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The work aims to explore a microscopic observation system of paper surface and achieve high-precision stereoscopic observation with detail characterization of paper surface morphology. Based on the DT-400E precision program-controlled three-dimensional translation stage and KEYENCE LJV-7200 two-dimensional laser scanner, the hardware parts of our own system are developed to scan and transmit point cloud data of paper surface morphology to the computer. The corresponding system software will automatically process the point cloud data acquired from the laser scanner and generate the corresponding vivid 3D model and height histogram. This system scans and characterizes four different types of paper samples, allowing the human eye to visually distinguish the differences in surface morphology as well as automatically calculate the numerical differences in paper surface morphology parameters. The results of the applicability test show that the system is highly efficient in acquiring, observing, and evaluating the topography of the paper surface. The system can not only predict the paper surface quality of printed paper, but can also be extended to the evaluation of 3D printed surfaces. [ABSTRACT FROM AUTHOR]

In the last 30 years, several distinguished researchers have proposed and discussed different fuzzy versions of well-known Saaty's Analytic Hierarchy Process (AHP). The paper recently published by K. Zhü in the European Journal of Operational Research heavily criticizes the fuzzy approaches to AHP, claiming the fallacy of all of them. Therefore, it seems to be necessary to clarify whether the criticisms are well-founded or not. The present paper aims to rebut Zhü's claims by showing that the evidences and the reasonings in his paper are very poor and far from proving the fallacy of fuzzy AHP. [ABSTRACT FROM AUTHOR]

In decision problems involving two dimensions (like several agents in uncertainty) the properties of expected utility ensure that the result of a two-stepped procedure evaluation does not depend on the order with which the aggregations of local evaluations are performed (e.g., agents first, uncertainty next, or the converse). We say that the aggregations on each dimension commute. In a previous conference paper, Ben Amor, Essghaier and Fargier have shown that this property holds when using pessimistic possibilistic integrals on each dimension, or optimistic ones, while it fails when using a pessimistic possibilistic integral on one dimension and an optimistic one on the other. This paper studies and completely solves this problem when more general Sugeno integrals are used in place of possibilistic integrals, leading to double Sugeno integrals. The results show that there are capacities other than possibility and necessity measures that ensure commutation of Sugeno integrals. Moreover, the relationship between two-dimensional capacities and the commutation property for their projections is investigated. [ABSTRACT FROM AUTHOR]

Ordered Weighted Averaging (OWA) operators are a family of aggregation functions for data fusion. If the data are real numbers, then OWA operators can be characterized either as a special kind of discrete Choquet integral or simply as an arithmetic mean of the given values previously ordered. This paper analyzes the possible generalizations of these characterizations when OWA operators are defined on a complete lattice. In addition, the set of all n-ary OWA operators is studied as a sublattice of the lattice of all the n-ary aggregation functions defined on a distributive lattice. [ABSTRACT FROM AUTHOR]

Since the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems published its first issue in 1993, it has made important contributions to the research field of computer science. In this study, based on the dataset of the publications published in this journal between 1993 and 2016 retrieved from Web of Science, a general overview of this journal is performed using bibliometric methods and visualized networks. First, the productive and influential publications, authors, institutions, countries/territories, and supraregions are analysed based on the total number of citations, publications, and different citation thresholds. Second, network visualization analysis is applied to illustrate the links and connections between terms by using the VOSviewer software. Moreover, the most cited journals and common author keywords of three continents, including North America, Europe, and Asia, are also presented. This paper will hopefully help researchers understand the research patterns of this journal. [ABSTRACT FROM AUTHOR]

In order to deal with the problem of encryption algorithms being overly simplistic, and the relatively low security of color images that creates potential to be attacked in the transmission process, this paper will introduce a new encryption algorithm that is designed to divide color images into R, G and B layers. In the scrambling operation: the first scrambling is aimed to block the clear text image scrambling; The second scrambling is the dynamic Arnold scrambling of the ciphertext after the first scrambling. In the diffusion operation, the scrambled ciphertext image was taken as the input, and the pseudo-random sequence generated by Tent mapping and Sine mapping was embedded. The sequence generated by Logistic mapping was used to select sub-blocks for block diffusion of the image. Tent-Sine mapping was applied to the second diffusion to obtain the final ciphertext image. The algorithm designed in this paper combines image block scrambling and dynamic Arnold scrambling, the scrambling degree of each layer of image pixels would be greatly improved, thus improving the security of color images. In the process of diffusion, chaos sequence is selected for diffusion operation, which increases the difficulty of decoding ciphertext. The simulation results show that the new algorithm has desirable encryption effect, strong key sensitivity and large key space, and complex encryption algorithm can effectively resist attacks, which certainly has value in image information security. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. It discusses the potential of fuzzy-based systems in statistical learning theory and their ability to handle uncertainty and ambiguity in real-time data. The editorial highlights recent advancements in integrating fuzzy systems with deep learning architectures and other machine learning techniques. The special issue aims to explore the intersection of fuzzy logic and statistical learning theory and includes twelve accepted papers that investigate the capabilities of fuzzy-based systems. The document provides a brief summary of each paper, covering topics such as pop-up text recognition, alignment of laser images, knowledge graph reasoning, land use changes, 3D printing in vehicle design, defect detection in electronic components, digital management process improvement, spectrum sharing in IoV, deep clustering for time series data, anti-collision algorithms, missing data filling, and video action recognition. The guest editor expresses gratitude to the authors and reviewers for their contributions and to the Editor-in-Chief for the opportunity to curate the special issue. [Extracted from the article]

