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Due to the restriction of duality structure, existing discounting methods in Lévy models have not met the requirements from psychologists and economists. To address this weakness, our paper invents a stochastic heterogeneous quasi-hyperbolic (SHQH) clock. The SHQH clock has the following strengths: (i) being invisible in the duality structure; (ii) capturing multi-period, multi-goods and multi-agent preferences; (iii) nesting many discount functions as its special cases. Using a self-driven approach, this paper applies the SHQH clock to an insurance policy-holder who is both a farsighted planner and a myopic doer. The time-consistent insurance strategy is explicitly solved via equivalent martingale measure. • Designing a preference tool based on psychological experiments. • Accommodating dynamic preferences varying over time, good & decision-maker. • Making time-consistent decision under Lévy process & time-inconsistency. • Adopting a self-guided and assumption-free approach. [ABSTRACT FROM AUTHOR]

• Setting a mathematical-model approach to solving selection of giant machine. • Validation of the problem-solving selection approach for the modernization process. • Methodology selection of bucket wheel excavators were proposed. • This methodology is reliable and suitable tool for the purpose. This paper presents a methodology for evaluating and ranking different bucket wheel excavators (BWE) for determining which machine should enter in the process of revitalization and modernization. Various multicriteria methods are used. Combining them achieves the best effect when choosing BVE for the modernization process. AHP, TOPSIS, VIKOR, and PROMETHEE methods are used. The hybrid models are developed from the multi-criteria decision making (MCDM) methods and they are used to evaluate the basic indicators that characterize the operation of a BWE. The methods will be applied to each group of the parameters, their sub-parameters and alternatives in the selection. The models are defined in two parts. In the first part, the priority vectors of parameters are defined by applying the AHP method. Other methods are applied in the second part where the evaluation of alternatives is made according to the defined parameters. That forms three partial hybrid models AHP-TOPSIS, AHP-VIKOR, AHP-PROMETHEE. The analysis parameters are qualified in four groups Technical, Working, Maintenance, and Economics parameters. Validation and selection of the optimal method will be performed in relation to the expert analysis with prepared questionnaires. The selected method should help in defining which machine will enter in the process of revitalization and modernization. According to this method, there is a 100% match with AHP-PROMETHEE, and the ranking given with AHP-TOPSIS corresponds to 75%, while AHP-VIKOR corresponds to 50%. Model AHP-PROMETHEE for the selecting of the BWE is a reliable and suitable tool for this purpose. In comparison to the classical statistical methods, this approach can achieve great saving. The BWE are very expensive machines and their value is 20–30 million euros. The investment in revitalization and modernization is 25–35% of the purchase price. This explains the importance of forming such an evaluation model. The presented analysis showed the possibility of the application of hybrid combined methods. The results in the paper presented the benefit from two aspects. The first aspect is the formation of a model that can be applied to other similar problems. Another aspect is the choice of the machine itself based on a complex analysis of a large number of parameters. Generally, the model becomes universal and can be used for later similar analyzes. [ABSTRACT FROM AUTHOR]

Prediction of clinical outcomes using the patient's medical data enhances clinical decision making and improves prognostic accuracy. Deep learning (DL) for medical decision support systems has particularly shown expert-level accuracy in predicting clinical outcomes. However, most of these machine learning and Artificial Intelligent (AI) models lack interpretability which causes major trust-related problems in healthcare. This necessitates the need for interpretable AI systems that can explain their decisions. This paper is designed to address the problem of how clinical decision support systems can be designed to be transparent, interpretable and therefore comprehensible for humans. In this view, this paper proposes an attentive hierarchical adaptive neuro-fuzzy inference system (AH-ANFIS), that combines fuzzy inference in a hierarchical architecture with attention for selecting the important rules. The proposed model benefits from the rule-based structure of ANFIS that enables the user to interpret the abstractions of hidden layers. The hierarchical structure in fuzzy modeling helps to overcome the rule explosion problem that arises with large medical data and improve interpretability. To ensure the improvement of interpretability with hierarchical architecture, an evolutionary algorithm (EA) is used to decompose the input space into an optimal permutation of input subsets which results in subsystems with independent meaning. The attention-based rule selector identifies the most activated rule to select important patient-specific features for predicting the clinical outcome. To verify the performance of the proposed AH-ANFIS and analyze the important features for classification, we perform experiments with two cancer diagnostic datasets. By pruning fuzzy if-then rules using recursive rule elimination (RRE), the complexity of the model is largely reduced while maintaining the performance of the system making it more interpretable. • The AH-ANFIS combines fuzzy inference in a hierarchical architecture. • The hierarchical structure in fuzzy modeling overcome the rule explosion problem. • Interpret the system's conclusions based on fuzzy rules extraction. • An EA is used to decompose the input space into the optimal input subsets. • Improve the interpretability by pruning fuzzy if-then rules. [ABSTRACT FROM AUTHOR]

Regarding to group decision-making, decision makers may express different decision preferences for the same target. Since existing decision-making methods only consider a single decision preference at most, it is difficult to integrate the multi-decision preferences of multiple decision makers effectively. In this paper, we explore a class of heterogeneous multi-attribute group decision-making (MAGDM) problems with multiple decisions. Over the framework of granular computing, we present a new MAGDM method with the aid of the valued outranking relations in the ELECTRE III method. Considering the non-compensability among attributes, we first define a special valued outranking relation on a heterogeneous attribute multi-decision information system, and then present the notion of valued outranking classes. Subsequently, given the fact that the fault tolerance of probability approximations can reduce the vulnerability of decision-making methods, we construct multi-decision multigranulation probabilistic rough sets (MD-MGPRSs) form different perspectives, which extend existing rough set models to a more general situation. After that, we establish a novel MAGDM method, and apply the method to a real medical case to illustrate its applicability. Lastly, the experimental results on seven datasets demonstrate the effectiveness, robustness, and superiority of the method. Our study not only enriches the theoretical research of rough sets, but also extends the application scope of decision-making theory. • A novel concept of valued outranking classes is presented. • Novel MD-MGRSs and MD-MGPRSs are constructed. • An MAGDM method based on MD-MGPRSs is established. • The robustness, validity and superiority of the established method are verified. [ABSTRACT FROM AUTHOR]

• A novel evaluation framework for storm surge disaster emergency plans is established. • The social network theory is introduced to evaluate the trust relationships. • An UPMSM operator is used to aggregate the unreasonable hybrid information. • A DELGDM model for storm surge disaster evaluation is proposed. Storm surges are natural disasters that cause abnormal fluctuations in seawater owing to severe atmospheric disturbances. Storm surges have the characteristics of a fierce, fast and destructive force, and can cause serious casualties and economic losses in coastal areas. To minimize losses within the shortest time and with less material consumption, it is technological to improve emergency decision-making capacity. Therefore, in this paper, a dynamic emergency large-group decision-making (DELGDM) model is proposed to technological evaluate multiple emergency plans. A novel evaluation framework of storm surge disaster emergency plans is firstly established from the two dimensions of the objectives of storm surge emergency response plan and the characteristics of storm surge emergency response action. The uncertain power Maclaurin symmetric mean (UPMSM) operator is then proposed by considering that the unreasonable assessment of large-group decision-makers (DMs) can affect the decision results and the interrelationship among input parameters. Moreover, the social network theory is introduced to evaluate the trust relationships among large-group DMs, and the weight of experts is determined by combining the influence of experts and their knowledge and experience level. Finally, the effectiveness of the proposed method is verified through a case study on storm surge emergency plan optimization. The method is applicable to the optimization of emergency decision-making and provides ideas for solving the selection of various emergency plans. [ABSTRACT FROM AUTHOR]

