Abstract 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

Abstract 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

Abstract As a strategic resource for the region to participate in global economic cooperation and competition, the coordinated development of ports within the port group is of great practical significance to promote its own and regional economic development. In order to better promote the port’s rational development and resource allocation, this paper uses the rough set reduction method based on information

Abstract There are two different perspectives to study single axioms for (S, T)-fuzzy rough approximation operators, that is, ordinary fuzzy operations and fuzzy product operations. However, it is too complex and tedious to characterize (S, T)-fuzzy rough approximation operators with ordinary fuzzy operations, such as intersection, union and so on. To remedy these defects, this paper further investigates

Abstract In this paper, we propose a method to estimate the number and locations of change points and further estimate parameters of different regions for piecewise stationary vector autoregressive models. The procedure decomposes the problem of change points detection and parameter estimation along the component series. By reformulating the change point detection problem as a variable selection one

Abstract Time series exist widely in either nature or society such that the research on analysis of time series has great significance. However, considering the nonlinearity and uncertainty, the prediction of time series is still an open problem. In this paper, by means of the intuitionistic fuzzy set theory, we proposed a novel time series prediction scheme based on intuitionistic fuzzy cognitive

Abstract We study notions of conditional probability and stochastic dependence/independence in an upgraded probability model in which the space of events is modeled by a full Łukasiewicz tribe of all measurable functions from some measurable space into [0, 1]. Our study is based on properties of joint experiments and the notion of stochastic channel, a construct equivalent to the notion of Markov kernel

Abstract New energy products (NEPs) are important driving forces for promoting economic transformation and green development. The current trend of NEPs development is propelled by consumers, spurred by governmental regulations, and implemented by manufacturers. However, the previous studies rarely discussed the behavioral strategies of consumers. The relationships among consumers, manufacturers, and

Abstract Failure mode and effect analysis (FMEA), a bottom-up method, is one of risk assessment tools to eliminate or reduce failures in design and process. It has been applied to many industries due to its flexibility and effectiveness. However, the conventional FMEA considers less about the subjectivity and vagueness in the process of risk assessment and assumes that three risk factors’ importance

Abstract Feature selection is a critical preprocess for constructing model in computer vision and machine learning, yet it is difficult to simultaneously satisfy both reducing features’ number and maintaining classification accuracy. Toward this problem, we propose dividing-based many-objective evolutionary algorithm for large-scale feature selection (DMEA-FS). Firstly, four novel objectives are established

Abstract In this paper, an urban hazmat transportation problem considering multiple factors that tangle with real-world applications (i.e., weather conditions, traffic conditions, population density, time window, link closure and half link closure) is investigated. Based on multiple depot capacitated vehicle routing problem, we provide a multi-level programming formulation for urban hazmat transportation

Abstract This paper focuses on the reliability of intelligent logistics for fresh food e-commerce. Based on the development of fresh food e-commerce, this paper analyses the factors that influence the reliability of fresh food logistics from the aspects of information technology, facilities and equipment, personnel operation and external environment. A Bayesian network is used to analyse the influence

Abstract This paper presents a new multi-objective discreet learnable evolution model (MODLEM) to address the vehicle routing problem with time windows (VRPTW). Learnable evolution model (LEM) includes a machine learning algorithm, like the decision trees, that can discover the correct directions of the evolution leading to significant improvements in the fitness of the individuals. We incorporate

Abstract The aim of this paper is to define two sorts of convergence in measure, that is, outer and inner statistical convergence, for double sequences of fuzzy-valued measurable functions and demonstrate that both kinds of convergence are equivalent in a finite measurable set. We also define the notion of statistical convergence in measure for double sequences of fuzzy-valued measurable functions

Abstract In this paper, the lifetimes of system components are assumed to have independent and nonidentical uncertainty distributions with uncertain parameters. The reliability functions and mean time to failure of the general systems are investigated according to the uncertainty theory. Basic models of the general systems with bi-uncertain variables are established and analyzed, including series,

Abstract Hollywood enjoys the position of being the biggest movie producers when it comes to global recognition among movie-making industries. Despite being the biggest movie producer, it has been facing high revenue losses lately since most of the films that it has created have failed to capture viewer’s attention in the first few weeks of its release resulting in a box-office flop. It has been observed

Abstract Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi

Abstract Clustering analysis has been widely used in image segmentation, face recognition, protein identification, intrusion detection, document clustering and so on. Most of the previous clustering algorithms are not suitable for complex situations with manifold structure and large variations in density. Clustering by density core (DCore) turns out to be a very effective clustering method for complex

Abstract In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions

Abstract The effective identification of critical failure modes of individual equipment components or processes and the development of plans for improvement are crucial for the manufacturing industry. Recently, the failure modes and effects analysis (FMEA) approach based on multiple criteria decision making (MCDM) has been utilized effectively for the assessment of primary failure modes and risks.

