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Machine Learning (ML) is revolutionizing the functioning of our societies and reshaping much of the economic tissue underlying them. The deep integration of ML into the fabric of our lives has changed to way we work and communicate and how we relate to each other. In this Topical Collection we reflect on the reach and impact of this AI (ML-driven) revolution in our society, critically analyzing some of the most important ethical, epistemological, scientific, and sociological issues underlying it. [ABSTRACT FROM AUTHOR]

The research on decision making driven by product rankings faces challenges due to the rise of extensive positive reviews and the widespread distribution of electronic word of mouth (eWOM) across multiple platforms. There is a limited body of research that examines the impact of platform credibility on the quality of product rankings. Hence, based on the basic uncertain linguistic information (BULI), which enables simultaneous representation of information and its credibility, we investigate the development of a ratings aggregation approach for cross-platform distribution (CPD) with the aim of facilitating decision-making processes, focusing specifically on the aspect of credibility. To begin with, this paper introduces the concept of BULI as a means to effectively represent both product ratings and their corresponding levels of credibility. Subsequently, we proceeded to devise the BULI-based aggregation functions that are well suited for the aggregation of CPD ratings and that can be degraded to the existing operator. In addition, we develop a credibility evaluation index system and credibility calculation model for the platform in order to derive a product BULI matrix consisting of ratings and their corresponding levels of credibility. In this study, we propose two models, namely the feature information-based user weighting model and the BULI distance measure-based technique for order preference by similarity to an ideal solution (BULI-TOPSIS) model, to enhance the product ratings aggregation approach for decision-making purposes. The utilization of the proposed method is exemplified through the case study of passenger car ranking, showcasing its practicality and efficiency. [ABSTRACT FROM AUTHOR]

Compared to traditional image recognition, Fine-Grained Image Recognition (FGIR) faces significant challenges due to the subtle distinctions among different categories and the notable variances within the same category. Furthermore, the complexity of backgrounds and the extraction of discriminative features limited to small local regions further exacerbate the difficulty. Recently, several studies have demonstrated the effectiveness of the Vision Transformer (ViT) in FGIR. However, these investigations have frequently overlooked critical information embedded within class tokens across different layers, while also neglecting the subtle local details hidden within patch tokens. To address these issues and enhance FGIR performance, we introduce a novel ViT-based network architecture MIFBF. The proposed model builds upon ViT by incorporating three modules: Complementary Class Tokens Combination module (CCTC), Patches Information Integration module (PII), and Attention Cropping Module (ACM). The CCTC module integrates multi-layer class tokens to capture complementary information, thereby enhancing the model's representational capacity. The PII module delves into the rich local details encoded in patch tokens to improve classification accuracy. The ACM module generates regions of interest based on ViT's self-attention weights and effectively filters background noise, thereby directing the model's attention to the most relevant image areas. Experiments conducted on three different datasets validate the effectiveness of the proposed model, yielding state-of-the-art results and highlighting its superiority in FGIR tasks. [ABSTRACT FROM AUTHOR]

Many attempts have been made to generalize the concept of intuitionistic fuzzy sets (IFS) like Pythagorean (PFS), q-rung orthopair (q-OFS), and linear Diophantine (LDFS). However, these generalizations have many advantages and disadvantages. Among the disadvantages, the main concern with these sets is that they cannot capture the situation where both or at least one of the memberships and non-membership grades are equal to 1. Secondly, how to reduce the dependency between the membership and non-membership grades. Thus, any data in the form X = {< x1; (1,0.9) > , < x2; (0.3,1) > , < x3; (1,1) >} is not handled by the IFS and other versions of IFS because 1 + 0.9 = 1.9 > 1, 0.3 + 1 = 1.3 > 1, and 1 + 1 = 2 > 1. We propose the new idea of the q-fractional fuzzy set ( q f r s ), which can handle all such situations, using the q-intercept of the straight line and letting both membership and non-membership grades approach 100% without depending on each other. The q = 2 is the smallest value for which all the situations in the first quadrant are tackled, and the sum of membership and non-membership grades is near 1. For all other values of q > 2, the sum of membership and non-membership grades approaches 0, i.e., the larger the value of q, i.e., the intercepts, the sum of memberships and non-membership grades approaches 0. For q = 1, the first intercept is simply the intuitionistic fuzzy set. We provide the basic properties of the q-fractional fuzzy set using the extension principle of fuzzy sets and develop some aggregation operators. We also developed a new q-fractional fuzzy neural network and provided an example. [ABSTRACT FROM AUTHOR]

Rough fuzzy K-means (RFKM) decomposes data into clusters using partial memberships by underlying structure of incomplete information, which emphasizes the uncertainty of objects located in cluster boundary. In this scheme, the settings of cluster boundary merely depend on subjective judgment of perceptual experience. When confronted with the data exhibiting heavily overlap and imbalance, the boundary regions obtained by existing empirical schemes vary greatly accompanied by skewing of cluster center, which exerts considerable influence on the accuracy and stability of RFKM. This paper seeks to analyze and address this deficiency and then proposes an improved rough fuzzy K-means clustering based on parametric decision-theoretic shadowed set (RFKM-DTSS). Three-way approximation is implemented by incorporating a novel fuzzy entropy into the decision-theoretic shadowed set, which rationalizes cluster boundary through minimizing fuzzy entropy loss. Under the secondary adjustment method and improved update strategy of cluster center, the proposed RFKM-DTSS is thus featured by a powerful processing ability on class overlap and imbalance commonly seen in scenarios, such as fault detection and medical diagnosis with unclear decision boundaries. The effectiveness and robustness of the RFKM-DTSS are verified by the results of comparative experiments, demonstrating the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. [ABSTRACT FROM AUTHOR]