With increasingly frequent and serious emergency problems, timely and optimal emergency decision-making (EDM) becomes the key to reducing casualties and losses. EDM is characterized by urgency, volatility, and uncertainty, thus it is crucial to deal with considerably ambiguous information. This paper mainly investigates the continuous dynamic EDM problem with picture fuzzy (PF) information. First, we construct a dissimilarity measure between picture fuzzy numbers (PFNs) to discriminate PF information. Next, we deduce the differential and integral of the PF function (PFF) based on the basic operations of PFNs and then apply the theory to time-varying (TV) PF circumstance such that the TV-PF weighted averaging operator can be defined according to the derived linear operation of time-varying PF integral. In order to reduce the computational difficulty of PF integral on each period, we adopt the way of numerical integration for approximation and divide the integration intervals into smaller and more ones to improve the accuracy. Based on these, we propose the aggregation method of the dynamic decision information represented by TV-PFNs and combine the TODIM method to establish a methodology of EDM with TVPF information. Finally, we apply the proposed methodology to an emergency case and illustrate the feasibility and validity through sensitivity analysis and comparison analysis. [ABSTRACT FROM AUTHOR]

We can formulate complex automation systems with advanced decision-making methods. This work proposes a new multi-criteria group-based decision-making (MCGDM) method based on the linguistic neutrosophic cloud (LNC). As an efficient linguistic expression, the linguistic neutrosophic set (LNS) introduces linguistic terminology into a neutrosophic set to make it more complex. However, there are inherent problems with linguistic values and neutrosophic sets. First, existing operators cannot handle linguistic neutrosophic numbers (LNN) with extreme values while producing distorted results. Second, the subscript-based computation of linguistic values does not reflect the change of ambiguity during the operation. Third, the literature review rarely considers the randomness of uncertain variables. To eliminate the drawbacks of previous studies, this paper proposes a multi-criteria group-based decision-making (MCGDM) method considering the linguistic neutrosophic cloud (LNC). The proposed method presents a distance measure for LNCs based on Wasserstein distance and develops an improved MCGDM method based on weighted modified partial Hausdorff distance. With an extensive simulation, the feasibility of the proposed method is verified by solving an auto part selection problem. Finally, we show the superiority of the proposed method through a comparison with four different aggregation operators of LNNs in the literature review. [ABSTRACT FROM AUTHOR]

Despite the turmoil in the global financial sector over the years, Asian banking stood still with many positive characteristics - stable, balanced competition, good governance, and profitable – becoming a fertile ground for performance studies. This paper departs from previous banking performance studies in Asia by proposing a novel stochastic Multi-Criteria Decision-Making (MCDM) based on sign decomposition, offering a novel perspective in five analytical fronts. First, criteria decorrelation and filtering from country and time impacts as an initial data treatment. Second, endogenous computation of bank criteria importance for the alternatives comprised by each bank and year under analysis, in a "one-stop-shop" approach. Third, derivation of partial performance scores for each criterion under each alternative, besides traditional overall performance scores for each alternative. Fourth, mapping the exogenous/endogenous relationships among bank filtered criteria from country and year impacts using transfer entropy. Fifth, prediction of the overall performance drivers in terms of country-related contextual variables using neural networks. Our findings indicate that bank liquidity, bank capitalization, net income as well as the ability to generate profit for the shareholders are significantly related to bank performance. In addition, we find that accounting standards have a significant impact on bank performance. Although inflation is found to be positively related to bank performance, the results show that bank performance is helpful to promote people's economic standard and life expectancy. Our study provides important implications to macroeconomic management to balance the development of macroeconomic development and bank performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ship energy systems (SESs) benchmarking model for performance measurement of sustainable transportation based on the extension of q-rung orthopair fuzzy rough sets (q-ROFRS) and multicriteria decision-making (MCDM) methods. The underlying research methodology consists of two main stages: (i) Formulation of the SES decision matrix between SESs and the sustainability, (ii) Development of a q-ROFRS and fuzzy-weighted zero-inconsistency (q-ROFRS–FWZIC) model to determine the weights of each criterion. The integrated model of the q-ROFRS and fuzzy decision by the opinion score method (q-ROFRS-FDOSM) is offered as a tool for benchmarking the SESs. Sixty-two SESs are evaluated and benchmarked according to the three layers of criteria concerning the five design alternatives. The analysis of the proposed q-ROFRS–FWZIC methodology revealed that decision support methods (C2) is the most important criterion with a weight of 0.4174, followed by gas emissions (C1.1.2) and economic criterion (C1.1.1) with weights of 0.1661 and 0.1498, respectively; and energy efficiency design index (C1.2.1) is the least important. Furthermore, the results from q-ROFRS-FDOSM reveal that SES62 is the most suitable SES followed by SES60, whereas SES37 is the least suitable. Finally, the robustness of the proposed method is assessed by conducting a sensitivity analysis. [ABSTRACT FROM AUTHOR]

• Management of a pandemic could be enhanced by a rational public health decision making process. • Decision Support Systems (DSS) can facilitate the exploration of pandemic-related questions. • A public health decision-making process could be formalized and embedded within a modular DSS. • A methodology for designing a modular DSS for public health decision-making is presented. • The modular DSS would include established data analytic methods such a data mining methods and DEA. The awareness of the occurrence of a new disease involves much uncertainty and the search for answers and also appropriate questions. In this paper we focus on the perspective of public health decision-makers. Typically, they would have a standard set of questions and supporting metrics that have been found in previous disease outbreaks to be useful in assessing the effectiveness of various 'solution' methods on the trajectory of the disease. There may be other relevant questions with which such public health domain experts may not be familiar and/or for which they are familiar but are not aware of methods for addressing such questions when there is limited data. Decision Support Systems (DSS) can be used to facilitate the exploration of established questions and some other relevant questions. Given an initial set of questions, the DSS designer should consider which sets of data analytic methods have the capabilities to adequately address. Some of these data analytic methods may also have the capability of addressing questions that could be of interest to the public health decision makers including researchers. In this paper we present a conceptual design for a relevant easy-to-construct DSS and an example of a multi-method DSS that is based on this conceptual design. Using publicly available data on the CoViD-19 pandemic, we illustrate benefits of the multi-method DSS in action. [ABSTRACT FROM AUTHOR]

