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Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area.

The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information.

The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants.

This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem.

The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process.

A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion.

The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

The paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.

The proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.

The proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.

The obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.

Many researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

The status of welding process is difficult to monitor because of the intense disturbance during the process. The purpose of this paper is to use multiple sensors to obtain information about the process from different aspects and use multi‐sensor information fusion technology to fuse the information, to obtain more precise information about the process than using a single sensor alone.

Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, weld voltage, weld pool's image, and weld sound about the pulsed gas tungsten‐arc welding (GTAW) process. Then special algorithms were used to extract the signal features of different information. Fuzzy measure and fuzzy integral method were used to fuse the extracted signal features to predict the penetration status about the welding process.

Experiment results show that fuzzy measure and fuzzy integral method can effectively utilize the information obtained by different sensors and obtain better prediction results than a single sensor.

Arc sensor, visual sensor, and sound sensor are used in pulsed GTAW at the same time to obtain information, and fuzzy measure and fuzzy integral method are used to fuse the different features in welding process for the first time; experiment results show that multi‐sensor information can obtain better results than single sensor, this provides a new method for monitoring welding status and to control the welding process more precisely.

Multi‐sensor data fusion (MSDF) is defined as the process of integrating information from multiple sources to produce the most specific and comprehensive unified data about an entity, activity or event. Multi‐sensor object recognition is one of the important technologies of MSDF. It has been widely applied in the fields of navigation, aviation, artificial intelligence, pattern recognition, fuzzy control, robot, and so on. Hence, aimed at the type recognition problem in which the characteristic values of object types and observations of sensors are in the form of triangular fuzzy numbers, the purpose of this paper is to propose a new fusion method from the viewpoint of decision‐making theory.

This work, first divides the comprehensive transaction process of sensor signal into two phases. Then, aimed at the type recognition problem, the paper gives the definition of similarity degree between two triangular fuzzy numbers. By solving the maximization optimization model, the vector of characteristic weights is objectively derived. A new fusion method is proposed according to the overall similarity degree.

The results of the experiments show that solving the maximization optimization model improves significantly the objectivity and accuracy of object recognition.

The paper studies the type recognition problem in which the characteristic values of object types and observations of sensors are in the form of triangular fuzzy numbers. By solving the maximization optimization model, the vector of characteristic weights is derived. A new fusion method is proposed. This method improves the objectivity and accuracy of object recognition.

Estimating the pose – position and orientation – of a moving object such as a robot is a necessary task for many applications, e.g., robot navigation control, environment mapping, and medical applications such as robotic surgery. The purpose of this paper is to introduce a novel method to fuse the information from several available sensors in order to improve the estimated pose from any individual sensor and calculate a more accurate pose for the moving platform.

Pose estimation is usually done by collecting the data obtained from several sensors mounted on the object/platform and fusing the acquired information. Assuming that the robot is moving in a three-dimensional (3D) world, its location is completely defined by six degrees of freedom (6DOF): three angles and three position coordinates. Some 3D sensors, such as IMUs and cameras, have been widely used for 3D localization. Yet, there are other sensors, like 2D Light Detection And Ranging (LiDAR), which can give a very precise estimation in a 2D plane but they are not employed for 3D estimation since the sensor is unable to obtain the full 6DOF. However, in some applications there is a considerable amount of time in which the robot is almost moving on a plane during the time interval between two sensor readings; e.g., a ground vehicle moving on a flat surface or a drone flying at an almost constant altitude to collect visual data. In this paper a novel method using a “fuzzy inference system” is proposed that employs a 2D LiDAR in a 3D localization algorithm in order to improve the pose estimation accuracy.

The method determines the trajectory of the robot and the sensor reliability between two readings and based on this information defines the weight of the 2D sensor in the final fused pose by adjusting “extended Kalman filter” parameters. Simulation and real world experiments show that the pose estimation error can be significantly decreased using the proposed method.

To the best of the authors’ knowledge this is the first time that a 2D LiDAR has been employed to improve the 3D pose estimation in an unknown environment without any previous knowledge. Simulation and real world experiments show that the pose estimation error can be significantly decreased using the proposed method.

There is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.

By introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.

This approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.

This approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Mobile Service of Big Data can be supported by the fused technologies of computing, communication and digital multimedia. The purpose of this paper is to propose new agent-based proactive migration method and system for Big Data Environment (BDE).

First, the authors have designed new relative fusion method for making decision based on fuzzy-neural network. The method can make the fusion belief degree to be improved. Then the authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. Finally, the authors have designed the application system, which can support proactive seamless migration function for big data. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device.

The authors have proposed agent-based proactive migrating method with service discovery and key frames selection strategy. The method has innovation in which mobile service task of big data can dynamically follow its mobile user from one device to another device. The designed system is convenient to work and use during mobility, and which is useful or helpful for mobile user in the BDE.

The authors have clarified and realizes how to transfer service tasks among different distances in Big Data Environment (BDE). The authors have given a formal description and classification of the mobile service task, which is independent of the realization mechanism. In the designed and developed application system, the new idea adopts fuzzy-neural control theory to make decision for task-oriented proactive seamless migration application.

Quality cost is usually considered as a means to measure the quality level in a quality system. Since the interrelationship among quality cost components is complex, a general quantitative model for describing their relationship is not easy to construct for improving the quality. In the assessments of quality cost, some hidden quality costs, such as the goodwill loss due to lost customers’ reliability, are often neglected in the existing analysis methods. This may lead to reaching a sub‐optimal decision. In addition, the assessments of quantitative quality cost items are usually approximated, and therefore are imprecise in nature. Based on these considerations, we propose fuzzy approaches to evaluate quality improvement alternatives because of its fuzzy nature. An evidence fusion technique, namely Choquet fuzzy integral, is employed to aggregate the quality cost information. A composite index is determined to find the best quality improvement alternative. Finally, a numerical example is used to demonstrate the applicability of the approach.