A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05852-9

This paper proposes a novel metaheuristic-based optimization technique called search space reduction (SSR) optimization algorithm. This algorithm attempts to solve the common pitfalls in the existing algorithms in the literature by randomly generating the search agents in every iteration instead of following the best solution. This new algorithm is simple, computationally efficient, which is based

In this research paper, we will solve problems of Bi-level linear fractional programming (BL-LFP) by proposing an interactive approach. Based on the imposition of the relationship DM. to obtained adequate solution, DMs will updating the minimal adequate level at upper level permanently, and that is through. Firstly, the decision makers uncertainty is described by introducing the membership function

In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze

The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called

Enhanced richness and size of the data on the web paves the path for increased online services, supporting the sophisticated usage of heterogeneous complex tasks by users. As deep web online services are increasing, several efficient techniques to explore the location of deep web interfaces are also leveraged thereby providing better support in user information exploration on the web concerning queries

Credit risk and business failure classification and prediction are a major topic in financial risk management and corporate finance decision making. In this work, an adaptive sequential-filtering learning system for credit risk modeling. It is basically a three-stage sequential system for credit risk and business failure classification is presented. First, different statistical filters are applied

A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05848-5

This paper introduces a new Runge–Kutta (RK) integration-based adaptive controller by considering control law as an ODE for nonlinear MIMO systems. It is aimed to derive a novel adaptive controller by regarding the control law as an ODE with limited information about control law structure. Adaptive parameters are adjusted via an RK predictive system model where Levenberg–Marquardt (LM) technique is

Sparse representation (SR) has been widely studied and successfully applied to many areas of computer science in recent years. However, whether sparsity is essential to improve the classification performance is still an open question. Some studies reveal that it is the collaborative representation (CR) rather than SR that truly improves the classification performance. In this paper, the advantage of

In this paper, some results related to properties of various versions in equality algebras are given by using the notion of hyper equality algebras. We introduce some types of filters such as implicative, positive implicative and fantastic filters on hyper equality algebras and prove some results determining the relation between these filters. Further, it is shown that every (\(S_\sim \)-reflexive)

Accurate evaluation of project performance is one of the biggest concerns of project-oriented organizations, and project managers are always looking for suitable tools and techniques to monitor and control the project activities. For a long time, the earned value management system is helping project managers in achieving this objective. However, its predominant focus on project cost and other limitations

Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output.

Failure modes and effects analysis (FMEA) is a commonly used method in risk evaluation. In this paper, a novel FMEA model based on neutrosophic analytic hierarchy process (NAHP) is proposed to overcome shortcomings of the risk prioritization of classical FMEA. Neutrosophic sets approach real-world decision-making problems from a three-aspect structure (i.e., truthiness, indeterminacy and falsity).

Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows’ intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA)

Concrete is a material of utmost importance in construction, and therefore various numerical models have been developed to satisfactorily represent its observed behavior. Within this perspective, damage models stand out, especially isotropic damage ones that can satisfactorily represent the nonlinearity characteristics resulting from stiffness deterioration having the added advantages of a few parameters

This paper proposes an adaptive fuzzy inference approach for color image steganography, taking into account the influence of image complexity such as pixel similarity, pixel brightness and color sensitivity. A fuzzy inference system is designed as a classifier which adopts the features of the cover image as its crisp input values and produces semantic concepts corresponding to the payload of image

A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05720-6

The heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work,

Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis

In this paper, the analysis of means test under the neutrosophic statistics is presented. The necessary procedure is given to apply the proposed neutrosophic analysis of means. The proposed neutrosophic analysis of means is explained with the aid of wind power data. The efficiency of the proposed neutrosophic analysis of means is compared with existing analysis of means under classical statistics.

In this paper, we considered the defined intuitionistic fuzzy numbers (IFNs) on real numbers and proposed a new method based on Hamacher operators to operate with membership and non-membership degrees of the given IFN in aggregation process. Also, a new method will be introduced to compute the distance between IFNs. These methods will be applied to solve multi-attribute group decision-making (MAGDM)

The main contribution of this paper is to give a new axiomatic definition of entropy measure and provide a constructing approach in the context of interval-valued intuitionistic fuzzy set (IVIFS). We give a new idea to define entropy on IVIFS: From the graphical representation, we consider the difference between a given IVIFS and its corresponding two interval fuzzy sets (IVFSs) by introducing a distance

The second-hand goods exchange matching problem with intelligent exchange platform is a valuable research topic, while research in the related field is relatively lacking. Thus, the goal of this paper is to develop a two-sided matching method for the second-hand goods exchange matching. In the method, not only the characteristic of loss aversion of buyers/sellers and the benefit of e-brokerage are

Due to the importance of correlation coefficient in data analysis, researchers have shown interest in the concept of correlation coefficient for the extensions of fuzzy sets, in particular, for a recent established extension known as hesitant fuzzy set (HFS). Most of the existing correlation coefficients for fuzzy sets return the value one to reflect a perfect linear relationship between objects, but

