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Many oversampling methods applied to imbalanced data generate samples according to local density distribution of minority samples. However, samples generated by these methods can only present a non-deterministic relationship between the local and global distributions. A generative adversarial network (GAN) is a suitable tool to learn an unknown global probability distribution. In this paper, we propose an improved GAN (I-GAN) to oversample according to the global underlying structure of minority samples. The originality of I-GAN stems from the fact it provides additional density distribution information of minority samples for GAN and generated samples. By building on this idea, three detailed strategies are presented: input random vectors of the generator are sampled from a rough estimate of the distribution of minority samples to orientate fake samples more believable; a residual about minority samples is added into the discriminator to strengthen the constraint of loss function; generated samples are redistributed with a reshaper. These three strategies provide innovative methodologies at various stages of GANs for the oversampling task. Compared with 22 classical and popular imbalanced sampling methods under metrics of G m , F 1 , and A U C on 24 benchmark imbalanced datasets, it is shown that I-GAN is effective and robust. The I-GAN implementation line procedure has been uploaded to Github (https://github.com/flowerbloom000/I-GAN). [ABSTRACT FROM AUTHOR]

This paper investigates decision making with normalized probabilistic linguistic preference relations (NPLPRs). Consistency analysis is indispensable for deriving the reasonable ranking. After recalling previous research, we find that all previous concepts cannot fully define consistent NPLPRs. As a fundamental topic of decision making with preference relations, it is necessary to further study the consistency of NPLPRs. For this purpose, an interactive algorithm for deriving disjunctive probabilistic additive linguistic preference relations (DP-ALPRs) is provided, by which an additive consistency concept for NPLPRs is defined. When NPLPRs are unacceptably consistent, models for obtaining acceptably additively consistent NPLPRs are built. Considering the situation where only incomplete NPLPRs are obtained, a disjunctive probability and additive consistency based interactive algorithm for ascertaining missing judgments is provided. Meanwhile, we discuss group decision making (GDM) with NPLPRs and offer a distance measure based formula to determine the weights of the decision makers. In addition, the method defines a consensus index and builds models for improving the consensus level. Under the additive consistency and consensus discussions, an interactive algorithm for GDM with NPLPRs is proposed. Finally, the new method is applied to select green raw material suppliers to illustrate the application and compared with several previous ones. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine). • CNN design based on the novel data representation method leads to preferred classifier. • CPSO is applied to acquire the available features reordering matrix with spatial information from many matrices. • FPF is employed to copy with CO problem, which combines the FIS to self-adaptively modify penalty parameter. • Visualization technique of t-SNE is applied to the proposed data representation method. • The proposed FCPSO is applied to solve the real-life problem of black plastic wastes. [ABSTRACT FROM AUTHOR]

Increases in population and prosperity are linked to a worldwide rise in garbage. The "classification" and "recycling" of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model. • Develop a 2-layer decision system for waste sorting using deep learning. • Construct MFDM using fusion scores, criteria, and deep waste sorting models. • Entropy and VIKOR methods rank criteria and deep learning models. [ABSTRACT FROM AUTHOR]

Information quality has attracted increasing attention in recent years. In this paper, we propose a measure to quantify the information quality of intuitionistic fuzzy information based on a pseudo probability transformation, and derive its induced order to rank intuitionistic fuzzy values. The proposed measure and the induced order are proved to possess some well-defined properties that ensure rationality. Further, in order to rank intuitionistic fuzzy alternatives in decision making, we turn the proposed information quality into a ranking-oriented information quality, and present its induced order to generate the ranking. The decision capacity of our method is founded with the goal of rationalizing, simulating and further facilitating the interpretability and transparency in human decision making. A comparative study has been conducted, demonstrating the effectiveness of the proposed approach to ranking alternatives. The proposed approach is shown to outperform existing ranking methods via case-by-case comparisons. Finally, an application involving decision making of drug selection and an application in multi-criteria decision analysis of supplier selection are provided. [ABSTRACT FROM AUTHOR]

The growth potential of a career reflects its future prospects and is an important consideration for individuals and organizations when career planning. There is still a lack of quantitative assessment tools for growth potential of careers. In this study, considering the key role of human capital in human resource management, as well as the excellent performance of complex network and machine learning in big data analysis and prediction, a career growth potential assessment model with human capital ensemble is proposed through human capital-based occupational mobility network and ensemble learning. First, an occupational mobility network is constructed based on online professional dataset to associate occupations with each other. Then, five dimensions of human capital measurements are designed to quantify human capital in terms of education, experience, social capital, occupational size, and concentration. These are then combined with the occupational mobility network to create a new network that depicts human capital flows among occupations. Finally, an ensemble framework for assessing career growth potential is constructed to integrate multidimensional human capital information in the network and obtain quantitative scores of growth potential. This study is the original attempt to adopt a data-driven idea and an intelligent approach to understand career growth potential. The experimental results show that it also makes a useful exploration for modeling human capital flows and intelligent assessment of career prospects. [ABSTRACT FROM AUTHOR]

Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker. [ABSTRACT FROM AUTHOR]

