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• Time series Covid-19 prediction problem, a case study in Vietnam. • A dual attention mechanism with RNN-based auto-encoding for handling chaotic time series prediction task. • Extensive experiments with realistic Covid-19 data in Vietnam. • Datasets and implementation source code are available for future studies. For preventing the outbreaks of Covid-19 infection in different countries, many organizations and governments have extensively studied and applied different kinds of quarantine isolation policies, medical treatments as well as organized massive/fast vaccination strategy for over-18 citizens. There are several valuable lessons have been achieved in different countries this Covid-19 battle. These studies have presented the usefulness of prompt actions in testing, isolating confirmed infectious cases from community as well as social resource planning/optimization through data-driven anticipation. In recent times, many studies have demonstrated the effectiveness of short/long-term forecasting in number of new Covid-19 cases in forms of time-series data. These predictions have directly supported to effectively optimize the available healthcare resources as well as imposing suitable policies for slowing down the Covid-19 spreads, especially in high-populated cities/regions/nations. There are several progresses of deep neural architectures, such as recurrent neural network (RNN) have demonstrated significant improvements in analyzing and learning the time-series datasets for conducting better predictions. However, most of recent RNN-based techniques are considered as unable to handle chaotic/non-smooth sequential datasets. The consecutive disturbances and lagged observations from chaotic time-series dataset like as routine Covid-19 confirmed cases have led to the low performance in temporal feature learning process through recent RNN-based models. To meet this challenge, in this paper, we proposed a novel dual attention-based sequential auto-encoding architecture, called as: DAttAE. Our proposed model supports to effectively learn and predict the new Covid-19 cases in forms of chaotic and non-smooth time series dataset. Specifically, the integration between dual self-attention mechanism in a given Bi-LSTM based auto-encoder in our proposed model supports to directly focus the model on a specific time-range sequence in order to achieve better prediction. We evaluated the performance of our proposed DAttAE model by comparing with multiple traditional and state-of-the-art deep learning-based techniques for time-series prediction task upon different real-world datasets. Experimental outputs demonstrated the effectiveness of our proposed attention-based deep neural approach in comparing with state-of-the-art RNN-based architectures for time series based Covid-19 outbreak prediction task. [ABSTRACT FROM AUTHOR]

• New concepts for ATrFPRs are defined that avoid the issues in previous ones. • New methods can cope with incomplete ATrFPRs while all previous research cannot. • New methods based the acceptable consistency have some merits than previous ones. • Optimization models are more reasonable than the iterative method. • Numerical examples and comparative analysis are made. Trapezoidal fuzzy numbers offer a suitable and flexible way to express vague judgments of decision makers. This paper studies decision making with additive trapezoidal fuzzy preference relations. It first analyzes the issues encountered in previous consistency concepts for additive trapezoidal fuzzy preference relations. Then, two new consistency concepts, namely, an additive consistency concept and a multiplicative consistency concept, are introduced. To verify the rationality, several of their properties are discussed. To efficiently assess the consistency of additive trapezoidal fuzzy preference relations, several corresponding optimization models are formed. When the objective function values of the built models are zero, the associated additive trapezoidal fuzzy preference relations are consistent. Considering the case where additive trapezoidal fuzzy preference relations are incomplete, optimization models based on the consistency analysis are built, by which identified unknown judgments have the highest consistency level with the known ones. In general, additive trapezoidal fuzzy preference relations are unacceptably consistent. To rank objects from additive trapezoidal fuzzy preference relations, several optimization models for improving the consistency level are built, which consider the self-confidence of the decision makers, the total adjustment and the number of adjusted elements. According to the above discussion, two frameworks for ranking objects from additive trapezoidal fuzzy preference relations are provided that are based on the additive and multiplicative consistency analysis, respectively. Finally, numerical examples are provided to highlight the concrete application and to deliver the comparative analysis. [ABSTRACT FROM AUTHOR]