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer (LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8%. This greatly improves the stability and security, which is exactly what the drilling process requires. [ABSTRACT FROM AUTHOR]

The product–service system (PSS) is a strategic design approach proposed to address sustainability in socio-economic systems amidst rapid industrialization and transition. Evaluating the concept design of a PSS is a crucial and initial step prior to implementation. This study presents an innovative framework for evaluating concept designs of sustainable PSS based on a well-defined evaluation index system via integrating quality function deployment (QFD) and the technique for order preference by similarity to ideal solution (TOPSIS) while accommodating extended basic uncertain linguistic information (EBULI). Specifically, a QFD-based framework is first developed to identify the requirements of various stakeholders and then to establish the multi-dimensional criteria for evaluating sustainable PSS. Then, a House of Quality-based relationship matrix is introduced to determine the weights of criteria more accurately. Further, an adaptive consensus-reaching process method based on an expert weighting optimization model is proposed to ensure a collective outputs recognized by multiple involved stakeholders. Finally, an improved EBULI-based TOPSIS method is presented to determine the priority ranking of alternative sustainable PSS concepts. A case study on a car-sharing PSS project demonstrates the viability and effectiveness of the proposed QFD–TOPSIS integrated approach under EBULI settings. The alternative PSS concept design, which demonstrates relatively good performance in criteria of high importance, is selected as the most suitable option. Moreover, relevant comparative and sensitivity analyses reveal that the proposed approach exhibits superiorities in appropriate criteria elicitation, accurate weights determination, and high consensus ranking outputs. [ABSTRACT FROM AUTHOR]

Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality. [ABSTRACT FROM AUTHOR]

The earliest detection of rumors across social media is the need to the hour in present global village. User's are seamlessly connected in an unstructured network leading to rapid flow of information. User's on the social media with malign intents may share defamatory content to contribute towards the fifth generation media warfare. The ingress of such defamatory content into society can result in panic, uncertainty and demoralization the peoples. Due to the huge amount of content over social platforms, the detection of malicious contents is hard. Earlier research while focuses on content profiling and flow of information, however, the reciprocal perspective of the source and following contents is missing. In this research, a novel Reciprocal Perspective fused Graph Convolutional Neural Network (RPf-GCN) is proposed. The proposed framework incorporates twin GCNs to encode both the bottom-up and top-down perspectives, enhancing the understanding of rumor propagation. Moreover convolutional operation is employed to fuse reciprocal perspective, providing a holistic view of the conversations. To validate the efficacy of the proposed framework, we conducted a series of experiments using real-world datasets, including PHEME and SemEval. Experimentation performed illustrates that the proposed framework outperformed over various baselines in two different evaluation metrics namely Macro F1 (for PHEME 0.736, for SemEval 0.461) and Accuracy (for PHEME 0.748, for SemEval 0.658). [ABSTRACT FROM AUTHOR]

In many practical applications of machine learning, there are a large number of partially labeled categorical data due to the high cost of labelling data. Semi-supervised learning algorithm is needed to deal with such data. This paper studies uncertainty measurement (UM) of partially labeled categorical data and considers semi-supervised attribute reduction in a partially labeled categorical decision information system (p-CDIS). The fact that a discernibility pair set for categorical data is actually a distinguishable relation is first stated. Then, a p-CDIS is divided into two categorical decision information systems: one is the labeled categorical decision information system (l-CDIS) and the other is the unlabeled categorical decision information system (u-CDIS). Next, based on the indistinguishable relation, distinguishable relation and dependence function, four degrees of importance are defined. They are the weighted sum of l-CDIS and u-CDIS determined by the label missing rate and can be considered as the UM of p-CDIS. Moreover, the numerical experiments and statistical tests on 10 datasets verify their effectiveness. In addition, an adaptive semi-supervised reduction algorithm based on the defined degrees of importance is proposed, which can automatically adapt to various label missing rates. Finally, the results of experiments and statistical tests on 10 datasets show the proposed algorithm is statistically better than some stat-of-the-art algorithms according to classification accuracy. [ABSTRACT FROM AUTHOR]

Imbalanced data constitute a significant challenge in practical applications, as standard classifiers are usually designed to work on data with balanced class label distributions. One of effective methods to solve the imbalanced problem is boundary oversampling method, which only focuses on the classification of boundary samples. However, most boundary oversampling methods roughly select boundary samples for oversampling without considering the potentially useful boundary characteristics inherent in majority (negative) class. To overcome this limitation, we propose a novel boundary-degree-based oversampling method (BDO) in this paper. The originality of BDO stemps from quantifying the degree to which each negative sample can be regarded as a boundary sample in terms of probability using information entropy. Applying the sigma rule on the quantified boundary degree, negative boundary samples are determined to indirectly select minority (positive) boundary samples for oversampling. In this way, a substantial amount of information hidden in the negative class can be mined. To further transfer the mined information to help oversample, BDO iteratively synthesizes aided boundary points along a fraudulent gradient. Oversampling finally is performed on both positive boundary samples and the aided boundary points. Experimental results completed on 15 benchmark imbalanced datasets, two multi-label datasets and one large-scale dataset in terms of G-mean, F-measure, AUC, accuracy, TPR and TNR show that BDO exhibits better performance, which is competitive with some commonly considered methods. [ABSTRACT FROM AUTHOR]

The massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public's perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public's perception of China's civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public's greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K-means clustering with a novel distance measure for evaluators combined with unsupervised K-means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K-means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results. [ABSTRACT FROM AUTHOR]

In this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation. Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy equipment for the practical application of the material sorting system of the black plastic wastes. [ABSTRACT FROM AUTHOR]

Recently, many studies have been conducted on developing cubic sets, like cubic intuitionistic fuzzy sets, cubic Pythagorean fuzzy sets, and cubic q-rung orthopair fuzzy sets. However, the cubic set combines interval-valued and fuzzy sets. But both parts of these fuzzy sets cannot attain the maximum value (equal to 1) due to the restriction at the sum of memberships and non-membership grades. For example if one has data of the form Ξ = { < x ; ([ 0.5 , 1 ] , [ 0.1 , 1 ]) , (1 , 0.9) > | x ∈ X } , then clearly these data cannot be handled through cubic q-rung orthopair fuzzy sets. To cover this situation, we introduce the notions of cubic q-fractional fuzzy sets ( C q f r F s ), combining the interval-valued q-fractional fuzzy sets ( I V q f r F s ) and q-fractional fuzzy sets ( q f r F s ) and allowing them to attain the maximum value by introducing a new parameter q ≥ 2. We first introduce the concept of interval-valued q-fractional fuzzy sets ( I V q f r F s ) with elemental properties. Then we propose the novel idea of cubic q-fractional fuzzy sets ( C q f r F s ) and discuss their sensitivity analysis. We also provide the fundamental arithmetic operations of cubic q-fractional fuzzy sets ( C q f r F s ) and properties. In the end, we propose the correlation coefficients to measure the relationship between cubic q-fractional fuzzy sets ( C q f r F s ). Finally, we presented a numerical example of the evaluation of using a digital library in the education department by considering its advantages and disadvantages using the developed correlation coefficients for user X. Thus, by knowing a user's priorities, the digital library database can be updated. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) is a popular strategy for pattern recognition and classification tasks. It leverages a large amount of data from the source domain to help train the model applied in the target domain. Supervised domain adaptation (SDA) approaches are desirable when only few labeled samples from the target domain are available. They can be easily adopted in many real-world applications where data collection is expensive. In this study, we propose a new supervision signal, namely center transfer loss (CTL), to efficiently align features under the SDA setting in the deep learning (DL) field. Unlike most previous SDA methods that rely on pairing up training samples, the proposed loss is trainable only using one-stream input based on the mini-batch strategy. The CTL exhibits two main functionalities in training to increase the performance of DL models, i.e., domain alignment and increasing the feature's discriminative power. The hyper-parameter to balance these two functionalities is waived in CTL, which is the second improvement from the previous approaches. Extensive experiments completed on well-known public datasets show that the proposed method performs better than recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In an uncertain and complex decision-making environment, limited by the scope of human cognition, traditional utility decision-making has a certain deviation to actual decision-making. The revision of behavioral decision-making (BDM) to traditional rational decision-making theory makes the new model more universal. In light of this point, we reveal a new three-way decision (3WD) model by virtue of prospect theory (PT) on multi-scale information systems (MS-ISs) for persuing multi-attribute decision-making (MADM) problems. By utilizing an expected evaluation, our newly designed value function can not only reflect the relative position of the object but also avoid the drawbacks of the reference point being too subjective. Through the value function, we obtain a more reasonable avail function to replace the loss function in the traditional 3WD model. At the same time, the weighting function of the object in different states can be calculated, by synthesizing avail function and the weighting function under different decision attitudes. The comprehensive prospect value and classification conditions of the object are calculated. Then, through data selected from the UCI database, we verify the effectiveness of the constructed method. Comparative and experimental analyses are also used to illustrate the superiority and stability of our designed method. [ABSTRACT FROM AUTHOR]

With the increasing complexity of real decision-making problems, some experts try to use multi-scale information to express expert opinions in group decision-making problems. Facing the problem of group decision-making with multi-scale information, this paper attempts to explore a multi-scale group decision-making method consisting of two stages to provide theoretical support and methodological basis for establishing a multi-scale decision analysis system. In the first stage, we introduce a newly ranking decision-making approach based on a reflexive fuzzy α-neighborhood operator to deal with single-scale ranking problems with a single expert, which greatly enriches the ranking decision analysis theory. We also use a numerical example and experimental analysis to detect the stability and validity of the method. In the second stage, considering that the decision-making opinions of multiple experts may appear at the same time in the decision-making process, we propose the score labeling approach and the decision fusion approach to obtain the comprehensive decision-making result, which provides a feasible research idea for the comprehensive analysis of group decision-making results. Combining these two stages, a complete multi-scale group decision-making method in a multi-scale environment is described in detail, which can effectively deal with multi-scale group decision-making problems. Moreover, a series of simulation calculations are conducted to test the validity and stability of the proposed group decision-making method. [ABSTRACT FROM AUTHOR]