Abstract Twist-structure representation theorems are established for De Morgan and Kleene lattices. While the former result relies essentially on the quasivariety of De Morgan lattices being finitely generated, the representation for Kleene lattices does not and can be extended to more general algebras. In particular, one can drop the double negation identity (involutivity). The resulting class of

Abstract Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures

Abstract Clustering problems widely exist in machine learning, pattern recognition, image analysis and information sciences, etc. Although many clustering algorithms have been proposed, it is unpractical to find a clustering algorithm suitable for all types of datasets. Fuzzy c-means (FCM) is one of the most frequently-used fuzzy clustering algorithm for the reason that it is efficient, straightforward

Unfortunately, the given names and the family name of the author Uchitha Jayawickrama are incorrectly published in the original article.

Abstract Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic

Abstract High-accuracy traffic flow forecasting is vital to the development of intelligent city transportation systems. Recently, traffic flow forecasting models based on the kernel method have been widely applied due to their great generalization capability. The aim of this article is twofold: A novel kernel learning method, relevance vector machine, is employed to short-term traffic flow forecasting

Abstract Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for

Abstract In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from

Abstract In this paper, a new optimization algorithm called the Hitchcock bird-inspired algorithm (HBIA) is proposed. It is inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller “The Birds.” It is noteworthy to emphasize that the bird’s behavior as shown in the movie is itself inspired by a considered natural birds behavior when faced with extreme conditions. HBIA

Abstract With the rapid development of the Internet of Things, the massive amount of big data generated by the Internet of Things terminals and the real-time processing requirements have brought enormous challenges. A two-tier computing model consisting solely of two entities, cloud and user, will not be sufficient to support processing large numbers of concurrent data requests. Therefore, fog computing

Abstract Nowadays, big data plays a substantial part in information knowledge analysis, manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets are a very challenging task due to the imbalance of data distribution, which could lead to a biased classification results and wrong decisions. The standard classifiers are not capable of handling such datasets. Hence, a new

Abstract Obtaining a useful and discriminative feature for facial expression recognition (FER) is a hot research topic in computer vision. In this paper, we propose a novel facial expression representation for FER. Firstly, we select the appropriate parameter of multi-scale block local binary pattern uniform histogram (MB-LBPUH) operator to filter the facial images for representing the holistic structural

Abstract Cloud computing has been one of the critical solutions to reduce heavy storage and computation burden of biometric identification. To protect the privacy of biometric data against untrusted cloud servers, outsourced biometric databases are usually encrypted by users. Performing biometric identification over encrypted data without revealing privacy to cloud servers attracts more and more attention

Abstract Rule induction (RI) is one of the known classification approaches in data mining. RI extracts hidden patterns from instances in terms of rules. This paper proposes a logic-based rule induction (LBRI) classifier based on a switching function approach. LBRI generates binary rules by using a novel minimization function, which depends on simple and powerful bitwise operations. Initially, LBRI

Abstract Image processing continues to be a challenging topic in many scientific fields such as medicine, computational physics and informatics especially with the discovery and development of 3D cases. Therefore, development of suitable tools that guarantee a best treatment is a necessity. Spherical shapes are a big class of 3D images whom processing necessitates adoptable tools. This encourages researchers

Abstract The banks serve in a highly dynamic and competitive environment and need to systematically evaluate their performance to improve their competitiveness. Performance evaluation is an important and complex process that requires flexible and analytic methods while handling the multidimensionality of the problem. This study presents a hybrid multi-criteria performance evaluation model for banking

Abstract In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonometry FLNN, Legendre FLNN (LeFLNN), Chebyshev FLNN, power series FLNN (PSFLNN) and Hermite FLNN. The recursive weights adjustment equations are derived using the combination of

Abstract The widespread availability of advanced digital image technology and powerful image editing tools has made it extremely easy to manipulate image content. One popular technique that is challenging for tampering detection methods is the copy–move forgery. Here, one part of the image is copied and is pasted into another part of the same image. Image tampering is very difficult to detect with

Abstract The simplicity and effectiveness of a recently proposed metaheuristic, butterfly optimization algorithm (BOA) have gained huge popularity among research community and are being used to solve optimization problems in various disciplines. However, the algorithm is suffering from poor exploitation ability and has a tendency to show premature convergence to local optima. On the other hand, the

Abstract The urban transit routing problem is NP-Hard, referring to the design of effective bus routes on the existing road networks. Current studies mainly focus on the models and the application of algorithms, and the improvements in the operation process in the algorithm such as the construction of initial solutions and the transformation methods are not investigated in detail. In order to optimize

Abstract In the case of contamination for skewed distributions, the modified Shewhart, modified weighted variance, and modified skewness correction methods are newly introduced by Karagöz (Hacet J Math Stat 47(1):223–242, 2018). In this study, we propose to modify these methods by considering simple random sampling (SRS), ranked set sampling (RSS) and median ranked set sampling (MRSS) designs under

Abstract A two-stage R2 indicator-based evolution algorithm (TS-R2EA) was proposed in the recent years. A good balance between convergence and diversity can be achieved, due to the R2 indicator and reference vector-guided selection strategy. However, TSR2-EA is sensitive to problem geometries. In order to address this issue, a weight vector-based selection strategy is introduced, and a weight vector