Breast cancer exhibits one of the highest incidence and mortality rates among all cancers affecting women. The early detection of breast cancer reduces mortality and is crucial for prolonging life expectancy. Although mammography is the most often used screening technique in clinical practice, previous studies reviewing mammograms diagnosed by radiologists have commonly revealed false negatives and false positives. Ongoing advances in machine learning techniques have triggered new motivation for the development of computer-aided diagnosis (CAD) systems, which could be applied to assist radiologists in improving final diagnostic accuracy. In this study, an automated methodology for detecting breast cancer in mammography images is proposed based on an ensemble classifier and feature weighting algorithms. First, a novel region extraction approach is proposed to constrain the search area for suspicious breast lesions and an original pectoral removal method is proposed to avoid interference when identifying a region of interest (ROI). In addition, an effective segmentation strategy is developed to automatically identify ROIs whose textural and morphological features are then fused and weighted to generate new feature vectors using a feature weighting algorithm. Finally, an ensemble classifier model is designed using k-nearest neighbor (KNN), bagging, and eigenvalue classification (EigenClass) to determine whether a mammogram contains normal, benign, or malignant tumors based on a majority voting rule. A series of experiments was conducted using the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets, the results of which demonstrated the proposed scheme outperformed comparable algorithms. • An automated methodology is proposed for detecting breast cancer in mammograms. • A region extraction method is introduced to constrain the suspicious breast lesions. • A pectoral removal technique is developed to lessen the unwanted muscle interference. • An ROI segmentation strategy is formulated to enhance efficiency of tumor detection. • A feature weighting algorithm is designed to improve accuracy of diagnostic results. [ABSTRACT FROM AUTHOR]

• Granulation of data using different encoding mechanisms. • Design of fuzzy rule-based models with interpretable relational factorization. • The factorization-oriented dimensionality reduction aimed at the design of rules. This study opens an original avenue of designing interpretable fuzzy rule-based models realized in the presence of data characterized by a large number of attributes (input variables). In the presence of such high-dimensional data, the development of rule-based models faces serious challenges both at the design and interpretability level. To address these two problems, we develop a new architecture of the rules and establish a two-step design process. First, the dimensionality reduction is realized with the aid of a fuzzy relational matrix factorization which transforms original fuzzy set-based encoded variables and yields a logic-oriented level of activation of individual rules. The results of relational factorization are logic expressions built over the encoded input variables forming condition parts of the rules. Second, the conclusions of the rules are built by optimizing local functions (both constant and linear functions are considered). A number of experiments are reported along with comparative studies involving a standard Takagi-Sugeno (TS) model. [ABSTRACT FROM AUTHOR]

Intelligent applications are highly susceptible to uncertainty and inconsistency due to the intense and intricate interactions among their autonomous components (or agents), making their verification theoretically and practically challenging. This paper presents the design and implementation of a new open-source and scalable software tool for modeling and verifying intelligent applications with commitment and trust protocols under both uncertainty and inconsistency settings, using reduction-based multi-valued model checking techniques. The proposed tool is equipped with original and novel algorithms that transform our logics of multi-valued commitment (mv-CTLC) and multi-value trust (mv-TCTL) that we recently introduced to their classical two-valued commitment (CTLC) and trust (TCTL) logic versions as well as to Computational Tree Logic (CTL). Moreover, the tool transforms the mv-CTL to CTL, and it is applicable for the classical model checking by transforming the classical logics of trust and commitment to CTL. To demonstrate the practicality and applicability of the proposed tool in real settings, we present and report experimental results over two blockchain-based applications in the healthcare domain. Finally, we provide discussions and comparisons between the proposed approaches regarding scalability and efficiency. Moreover, we provide packages of more than 11 experiments, including the ones we conduct in this paper and enhanced experiments from previous works. Our findings ensure that the proposed approaches and the software tool that implements them are highly efficient and scalable, giving accurate results under varying conditions. • Design and implementation of an open-source scalable tool for systems model checking. • System modeling with multi-valued logic capturing uncertainty and inconsistency. • Practical demonstration using blockchain-based applications in healthcare domain. • Extensive experiments showing scalability, efficiency and reliability of the tool. [ABSTRACT FROM AUTHOR]