In modern logistics, port transportation plays an important role in the development of the surrounding economy. However, to determine the level of port group competitiveness, regional ports must be classified. To realize this classification, this paper introduces a novel multiple criteria sorting method, ORESTE-SORT, together with its main characteristics and properties. This approach can help in handling three types of preference relationships among alternatives and profiles: preference, indifference, and incomparability. In addition, a novel sorting rule, the assignment rule driven by attitudes (ARDA), is introduced to allocate alternatives to predefined categories based on the generalization of two classical sorting rules: optimistic and pessimistic rules. We also perform a case study to illustrate the suitability of the proposed sorting method for sorting port group competitiveness. Finally, sensitivity analysis and comparative analysis are performed with the ELECTRE-SORT method to determine the effectiveness of ORESTE-SORT, following which the capability of this new sorting methodology is further discussed. [ABSTRACT FROM AUTHOR]

Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm is easily affected by the initial clustering centers. Currently, scholars often use the multiple population genetic algorithm (MPGA) to optimize the clustering centers, but the MPGA has insufficient global search ability and lacks self-adaptability, is prone to premature convergence, and has poor initial clustering centers. Therefore, this paper proposes an adaptive FCM clustering algorithm DMGA-FCM based on a derivative multiple population genetic algorithm (DMGA). In DMGA-FCM algorithm, firstly, the derivative operator, which is proposed for the first time in this paper, performs derivative operations on initialized populations to improve the algorithm's searchability and deal with the lack of inter-population search ability. Secondly, the adaptive probability fuzzy control operator is used to dynamically adjust the genetic probability to improve the adaptability of the algorithm, which in turn enhances the global merit-seeking ability of the DMGA algorithm and avoids premature convergence. Finally, the initial clustering center of FCM algorithm is optimized with DMGA to enhance the clustering effect of the algorithm. The analysis of simulation experiments and MRI brain map application results show that the DMGA-FCM algorithm can obtain a better clustering effect of medical data and image clustering segmentation effect compared with other related FCM algorithms. [ABSTRACT FROM AUTHOR]

Recently, with the rapid developments of the Internet and social networks, there have been tremendous increase in the amount of complex-structured text resources. These information explosions require extensive studies as well as more advanced methods in order to better understand and effectively model/learn these high-dimensional/structure-complicated textual datasets. Moving along with the recent progresses in deep learning and textual representation learning approaches, many researchers in this domain have been attracted by utilizing different deep neural architectures for learning essential features from texts. These novel neural architectures must enable to handle complex textual feature engineering. Moreover, it also has to be able to extract deeper semantic and structural information from textual resources. Recently, there are several integrations between advanced deep learning architectures, such as recurrent neural networks (RNNs), sequence-to-sequence (seq2seq) and transformers in text classification have been proposed. These hybrid deep neural architectures have shed light on how computers can comprehensively process sequential information from texts to fine-tune for leveraging the performance of multiple tasks in natural language processing, including classification. However, most of recent RNN-based techniques still suffer from several limitations. These limitations are mainly related to the capability of capturing the global long-range dependent as well syntactical structures of the given text corpus. There are some recent studies have shown that a combination of graph-based text representation and graph neural network (GNN) approaches can cope with these challenges. In this survey works, we mainly focus on discussing about recent state-of-the-art studies which are mainly dedicated on the text graph representation learning through GNN, named as TG-GNN. In addition, beside the TG-GNN based models' features and capability discussions, we also mentioned about the pros/cons. Extensive comparative studies of TG-GNN based techniques in benchmark datasets for text classification problem are also provided in this survey. Finally, we highlight existing challenges as well as identify perspectives which might be useful for future improvements in this research direction. [ABSTRACT FROM AUTHOR]

Anomaly detection is a challenging problem in science and engineering that appeals to numerous scholars. It is of great relevance to detect anomalies and analyze their potential implications. In this study, a multi-level anomaly detection framework with information granules of higher type and higher order is developed based on the principle of justifiable granularity and Fuzzy C-Means (FCM) clustering algorithm, including two different types of approaches, namely abstract level approach (ALA) and detailed level approach (DLA). The ALA approach is implemented at a comparatively abstract level (viz., level-1), in which two distinct types of information granules of order-1 (viz., information granules of type-1 and type-2) are employed for anomaly detection. The DLA approach is formulated and derived from the ALA approach at a more detailed level (viz., level-2), which generates more detailed information granules, namely information granules of order-2, through successive splitting information granules and the FCM clustering algorithm to refine the problem at various levels. Furthermore, a similarity measurement algorithm is designed for anomaly detection utilizing information granules of higher type and higher order. Comprehensive performance indexes are produced to quantify the performance of the proposed framework compared with the methods of two single-level approaches and two multi-level approaches. Synthetic data and several real-world data coming from various areas are engaged to demonstrate and support the superiority of the proposed approaches over other classical methods in terms of detection accuracy and data anomaly resolution. [ABSTRACT FROM AUTHOR]

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost. [ABSTRACT FROM AUTHOR]

We present a simple yet effective idea, perturbation-based oversampling (POS), to tackle imbalanced classification problems. In this method, we perturb each feature of a given minority instance to generate a new instance. The originality and advantage of the POS is that a hyperparameter p is introduced to control the variance of the perturbation, which provides flexibility to adapt the algorithm to data with different characteristics. Experimental results yielded by using five types of classifiers and 11 performance metrics on 103 imbalanced datasets show that the POS offers comparable or better results than those yielded by 11 reference methods in terms of multiple performance metrics. An important finding of this work is that a simple perturbation-based oversampling method is able to yield better classification results than many advanced oversampling methods by controlling the variance of input perturbation. This reminds us it may need to conduct comparisons with simple oversampling methods, e.g., POS, when designing new oversampling approaches. [ABSTRACT FROM AUTHOR]