This paper focuses on the generalized Calogero–Bogoyavlenskii–Schiff equation to extract new complex solutions by using two analytical methods, namely, Bernoulli sub-equation function method, Modified exponential function method. For better understanding of physical meanings of solutions, simulations are reported by using a computational package program. Moreover, strain conditions for validity of

A correction to this paper has been published: https://doi.org/10.1007/s00500-021-05804-3

The partner selection problem (PSP) is a key issue in constituting and reconfiguring strategic alliances. In this paper, we seek to address PSP under time, budget, activity precedence, and resource constraints. Multiple objectives are considered, our proposed approach simultaneously minimizing total cost and project duration while maximizing average quality. For these purposes, we present a novel multi-objective

Antenna arrays have potential applications in Radar, mobile, broadcasting, astronomy and other defense and commercial platforms. The design of antenna arrays is essential as they are capable of controlling the radiation pattern in terms of sidelobe level (SLL), beamwidth (BW), nulls and beam steering. Among several geometries of antenna arrays, the circular antenna arrays have the advantage in terms

Early supplier involvement in new product development is strategically important for the success of most firms. We therefore discuss how to configure an appropriate supply chain network for new product development. First, we propose a supply chain configuration model under a multi-objective optimization framework. We then develop a solution procedure based on a multi-objective evolutionary algorithm

Developing a hedging strategy to reduce risk of losses for a given set of stocks in a portfolio is a difficult task due to cost of the hedge. In Vietnam stock market, cross-hedge is involved hedging a long position of a stock because there is no put option for the stock. In addition, only VN30 stock index futures contracts are traded on Hanoi Stock Exchange. Inspired by recently achievement of deep

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the

We introduce the Einstein operations based on cubic linguistic uncertain fuzzy numbers. We discuss three arithmetic averaging operators, viz. the cubic linguistic uncertain fuzzy Einstein weighted averaging operator, the cubic linguistic uncertain fuzzy Einstein ordered weighted averaging operator, and the cubic linguistic uncertain fuzzy Einstein hybrid weighted averaging operator, for gathering cubic

The q-rung orthopair fuzzy sets dynamically change the range of indication of decision knowledge by adjusting a parameter q from decision makers, where \(q \ge 1\), and outperform the conventional intuitionistic fuzzy sets and Pythagorean fuzzy sets. Linguistic q-rung orthopair fuzzy sets (Lq-ROFSs), a qualitative type of q-rung orthopair fuzzy sets, are characterized by a degree of linguistic membership

In this paper, traditional and meta-heuristic approaches for optimizing deep neural networks (DNN) have been surveyed, and a genetic algorithm (GA)-based approach involving two optimization phases for hyper-parameter discovery and optimal data subset determination has been proposed. The first phase aims to quickly select an optimal combination of the network hyper-parameters to design a DNN. Compared

A flexible manufacturing system (FMS) is the model used for the system produced in the manufacturing industry, and it consists of the number of interconnected workstation. Inflexible manufacturing system scheduling of jobs has become a serious problem, even for a short breakdown of the machine and for the unexpected arrival of the product. To overcome this problem, a flexible manufacturing system using

This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy C-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi–Sugeno (T–S) fuzzy model. A lately and important proposed integral inequality is considered

This study designs a novel decision support model to address group decision-making (GDM) problems with Pythagorean fuzzy linguistic information. To do so, a new concept of Pythagorean fuzzy linguistic preference relations (PFLPRs) is first introduced to describe fuzzy and uncertain information, where the Pythagorean fuzzy linguistic values (PFLVs) are represented by the linguistic membership degree

When modeling the kink regression model, it is possible to have an excessive number of explanatory variables and their corresponding coefficients, thereby leading to the over-parameterization and multicollinearity problems. Motivated by these problems, five sparse estimation methods, namely LASSO, sparse Ridge, SCAD, MCP, and Bridge, are considered to perform simultaneous variable selection and parameter

Currently, the high dropout rate of massive open online course (MOOC) has seriously affected its popularity and promotion. How to effectively predict the dropout status of students in MOOC so as to intervene as early as possible has become a hot topic. As we know, different students in MOOC have big differences in learning behaviors, learning habits, and learning time, etc. This leads to different

With the extensive growth of data that has been joined with the thriving development of the Internet in this century, finding or getting valuable information and knowledge from these huge noisy data became harder. The Concept of Knowledge Graph (KG) is one of the concepts that has come into the public view as a result of this development. In addition, with that thriving development especially in the

Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification

In this paper, we propose a generalization for fuzzy graphs in order to model reactive systems with fuzziness. As we will show, the resulting fuzzy structure, called fuzzy reactive graphs (FRG), is able to model dynamical aspects of some entities which generally appear in: biology, computer science and some other fields. The dynamical aspect is captured by a transition function which updates the values