Trust and social commitments have been studied with different objectives for communication in Multi-Agent Systems (MASs) separately. The purpose of this paper is to present the first logical framework to explore the relationship between two key concepts in autonomous MASs, namely trust and commitments among agents, along with its model checking algorithms. An analysis is performed on this relationship, with a specific emphasis on the perspectives of semantics and model checking. The analysis is carried out within the framework of Trust Computation Tree Logic with Commitments (CTLC) logic, an extended logic built on top of the standard branching temporal Computation Tree Logic (CTL) by including modalities for reasoning about commitments and their fulfillment. Moreover, the study employs a trust operator to facilitate trust reasoning. We propose thus Trust Computation Tree Logic with Commitments (TCTLC), a new logic able to express properties about trust and commitments simultaneously. Such rich properties cannot be expressed in other languages. We introduce the semantics using the extended formalism of interpreted systems. We propose some postulates and proofs that show how we can reason about combined trust and commitments in the logic proposed. New Binary Decision Diagram (BDD)-based algorithms for our logic are presented and implemented as additional libraries of the Model Checker for Multi-Agent Systems (MCMAS), the default model checker of MASs. Furthermore, we prove that although TCTLC extends CTL, its model checking algorithm remains polynomial (P-complete) with respect to the size of the model and the length of the formula. Similarly, the space complexity for concurrent programs also remains unchanged, which is polynomial (PSPACE-complete) with respect to the size of the components within these programs. Finally, to evaluate the proposed technique, we report the experimental results using an industrial case study and compare the results with those obtained on relevant benchmarks. • We introduce a new logical language combining trust and social commitments. • We introduce a new model checking algorithm for the new trust and commitment logic. • We propose reasoning postulates and proofs to reason about trust and commitments. • We implement the new algorithm and report its practical efficiency on a case study. [ABSTRACT FROM AUTHOR]

• Design a type of multi-dimensional Gaussian information granule. • Consider correlation among different variables of data. • Adopt confidence level to construct information granule. • Propose a strategy to exclude inhibitory data. In this study, based on the principle of justifiable granularity, a method for designing multi-dimensional information granules is proposed. To design information granules, the correlations among the different variables within the data and their confidence levels are considered. The designed information granules reveal the relationships present in the experimental data and help to capture more features of the original data. In addition, a strategy for the exclusion of inhibitory data is considered, making the design of information granules more focused. Several experimental studies are conducted to quantify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Semi-supervised learning (SSL) methods, which exploit both the labeled and unlabeled data, have attracted a lot of attention. One of the major categories of SSL methods, graph-based semi-supervised learning (GBSSL) learns labels of unlabeled data on an adjacency graph, where neighborhood sparse graph is often used to reduce computational complexity. However, the neighborhood size is difficult to set. Instead of assigning a concrete value of neighborhood size, we propose a new label propagation algorithm called multi granularity based label propagation (MGLP) and developed from the view of granular computing. In MGLP, labels of unlabeled data are learned by two classic label propagation processes with diverse neighborhood size k , where granular computing delivers a guiding strategy to leverage multiple level neighborhood information granules, and three-way decision acts as an active learning strategy to select the unlabeled data for further annotating. Through the iterative procedures of label propagating, data annotating and data subset updating, the ultimate pseudo label accuracy of unlabeled data may be higher. Theoretically, the accuracy of pseudo labels is enhanced in some scenarios. Experimentally, the results of simulation studies on ten benchmark datasets, show that the proposed method MGLP can rise pseudo labels accuracy by 8.6% than LP (label propagation), 6.5% than LNP (linear neighborhood propagation), 6.4% than LPSN (label propagation through sparse neighborhood), 4.5% than Adaptive-NP (adaptive neighborhood propagation) and 4.6% than CRLP (consensus rate-based label propagation). It also provides a novel way to annotate data. • Granular computing offering some guidelines for sound structured thinking. • Detailed analysis of the impact of neighborhood size k. • Learning labels by two label propagation processes with diverse neighborhood size k. • Three-way decision and active learning applied for further annotating data. • Better results compared with the random data labeling methods. [ABSTRACT FROM AUTHOR]