• The score function is proposed for hesitant Fermatean fuzzy set. • Unify the framework of Regional Green Development Level Evaluation. • Improve MULTIMOORA method is proposed with hesitant Fermatean fuzzy information. • Level Difference Maximization Method to combine subjective and objective weights. • Case study of RGDL is presented to show the potentiality of the developed method. Economic green development has received increase attention. However, the uncertainty decision processes in the development of green economic growth has brought challenges to the evaluation, which leads to the evaluation index system has not yet reached a consensus. In order to solve the above problems, this paper, based on the established and developed regional green development level evaluation (RGDLE) system in existing literature, employed improved MULTIMOORA group decision model under the hesitant fuzzy Fermatean environment (HFFS-MULTIMOORA) and an innovative weighting method to develop the RGDLE process. This evaluation framework describes the complexity of the RGDLE process. The Level Difference Maximization Method to combine the average-based subjective weight-determining method and the Shannon-entropy-based objective weight-determining method is developed to fit RGDLE problems with cognitive complex information. This innovative index system and HFFS-MULTIMOORA method based group decision-making framework is further applied to evaluate the green development level of five provinces and municipalities along the Yangtze River Economic Zone, China. Results showed the optimal green development levels ranking of the five alternative provinces and municipalities of the Yangtze River Economic Zone, China. Moreover, the effectiveness and feasibility of the model framework was verified by robustness and comparative analysis between the existing method. This paper contributes not only to the literature on RGDLE but also to giving decision-makers and researchers inside and outside China valuable inspiration and references for improving the evaluation efficiency and giving policy suggestions. [ABSTRACT FROM AUTHOR]

• Estimate the relevance of contextual information for music recommendation. • Model users' context-sensitive preference for music. • Recommendation quality is improved by treating criteria preferences as contexts. • Experimental results show the advantages of the proposed method. • The method outperforms both traditional baselines and context-aware models. Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user's context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main developments on sorting approaches. • The categorization of multiple-criteria decision-making sorting methods. • Applications of the multiple-criteria decision-making sorting methods. • Trends and future directions of MCDM Sorting. Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods' trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research. [ABSTRACT FROM AUTHOR]

• Proposing a novel MCDM method. • Developing the BWM for considering fuzzy pairwise comparisons. • Obtaining crisp weights from a fuzzy pairwise comparison matrix. • Superiority to the two well-known previous BWMs. One of the latest multi-criteria decision-making methods is best-worst method (BWM). In the procedure of BWM, decision maker (DM) identifies the most and the least important criteria namely, best and worst. Thereafter, DM identifies the degrees which he believes the best criterion is better than the other criteria, and identifies the degrees which he believes the other criteria are better than the worst criterion. Because of uncertainties in comparisons due to using linguistic variables for pairwise comparisons and also the lack of complete information, the crisp values of pairwise comparisons cannot appropriately model the problems. This paper develops the BWM for considering fuzzy pairwise comparisons (FBWM) by proposing a new fuzzy mathematical model which yields crisp weights from a fuzzy pairwise comparison matrix. Unlike to some previous papers that obtains fuzzy weights from fuzzy pairwise comparison matrix, the crisp weights of the proposed method of this paper eliminates the supplementary aggregation of fuzzy weights and ranking procedures. Moreover, the proposed method avoids obtaining the different ranking results due to the different ranking procedures of fuzzy numbers. Another outstanding advantage of this paper is that the obtained weights of the proposed method better satisfy the initial judgments compared to previous methods, while as we know, the satisfaction of the initial judgments is essential for pairwise comparison judgments. This paper presents several numerical examples to prove the good performance and the merit of the proposed method. According to the provided numerical examples, the proposed method of this paper absolutely outperforms the two well-known previous methods. [ABSTRACT FROM AUTHOR]

• Link prediction in multiple networks is regarded as an MADM problem. • The TOPSIS method is employed to rank potential links. • The interlayer relevance is used to gauge the weight for each layer. • Experiments show the proposed method achieves superior performance. This paper investigates the link prediction in multiplex networks. Multiplex networks that represent multiple types of interaction between the same group of individuals are a special case of complex networks. Each type of interaction is modeled as a layer in a multiplex network. Usually, the topological structures between different layers of a multiplex network have a certain extent of correlation. As a result, the accuracy of link prediction in multiplex networks can be enhanced by combining the information of different layers. In this paper, link prediction in multiplex networks is regarded as a multiple-attribute decision-making problem, in which the potential links in the target layer are considered as alternatives, layers are viewed as attributes, and the similarity score of a potential link in each layer is an attribute value. In implementation, the TOPSIS method is employed to rank alternatives, and interlayer relevance is used to weight the attributes. The experimental results show that the proposed method is not sensitive to the parameter and the interlayer relevance measure, and achieves superior prediction accuracy. [ABSTRACT FROM AUTHOR]

• The interval type 2 fuzzy Bonferroni mean Ordered Weighted Averaging is developed. • Seven Bonferroni mean quantifier Ordered Weighted Averaging models are formulated. • The optimal weights are solved from the mixed integer linear programming models. • The models are applied to various linguistic decision making aggregation schemes. In this paper, the new aggregation models for the interrelated multi-criteria decision making (MCDM) problems based on the quantifier guided ordered weighted averaging (QGOWA) and Bonferroni mean (BM) operator with interval type 2 fuzzy sets (IT2FS) are developed. In most MCDM problems, the decision criteria might not be totally independent. For some MCDM methodologies that are not considering the interrelationship of the criteria, the decision results suggested are meaningless. The BM operator can express the interrelationship of the input arguments, which serves as a mean type aggregator. Yager introduced the OWA operator which is associated with the orness level (attitudinal character) by means of the quantifiers. Different quantifier functions are associated with the respective different orness levels. This is referred to as the QGOWA operator. Besides, the real world MCDM problems are mostly under uncertain environments. To address such MCDM problems, the linguistic criteria weights and the alternative rates are better characterized by IT2FS. The major contributions of this paper are to propose the interrelation MCDM aggregation models with various extensions, to construct the mixed integer linear programming models for obtaining the optimal QGOWA BM IT2FS weights, and to formulate a new interrelation MCDM paradigm. The developed aggregation models are: (1). Ordinary MCDM aggregation; (2). BM with OWA weights; (3). BM with OWA weights and personal importance; (4). BM with QGOWA weights; (5). BM with QGOWA weights and personal importance; (6). BM with QGOWA weights and attitudinal characters; (7). BM with QGOWA weights, attitudinal characters and personal importance. A new MCDM aggregation methodology with application based on the developed models is introduced. The application results from the MCDM aggregation methodology demonstrate that the final decision prioritization are actually affected by the various orness levels predetermined by the decision experts. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) have obtained wide attention in recent years, which is relied on their attractive socio-economic benefits and potential to improve safety. One of the main characteristics of MASS is that it can use perception information to replace Officers on Watch (OOW) to make different navigation decisions through expert and intelligent systems, so as to make navigation more intelligent, reliable and safer, which is mainly based on the Maritime Autonomous Navigation Decision-making System (MANDS). However, the state-of-the-art study lacks a systematic description and research on MANDS, and most of them mainly focus on solving a single navigation decision-making task, which is not aligned with the meaning of autonomous navigation. Therefore, this study develops a novel MANDS and installs it on a 300TEU intelligent container ship to successfully carry out real ship trials of autonomous navigation. In this paper, the definition of autonomous navigation is first proposed, which mainly contains four different navigation tasks, namely "Keep route", "Route Optimization", "Collision Avoidance", "Return Back". A well-designed architecture for MANDS is presented which includes corresponding decision-making models and communication interfaces with external systems. Furthermore, in order to realize the switching and deployment between different navigation tasks in the process of autonomous navigation, a novel hierarchical decision-making mechanism is proposed and analyzed, and finally the MANDS is integrated with perception system and ship control system respectively. Before the real ship trial, this system is tested and verified in a simulation-based environment which is composed of the electronic chart platform and ship motion simulator, we also design a scenario library for the testing work. At last, real ship trials of autonomous navigation for MANDS are carried out, which can convincingly prove the performance of MANDS. This study proposes a relative complete solution for autonomous navigation, including system modelling, integration and real ship trial. These findings offer new insight into the research of ship autonomous navigation and promote the improvement of ship intelligence and informatization level. [ABSTRACT FROM AUTHOR]

