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Recently, there have been many studies on solving different kinds of fuzzy equations. In this paper, the solution of a trapezoidal fully fuzzy linear system (FFLS) is studied. Uzawa approach, which is a popular iterative technique for saddle point problems, is considered for solving such FFLSs. In our...

The hesitant fuzzy linguistic term set (HFLTS) and the linguistic distribution (LD) are becoming popular tools to describe decision makers’ linguistic preferences. By combining HFLTS and LD, this paper proposes a new concept called hesitant linguistic distribution (HLD), and then presents the transformation...

We describe an applied methodology to build fuzzy models of geographical expressions, which are meant to be used for natural language generation purposes. Our approach encompasses a language grounding task within the development of an actual data-to-text system for the generation of textual descriptions...

This study presents the normative knowledge source for the belief space of cultural algorithm(CA) based on an adaptive Radial Basis Function Neural Network (RBFNN). The use of the RBFNN makes it possible to use the previous upper and lower bounds of the normative knowledge to update them and to extract...

The aim of this paper is to present a novel approach for deriving weights of the decision criteria or alternatives in multi-attribute decision making (MADM) under intuitionistic fuzzy (IF) environment. In order to tackle the uncertainty and imprecision of the practical situations, decision makers’...

As a primary means for representing and reasoning about knowledge, Answer Set Programming (ASP) has been applying in many areas such as planning, decision making, fault diagnosing and increasingly prevalent e-service. Based on the stable model semantics of logic programming, ASP can be used to solve...

In a context of lattice-valued functions (also called lattice-valued fuzzy sets), where the codomain is a complete lattice L, an equivalence relation defined on L by the equality of related cuts is investigated. It is known that this relation is a complete congruence on the join-semilattice reduct of...

Information fusion has been a hot topic currently, how to make information fusion for intelligent decision is a challenge. Although the applications of random set theory attract many researchers, the probability function distribution is still imprecise. In this paper, we give a new definition of probability...

Slope sliding force can be measured by an anchor cable sensor with the negative Poisson's ratio (NPR) property. It is capable of reflecting the stability of the slope intuitively. Thus, predicting the variation trend of the sliding force is able to achieve early warning for landslide disaster, thereby...

Network reconfiguration of a power distribution system is an operation to alter the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie switches. Network reconfiguration balances feeder loads and helps in managing overload conditions of the network by...

This paper presents a new approach to crime hotspot detection and monitoring. The approach consists of three phases’ namely: video analysis, crime prediction and crime mapping. In video analysis, crime indicator events are modelled using statistical distribution of semantic concepts. In crime prediction,...

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights...

A novel froth image analysis based production condition recognition method is presented to identify the froth phases under various production conditions. Gabor wavelet transformation is employed to froth image processing firstly due to the ability of Gabor functions in simulating the response of the...

To be cost-effective, most commercial off-the-shelf industrial controllers have low system order and a predefined internal structure. When operating in an industrial environment, the system output is often specified by a reference model, and the control system must closely match the model’s response....

Ever since the introduction of Pythagorean fuzzy (PF) sets, many scholars have focused on solving multicriteria decision-making (MCDM) problems with PF information. The technique for order preference by similarity to ideal solutions (TOPSIS) is a well-known and effective method for MCDM problems. The...

Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly,we also use color histogram(CH) and histogram of orientated gradients(HOG) to represent...

Uninorms play a prominent role both in the theory and the applications of aggregations and fuzzy logic. In this paper a class of uninorms, called group-like uninorms will be introduced and a complete structural description will be given for a large subclass of them. First, the four versions of a general...

In the learning process of the conventional SOM, the neuron which is closer to the winner neuron learns more than the neuron which is farther away from the winner neuron. The neurons farther away from input are not able to learn properly and some dead units are left on the map. To decrease dead unit...

Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN). Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main...

Recent deep convolutional neural network-based object detectors have shown promising performance when detecting large objects, but they are still limited in detecting small or partially occluded ones—in part because such objects convey limited information due to the small areas they occupy in images....

Computer-aided detection(CAD) system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and costsensitive support vector machine(C-SVM) classifiers is proposed for detecting both...

This paper proposes a novel approach to decision making based on grey relational analysis and MYCIN certainty factor, which describes decision making problems with fuzzy soft sets. Firstly, we utilize grey relational analysis to obtain the grey mean relational degree, and the uncertain degree of various...

Effective channel order determination is an important problem in convolutive blind channel identification. The classical techniques are based on information theoretic criteria, which show a great potentiality to estimate the effective channel order. However, these methods are just effective for the overdetermined...

Data stream mining has recently grown in popularity, thanks to an increasing number of applications which need continuous and fast analysis of streaming data. Such data are generally produced in application domains that require immediate reactions with strict temporal constraints. These particular characteristics...