Linguistic variables are flexible and intuitive attraction for expressing the wording of decision makers. This paper introduces a new type of linguistic fuzzy sets called linguistic dual hesitant fuzzy sets to express the hesitancy of decision makers' qualitative preferences and non-preferences. Considering the application in decision making, linguistic dual hesitant fuzzy preference relations (LDHFPRs) are introduced that permit the decision makers to apply several linguistic variables to indicate a qualitative preferred judgment and a qualitative non-preferred judgment, respectively. To rank objects from LDHFPRs rationally, a consistency concept is first presented. Then, two optimal models are built to judge the consistency of LDHFPRs. When LDHFPRs are inconsistent, an optimal model-based iteration algorithm for obtaining consistent LDHFPRs is offered. Based on consistent linguistic intuitionistic fuzzy preference relations, a method for calculating the weighted linguistic intuitionistic fuzzy priority vector is introduced. In the setting of group decision making (GDM), a consensus measure based on individually weighted consistent reverse complementary linguistic intuitionistic fuzzy preference relations is defined. When the consensus does not satisfy the requirement, a two-step optimal model-based method for increasing the consensus level is offered. Furthermore, an approach for GDM with LDHFPRs is developed. Finally, an illustrative example concerning the evaluation of basic services internet enterprise websites is provided to show the efficiency of the new method. [ABSTRACT FROM AUTHOR]

This study elaborates on a novel Hidden Markov Model (HMM)-based fuzzy model for time series prediction. Fuzzy rules (rule-based models) are employed to describe and quantify the relationship between the input and output time series, while the HMM is regarded as a vehicle for efficiently capturing the temporal behavior or changes of the multivariate time series which are not capable to capture through commonly encountered fuzzy rule-based models. Essentially, the proposed strategies control the contribution of different fuzzy rules so that the proposed model can well model the dynamic behavior of time series. Fuzzy C-Means clustering technique is an alternative way to construct fuzzy rules. Particle swarm optimization serves as a tool to optimize the parameters of the model (e.g., transition matrix and emission matrix). We construct and investigate the performance of the HMM-based fuzzy model by using a series of synthetic and publicly available multivariate time series. Experimental results demonstrate that the proposed model shows better performance than the fuzzy rule-based models used without the involvement of HMMs. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Concept Design Evaluation of Sustainable Product-Service Systems: A QFD-TOPSIS Integrated Framework with Basic Uncertain Linguistic Information" published in the journal Group Decision & Negotiation. The correction adds the missing Acknowledgements section, which acknowledges the support received from various funding sources. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are Qiang Yang, Zhen-Song Chen, Jiang-Hong Zhu, Luis Martínez, Witold Pedrycz, and Mirosław J. Skibniewski. [Extracted from the article]

In group decision-making problems, experts often form multiple subgroups depending upon their backgrounds or attitudes. To explore the effect of different types of subgroups' formation on the final decision and to make the decision process more conforming to human custom, we propose a comprehensive study on the effect of "multi-subgroup background" on a basis of weights optimization and Granular Computing techniques. Firstly, a comparison of different types of clustering strategies is realized to find the most suitable method for clustering experts. Since Analytical Hierarchy Process is selected as the fundamental model, it becomes a reciprocal matrices' or eigenvectors' clustering task. Secondly, allocation strategies of information granularity to each expert along with weight to each subgroup are carefully designed and optimized. Information granularity is viewed as a design asset to provide experts with some flexibility to adjust their evaluation results, whereas weights reflect the importance degrees of different subgroups and further help to increase the consensus. A granulation-degranulation process is developed to evaluate the performance of a set of weights and granularities under a multi-criteria objective's guidance. Thirdly, a proper evolutionary optimization method like particle swarm optimization is redesigned to iteratively generate the best set of information granularities and weights under an equality constraint condition. A series of numerical studies on synthetic data and real-world data are executed to verify the effectiveness of our method. The results show same trend present across experts. [ABSTRACT FROM AUTHOR]

Every real-world physical problem is inherently based on uncertainty. It is essential to model the uncertainty then solve, analyze and interpret the result one encounters in the world of vagueness. Generally, science and engineering problems are governed by differential equations. But the parameters, variables and initial conditions involved in the system contain uncertainty due to the lack of information in measurement, observations and experiment. However, It is necessary to develop a comprehensive approach for solving differential equations in an uncertain environment. The purpose of this work is to study and investigate the fuzzy solution of linear third-order fuzzy differential equations using the concept of strongly generalized Hukuhara differentiability (SGHD). To make our analysis possible, we apply the first and second differentiability up to the third-order fuzzy derivative of the fuzzy-valued function. Moreover, we develop an important result concerning the relationship between Laplace transform of fuzzy-valued function and third-order derivative. We construct an algorithm to determine a potential solution of linear third-order fuzzy initial-value problem using the Laplace transform technique. All these solutions are represented in terms of the Mittag-Leffler function involving a single series. Furthermore, we discuss the switching points of linear third-order differential equations and their corresponding solutions in fuzzy environments. To enhance the novelty of the proposed technique, some illustrative examples are presented as applications are analyzed to visualize and support theoretical results. [ABSTRACT FROM AUTHOR]