The potential of using computer vision to estimate the weight of pigs has been demonstrated in several studies. In this paper, we propose a Vision Analysis and Prediction (VAP) system, which consisted of two main parts: (1) computer vision – a vision algorithm was developed to segment the pigs and identify which pig area could be used to estimate the corresponding weight; (2) statistical analysis – a statistical method for predicting pig weight using the area was presented, including BLUP model and linear regression as well as a prediction function. The results showed that our approach has successfully estimated the weight of growing pigs (approximate ranges from 20 to 105 kg) with an accuracy of weight estimation of 97.76% on average for the predicted mean weight at pen level. The proposed system does not require modifications to the building and shows a strong potential to be utilized in pig weight estimation in slaughter pens. • A VAP system has great potential for pig weight estimation in slaughter pens. • The VAP includes two parts: a computer vision algorithm and a predicting model. • The VAP can accurately predict pig weights from 20 to 105 kg in a slaughter pen. • The VAP can help for optimal decision making to improve animal productivity. [ABSTRACT FROM AUTHOR]

• The student dormitory design plan is selected using novel hybrid subjective and objective methods. • The proposed method is examined for the student dormitory design plan with subjective and objective methods. • The subjective and objective methods are constructed via the analysis of the advantages and disadvantages of weighting methods. • Methodology is showed a potential assessment tool for the student dormitory design plan. • The subjective and objective methods are used to study and evaluate the student dormitory design plan. To solve multi-attribute decision making problem of limited schemes, multiple attribute indexes is generally synthesized to a evaluation index. So it is necessary to determine the weighting coefficient of each attribute index. In this paper we propose a method of multi-attribute decision making based on subjective and objective weighting coefficients range. The hybid subjective and objective method is applied to study and evaluate the student dormitory design plan. Through analysis and comparison, weighting coefficient of each index may be calculated with the method, provided weighting range of choosing a best dormitory design plan index and its comprehensive evaluation value are given. The method not only meets the needs of contemporary students, but also conforms to the development of the school, and has certain reference value for the future construction of student dormitories. [ABSTRACT FROM AUTHOR]

In recent years, multi-access edge computing (MEC) has become a hot topic. With its distributed characteristics, MEC provides more possibilities for delay-sensitive tasks. In this paper, we study a task offloading problem to shorten task delay. The problem consists of two aspects, bandwidth allocation and task offloading decision-making. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Game participants are all users. We take into account the dissatisfaction of each alliance and find the least dissatisfaction of the alliance. Then, we formulate the task offloading decision-making to minimize task delay. Task delay consists of communication delay and execution delay. Computing and storage capacity are treated as limiting conditions for decision-making. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm. • The alliance game is used to program the bandwidth allocation problem. • A delay sensitive task offloading problem is formulated. • Multi-objective particle swarm algorithm is used to optimize bandwidth allocation. • The Branch and Bound method is used to construct the propagation tree. [ABSTRACT FROM AUTHOR]

• A novel consensus method has been developed for multi-agent decision systems. • The method is specially designed to allow large groups of decision-makers. • Social rules and component division are introduced to improve the method response. • The method allows to manage previous influence. • HIVES can be applied beforehand to obtain group decisions for any MCDM method. Aggregating multiple opinions or assessments in a decision has always been a challenging field topic for researchers. Over the last decade, different approaches, mainly based on weighting data sources or decision-makers (DMs), have been proposed to resolve this issue, although social choice theory, focused on frameworks to combine individual opinions, is generally overlooked. To resolve this situation, a novel methodology is developed in this paper based on social choice theory and statistical mathematics. This method innovates by dividing the assessment into components which provides a multiple assessment analysis, assigning weights to each source regarding their position compared to the group for each considered criteria. This multiple-weighting process maximises individual and group satisfaction. Furthermore, the method makes it possible to manage previously assigned influence. An example is given to illustrate the proposed methodology. Additionally, sensitivity analysis is performed and comparisons with other methods are made. Finally, conclusions are presented. [ABSTRACT FROM AUTHOR]

• The modeling of fog/cloud offloading using replicator dynamics is scalable. • It saves 180% of the time compared to the best state-of-the-art methods. • It saves 380% of the energy compared to the best state-of-the-art methods. • The proposed method gains more users' latency satisfaction. • The proposed method is promising for energy-constraint applications. Due to the growing demand for delay-sensitive applications, partial offloading is very important in fog/cloud computing. In partial offloading, different parts of each task are run in parallel on the end device, fog device, and remote cloud. Generally, a fog/cloud environment consists of independent, selfish agents who want to get more resources. Game theory is a well-known mathematical tool for modeling the interactions of these selfish agents. In traditional game-theoretical models, the mobile user is considered a player. A significant problem with such modeling is scalability. When the number of players is very large, the algorithm may require a long time to converge. The reason is that with the increase in the number of users, the search space for strategies becomes larger. The player must have complete knowledge about competitors to make the best decision. Moreover, the dynamic changes in the fog environment have been ignored in almost all previous studies. This paper presents dual-population modeling using replicator dynamics considering the interaction between the user and the fog environment. Unlike classical game theory, here, the offloading decision (strategy) is viewed as the player. The proposed method can very efficiently model the growing/shrinking behavior of two populations of CPU cycles on both the user and the network sides. It is scalable and converges to a stable equilibrium point regardless of the number of mobile users. We derive conditions under which agents can update their decisions according to the dynamics of the environment. The simulation results reveal the superiority of the proposed method over the full local execution, full offloading, evolutionary algorithms, and the Stackelberg methods. It saves 180% and 380% of the time and energy compared to the best state-of-the-art and baseline methods. [ABSTRACT FROM AUTHOR]

New Unmanned Aerial Systems (UAS) applications will increase air traffic densities in metropolitan regions. Collision avoidance systems (CAS) are a key component in integrating a high number of UAS into the airspace in a safe way. This paper presents a distributed, autonomous, and knowledge-based CAS, called Dronetology System (DroS), for UASs. The CAS proposed here is managed using a novel ontology, called Dronetology-cas, which allows to make autonomous decisions according to the knowledge inferred from the data gathered by the UAS. DroS is deployed as part of the payload of the UAS. So, it is designed to run in an embedded platform with limited processing capacity and low battery consumption. DroS collects data from sensors and collaborative elements to make smart decisions using knowledge obtained from collaborative UASs, adapting the maneuvers of the aerial vehicles to their original flight plans, their kind of vehicle, and the collision scenario. DroS accountability involves recording its internal operation to assist with reconstructing the circumstances surrounding an autonomous maneuver or the details previous to a collision. DroS has been verified using the hardware in the loop (HIL) technique with a UAS traffic environment simulator. Results obtained show a significant improvement in terms of safety by avoiding collisions. • Distributed, autonomous and knowledge-based collision avoidance system for UASs. • Autonomous decisions according to the knowledge inferred from the data gathered. • Accountability of the circumstances surrounding a maneuver or collision. • Verified using HIL with an UAS traffic environment simulator. • Significant improvement in terms of safety by avoiding collisions. [ABSTRACT FROM AUTHOR]