In this paper, we investigate a class of stochastic shunting inhibitory cellular neural networks with time-varying delays. Applying integral inequality, some sufficient conditions on the existence and p-exponential stability of periodic solutions for stochastic shunting inhibitory cellular neural networks...

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order...

The content authenticity of the multimedia delivery is important issue with rapid development and widely used of multimedia technology. Till now many authentication solutions had been proposed, such as cryptology and watermarking based methods. However, in latest heterogeneous network the video stream...

As a relatively new collaborative service mode, Mobile Instant Messaging (MIM) is gaining ever greater popularity. MIM users interact within a complex system to influence others' adoption decisions, thus resulting in behavioral evolution. An agent-based computational approach is a methodology well suited...

Image segmentation plays crucial role in medical image analysis and forms the basis for clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes...

The purpose of a brain–computer interface (BCI) is the recording of brain signals to translate them into commands. This work proposes new naturalistic and intuitive motor imagery (MI) BCI task for aircraft Pilots for a BCI System, and explore if they take advantage of their previous motor experience...

We present a model made up of linguistic multi-criteria decision making processes to integrate the answers to heterogeneous questionnaires, based on a five-point Likert scale, into a unique form rooted in the widespread course experience questionnaire. The main advantage of having the resulting integrated...

In this paper, a novel fusion method based on Total Error Rate (TER) and multiple hidden layer probabilistic extreme learning machine is proposed. At first, the study transfers the matching scores into TER based on corresponding False Reject Rates (FRR) and False Accept Rates (FAR) aims at avoiding to...

The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution...

The high-tech consumer goods market is a rapidly changing environment. Project evaluation and selection play important roles in an organization to increase its competitive advantages successfully. When handling new product project decision problems, it is important to collect information to make the...

One of key issues for -() ary resolution automated reasoning based on lattice-valued logic with truth-value in a lattice implication algebra is to investigate the -() ary resolution of some generalized literals. In this article, the determination of -resolution of any 3-ary generalized literals which...

Today, computer networks are complex and illegal wiretapping is possible. This poses a real problem for the security during transmission of data. For ethical reasons, the transfer of sensitive data cannot be done with such a danger and must protect themselves. Protection best suited for this type of...

Access control is an important element in network security. Meanwhile, trust provides a new direction for access control in open network environments. Based on the dynamic nature of trust, we study the temporal and spatial characteristics in the security of society and propose the concept of scenario...

For decades, image enhancement has been considered one of the most important aspects in computer science because of its influence on a number of fields including but not limited to medical, security, banking and financial sectors. In this paper, a new gray level image (edge preserving) enhancement method...

Real world problems for prediction usually try to predict rare occurrences. Application of standard classification algorithm is biased toward against these rare events, due to this data imbalance. Typical approaches to solve this data imbalance involve oversampling these “rare events” or under sampling...

Rainfall prediction has emerged as a challenging time-series prediction problem in recent years. In this paper, we propose a novel rainfall prediction technique using effective feature indicators and a hybrid technique. Our proposed model consists of three phases, namely, layer model simulation, training...

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling...

The population migration algorithm is a very effective evolutionary algorithm for solving single-objective optimization problems, but very few applications are available for solving multi-objective optimization problems (MOPs). The current study proposes an improved population migration algorithm for...

Integrating aspects of the risk perception attitude framework and perceived interactivity, this study investigates the impact of the social media interaction environment on users' motivation and behavioral intention to reproductive health communication. A survey was conducted, and structural equation...

The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm...

Based on the fully implicational idea, we investigate the interval-valued fuzzy reasoning with multiantecedent rules. First, we construct a class of interval-valued fuzzy implications by means of a type of implications and a parameter on the unit interval, then use them to establish three kinds of fully...

Prior to every academic semester, every department's administrator is required to offer the best overall set of courses to meet student requirements, instructor needs and department regulations. The key contributions of this research is firstly, determining the potential factors that influence student...

The modeling of biological phenomena and its adaptations to distributed computing are promising research areas. The computational modeling of neurobiological phenomena, such as cognition and consciousness, has potential for applications into bio-inspired distributed computing. The functioning of neurological...

The flexible job shop scheduling problem (FJSP) is one of the most important problems in the field of production scheduling, which is the abstract of some practical production processes. It is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation...

Cloud computing bring a tremendous complexity to information security. Remote attestation can be used to establish trust relationship in cloud. TBVMM is designed to extend the existing chain of trust into the software layers to support dynamic remote attestation for cloud computing. TBVMM uses Bayesian...

For actual decision making problems, decision makers sometimes may have difficulty to provide all the preference information over alternatives through pairwise comparisons. In this paper, we focus on estimating missing elements for an incomplete uncertain 2-tuple linguistic preference relation. First,...