With the rapid development of intelligent vehicle technology, traditional vehicle design and manufacturing methods are facing many challenges. Traditional vehicle design and manufacturing methods have problems such as high cost, long development cycle and lack of personalized design. This paper took advantage of the high degree of flexibility, rapid customization and cost savings of 3D printing technology, which can provide entirely new solutions for intelligent vehicle design. This paper mainly analyzed the application of three-dimensional (3D) printing and manufacturing technology in intelligent vehicle design. In intelligent vehicle design, 3D printing can carry out 3D printed body structure design to make parts lightweight. The displacement sensor in the intelligent sensor can measure the displacement, deformation and vibration of the body components, and detect whether the body structure exceeds the safety range, which can provide safety feedback to the owner. 3D printing technology has been involved in automotive dashboard, interior design and customization. In this paper, the effects of vehicles designed under traditional methods and vehicles designed under 3D printing manufacturing technology were compared. The research and development cycle of class A, Class B, Class C and Class D in brand 1 under traditional manufacturing was 3.85 years, 3.13 years, 4.27 years and 4.32 years respectively. The R&D (research and development) cycle of grade A, B, C and D in brand 1 under 3D printing technology was 2.43 years, 2.05 years, 1.64 years and 1.73 years respectively. Under the traditional method, the average R&D cost of different levels of vehicles in brand 1, brand 2 and brand 3 was 191,000 yuan, 20900 yuan and 227,200 yuan respectively. Under 3D printing technology, the average R&D cost of different levels of vehicles in brand 1, brand 2 and brand 3 was 132,600 yuan, 145,300 yuan and 157,500 yuan respectively. The results of this paper emphasize that the application of 3D printing manufacturing technology shortens the development cycle of intelligent vehicles, reduces the development cost, and can be customized, which can be more in line with the needs of users. [ABSTRACT FROM AUTHOR]

To effectively promote the efficient dissemination of sci-tech journals and improve the influence of sci-tech journals, a precise push method of sci-tech journals based on knowledge graph reasoning is proposed, using knowledge graph to build network to realize push reasoning of scientific and Technological Journals. Based on the one-way author relationship network diagram and the two-way keyword network diagram, the PageRank algorithm is used to calculate the weight of network vertices, quantitatively and accurately identify the research direction of push customers and predict customers, and realize the accurate push management of content, so as to obtain the basis for the construction of network diagram. Based on the previous article in the Journal of the information society of science and technology of China, this paper constructs a knowledge reasoning diagram, mines 52 co-author cases and 60 related papers as experimental data, and verifies the progressiveness method through customer value. According to the network diagram, the progressiveness of this method is verified. The experimental results show that the knowledge map network reasoning method proposed in this paper has high accuracy in identifying the push objects of scientific and technological journals and accurately predicting the research direction. The matching degree between the number of customers and the pushed content. Using knowledge map network can ensure the timeliness and accuracy of pushing scientific and technological journals. [ABSTRACT FROM AUTHOR]

The time-varying delay is a peculiar phenomenon that occurs in almost all systems. It can cause numerous problems and instability during system operation. In this paper, the time-varying delay is considered in both the states and input vectors, which is a significant distinction between the proposed method here and previous algorithms. Furthermore, the time-varying delay is unknown but bounded. To address this issue, the Razumikhin approach is applied to the proposed method, as it incorporates a Lyapunov function with the original non-augmented state space of the system models, in contrast to the Krasovskii formula. Moreover, the Razumikhin method performs better and avoids the inherent complexity of the Krasovskii method, particularly when dealing with large delays and disturbances. For achieving output stabilization, the model predictive control (MPC) is designed for the system. The considered system in this paper is an interval type-2 (IT2) fuzzy T-S model, which provides a more accurate estimation of the dynamic model of the system. The online optimization problems are solved using linear matrix inequalities (LMIs), which reduces the computational burden and online computational costs compared to offline and non-LMI approaches. Finally, an example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the problem of robust nonfragile guaranteed cost control for uncertain discrete-time Takagi–Sugeno fuzzy systems with Markov jumping parameters and time-varying delay. A nonfragile fuzzy-basis-dependent and mode-dependent controller is designed and a sufficient condition is developed to ensure that the resulting closed-loop system is robust asymptotically stable in mean square with guaranteed cost index not exceeding the specified upper bound. Subsequently, the controller gain and minimum upper bound of the guaranteed cost index can be obtained by solving a set of linear matrix inequalities. Compared with the existing literature, this paper enormously reduces the conservatism of the result obtained. Finally, numerical and practical examples are provided to demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios, and it is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper investigates the Constrained Multi-Objective Travelling Salesman Problem (CMOTSP) and the Constrained Multi-Objective Solid Travelling Salesman Problem (CMOSTSP) under an uncertain environment with zigzag uncertain variables. To solve CMOTSP and CMOSTSP models under uncertain environment, the expected value and optimistic value models are developed using two different ranking criteria of uncertainty theory. The models are transformed to their deterministic forms using the fundamentals of uncertainty. The Models are solved using two solution methodologies Aspiration level-based Multi-Objective Quasi Oppositional Jaya Algorithm (AL-based MOQO Jaya) and Fuzzy Programming Technique (FPT) with linear membership function. Further, the numerical illustration is solved using both methodologies to demonstrate its application. The sensitivity of the OVM model's objective functions regarding confidence levels is also investigated to look at the variation in the objective function. The paper concludes that the developed approach has solved CMOTSP and CMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM. [ABSTRACT FROM AUTHOR]