• The Wasserstein method measures the distance between two Z-numbers. • The Exp-TODIM method for MADM is built on the Z-Wasserstein distance. • A case study for choosing a reasonable carbon storage site is given. • The sensitivity analysis is given to illustrate the stability of the method. • Some comparative analysis is used to state the advantages of the method. Z-numbers, as relatively emerging fuzzy numbers, are to a large extent close to human language. For this reason, the Z-number is a powerful tool for representing expert evaluation information. However, the Z-number is more complex than the general structure of fuzzy numbers since it consists of both the fuzzy restriction A and the reliability measure B. As a result, calculating of the Z-number is a very complex process. This paper uses a modified Wasserstein distance to measure the distance between two Z-numbers, which avoids the loss of information better than the existing metric. Then a new decision model is constructed by combining the Z-Wasserstein distance with the exponential TODIM method(exp-TODIM), which is less susceptible to changes in parameters and has good stability. Next, a detailed example of choosing a reasonable carbon storage site is given to illustrate the feasibility of the exp-TODIM method with wasserstein distance. Finally, a sensitivity analysis is given to illustrate the stability of the method, and a comparative analysis is used to state the advantages of the method. [ABSTRACT FROM AUTHOR]

• OCRs are integrated with PLTS. • PL-ITARA is designed for determining m-payment attributes' importance. • The ELECTRE I method is improved to rank m-payment services. The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology. [ABSTRACT FROM AUTHOR]

The problem of guaranteeing the fairness of automatic decision making systems has become a topic of considerable interest. Many competing definitions of fairness have been proposed, as well as methods aiming to achieve or approximate them while maintaining the ability to train useful models. The complimentary question of testing the fairness of an existing predictor is important both to the creators of machine learning systems, and to users. More specifically, it is important for users to be able to prove that an unfair system that affects them is indeed unfair, even when full and direct access to the system internals is denied. In this paper, we propose a framework that enables us to prove the unfairness of predictors which have known accuracy properties, without direct access to the model, the features it is based on, or even individual predictions. To do so, we analyze the fairness-accuracy trade-off under the definition of demographic parity. We develop an information-theoretic method that uses only an external dataset containing the protected attributes and the targets and provides a bound on the accuracy of any fair model that predicts the same targets, regardless of the features it is based on. The result is an algorithm that enables proof of unfairness, with absolutely no cooperation from the system owners. • Fairness of automatic decision-making systems is highly important to users. • Users typically do not have direct access to the system internals. • We show that unfairness can be proven without direct system access. [ABSTRACT FROM AUTHOR]

In-spite of availability of wide range of algorithms for constructing multi-class classifier, there are applications like Decision support system, Game prediction in the sports, where the multi-class classifier performs relatively poor in terms of achieving the reasonable classification accuracy. In this paper, poorly performing multi-class classifier (constructed using the classical methods like Artificial Neural Network, cascade of Support Vector Machine, etc.) is treated as the discrete memoryless channel model with known transition probabilities (channel matrix) and the unknown priors. It is further used to construct M-ary Mini-Max technique based randomized decision rule to improve the performance of the multi-class classifier in terms of the classification accuracy. The prior probabilities and the probabilities associated with the M-ary randomized decision rule are further solved using the Particle Swarm Optimization. The experimental results based on the Monte-Carlo simulation using the synthetic data set and the real data set reveal the consistent improvement in the performance of the poorly performing classifier using the proposed technique. • Discrete Memoryless Channel using the trained classifier's confusion matrix. • Particle Swarm Optimization (PSO) based M-ary mini-max Hypothesis Testing. • Bayes rules at prior probabilities using the matrix S for M-ary case. • Improvement of the classification accuracy of the under-performing classifier. • Demonstrating effectiveness in Human Decision Making based on experience. [ABSTRACT FROM AUTHOR]

• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

Random forest is an efficient and accurate classification model, which makes decisions by aggregating a set of trees, either by voting or by averaging class posterior probability estimates. However, tree outputs may be unreliable in presence of scarce data. The imprecise Dirichlet model (IDM) provides workaround, by replacing point probability estimates with interval-valued ones. This paper investigates a new tree aggregation method based on the theory of belief functions to combine such probability intervals, resulting in a cautious random forest classifier. In particular, we propose a strategy for computing tree weights based on the minimization of a convex cost function, which takes both determinacy and accuracy into account and makes it possible to adjust the level of cautiousness of the model. The proposed model is evaluated on 25 UCI datasets and is demonstrated to be more adaptive to the noise in training data and to achieve a better compromise between informativeness and cautiousness. • A cautious classifier based on random forests and belief functions is proposed. • Tree weights are automatically learned from data using a new cost function. • The cautiousness of the model is tuned using a single parameter. • Extensive experiments demonstrate the interests of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of decision making, three-way decision making has been proven more fruitful for providing scopes to make delayed decision along with acceptance and rejection simultaneously. As a result, the decision risk and loss, which could occur due to take rapid decisions in traditional two-way decision making, are effectively reduced. Accordingly, this paper offers a novel three-way multi-attribute decision making model by combining three-way decision making and multi-attribute decision making under an incomplete information system. The incertitude in the information system is illustrated by introducing interval-valued Fermatean connection number based on interval-valued Fermatean fuzzy number and set pair analysis theory. Thereafter, the achievement of this study is five-fold. First, a possibility dominance relation is developed under incomplete information system in the basis of possibility degree measure of interval-valued Fermatean connection number. Second, we put forward a novel procedure to set up the fuzzy state set. Third, the conditional probability is estimated in light of the fuzzy state set and probability dominance relation. Fourth, the relative utility functions are determined in the virtue of regret theory. Lastly, a three-way multi-attribute decision making model is implemented in incomplete information system and exploited to deal with incomplete multi-attribute decision making problems. Eventually, the propriety, stability and superiority of the proposed model is established via conducting the comparative and experimental analysis. • Introducing a novel uncertainty, namely, interval-valued Fermatean connection number. • Developing a new approach to derive fuzzy state set and conditional probability. • Calculating relative utility functions by using regret theory. • Proposing a novel three-way multi-attribute decision making model. [ABSTRACT FROM AUTHOR]

Multimodal optimization problems (MMOPs) are single-objective optimization problems with multiple optimal solutions. It is essential to find as many optimal solutions as possible to make a robust decision. However, it is a very challenging task. In this paper, an enhanced adaptive neighborhood-based speciation differential evolution (EANSDE) is proposed to solve the MMOPs effectively, which can be featured as: (i) The parameters are adaptively controlled to alleviate the fine-tuning of parameters by the users. (ii) An external archive is introduced to store the inferior solutions discarded in each generation. When the population size is reduced to a preset threshold, the solutions in the archive are merged with the current population in the following search. (iii) The crowding relieving mechanism is proposed to remove extremely similar individuals from population. Compared with related methods, experimental results indicate the superiority of EANSDE on the 20 benchmark MMOPs in CEC-2013. • An adaptive neighborhood-based speciation DE is proposed. • An external archive is used to fully utilize useful information. • Diversity is enhanced with a crowding relieving mechanism. • Results indicate the superiority of our approach. [ABSTRACT FROM AUTHOR]