Transformers are Deep Neural Networks (DNN) that utilize a self-attention mechanism to capture contextual relationships within sequential data. Unlike traditional neural networks and variants of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), Transformer models excel at managing long dependencies among input sequence elements and facilitate parallel processing. Consequently, Transformer-based models have garnered significant attention from researchers in the field of artificial intelligence. This is due to their tremendous potential and impressive accomplishments, which extend beyond Natural Language Processing (NLP) tasks to encompass various domains, including Computer Vision (CV), audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published, spotlighting the Transformer's contributions in specific fields, architectural disparities, or performance assessments, there remains a notable absence of a comprehensive survey paper that encompasses its major applications across diverse domains. Therefore, this paper addresses this gap by conducting an extensive survey of proposed Transformer models spanning from 2017 to 2022. Our survey encompasses the identification of the top five application domains for Transformer-based models, namely: NLP, CV, multi-modality, audio and speech processing, and signal processing. We analyze the influence of highly impactful Transformer-based models within these domains and subsequently categorize them according to their respective tasks, employing a novel taxonomy. Our primary objective is to illuminate the existing potential and future prospects of Transformers for researchers who are passionate about this area, thereby contributing to a more comprehensive understanding of this groundbreaking technology. • The paper presents a comprehensive survey on transformers for deep learning tasks. • The paper conducts a thorough analysis on highly effective models in five domains. • The paper classifies the models based on respective tasks using a proposed taxonomy. • The characteristics of the surveyed models are deeply explored and analyzed. • Future directions and challenges for transformer-based models are deciphered. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy (IF) theory has become main approach to representing imprecision and vagueness. The IF divergence measure (IFDivM) based on Jensen–Shannon divergence is perhaps the most widely used measure to compare the similarity of multiple intuitionistic fuzzy sets (IFSs). In the present paper, this IFDivM is examined and applied to multiple examples. It is found that some extant IFDivMs hardly satisfy the axiomatic definition, and in a few cases even unable to show divergence of trivial IFSs. To address these inconsistencies, a new IFDivM based on Jensen–Shannon divergence is proposed, free from these problems. The effectiveness of the proposed IFDivM is tested on several critical cases, and precise analysis of its properties is performed. It is proved that the proposed IFDivM satisfies the axiomatic definition of IFDivMs. To illustrate the practical significance of the IFDivM, a novel intuitionistic fuzzy (IF) TODIM method, based on the proposed IFDivM, is developed, termed as GIF-TODIM method. Unlike the existing IF-TODIM methods, GIF-TODIM does not suffer from the revere ordering inconsistencies. The proposed GIF-TODIM method and the proposed IFDivM are applied to a real-world case study on supplier selection. A detailed comparative analysis is performed taking the TOPSIS method and other IFDivMs as baselines. The role of attitude on the final choice is analyzed in great detail. It is found that the proposed GIF-TODIM method is indeed useful, effective, and superior to the counterpart methods, when it comes to real-world situations. Concomitantly, in the present work, it is also revealed that the TOPSIS method based on the 2-D Hamming distance is a special form of the proposed GIF-TODIM method, when decision-makers have the same attitude towards losses and gains. Thus, an interesting relationship between TOPSIS and TODIM is identified under the intuitionistic fuzzy environment, which is bound to propel significant research in the area of decision making under uncertain conditions. As a whole, the article offers comprehensive analyses of IFDivMs and the TODIM method under the intuitionistic fuzzy environment. • Construct a new IF divergence measure (IFDivM) based on Jensen–Shannon divergence. • Develop a GIF-TODIM method based on the new IFDivM. • Apply the proposed GIF-TODIM method to a supplier selection problem. • Provide parameter analysis and comparative analysis with TOPSIS and other IFDivMs. • Show that TOPSIS based on 2-D Hamming distance is a special form of our GIF-TODIM. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new "Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency" (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, "Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method" (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C 21 latency and C 22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C 15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including "virtualized cloud RAN" coming as the optimal one, followed by "fog RAN," "cloud RAN," and finally "heterogeneous cloud RAN." The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications. [ABSTRACT FROM AUTHOR]

This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic hesitant fuzzy set (PSVNHFS) environment to benchmark Hybrid Multi Deep Transfer and Machine Learning (HMDTML) models. The novel DLBD method is proposed to generate a dynamic localisation decision matrix based on the upper and lower boundaries and the length of the scale. The superiority of DLBD derives from its ability to manage dynamic changes with boundary value consequences. In addition, the utilization of PSVNHFS in conjunction with DLBD and FWZIC has proven to effectively address the challenges posed by vagueness, uncertainty and hesitancy in the benchmarking procedure. The proposed methodology consists of three primary three steps: i) the adaptation of 48 HMDTML models, including 4 deep transfer learning models and 12 machine learning models trained on a dataset of 936 chest X-ray images obtained from both COVID-19 patients and individuals without the disease. Then, these models were evaluated based on seven evaluation criteria, and a decision matrix was proposed. ii) The development of a PSVNH–FWZIC to assign weights to the evaluation criteria. iii) The formulation of a PSVNH–DLBD for the purpose of benchmarking HMDTML models. Results of the PSVNH–FWZIC revealed that AUC and time were the most important evaluation criteria, while precision was the least important. Furthermore, the results from PSVNH–DLBD, reveal that Model M24 (Painters-Decision Tree) earned the highest rank when λ = 2 , 3 , 4 , 5 a n d 6 , followed by Model M25 (SqueezeNet-AdaBoost) and Model M34 (DeepLoc-kNN), while Model M39 (DeepLoc-SVM) had the lowest rank (rank = 48) across all λ values. The proposed method underwent sensitivity and comparison analyses to confirm its reliability and robustness. [ABSTRACT FROM AUTHOR]