Countries generally aim to attain several different or sometimes contrary goals. To this end, a Goal Programming (GP) model integrated with an Input–Output (I-O) one was implemented in a number of previous studies. This paper attempts to integrate a GP with a Social Accounting Matrix (SAM) framework to allocate economic resources of an economy among alternative sectors in order to achieve a number

In this paper, the interval approach for Markov switching capital asset pricing model (MS-CAPM) is proposed to quantify the beta risk in two different regimes, namely a bull and a bear regimes. Instead of fitting a MS-CAPM on specific fixed reference points, such as midpoints (center method), and lower and upper bounds (MinMax method), this study suggests choosing the reference points that better represent

We present a numerical attribute dependency method for massive datasets based on the concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts for numerical attribute dependency in data analysis: Therein, the multi-dimensional inverse fuzzy transform was useful for approximating a regression function. Here we give an extension of this method in massive datasets because

This research integrates business analytics with enterprise risk management to help firms implement a decision support war-room. In particular, this study explores the hidden states of the small- and medium-sized enterprises (SMEs) which financial performances tend to intensively rise and fall. In practice, managers need to know what indicators can be good predictors to predict bankruptcy and what

Classification is one of the tasks that are most frequently carried out in real world applications. A large number of techniques have been developed based on statistics and machine learning methods. These classification techniques usually suffer from various limitations, and there is no single technique that works best for all classification problems. Two major drawbacks in existing techniques are

The essential protein detection on protein–protein interaction (PPI) network can not only promote the research of life science, but also have important applications in disease diagnosis and drug target cell identifying. A large number of computation-based essential protein detection algorithms have been presented recently. Most of those methods detect the essential proteins according to the centrality

The major purpose of this article is to enhance the performance of GOA algorithm by integrating a new mutation operator to the standard GOA algorithm. A series of six different variants of enhanced GOA is proposed by integrating GOA with six different variants of the mutation operator. The new enhanced metaheuristic optimization method is called EGOAs. EGOA aims to address the problems of slow convergence

Multiple criteria decision making is a mathematical tool for dealing with complex problems like best or optimal selection from various choices. Fuzzy set theory provides a soft computing framework for precise and rigorous study of vague conceptual environment. In this paper, a new hybrid multi-criteria decision-making method consists of group fuzzy COPRAS, and fuzzy BCM is proposed. The aim of the

The capsule network (CapsNet) is a promising model in computer vision. It has achieved excellent results on MNIST, but it is still slightly insufficient in real images. Deepening capsule architectures is an effective way to improve performance, but the computational cost hinders their development. To overcome parameter growth and build an efficient architecture, this paper proposes a tensor capsule

Sensor network-based home automation systems are familiar over the recent decades. Incorporating the benefits of the sensor network, energy management systems (EMS), is introduced to benefit end-user through periodic information sharing and remote access. WSN opted for energy harvesters to reduce the maintenance costs and maximize the lifetime of network. It is a perfect match for wireless devices

This paper addresses coordination of pricing and cooperative advertising policies in a two-echelon supply chain under fuzziness of demand function’s parameters and manufacturing costs. Three different decentralized scenarios are introduced with regard to the players’ market power: (1) manufacturer-Stackelberg game where the manufacturer has the dominant power in the channel, (2) retailer-Stackelberg

We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation

Fuzzy soft sets represent a generalization of fuzzy sets with considerable application potential in decision-making problems, optimization, and computer science. In the paper, we use the fact that the fuzzy soft sets powerset theory is defined by the monad and, using this theory, we introduce the concept of fuzzy soft relations defined by the monad. Using that general method, we can also introduce

In this paper, a cooperative bat searching algorithm (CBA) is proposed by using a communication topology to share information among all the bats in bat algorithm (BA). Inspired by the cooperation mechanism in the distributed control theory, a cooperative term is added to the original BA to accelerate the searching process. The convergence issue is rigorously studied for CBA by using the Jury’s test

Suppose that we know the probability distribution corresponding to some property A. How can we then describe the distribution describing the negation of this property? In this paper, we show that, in general, we need additional information to uniquely determine the desired probability distribution. If we do not have this additional information and we still need to select one of the possible distributions

Increase in population density in large cities has increased the environmental noise present in these environments, causing negative effects on human health. There are different sources of environmental noise; however, noise from road traffic is the most prevalent in cities. Therefore, it is necessary to have tools that allow noise characterization to establish strategies that permit obtaining levels

Classification is an essential task in the field of machine learning, where finding a maximum margin classifier is one of its central problems. In this work, an evolutionary algorithm is constructed, relying on the convexity properties of the version space, to evolve a population of perceptron classifiers in order to find a solution that approximates the maximum margin. Unlike other methods whose solutions