With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based on deep neural network can avoid studying electrochemical phenomena and manual extracting the features in battery. But single neural network has the different prediction accuracy and features extraction on different dataset. In this study, an ensemble method for the heterogeneous neural network is proposed, which integrates the prediction results of multiple heterogeneous neural networks with the adaptive weight. The weight of the neural network is higher with the closer correlation to the majority prediction results, vice versa. Furthermore, the weight of the neural network is adjusted via the predicting results for neural network on the different dataset, so that the computed weight of the neural network is adapted to the various dataset, and the effects of poor predictions of certain neural networks can be reduced sufficiently. The effectiveness of the ensemble method is verified on MIT-Stanford LIB degradation dataset, and the results show that the proposed method has higher accuracy than the existing ensemble methods for neural network.

When a small portion of the decision makers hold the correct information and the majority hold the opposite, the correct ranking of the alternatives for the group decision-making cannot be obtained with the current methods. A novel method is thus developed to tackle this challenge in this article. The priori probabilities of each alternative can be calculated via the opinions of the group decision makers, which are presented as the pairwise comparisons of the alternatives in the form of the linguistic preference relation. Based on the aggregated probabilities of the alternatives in the group of the decision makers, the normalized-prediction selection rate (NPSR) is defined and calculated accordingly. The alternative with maximal NPSR is selected as the correct answer, whereas the accuracy of the correct alternative selection (CAS) is guaranteed by two propositions. The iterative algorithm is first devised to determine the ranking of the alternatives depending on the CAS. For the proposed method, the decision makers require no modification of the opinions as can avoid the consensus problem, and the CAS can be obtained under the circumstances that the correct information is held by the minority of the group. Finally, the experiment has been conducted to demonstrate the efficacy of the proposed method to obtain the CAS, and the main limitations of proposed method are carefully addressed as well.

In recent years, Complex Network theory and graph algorithms have been proved to be effective in predicting software bugs. On the other hand, as a widely-used algorithm in Complex Network theory, k-core decomposition has been used in software engineering domain to identify key classes. Intuitively, key classes are more likely to be buggy since they participate in more functions or have more interactions and dependencies. However, there is no existing research uses k-core decomposition to analyze software bugs. To fill this gap, we first use k-core decomposition on Class Dependency Networks to analyze software bug distribution from a new perspective. An interesting and widely existed tendency is observed: for classes in k-cores with larger k values, there is a stronger possibility for them to be buggy. Based on this observation, we then propose a simple but effective equation named as top-core which improves the order of classes in the suspicious class list produced by effort-aware bug prediction models. Based on an empirical study on 18 open-source Java systems, we show that the bug prediction models' performances are significantly improved in 85.2 percent experiments in the cross-validation scenario and in 80.95 percent experiments in the forward-release scenario, after using top-core. The models' average performances are improved by 11.5 and 12.6 percent, respectively. It is concluded that the proposed top-core equation can help the testers or code reviewers locate the real bugs more quickly and easily in software bug prediction practices.

Pythagorean Fuzzy Preference Relations (PFPRs) have been considered in recent literature more powerful and flexible than the popular intuitionistic fuzzy preference relation in dealing with the linguistic imprecision for decision makers in the large scale group decision making. Following on this promising trend, a novel approach based on the PFPRs is proposed for decision support. In particular, the proposed work starts with the acquisition of the optimal comparison matrices, which essentially record the pairwise comparison of the alternatives from the positive and negative opinions. The proposed consensus reaching process is then utilised to guide the decision makers to revise the provided information in order to reach the overall group consensus, before the derivation of rankings of the alternatives. Experimental studies are provided to demonstrate the workings and effectiveness of the proposed approach in comparison with two state-of-the-art methods.

Sentiment analysis mines people's opinions and attitudes regarding a certain issue from source materials. Recently, it has drawn significant attention in a number of application areas. The sentiment analysis of healthcare in general and that of users' drug experience in particular could shed significant light on how to improve public health and make the right decisions. However, one of the major challenges in sentiment classification lies in the very large number of extracted features. Fuzzy-rough feature selection provides a means by which discrete or real-valued noisy data can be effectively reduced without human intervention. This paper proposes an implementation for automatic sentiment classification of drug reviews employing fuzzy rough feature selection. Experimental results demonstrate that the employment of fuzzy-rough feature selection can indeed significantly reduce the complexity of feature space and the classification run-time overheads while maintaining classification accuracy.

The methods with consensus reaching process can obtain a collective solution which is supported by most of decision makers in larger-scale group decision making. However, in case decision makers who could give correct opinions are from the minority, the conventional methods with consensus reaching process can not obtain the correct answer. In this paper, a novel method is developed to tackle this challenge. The decision makers give the opinions utilizing pairwise comparisons of the alternatives from positive and negative views based on intuitionistic fuzzy preference relation. The obtained opinions are translated into intuitionistic fuzzy numbers, and are further grouped and aggregated according to the alternatives. Based on the aggregated intuitionistic fuzzy numbers, the prediction normalized rate is defined and calculated for each alternative, the alternative with the minimal prediction normalized rate is selected as correct one. The experimental results show that the proposed method can obtain the correct answer even when the actual correct opinions are reflected by a small number of decision makers.