Graph embedding is one of the most efficient dimensionality reduction methods in machine learning and pattern recognition. Many local or global graph embedding methods have been proposed and impressive results have been achieved. However, little attention has been paid to the methods that integrate both local and global structural information without constructing complex graphs. In this paper, we propose a simple and effective global structure guided neighborhood preserving embedding method for dimensionality reduction called GSGNPE. Specifically, instead of constructing global graph, principal component analysis (PCA) projection matrix is first introduced to extract the global structural information of the original data, and then the induced global information is integrated with local neighborhood preserving structure to generate a discriminant projection. Moreover, the L 2 , 1 -norm regularization is employed in our method to enhance the robustness to occlusion. Finally, we propose an iterative optimization algorithm to solve the proposed problem, and its convergence is also theoretically analyzed. Extensive experiments on four face and six non-face benchmark data sets demonstrate the competitive performance of our proposed method in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Time series approximation techniques can provide approximate results for the data in another new space by dimensionality reduction or feature extraction. In this study, we propose a new time series approximation strategy based on the Fuzzy C-Means (FCM) clustering and elaborate on a comprehensive analysis of relationships between reconstruction error and classification performance when dealing with various representation (approximation) mechanisms of time series. Typically, time series approximation leads to the representation of original time series in the space of lower dimensionality compared to the dimensionality of the original input space. We reveal, quantify, and visualize the relationships between the reconstruction error and classification error (classification rate) for several commonly encountered representation methods. Through carefully structured experiments completed for sixteen publicly available datasets, we demonstrate experimentally and analytically that the classification error obtained for time series in the developed representation space becomes smaller than when dealing with original time series. It has been also observed that the reconstruction error decreases when increasing the dimensionality of the representation space. In addition, when compared with the state-of-the-art algorithms reported in the literature, experimental results show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The object recognition problems realized in uncertain environments have played a paramount role in decision-making. In recent years, neutrosophic soft sets (NS-sets), a combination of soft and neutrosophic sets, have emerged as outstanding candidates in this field. If neutrosophic sets are used to handle problems involving imprecise, indeterminate, and inconsistent data, soft sets are used to deal with uncertainties that classical tools cannot control. This paper defines a new concept based on NS-sets, called the sequence of NS-sets (NSS-sequence). Their inclusions, special types, operations, distances are determined with reasonable, convincing, and well-proven properties. Furthermore, we also propose an algorithm for the decision-making problem on NS-sequence and apply it in medical diagnosis by a real-life experiment. Finally, intending to verify its validity and feasibility, we compare our algorithm to the algorithm for the decision-making problem on time NS-set (tNS-set) through the real-life mentioned earlier by Alkhazaleh. Our work also shows that the proposed algorithm on NS-sequence has the same results as that offered by Alkhazaleh, and the tNS-set is just a particular case of the NSS-sequence. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy linguistic term sets (HFLTSs) and double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) are two frequently used linguistic information forms in uncertain decision-making environments. However, they only include membership grades and cannot yield fuzzy information from a negative aspect. A bipolar fuzzy set can quantify evaluation information from positive and negative sides using positive and negative memberships, respectively. To address this issue, double hierarchy bipolar hesitant fuzzy linguistic term sets (DHBHFLTSs) are proposed, which can highlight the importance of the negative membership degree, and the objects can be evaluated from positive and negative aspects. Furthermore, DHBHFLTSs increase the reasonableness and comprehension of the evaluation information in the process of optimal talent selection. This paper proposed a framework involving the stepwise weight assessment ratio analysis (SWARA) method and the extended weighted aggregated sum product assessment (WASPAS) method. The extended WASPAS method is utilized to aggregate the evaluation information of all the alternatives under the DHBHFLTSs context. So, this proposed method increases the ranking accuracy. The SWARA method is extended to DHBHFLTSs to rank and determine the criteria. This weight determination method is helpful for coordinating and gathering data from experts. Therefore, the proposed method can obtain the weight values efficiently. Subsequently, a case of talent selection is utilized to show the feasibility and applicability of the proposed framework. Finally, the accuracy and comparison analyses with other methods illustrate the superiority of this framework. [ABSTRACT FROM AUTHOR]

Semi-supervised real-time generation multi-view multi-label data sets are widely encountered in practical applications. A key issue is how to process the data whose information including labels or features may be lost due to some unforeknowable factors. In our work, we develop a multi-view multi-label-based online method with threefold correlations and dynamic updating multi-region (M2CR) to solve this issue. First, we adopt three kinds of correlations between features and labels to recover the missing information. Second, we process new arriving instances with dynamic updating multi-region. Experiments on classical multi-view multi-label data sets validate the effectiveness of M2CR in terms of classification, time performance, convergence, etc. [ABSTRACT FROM AUTHOR]

Multilevel programming is a mathematical programming problem with hierarchical structure. A typical feature of multilevel programming is that the upper level exhibits a priority over the lower level. However, the solutions obtained by most existing programming methods either violate this rule or ignore the participants' desire for a win–win outcome. The objective of this study is to propose new multilevel programming approaches for obtaining desirable solutions. First, three types of membership functions in neutrosophic set are defined to comprehensively describe fuzzy cognition of decision makers. Then, considering dissimilar intentions of experts, three different interactive approaches are proposed to solve multilevel programming problems. To demonstrate the feasibility of the proposed approaches, a case of pricing decision-making of data products is investigated and the impacts of four key parameters are discussed. Finally, several numerical examples are studied by using the proposed approaches and other existing methods. Two evaluation indexes, the equilibrium coefficient and distance measure, are utilized to appraise the performance of the developed programming methods. The results demonstrate that the proposed approaches can obtain sound solutions which obey the rule of multilevel programming, realize the mutual benefits of participants, and can provide guidelines for the pricing of satellite image data products. [ABSTRACT FROM AUTHOR]