Individual consistency and group consensus are the two protagonists in group decision-making (GDM). The former is considered as the logical basis of decision information. The latter is regarded as the guarantee of obtaining a widely accepted solution. In this study, two novel indexes for measuring consistency and consensus levels are defined using relative projection between multiplicative reciprocal matrices (MRMs) originating in Analytic Hierarchy Process (AHP). They are called the relative projection consistency index (RPCI) and the geometric relative projection consensus index (GRPCI), respectively. In order to test acceptable consistency of MRMs, the thresholds of RPCI are determined using the simulation method. Then, an optimization method for improving consistency of MRMs is proposed. By considering acceptable consistency of individual MRMs, a model of optimizing group consensus is further established by minimizing the GRPCI. Finally, a novel GDM algorithm is elaborated on, where the degrees of individual consistency and group consensus are optimized. A case study is carried out to illustrate the proposed models, where the Gaussian quantum behavior particle swarm optimization (GQPSO) algorithm is used to solve the constructed optimization models. Comparison with these previous studies reveals that a novel perspective is offered to simultaneously measure consistency degree of MRMs and group consensus level in GDM. The constructed optimization models are effective for improving consistency and reaching consensus in GDM with MRMs. • A novel consistency index of MRMs is proposed. • An optimization model is constructed to improve consistency of MRMs. • A novel consensus level is proposed in GDM. • An optimization model is established to improve group consensus level. • A novel GDM algorithm is reported and some comparisons are offered. [ABSTRACT FROM AUTHOR]

In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy. [ABSTRACT FROM AUTHOR]

Concept lattice has been widely studied and it is still attracting researchers in many fields by virtue of its well structured knowledge representation. However, it is more obvious that the number of concept lattice nodes generated from the initial formal context is enormous and results in more storage and lower efficiency of building, especially with the increasing of data to deal with. Meanwhile, users are not interested in the whole intents composed of all the attribute sets and it is a time consuming and troublesome job for them to acquire interesting knowledge. In order to deal with these deficiencies, we propose a constrained concept lattice structure referred to as a reduction concept lattice (RCL) guided by user preferences. This type of lattice structure not only can effectively reduce storage space and construction time, but also help to extract the knowledge that users are interested in. In this study, the definition of RCL is first given based on users' three types of interesting background knowledge described by predicate logic, together with some RCL's examples to illustrate this type of structure. Next, an algebraic structure of RCL is defined and its completeness is proved, and accordingly a new way is provided for reduction of concept lattice based on user preferences. • Three types of interesting background knowledge depicts user preferences. • Reduction concept lattice guided by user preferences. • Reduction concept lattice guided by user helps to quickly acquire knowledge. • Interesting knowledge can be efficiently obtained from reduction concept lattice. [ABSTRACT FROM AUTHOR]

• Driver well-being is important to prevent accidents, aggression and absenteeism. • We use topic modeling to extract central ideas on driver fatigue, stress, and burnout. • Drivers need more breaks to improve health, control aggression and prevent accidents. • An integrated model is created to improve driver well-being and minimize travel time. • A hybrid genetic algorithm is used to solve the ensuing fuzzy vehicle routing problem. Drivers are essential to any industry offering transportation and logistics services. Ensuring their well-being ensures smooth business and reduces stress factors that cause driver burnout and increase fatigue and stress. Burnout is usually responsible for accidents, absenteeism, and other similar problems, that are best prevented. Therefore, understanding these stress factors and determining ways to overcome them would benefit any related industry. In the proposed approach, we leverage the benefits of natural language processing and the availability of numerous studies on driver burnout, fatigue, and stress to determine the various stress factors and to understand how to address those using a vehicle routing problem. First, topic modeling, a popular natural language processing technique, is used to extract the different topics of discussion around driver burnout, stress, and fatigue. Next, the extracted cases are qualitatively analyzed to ascertain the stress factors that can be controlled through a routing problem and how to do so. Since uncertainty is prevalent in real life, pairwise travel times are assumed to follow different functional forms. Finally, an integrated routing model is developed, and a hybrid genetic algorithm is coded to solve the model. The use of various sources and types of data, viz., structured data for routing and unstructured data for topic modeling, to obtain solutions for routing and driver well-being, simultaneously, is a notable contribution of the proposed approach. Also, the integrated use of qualitative and quantitative research methods and the combination of text analytics and combinatorial optimization to model social problems is a relatively new concept in the vehicle routing literature. Experimental studies on existing datasets provide solutions that illustrate the advantages of the approach. Insights are also provided to assist managers in decision-making under similar scenarios. [ABSTRACT FROM AUTHOR]

