Li-ion battery SOH estimation under complex stress conditions, an estimation method of SOH for Li-ion battery using the adaptive dual extended Kalman filter-based fuzzy inference system (ADEKF-FIS) is proposed. First, Li-ion battery

Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications

Optimal design of controllers without considering uncertainty in the plant dynamics can induce feedback instabilities and lead to obtaining infeasible controllers in practice. This paper presents a multi-objective evolutionary algorithm integrated with Monte Carlo simulations (MCS) to perform the optimal stochastic design of robust controllers for uncertain time-delay systems. Each potential optimal

Clustering is extensively realistic and considered in computer vision that follows unsupervised learning principles. In this, the performance of a clustering process mainly depends on the feature representation. Generally, the clustering process may have an error rate, and this affects the feature representation. To avoid this, unsupervised Learning (USL) provides an alternative path to obtain the

Software has made a profound influence in all walks of life. Developing quality software is a major challenge, and the consistency and completeness of the design has a prime role in the development of quality software. Many a times, the process of consistency checking in industries is manual. Artificial intelligence techniques can replace many of these manual efforts to make the development of software

This paper presents the stability analysis problem of fractional-order nonlinear systems with time-varying delay. After formulating the problem and selecting the nonlinear model as the system under study, stability analysis and expression of the sufficient conditions for fractional-order nonlinear systems with time-varying delay are obtained using two different methods. In these methods, sufficient

The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error

The article Adaptive ELM neural computing framework with fuzzy PI controller for speed regulation in permanent magnet synchronous motors, written by F. Vijay Amirtha Raj and V. Kamatchi Kannan, was originally published electronically on the publisher’s Internet portal (currently SpringerLink) on 06 May 2020 with open access.

The belief interval-valued soft set (BIVSS) combines soft set theory and belief interval value (Dempster–Shafer theory). In this study, we propose a generalized belief interval-valued soft set (GBIVSS) approach and explore the associated properties of this approach in decision-making applications. Using the score function, the scoring function and similarity measure used to compare the relationships

We present and discuss a formal approach for describing the quantum to classical crossover based on the group-theoretic construction of generalized coherent states. The method was originally introduced by Yaffe (Rev Mod Phys 54:407, 1982) in 1982 for tackling large-N quantum field theories and has been recently used for studying open quantum systems whose environment, while becoming macroscopic, may

Contrary to previous methods in production management, today’s approaches mainly focus on the whole supply chain parties’ considerations. Considering production planning and distribution, as the two main functions in supply chain (SC) management, in an integrated manner in order to enhance the SC advantages is one of today’s main dilemma. Here, we have firstly proposed and investigated the integrated

As an emerging technology, interval type-2 fuzzy logic systems (IT2 FLSs) have drawn great attentions in the past decade years. However, the computational intensive and time consuming type-reduction (TR) block may hinder the real applications IT2 FLSs. Unlike the most popular Karnik–Mendel (KM) iterative algorithms, the noniterative algorithms decrease the computational cost greatly. The comparison

Human factor is an inevitable element of safety-critical control room operations in nuclear power plants. Identifying human factors influencing operator error occurrence in such critical application is important to mitigate human errors. Conventional methods employed for human factor identification are often static, unable to deal with data and model uncertainty, and to consider independencies among

Thanks to the advent of powerful computers, searching for optimal solutions to engineering design problems becomes easier every day. Numerous researchers are still developing modern optimization algorithms, and the competition for “the most efficient optimization algorithm” continues apace. This study evaluates the performances of the Crow Search Algorithm (CSA) and a slightly modified variant (CSAM)

The purpose of this research study is to extend the multi-attribute group decision-making (MAGDM) methods to hesitant fuzzy soft set (HFS-set), hesitant fuzzy soft topology (HFS-topology) and HFS-Hausdorff spaces in group decision-making environment, as HFS-set is more superior tool to capture vagueness, hesitancy and incompleteness in individual evaluations. In order to obtain optimal decisions in

We will introduce a neutrosophic exponentially weighted moving average (NEWMA) statistic for the attribute data. We will use the proposed NEWMA to design an attribute control chart. We will introduce the neutrosophic Monte Carlo simulation to find the neutrosophic average run length (NARL). The comparative study shows the efficiency of the proposed NEWMA attribute. Two examples of having neutrosophic

Image classification has received extensive attention as an important technical means of acquiring image information. It has been widely used in various engineering fields. Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy and weak

Regression analysis is a mathematical tool to estimate the relationship between explanatory variables and response variable. This paper defines a likelihood function in the sense of uncertain measure to represent the likelihood of unknown parameters. Furthermore, the method of maximum likelihood estimation is used for the parameter estimation of uncertain regression models, and the uncertainty distribution

While typesetting the article, the surname of the second author has been incorrectly published.

The article On ε-soft topological semigroups, written by A. A. Bahredar and N. Kouhestani, was originally published electronically on the publisher’s internet portal

With the development of modern mobile communication, artificial intelligence (AI) has begun to enter people’s life, and it is also constantly changing the modern education mode. First, the structure of BPN (back-propagation network) is designed in this paper. On this basis, genetic algorithm is applied to optimize, which can accelerate the convergence speed and achieve the effect of global optimization

In this paper, we are reporting a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. We used HOG and SVM in the detection part to detect the road

The operational data of advanced process systems have met with explosive growth, but its fluctuations are so slight that the number of the extracted representative samples is quite limited, making it difficult to reflect the nature of the process and to establish prediction models. In this study, inspired by the process of fisherman repairing nets, a Kriging-based virtual sample generation (VSG) named

A simple jerk system with only one exponential nonlinearity is proposed and discussed. Dynamic analysis of the integer-order jerk system shows the existence of chaotic oscillations. A model for the fractional-order jerk system is derived. The Adomian decomposition method is used to analyse the fractional-order jerk system. Stability analysis of the fractional-order jerk system shows that chaotic oscillations

Type 2 intuitionistic fuzzy set (T2IFS) is one of the most important concepts to describe fuzzy information by providing an additional degree of freedom to the decision maker to decide the decision-making process. The fundamental superiority of the T2IFS, over the intuitionistic fuzzy set, has been its ability to capture the degrees of membership of relevant membership values, where the uncertainty