With the growing demands of data products, the selection of satellite image data products becomes a challenging decision issue for customers. The objective of this study is to propose a practically sound decision-making approach for solving the satellite image data products selection problems. First, the influencing factors of selecting satellite image data products are identified. Then, hybrid evaluation information is recommended to represent these criteria. That is, numerical and interval-valued quantification is used for quantitative criteria, and picture fuzzy numbers (PFNs) are considered to express qualitative criteria. To reflect decision makers' preferences, a non-linear optimization is implemented to treat criteria weights with constraints. Thereafter, some penalty functions are defined and the artificial fish swarm algorithm (AFSA) is improved to calculate weight values. Furthermore, six main parameters of AFSA are analyzed. Compared with other commonly used algorithms (such as genetic algorithm (GA) and particle swarm optimization (PSO)), the largest advantage of AFSA is its high robustness of parameters and initial values. Finally, the traditional multi-attributive border approximation area comparison (MABAC) is modified with likelihood measures to obtain the best data product in hybrid evaluation environments. Furthermore, the feasibility and effectiveness of the proposed approach is validated by comparing with existing methods in some representative literature. The results demonstrate that the proposed method is feasible and can provide useful guidelines for the selection and pricing of satellite image data products. [ABSTRACT FROM AUTHOR]

The notion of Yager's q-rung orthopair fuzzy set (QROFS) have gained considerable and continuously increasing attention as a useful tool for imprecision and uncertainty representation due to its capability to discard the constraints on the membership and nonmembership functions as generally required by its intuitionistic fuzzy counterpart. Among the generalizations and variants established in the past few years, the interval-valued QROFSs (IVQROFSs) have been diffusely considered to be a powerful generalization of the interval-valued fuzzy sets. The continuous ordered weighted averaging (COWA) operator has been extended successfully to some special cases of IVQROFSs, including interval-valued intuitionistic and Pythagorean fuzzy sets. Thus, to expand on previous studies, several continuous IVQROF (C-IVQROF) aggregation operators are proposed in this study. First, the dual C-GOWA operator is defined on the basis of the continuous generalized ordered weighted averaging (C-GOWA) operator and Yager class of fuzzy negation. Subsequently, the C-IVQROFOWA operator with two independent parameters is constructed, and the weighted C-IVQROFOWA operator is then proposed for aggregating a collection of IVQROFSs. The C-IVQROFOWA operator and its weighted version can model commendably the attitudinal characteristics of the decision-maker. Second, a parameter optimization model and its algorithm-solving strategy driven by consensus measures are built to develop a group decision-making method. Finally, a case study to evaluate the SmartWatch design alternatives is provided to demonstrate the proposed approach, and the results of a comparative analysis verify the rationality and efficiency of the proposed operators. [ABSTRACT FROM AUTHOR]

The concepts of consistency definition and consistency index are usually used to measure the consistency of a preference relation. When interval numbers are used to express the preference information, the consistency of the derived interval-valued preference relations (IVPRs) is worth being investigated. In this study, a comment is provided for the ideas behind consistency definitions and consistency indexes of interval multiplicative reciprocal matrices (IMRMs) and interval additive reciprocal matrices (IARMs), respectively. A comparison is made by considering the two kinds of consistency definitions of IVPRs. It is found that the method of defining the consistency of IVPRs in terms of the imaginary intervals is equivalent to that of defining the approximate consistency. Numerical examples are reported to illustrate the differences of the two consistency definitions of IVPRs. The observations illustrate that the fundamental inconsistency of IVPRs is compatible with the underlying idea of fuzzy sets. It is revealed that a consistent preference relation is only a particular case with a fixed value of the defined consistency index. In general, the consistency index could be used to quantify the deviation degree from a consistent real-valued preference relation. [ABSTRACT FROM AUTHOR]

Compensatory Fuzzy Logic is a transdisciplinary axiomatic theory, different from the Classical Norm and Conorm approach to improving interpretability by natural language. Archimedean Compensatory Fuzzy Logic (ACFL), introduced recently, uses different properties and interpretations of involved truth values. Membership functions involved are not studied explicitly in fuzzy theories, even though it is essential in solving problems. The definition of parameterized families of membership functions is not rare in fuzzy literature. However, according to our review, each of those families has the same shape except the recently introduced Continuous Linguistic Variables. That has been a limitation in the expressiveness of linguistic values. Besides, except for Dombi's theory, these functions are often not related to logical operators. This paper aims to use ACFL to overcome each of these drawbacks. We generalize some fuzzy concepts, only using the ACFL generator function. A Generalized Sigmoidal Function and a Generalized Linguistic Modifier are s-shaped functions generated by it. Those elements define a parameterized family containing different shape functions like an increasing sigmoidal, decreasing sigmoidal and convex function; we call it a Generalized Continuous Linguistic Variable. This paper improves ACFL by unifying it into single theory elements like logic generator functions, linguistic modifiers, membership functions, and linguistic variables. The improved ACFL is not just a Pluralist Logic that makes compatible the classical approach of Norm and Conorm with CFL theory, but a contextual pluralist logic able to select a logic that better expresses specific contextual knowledge. This theory is valuable in Knowledge Discovery; because it creates new searching elements that allow selecting the 'best logic´ for a particular dataset. We develop knowledge discovery cases for different databases to illustrate it and show its data sensitivity. [ABSTRACT FROM AUTHOR]