The neutrosophic fuzzy set (NF-set) is a unique hybrid structure that combines the essence of the fuzzy and neutrosophic sets. It is an effective mathematical tool for dealing with indeterminate and inconsistent information in situations where the data are imprecise or vague. This paper defines the concepts of similarity-based information measures, including entropy and cross-entropy, between NF-sets. It is the first time these concepts have been introduced since the NF-sets were defined. We provide their interesting properties through well-proven theorems. In addition, we also propose a novel and efficient algorithm to make multi-criteria decision(s) using these information measures with a clear step-by-step illustration. It overcomes the limitations of the original algorithm when evaluating the criteria in two aspects: qualitative and quantitative. A real-world experiment is then conducted to assist students in choosing the optimal subject group(s) for the Vietnamese national high school graduation examination. Experimental results show that our method outperforms the previous original method when giving recommendations with sixteen correct cases, three acceptable cases, and one noisy case out of twenty real-life cases. Finally, the experimental results are presented visually, analyzed rigorously, and discussed carefully, intending to verify the validity and feasibility of the proposed algorithm. The Python source code for experiments is publicly available on Github. [ABSTRACT FROM AUTHOR]

As environmental problems are increasingly challenging and sustainable development win support among the people, the energy-efficient hybrid flow-shop scheduling problem (HFSP), as a scheduling problem with great application value, has been widely concerned. However, most existing research has focused on deterministic cases and uncertainty is rarely considered in energy-efficient HFSP (EHFSP), especially with various machine speed constraints. Uncertainty is often caused by some uncontrollable factors, such as human factors and ignoring uncertainty will greatly reduce the application value of the problem solutions. In this study, an energy-efficient fuzzy HFSP (EFHFSP) at a variable machine speed is considered and the existing non-dominated sorting genetic algorithm-II (NSGA-II) is extended to minimize fuzzy make-span and total fuzzy energy consumption simultaneously. The computation of total fuzzy energy consumption is given and reverse learning is proposed to produce the initial population. ENSGA-II adopts an effective genetic operator and its parameters (P c and P m) are adjustive. A novel strategy based on history information is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of ENSGA-II. ENSGA-II can provide promising results for EFHFSP. • A multi-objective mathematical model for energy-efficient fuzzy hybrid slow-shop scheduling with various machine speed is formulated. • In order to solve the problem of the subtraction of triangular fuzzy number not satisfying the closure, a repairing measure is proposed. • The operands of NSGA-II are changed from crisp numbers to triangular fuzzy numbers. • The main loop of NSGA-II is improved to enhance the efficiency of extended NSGA-II. [ABSTRACT FROM AUTHOR]

Intuitionistic multiplicative preference relation (IMPR) has been successfully and widely used to model the decision makers' preferences elicited by pairwise comparisons. As the critical links of group decision-making (GDM) procedure, the consistency improvement process (CIP) and consensus reaching process (CRP) have been investigated intensively; nevertheless, the extant methods explored the CIP and CRP of IMPRs without considering the situations of whether decision makers participate in the reassessment task. To overcome the limitation, this paper proposes two novel IMPR-based GDM approaches with CIP and CRP under different situations. First, when decision makers only express their original preferences, the novel consistency index and group consensus measure of IMPRs are defined to construct the CIP and CRP without feedback mechanism, in which the original preferences can be updated automatically. Second, when decision makers are required to modify their opinions, a series of consistency, consensus, and proximity degrees are put forwards to generate the identification and direction rules for improving the inconsistent and conflict preferences. Subsequently, the induced intuitionistic multiplicative ordered geometric averaging operator is constructed to fuse the individual preferences for determining the GDM results, in which the collective IMPR can maintain the acceptable consistency. Finally, a numerical example is applied to illustrate the feasibility and advantages of the proposed methods, which can improve the consistency and consensus levels of IMPRs according to the degrees of decision makers' involvement in the re-evaluation process. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA), as a powerful and effective risk assessment tool in reliability and safety analysis, has been extensively applied in different fields to enhance the reliability of a system. However, the traditional FMEA method has exposed some significant defects in practical applications. Furthermore, the risk ranking of failure modes is not convenient for subsequent maintenance strategies, and the interactions between risk criteria are not considered in most multiple criteria decision making (MCDM)-based methods. In this paper, we construct a novel FMEA model based on criteria importance through the inter-criteria correlation (CRITIC) method and three-way decisions to improve the performance of the conventional FMEA method and aid making a maintenance plan. Firstly, we introduce the decision-theoretic rough sets (DTRSs) into an interval 2-tuple linguistic (I2TL) environment to define interval 2-tuple linguistic decision-theoretic rough sets (I2TLDTRSs). Secondly, the CRITIC method is employed to calculate the weight of risk criteria. Thirdly, the conditional probability is obtained by a similarity-based method with the help of the ideas of the TOPSIS approach, and a relative loss function is designed by introducing risk avoidance coefficient. Fourthly, the classification of failure modes is derived according to the three-way decision rules. Finally, two numerical examples of the train door system are given to verify the effectiveness and practicality of the presented model. The presented model both extends the theory and application of three-way decisions and provides a solution beneficial for making a maintenance strategy to reduce the risk of failure modes. [ABSTRACT FROM AUTHOR]