Crowd intelligence opens up new ways for decision making in open environments, traditional decision making is unable to effectively make correct decisions in open environments. In this paper, positive and negative comparing method using linguistic scale is proposed to make decisions in the open environments with crowd intelligence. Firstly, the crowd participants compare the alternative with the corresponding positive and negative assessment points, and give their evaluations using linguistic scales form positive and negative views. The crowd participants' evaluations can be translated into Intuitionistic Fuzzy Numbers (IFNs). In the proposed methods, the evaluations given by the crowd participants do not depend on the pairwise comparisons of the alternatives, the consistent problem can be avoided. Secondly, the consensus measures between aggregating results and IFNs are proposed. Based on these concepts, the aggregating methods that without discarding any IFNs are proposed and studied. The studying results show that the proposed methods can improve the consensus measures between the aggregating result and evaluations given by crowd participants.

Aggregation operators for intuitionistic fuzzy information, the popular methods in group decision, face the challenge in this area - counter-intuitive result (the decision result is conflict with people's intuition under specific inputs). In this paper, the Mixed Intuitionistic Fuzzy Aggregation Operators (MIFAOs) are proposed to relieve this problem. Firstly, the Bivariate Mixed Intuitionistic Fuzzy Aggregation Operators (BMIFAOs) are introduced based on the proposed extensions of t-conorms and t-norms. Some basic operational laws are proposed for Archimedean t-conorms and t-norms. Thus, the effects of unusual Intuitionistic Fuzzy Numbers (IFNs) would be decreased on final aggregating results. Secondly, the Multivariate Mixed Intuitionistic Fuzzy Aggregation Operators (MMIFAOs) are proposed, by extending BMIFAOs to higher dimensions. The basic operators are deduced for the Algebraic t-conorms and t-norms. Finally, an illustrative example is utilized to demonstrate the process of aggregating the IFNs by utilizing the proposed MMIFAOs in this paper. The results show that the proposed MIFAOs can relieve the counter-intuitive results for aggregation operators on unusual IFNs.

Linguistic pairwise comparison matrices are widely used in decision-making procedures. However, the matrices often give conflicting results when there are multiple criteria under consideration. Despite intensive research, achieving consistency of such matrices remains a daunting task. In this paper, a novel approach based on linguistic discrete region is proposed to address the challenge. Unlike existing methods that require a single value for each comparison, our approach allows a comparison to be expressed by a discrete region with multiple linguistic terms. Such front-end gives users more freedom to express their opinions. In the back-end, we propose an iterative searching algorithm that is able to achieve approximate optimal consistency for the comparison matrices with discrete region values. The final results are single-value matrices that not only guarantee approximate optimal consistency but comply with evaluators' intentions a well, as our approach does not modify any linguistic values like many existing methods. We have conducted extensive evaluations, and our empirical study confirms that the linguistic discrete region-based approach significantly improves the consistency of linguistic pairwise comparison matrices.

Intuitionistic Fuzzy Set (IFS) is considered as a nature solution for information fusion. How to transform the information with non-uniform distribution into IFS? In this paper, the authors proposed an approach to deal with this challenge. First, the intensive region (IR) of non-uniform distribution data is searched. Second, the evaluation grades are assigned to IR and other regions. IR is assigned to more grades, because of the higher data rate in it. Finally, the non-uniform data is translated into IFS based on the suitable grades assignment. The experiment is conducted to study the effectiveness and advantage of this approach.

H. Zhang, Q. Zheng, and T. Liu et al. proposed a discrete region based approach to improve the consistency of the pair-wise comparison matrix. The approach is able to significantly improve the consistency of pair-wise comparison matrix without to revise the decision maker's opinion. In the approach, a discrete region matrix is transformed into a set-matrix in which the elements are the real number set. In this paper, a discrete region matrix is transformed into a reciprocal interval matrix. A new iterative searching algorithm (NISA) is proposed to find the pair-wise comparison matrix with approximate optimum consistency from the reciprocal interval matrix. Based on the similarly principle, a new algorithm is proposed to derive the interval weight vector for the reciprocal interval matrix. The key character of this algorithm is that the derived interval weight vector includes the weight vector got by NISA for the same reciprocal interval matrix. In the experiment, five experimental strategies are designed, and the experimental results show that H. Zhang et al. proposed approach and NISA can get approximately similar weight vector according to the same pair-wise comparison matrix used the discrete region.

The consistency of pair-wise comparison matrix is a serious challenge for the multiple-criteria decision-making problem. However, existing methods are either too complicated to be applied in the revising process of the inconsistent comparison matrix or are difficult to preserve most of the original comparison information due to the use of a new pairwise comparison matrix. In this paper, a discrete region-based approach is proposed to improve the consistency of the pair-wise comparison matrix. When the decision makers feel confused or uncertain, they could express their evaluation as a discrete region containing multiple judgments, instead of a single result. A new data structure, named as set-matrix, is designed to store the combinations of those multiple judgments. An iterative searching algorithm is designed to find the pair-wise comparison matrix with approximate optimal consistency from the set-matrix. The experiments show that: 1) the consistency is significantly improved when the experts apply the discrete region evaluation instead of the single evaluation; and 2) the users can find the matrix with approximate optimal consistency quickly exploiting the iterative searching algorithm.