Learning to rank (LTR) is a method of ranking search results using machine learning techniques. Currently, the reinforcement-learning-based ranking models have achieved some success in LTR task. However, these models have disadvantages like high variance gradient estimates and train inefficiency, which bring great challenges to the convergence and accuracy of the ranking model. Combining short- and long-term returns, this paper proposes AMRank, an adversarial Markov ranking model, which is based on reinforcement learning and formalizes the ranking task as a Markov decision process. To address the aforementioned weaknesses, in AMRank, we present a sequence discriminator to output a long-term return with a smaller variance and conduct single step updates, and use a document discriminator to yield a short-term return. The two discriminators are trained simultaneously before the decision is made. In the training process, the policy network is applied as a generator to sample candidate documents and get negative samples. At the beginning of the decision, the discriminator outputs the returns based on the environment state and the policy, and finally updates the parameters of the policy network using the policy gradient method. Experimental results on three LETOR benchmark datasets, OHUSMED, MQ2007 and MQ2008, demonstrate that the proposed AMRank outperforms the baseline models in document ranking task. • AMRank, a novel document ranking model, is proposed, which combines MDP and GAN. • AMRank employs long- and short-term returns to improve decision making. • A sequence discriminator is presented to generate long-term returns. • AMRank can realize one-step update and output return with less variance. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

This paper introduces and investigates ranked hesitant fuzzy sets, a novel extension of hesitant fuzzy sets that is less demanding than both probabilistic and proportional hesitant fuzzy sets. This new extension incorporates hierarchical knowledge about the various evaluations submitted for each alternative. These evaluations are ranked (for example by their plausibility, acceptability, or credibility), but their position does not necessarily derive from supplementary numerical information (as in probabilistic and proportional hesitant fuzzy sets). In particular, strictly ranked hesitant fuzzy sets arise when no ties exist, i.e., when for any fixed alternative, each submitted evaluation is either strictly more plausible or strictly less plausible than any other submitted evaluation. A detailed comparison with similar models from the literature is performed. Then in order to produce a natural strategy for multi-criteria multi-agent decisions with ranked hesitant fuzzy sets, canonical representations, scores and aggregation operators are designed in the framework of ranked hesitant fuzzy sets. In order to help implementation of this model, Mathematica code is provided for the computation of both scores and aggregators. The decision-making technique that is prescribed is tested with a comparative analysis with four methodologies based on probabilistic hesitant fuzzy information. A conclusion of this numerical exercise is that this methodology is reliable, applicable and robust. All these evidences show that ranked hesitant fuzzy sets are an intuitive extension of the hesitant fuzzy set model designed by V. Torra, that can be implemented in practice with the aid of computationally assisted algorithms. • We introduce a new model called ranked hesitant fuzzy sets. • We develop scores in this framework. • An algorithm produces information fusion of ranked information under hesitancy. • Mathematica codes facilitate reproducibility of these computations. • We apply these elements to multi-criteria multi-agent decisions. [ABSTRACT FROM AUTHOR]

Since the different matching schemes between manufacturing tasks and services have a great influence on the life cycle sustainability of complex product system (CoPS), the strategy used to find cost-effective, environmentally friendly, and socially acceptable matching results is essential for sustainable development. This paper proposed a novel two-sided matching model for CoPS based on life-cycle sustainability assessment (TSMM-CoPS-LCSA). First, the two-sided matching model based on the life-cycle sustainability assessment of manufacturing tasks and services for CoPS is presented. Then, a bi-level decision-making method is proposed, where learning and synergy effect are considered to ensure stability in the matching process. Sequentially, to facilitate the optimization solution, the Karush-Kuhn-Tucker (KKT) conditions are adopted to convert the bi-level model to a single model which aims to minimize the total cost, environmental impact, and social impact. Subsequently, for solving this nonlinear and complex multi-objective decision-making model, an improved hybrid algorithm that integrated Tabu Search and non-dominated sorting genetic algorithm II (TS-NSGA-II) is applied to solve the above model. Finally, a case study of CoPS manufacturing project is illustrated to demonstrate the effectiveness and superiority of the proposed TSMM-CoPS-LCSA model. The results indicate that the proposed method can find a better services scheme in line with actual demand, which provides a guiding significance for promoting the sustainable development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

• A new Multi-Criteria Decision-Making Method, ABAC, is proposed. • ABAC is simple, more scalable and free from rank reversal problem. • ABAC is illustrated and validated using cloud service selection. • Various case studies using ABAC in different domains are provided. Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%–95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ − ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers. [ABSTRACT FROM AUTHOR]

• Addresses the non-compensation between risk factors in the hospital's context. • Applies a consensus reaching process technique in an operating room risk assessment. • Proposes the mitigation complexity as a new risk factor for FMEA. • Applies DHHFLTS in a hospital context. • Combines DHHFLTS with ELECTRE TRI. The operating room (OR) is one of the most important departments of any hospital. Mismanagement can lead to severe problems with patient's safety and satisfaction, costs, and profitability. The Failure Mode and Effects Analysis (FMEA) is a risk assessment tool used to identify potential failure modes (FM) which should be prioritized to be mitigated. To assess the risks of an OR, some important aspects cannot be neglected: it should be conducted by a team of experts from different functional areas; address the non-compensation between criteria; classify FM, instead of ranking it, since it is easier to understand and support decision making; address the subjectivity present on experts' opinion; and consider the mitigation complexity, since hospital's resources are limited. However, were not found studies in the literature which simultaneously covered all these aspects. Therefore, this paper proposes a new group decision model, which combines a consensus reaching process with ELECTRE TRI and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS). A pilot application of the model was conducted in an OR of a Brazilian hospital. The results have shown that the consensus technique, the mitigation complexity, and the non-compensation can modify the failure modes classification. Also, the DHHFLTS allowed the experts to use more complex linguistic terms to express their opinion. In addition, the pilot application was carried out during the covid-19 pandemic and the results have shown that due to the high number of elective surgeries being rescheduled to give space to urgent surgeries, the patients' health problems become even worst and they turn to urgent surgeries. [ABSTRACT FROM AUTHOR]

• Performance measurement complexities during relief operations are key motivations. • Strategies aimed to improve the performance measurement in HSC were proposed. • Proposed strategies were modelled using the grey-DEMATEL and m-TISM approach. • The proposed framework may help managers improve performance measurement in HSC. Performance measurement activities (PMA) in the humanitarian supply chain (HSC) face several issues. Many of these issues are avoidable and inter-related, subsequently producing undesirable cascading effects. HSC Stakeholders are actively looking for means to reduce the complexities associated with PMA and improve its efficiency. In this paper, we identify and model the eleven strategies that can improve both PMA and the performance measurement system (PMS). Strategies were identified using detailed literature reviews and discussions with the experts and were modelled using the grey DEMATEL and m-TISM methodology. The results indicate that the most effective strategies are fundraising for PMA, Digital supply chain adoption, Top management support, Public awareness activities and Relief worker training. Four strategies can be grouped into the cause side and seven strategies on the effect side. The key recommendation is to rapidly adopt digital supply chain applications in the HSC. HSC stakeholders also need to pay increased attentions towards fundraising activities targeting PMS. This study advances the understanding of PMS in the context of HSC. The decision-maker can adopt the study's findings to reduce the complexities that occurred during PMA. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new decision making method for selection problems under uncertainty is proposed. • The method considers both the objective weights and the subjective preferences. • The method is applied to the selection of renewable energy technologies. • The best option varies depending on the coefficient factor of the subjective weight. • The method is applicable to other selection problems in different areas. The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives' scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations. [ABSTRACT FROM AUTHOR]