Abstract In the presented paper, the functioning and the results of the work of two metaheuristic algorithms, namely cuckoo search algorithm (CS) and firefly algorithm (FA), are described using the apparatus of generalized nets (GNs), which is an appropriate and efficient tool for describing the essence of various optimization methods. The two developed GN-models mimic the optimization processes based

Abstract This paper examines the influence of subsidies provided by traditional (e.g., a bricks-and-mortar retail mall) and electronic (e.g., Alibaba and Amazon) retail platforms to consumers. We look at the competition between traditional retailers, who sell in a shopping mall, and e-tailers, who sell on the electronic platform. Our focus is on analyzing and demonstrating how these channel subsidies

Abstract Diarrhea is one of the leading public health problems and the third main cause of death among young children in developing countries. Solutions to tackling the infectious disease require both preventive and control efforts. However, efforts toward improving the control measures require comprehending the factors associated with diarrhea incidence and the ability to accurately forecast the incidence

Abstract Due to mounting infiltration of solar and wind energy sources, it becomes essential to investigate its brunt on the dynamic economic dispatch. Here, solar–wind–thermal system integrating pumped-storage hydraulic unit has been considered. This work recommends chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving dynamic economic dispatch problem incorporating

Abstract Connectivity index of graph plays a significant role in chemistry, pharmacology, etc. This paper brings in connectivity index of a bipolar fuzzy graph (BFG) with its boundedness. We examine the changes of connectivity index for a BFG when a vertex or an edge is removed. Some theorems related to these are established. The parameter, average connectivity index of a BFG, is introduced with some

Abstract Probabilistic simplified neutrosophic set \( \left( {PSNS} \right) \) is an important tool to describe the vagueness existing in the real life. In this study, we define a PSNS and discuss some of theoretical set operations of \( PSNSs \). Also we propose the concepts of module on \( PSNSs \), as well as inner product and projection operator between two \( PSNSs \). In relation to this new

Abstract We present a model to simulate the subjective evaluation and compare various fusion algorithms. First, we produce an all-focus image and two multi-focus images by the help of two filters. Second, we fuse two multi-focus images with two representative algorithms WT and CT. Third, we decide the optimal fusion rules of WT and CT by comparing the fused images with an all-focus image. Finally,

Abstract This paper presents a method for the extraction of twofold information set (TFIS) features for the text-independent speaker recognition. The method takes the Mel frequency cepstral coefficients from the frames of a sample speech signal and forms a matrix. From this, both spatial and temporal information components are derived based on the information set concept using the entropy framework

Abstract The product low-carbon design is the key to decrease carbon emissions in manufacturing. The multiple criteria decision-making (MCDM) method has been widely used in solving the design schemes preference choosing problems. However, the existed MCDM method has two primary problems facing the product low-carbon design cases: (i) How to clarify the coupling relationship between low-carbon decision

Abstract Nowadays, with the emergence of new technologies such as smart grid and increasing the use of renewable energy in the grid, energy prediction has become more important in the electricity industry. Furthermore, with growing the integration of power generated from renewable energy sources into grids, an accurate forecasting tool for the reduction in undesirable effects of this scenario is essential

Generalized Boolean quasirings are ring-like structures used as algebraic models in the foundations of axiomatic quantum mechanics. The quantum mechanical system corresponding to such a quasiring turns out to be a classical one if and only if this quasiring is a Boolean ring with unit. We characterize this situation by a single identity.

For an algebra [Formula: see text] belonging to a quasivariety [Formula: see text], the quotient [Formula: see text] need not belong to [Formula: see text] for every [Formula: see text]. The natural question arises for which [Formula: see text]. We consider algebras [Formula: see text] of type (2, 0) where a partial order relation is determined by the operations [Formula: see text] and 1. Within these

It is known that a quasimetric space can be represented by means of a metric space; the points of the former space become closed subsets of the latter one, and the role of the quasimetric is assumed by the Hausdorff quasidistance. In this paper, we show that, in a slightly more special context, a sharpened version of this representation theorem holds. Namely, we assume a quasimetric to fulfil separability

Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among

We study conditions under which the lattice Id R of ideals of a given a commutative semiring R is complemented. At first we check when the annihilator I ∗ of a given ideal I of R is a complement of I. Further, we study complements of annihilator ideals. Next we investigate so-called Łukasiewicz semirings. These form a counterpart to MV-algebras which are used in quantum structures as they form an algebraic

The so-called non-associative MV-algebras were introduced recently by the first author and J. Kühr in order to have an appropriate tool for certain logics used in expert systems where associativity of the binary operation is excluded, see, e.g., Botur and Halaš (Arch Math Log 48:243-255, 2009). Since implication is an important logical connective in practically every propositional logic, in the present

This paper is a contribution to graded model theory, in the context of mathematical fuzzy logic. We study characterizations of classes of graded structures in terms of the syntactic form of their first-order axiomatization. We focus on classes given by universal and universal-existential sentences. In particular, we prove two amalgamation results using the technique of diagrams in the setting of structures