The aim of image fusion is to obtain a clear image by combining useful information coming from multiple images, so it is crucial to extract the salient features of source images as the activity level measurement effectively. In this paper, a novel algorithm called fractional-order differentiation based sparse representation (FD-SR) is presented for multi-focus image fusion. In this algorithm, the source images are first convoluted with fractional-order differentiation masks to acquire the feature maps, from which the histograms of oriented gradients (HOG) are computed to capture human vision-related salient information. Next, to construct a representative dictionary for sparse representation, the HOG patterns are then partitioned into many patches which are clustered to retain the structural information. From these clusters, compact sub-dictionaries are learned using orthogonal matching pursuit (OMP) respectively and then combined to form the overcomplete dictionary. Finally, the fused sub-images are reconstructed with the dictionary based on max l1 rule, and all these sub-images constitute the whole fused image. The experimental results on multi-focus image datasets and medical image dataset validate the effectiveness of the proposed method for image fusion task. [ABSTRACT FROM AUTHOR]

Inspired by the collaborative mechanism among biological nervous, endocrine and immune systems, this paper proposes an algorithm of adaptive evolutionary based on biological cooperation (BCAE). This method can solve the dynamic multi-objective optimization problem of Industrial Internet of Things (IIoT) services to reduce the total service cost and service time. The BCAE algorithm consists of two parts: bottom level and top level. In the bottom level, different Pareto frontiers are obtained by coevolution of multiple subpopulations. In the top level, according to the distance between the service request and the service provider and the unit energy consumption of the service provider, the connection weight sequence is designed, and then the affinity matrix is constructed according to the connection weight sequence. Finally, the multi factor genetic algorithm (MFEA-II) is used to mate and imitate the service providers with different affinity, and the total service cost and total service time of the optimal solution are obtained, which are recorded in the top-level optimal antigen solution set. On the basis of single service strategy and collaborative service strategy, the IIoT services with dynamic requests are studied under different distributions. The obtained simulation results show that the performance of BCAE is better than the performance of the four existing algorithms, especially when solving high-dimensional problems. [ABSTRACT FROM AUTHOR]

In this paper, we approximate the integral of fuzzy-number-valued functions using generalized quadrature rule and obtain its error estimate. Utilizing the generalized quadrature rule and successive approximations method, we construct an iterative approach to find the numerical approximation of solutions. Moreover, we investigate the error analysis of the numerical method, which guarantees pointwise convergence. Then we apply the presented method to two numerical experiments to present the accuracy and convergence of the method. [ABSTRACT FROM AUTHOR]

Membership function estimation is one of the less explored, albeit important, areas in fuzzy sets. This paper aims to define a new family of fuzzy sets called general continuous linguistic variables (GCLV), which represents a linguistic variable rather than a set of linguistic values. We refer to it as the principle of representation of linguistic variables. They are based on the well-known sigmoidal functions and contain at least three different classes of membership functions, namely, an increasing sigmoidal function, a decreasing sigmoidal function, and a convex one. These diverse features are essential to represent linguistic values exhibiting different semantics. We explore the properties of GCLV, including those ones over that allow us to approximate every continuous membership function. Finally, we illustrate the applicability of GCLV as a fuzzy tool. This leads to the development of the foundations of a new vehicle in fuzzy sets useful in data mining and time series prediction. [ABSTRACT FROM AUTHOR]

Saaty's analytic hierarchy process (AHP) is widely used in many decision-making problems such as a choice of alternatives, prioritization, or ranking. Despite being a valuable tool based on pairwise comparisons of a set of alternatives the method is strongly connected with numeric or linguistic descriptors of the preferences. This can form a limitation to the users who do not feel comfortable with numbers or words strictly related with the articulation of the meaning of preference, i.e., with a predefined scale. Therefore, in this study, we develop a comprehensive approach based on a simple graphic interface. The results and their consistency as well as stability of the method are examined. Moreover, through a suite of experiments we observe how the method works when a group of experts does not provide answers to all questions. Finally, we analyze four variants of non-linear transforms which are used to minimize the inconsistency ratio of the AHP (fuzzy AHP) process. [ABSTRACT FROM AUTHOR]

With the rapid development of computer vision and artificial intelligence, crowd counting has attracted significant attention from researchers and many well-known methods were proposed. However, due to interocclusions, perspective distortion, and uneven crowd distribution, crowd counting is still a highly challenging task in crowd analysis. Motivated by granular computing, a novel end-to-end crowd counting network (GrCNet) is proposed to enable the problem of crowd counting to be conceptualized at different levels of granularity, and to map problem into computationally tractable subproblems. It shows that by adaptively dividing the image into granules and then feeding the granules into different counting subnetworks separately, the scale variation range of image is narrowed and the the adaptability of counting algorithm to different scenarios is improved. Experiments on four well-known crowd counting benchmark datasets indicate that GrCNet achieves state-of-the-art counting performance and high robustness in dense crowd counting. [ABSTRACT FROM AUTHOR]

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and challenging. In view of these problems, we propose a population randomization-based multi-objective genetic algorithm. The gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time. [ABSTRACT FROM AUTHOR]