Purulia district is a drought-prone area in West Bengal, India. Naturally, the farmers of the Purulia district could not plant the crops due to the absence of water resources. Water resource management (WRM) has the potential mechanism to address the available water resources. This paper developed a multi-criterion water resource management technique for defining the available water resources in the agriculture field. We have introduced a novel ranking method of generalized single-valued non-linear bipolar neutrosophic numbers based on possibility measures. Additionally, we have first defined the possibilistic mean of generalized single-valued non-linear bipolar neutrosophic number. Using the possibilistic mean, we have invented the positive and negative rank expositors ( S μ + , S μ − ) for authenticity, hesitate, and falsity membership function. In the present situation, water resource management in agriculture has become a big problem worldwide. Multi-criteria decision-making technique is necessary for developing this situation. Using the proposed multi-criteria decision-making method, we have solved a real water resource management problem in the Purulia district under GSVnTbN-environment. In this decision-making problem, we have considered the GSVnTbN-environment. Because during the summertime, water demand is very high, and at the end of summer, when monsoon season comes, water demand is deficient in the agriculture field of Purulia district. Sometimes the monsoon season, rainwater does not occur entirely. At that time, monsoon crop planting was tough in the agriculture field. So, this district's water scarcity nature is a non-linear and uncertain type of nature. Therefore, we proposed an MCDM method for water resource management problems under GSVnTbN-environment. • MCDM technique of water resource management (WRM) problem. • Possibilistic mean of GSVnbN number. • Positive and negative rank expositor ( S μ + , S μ − ) of GSVnbN number. • WRM problem of agriculture field in the Purulia district. • WRM problem solved under GSVnbN environment. [ABSTRACT FROM AUTHOR]

• Propose a novel knowledge-based method for tool path planning. • Extract knowledge from the process documents based on syntactic dependency analysis. • Propose a hybrid clustering method for point cloud semantic segmentation. • The efficiency of the method is validated by the application in automotive exterior painting. Proper tool paths ensure the high-efficiency and high-quality machining of workpiece. The current tool path planning method is inadequate to meet the complex process requirements for the complex workpiece of abundant local features. Due to the lack of ability of independent process decision-making, the planning effect is still heavily dependent on human experience and the planning is time-consuming and laborious, which is not conducive to shortening development cycle of new products. Therefore, a novel knowledge-based method for tool path planning is proposed in this paper. The proposed method can automatically plan the tool path that meets the requirements of the machining process by the adaptive process decision-making according to the perceived features of complex workpiece. The knowledge extraction method based on the syntactic dependency analysis is used to extract named entities in the process document. And the correctly classified entities are filled in the nodes of knowledge model represented by the form of frame to finish the establishment of process knowledge base. Then, with the help of feature extraction module based on the hybrid clustering method, the semantic segmentation of the point cloud of the workpiece is completed, and the Feature Matrix (FM) is formed. This matrix is used to provide feature information required for process decision, including Feature Vector (FV) and Structure Description Matrix (SDM). Finally, the tool path is generated in the Path Generator. At last, an automatic path planning system is developed for the painting process of automotive exterior. The planned paths are tested for 20 types of bumpers, where the chromatic aberration Δ E of each product is less than 1.4; both the average thickness of each paint and the requirement of production rate are satisfactory. These test results verify the effectiveness of the proposed tool path planning method. [ABSTRACT FROM AUTHOR]

• A new knowledge description form with adaptive parameters is designed. • A data-driven interpretable associative reasoning model is established. • An associative learning method to obtain adaptivity in dynamic system is proposed. • A continuous decision-making model is built to obtain optimal scheme. Adaptive decision making in continuous production process is challenging for the uncertain characteristics of dynamics system state, decision-making time, as well as the correlation between the previous decision and the subsequent decision. In this paper, a data-driven continuous decision-making model with self-adaption and interpretability is proposed. For the uncertainty of industrial data, a new form of information granule with variable parameters is designed to describe the knowledge extracted from data. For the continuity of the decision-making process, a data-driven interpretable associative reasoning model is established by considering the similarity of knowledge and the attention mechanism. Aiming at the dynamic feature in the decision-making process, a data-driven associative learning method is proposed to obtain the adaptivity of the proposed model, which generates and updates routes according to the co-activation and simultaneous activities of the information granules. To reason decision results, the proposed model searches and records the reasoning routes according to the reasoning results and the objective at the decision points. Noisy chaotic time series data and a number of classification datasets are employed for experimental analyses, and the results indicate that the proposed method exhibits high reasoning accuracy, anti-noise ability, and interpretability. In addition, the experimental results based on industrial data indicate that the decision-making model is capable of giving a reasonable, effective and interpretable decision scheme and suitable for establishing a credible decision-making system. [ABSTRACT FROM AUTHOR]

2-dimension linguistic intuitionistic fuzzy set (2DLIFS) includes two classes of linguistic intuitionistic fuzzy variables, which has a more powerful and flexible ability in representing and handling uncertainty than 2-dimension linguistic fuzzy set (2DLFS) and intuitionistic fuzzy set (IFS). Distance and similarity are two important tools to quantify the difference and closeness of 2DLIFSs. This paper proposes the Euclidean distance and the cosine similarity measures for 2DLIFSs, respectively. Due to the limitation of the Euclidean distance and the cosine similarity measures for 2DLIFSs, a hybrid distance for 2DLIFSs is introduced by integrating these two measures. Further, based on the hybrid distance for 2DLIFSs, 2DLIF-PROMETHEE methods are introduced to deal with the multiple attribute decision making (MADM) problems under 2DLIF environment. Finally, a numerical example is provided to demonstrate the advantages and practicality of 2DLIF-PROMETHEE methods. • Euclidean distance for 2DLIFSs is proposed to measure the difference of 2DLIFSs. • Cosine similarity for 2DLIFSs is introduced to measure the similarity of 2DLIFSs. • A hybrid distance for 2DLIFSs is presented by combining the above two measures. • Six improved functions are proposed based on the hybrid distance for 2DLIFSs. • The 2DLIF-PROMETHEE methods are developed to handle MADM problems. [ABSTRACT FROM AUTHOR]