• The convolution-base and composite kernel are used for alleviating overfitting. • The composite kernel could adjust the nonlinear fitting ability of the classifier. • Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion. • The effectiveness of the HFM-SVM is demonstrated by three practical applications. Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification performance in high-dimensional applications. The key points of the proposed HFM SVM are enumerated as follows: 1) The convolution-base of the proposed classifier extracts features. The extracted features exhibit flexibility and applicability in high-dimensional applications. 2) The proposed HFM SVM designed with the composite kernel could adjust the nonlinear fitting ability for improving classification performance. The procedure of the proposed HFM SVM is described as follows: Convolution-base is considered as a preprocessing unit for extracting features. The features are not always linearly separable especially when being extracted from high dimensional data. A composite kernel function is constructed by considering the complicated (nonlinear) classification boundary into several local linear boundaries. The structure of the extracted features is captured by FCM clustering and integrated into the composite kernel function for enhancing the nonlinear fitting ability of the proposed classifier. The nonlinearity of the composite function can fill the gap between linear and nonlinear kernel functions by adjusting the number of clusters obtained by the FCM clustering algorithm. The proposed HFM SVM classifier based on composite kernel could improve the classification performance on high dimensional datasets. The performance of the proposed HFM SVM is experimented with and demonstrated by using three high-dimensional applications to show the effectiveness as well as performance improvement. [ABSTRACT FROM AUTHOR]

• We propose a method for the MAGDM with the flexible linguistic expression. • We define an average consistency index (ACI) of flexible linguistic preference relation. • We develop a consistency-driven model to determine the personalized individual semantics. • We justify the effectiveness of the PIS-based FLE-MAGDM approach. Multi-attribute group decision making (MAGDM) model which uncovers the preferences of individuals has become increasingly important in enhancing the efficiency of expert systems. Meanwhile, the use of flexible linguistic expression (FLE) and the personalized individual semantics (PIS) bring challenges to MAGDM. Therefore, in this paper, we devote to design a method based on personalized individual semantics for MAGDM with flexible linguistic expression, named PIS-based FLE-MAGDM. In the PIS-based FLE-MAGDM, we firstly develop a consistency-driven PIS model to derive personalized numerical scales (PNS) for linguistic terms with flexible linguistic preference relation (FLPR) and obtain the heterogeneous attribute weight by different decision makers. Then, we propose the individual selection process for getting the individual ranking of alternatives. Next, we provide the collective selection process to aggregate the individual ranking into the collective ranking. Finally, an application under the background of airline selection and some comparative analyses are presented to illustrate and justify the effectiveness of our proposals. [ABSTRACT FROM AUTHOR]

• Take morphology and shape features into account for granularity representation. • Using interval information granules to achieve system modelling. • A granular model is proposed and used in the field of anomaly detection. • Evaluation indexes are established to quantify the performance. • Greatly reduce the data volume and improve the detection performance. Since time series are characterized by a substantial volume of data, high levels of noise and the correlation between data in the time series attributes, it becomes challenging to mine crucial information from the series and apply it to anomaly detection. In this study, inspired by the concept of information granularity being applied to the process of system modelling, a granular Markov model is proposed for time series anomaly detection. Anomalies are generally caused by the changes in amplitude and shape; in this study we take both the original time series data and their amplitude change data into consideration. First, we utilize an interval information granularity representation based on the principle of justifiable granularity to represent the original time series data in an abstract manner to arrive at the corresponding representation results-- that is, interval information granules. Then, based on the results of the interval information granularity representation and the Fuzzy C-Means (FCM) clustering algorithm, a granular Markov model is developed to produce anomaly scores to quantify possible anomalies. Compared with state-of-the-art methods, experimental studies completed for a large number of datasets demonstrate that the proposed method can significantly improve the anomaly detection process with higher data anomaly resolution. The obtained results are consistent across all datasets. [ABSTRACT FROM AUTHOR]

• Apply dynamic utility calculation framework to mine closed high utility itemsets. • Dramatically reduce the cost of database scans by adopting the P-set structure. • Propose a new CHUIM algorithm, named iEFIM-Closed. • Conduct experiments to measure the performance of the proposed algorithm. The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms' traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability. [ABSTRACT FROM AUTHOR]