• An integrated MADM approach for identifying Micro-Enterprises accident causes is proposed. • Cause-effect relation between the factors contributing to the micro-enterprises accidents can be determined and understood. • Hierarchal structure of the factors contributing to the micro-enterprises accidents can be determined and understood. Micro-enterprises are acknowledged as one of the most high-risk workplaces often due to poor safety practices and performance. Despite ongoing efforts to reduce these workplace accidents, they remain as a serious problem. Therefore, understanding the relationship between the contributing factors to workplace accidents in micro-enterprises remains a key area of interest for safety professionals. This paper presents a new integrated approach comprised of a D number Decision Making Trial and Evaluation Laboratory (D-DEMATEL) technique, and an Intuitionistic Fuzzy Interpretive Structural Modeling (IFISM) technique. The DEMATEL technique is applied to determine the cause-and-effect relationships of contributing factors, and D number theory is applied to take into account their uncertain characteristics. Lastly the IFISM technique is used to examine the hierarchical structure of driver-driven relationships. Results showed that sixteen factors contribute to the micro-enterprises' workplace accidents in which "repairing or maintaining equipment under unsafe conditions" and "improper use of tools" are the most important causes. The hierarchal structure analysis showed that the root and basic causes of micro-enterprises' workplace accidents based on the ISM method were the "improper use of tools" and "repairing or maintaining equipment under unsafe conditions". This study is the first of its kind to propose a hybrid D-DEMATEL–IFISM method for identifying and evaluating the contributing factors affecting the occurrence of workplace accidents in micro-enterprises. The new method allows insights on contributing factors to be based not only importance, intensity and impact of each identified contributing factor but also on the relationship amongst the contributing factors. The study presents key information on the main contributing factors to micro enterprise workplace accidents. These results provide safety professionals with evidence which can be used to focus safety efforts, with a view to reducing workplace accidents in micro enterprises. [ABSTRACT FROM AUTHOR]

The management of sick leaves is a critical task that public and private health systems carry out. This enables the good care of sick workers and guarantees a safe return to their jobs. Most health systems enforce regulations that establish the duration of sick leave according to general rules for groups of diagnoses. However, these regulations do not account for the particularities of workers. On the one hand, an early return to work is sometimes possible, but this does not happen unless the worker pro-actively requests it. On the other hand, the worker's health condition could demand for one or more leave extensions, but the system requires mandatory and sometimes unnecessary follow-ups, adding nuisance to patients and overhead to health systems. In both cases, the lack and excess of action by the health system represents extra costs for society. This paper proposes the analysis of a voluminous historical dataset of sick leaves (including medical and personal data) to predict the duration of future sick leaves for patients. The data mining process is performed for a large number of diagnoses to assess the possibility of using data driven models for broad decision-making. The nature and characteristics of the data makes it difficult to obtain models using classical methods, which is why the analysis focuses on non-linear machine learning-based survival analysis methods. In sight of the models performance, we move forward to its practical implementation, proposing a tool to manage the decision of what patients should be contacted at a given date using the predictions of the trained models. This tool will manage the whole cycle, continuously training on new data, performing daily predictions, and presenting the results to the health-care decision-maker for their assessment. • Modelling of Patient sick leaves at large scale from historical data. • Studying non-linear survival analysis methods for decision support systems. • Analysing attribute relevance extracted from decision trees survival models. • Building a Decision Support System for sick leaves management. [ABSTRACT FROM AUTHOR]

With the increase of fall incidents in emergency cases and expenditures in the healthcare sector, fall prevention has become a very important study. The aim of this paper is to develop a ranking system to capture the risky gaits to aid in the reduction of fall incidents. With this, an approach driven framework under the interval-valued intuitionistic fuzzy environment is developed for decision making. The proposed framework is validated by experimental data and is seen to be effective and reliable. It is applied to risky gaits and a ranking is obtained based on the mindset one chooses. Technically, we also extended the entropy measure to incorporate the hesitancy factor to interval-valued region where a new measuring function is introduced. We believe that the proposed system will provide a better support for rehabilitation arrangements. • We developed a system to prevent falls by ranking risky gaits. • Interval-valued intuitionistic fuzzy sets are used to model experts knowledge. • Gaits are evaluated and optimistic, pessimistic and neutral rankings are provided. • A new measure score helps in ranking risk of falling of the subjects. • Entropy measure incorporating hesitancy factor is extended to interval valued region. [ABSTRACT FROM AUTHOR]

This paper proposes an obstacle avoidance strategy for small multi-rotor drones with a monocular camera using deep reinforcement learning. The proposed method is composed of two steps: depth estimation and navigation decision making. For the depth estimation step, a pre-trained depth estimation algorithm based on the convolutional neural network is used. On the navigation decision making step, a dueling double deep Q-network is employed with a well-designed reward function. The network is trained using the robot operating system and Gazebo simulation environment. To validate the performance and robustness of the proposed approach, simulations and real experiments have been carried out using a Parrot Bebop2 drone in various complex indoor environments. We demonstrate that the proposed algorithm successfully travels along the narrow corridors with the texture free walls, people, and boxes. • A reinforcement learning-based obstacle avoidance algorithm for small drones. • A monocular vision-based obstacle avoidance using the estimated depth information. • A newly-designed reward function for the fast and safe algorithm. • Applying the learned policy from simulations to real worlds without further training. [ABSTRACT FROM AUTHOR]

Most online service providers utilize a recommender system to help their customers' decision making process by producing referrals. If a customer requests a suggestion for a specific item, the recommender systems produce predictions for it. On the other hand, it is also possible to create top- n lists containing the products that the customer might like the most. Recommender systems' outcomes depend on individuals' preferences which can be provided by considering a single criterion or multiple criteria about the services or products. Therefore, there must be methods to produce predictions and top- n lists for single and multiple-criteria datasets. Although the researchers introduced several algorithms on single criterion-based ratings for producing single predictions and top- n lists, there are only methods for producing referrals for a specific item on multi-criteria data. Accordingly, this paper proposes an intuitionistic fuzzy set-based top- n recommender system method with a novel neighborhood formation process for multi-criteria datasets. The proposed method consists of two crucial points: (i) Determining the relational structure between products; (ii) Investigating user tendencies, as well as their distinctive structures and rating distributions. The rating distribution and the relational structure between the products are determined with association rule mining and entropy measure, while the attitudes and tendencies of the users during the evaluation are analyzed with intuitionistic fuzzy sets. We also adopt a single-criterion top- n method to a multi-criteria recommender system, and we employ crisp ratings instead of fuzzy ones to compare the performance of the proposed method. The measurements of serendipity, diversity, and novelty are utilized to show how the experimental results are compelling. When the experiments' results are examined, it is concluded that our method can generate successful top- n lists. • Novel multi-criteria top- n RS based on a new neighborhood selection process (NSP) is developed. • The new NSP based on the different properties of the products is applied using entropy and ARM. • Effective analysis of products and users has improved the quality of the RS. • The criteria weighting is carried out using entropy. [ABSTRACT FROM AUTHOR]

For estimation performance ranking, the ranking vector (RV) approach using pairwise comparison information is efficient and processes advanced properties (e,g, homogeneity, invariance, monotonicity, decisiveness, and getting around intransitivity problem). However, the existing RV approach does not use quantity information of comparison and thus may be unreliable for inadequate number of error measures. To address this drawback, we develop two extended versions of the previously proposed RV approach in this paper. The first one focusing on using quantity information is based on a novel data normalization approach named pairwise data normalization. It quantifies the relative distance (goodness) of two elements with respect to a perfect point as the reference. The second version accounts for attribute difference and determines the weights of attributes accordingly. If the data of an error measure are highly different from those of others, this error measure should be weighted more in the performance ranking because it definitely reflects a different performance aspect. We compare different RV approaches, analyze their pros and cons, and discuss how to apply them for different cases. Examples are also provided to illustrate the application of these approaches to estimation performance ranking. • A new data normalization method named pairwise data normalization is proposed. • We propose an extended RV approach by considering the quantity information. • We propose another extended version by considering attributes differences. • We fully compare different RV approaches and analyze their pros and cons. [ABSTRACT FROM AUTHOR]