The idea of nondeterministic typical hesitant fuzzy automata is a generalization of the fuzzy automata presented by Costa and Bedregal. This paper, presents the sufficient and necessary conditions for a typical hesitant fuzzy language to be computed by nondeterministic typical hesitant fuzzy automata. Besides, the paper introduces a new class of typical hesitant fuzzy automata with crisp transitions, and we will show that this new class is equivalent to the original class introduced by Costa and Bedregal. [ABSTRACT FROM AUTHOR]

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means + + (MRK + +) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means + + ; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means + + clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means + + clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means + + - RBM model is compared with any single model, it provides the highest accuracy. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

A person's preference to select or reject certain meals is influenced by several aspects, including colour. In this paper, we study the relevance of food colour for such preferences. To this end, a set of images of meals is processed by an automatic method that associates mood adjectives that capture such meal preferences. These adjectives are obtained by analyzing the colour palettes in the image, using a method based in Kobayashi's model of harmonic colour combinations. The paper also validates that the colour palettes calculated for each image are harmonic by developing a rating model to predict how much a user would like the colour palettes obtained. This rating is computed using a regression model based on the COLOURlovers dataset implemented to learn users' preferences. Finally, the adjectives associated automatically with images of dishes are validated by a survey which was responded by 178 people and demonstrates that the labels are adequate. The results obtained in this paper have applications in tourism marketing, to help in the design of marketing multimedia material, especially for promoting restaurants and gastronomic destinations. [ABSTRACT FROM AUTHOR]

Associated path detection is considered as the major concern of the traditional shortest path issue. The associated path is generally represented by the shortest distance among the source and destination. In the transportation network, distance or cost detection may identify this associated path. Specifically, it is very important to discover the shortest distance that has a minimum number of nodes, and it will give the most optimized result. In this paper, the Fuzzy based Pareto Optimal (FPO) approach is used to discover the shortest paths in a network graph. Initially, the FPO technique finds the shortest paths in a network by using set of rules. Then, the Lexicographical model uses a set of rules to rank the shortest distance based on minimum distance value. From the ranking results, the optimal shortest path is selected based on the proposed Ant Lion Optimization (ALO) algorithm. So, this paper achieves multi objectives like shortest path ranking and selection of the optimal shortest path. Time, distance or cost, convergence time, fitness function, and mean square error are the parameters used to relate the performance of the proposed technique with state-of-the-art techniques. Comparative results display the robustness and proficiency of the proposed system with several works. [ABSTRACT FROM AUTHOR]

This paper studies robust fuzzy model predictive control of discrete-time fuzzy Takagi-Sugeno large-scale systems based on hierarchical optimization and H_ ∞ Performance. The considered system in this paper is a large-scale system. So, the Takagi-Sugeno (T-S) fuzzy approach which uses nonlinear local models is applied to estimate the nonlinear system to reduce the burden of modeling. The model predictive control (MPC) is a practical application that is useful for all systems but the problem is the online computational cost. For large-scale systems, this problem is the online computational burden. Besides, it leads to conservative or even no solution. By the proposed method, more relaxed solutions are achieved. First, the T-S system with nonlinear local models is adopted. Second, the hierarchical optimization scheme is considered for the optimization part of the MPC. Third, by inspiring the H ∞ performance, uncertainties and disturbances that are appeared are reduced. Moreover, the linear matrix inequalities (LMIs) are used to solve the proposed method that the computational burden significantly is reduced at the same time. Simulation results are finally presented to show the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) lending brings many benefits for both lenders and borrowers, as well as risk for them, especially for the lenders. To help the lenders select a reliable P2P lending platform with high return and low risk, a more comprehensive selection method is necessary. However, vast information with fluctuant, fuzzy and subjective data on the P2P lending platform make it more difficult for lenders to make the right choice. This paper intends to propose a selection method by using intuitionistic fuzzy soft set to organize the data, and using D-S theory of evidence to integrate the intuitionistic fuzzy values. Besides, the paper analyses the critical factors affecting the lenders' decision, and presents some indicators for evaluating. Finally, an empirical experiment was given by choosing several P2P lending platforms with real data, and the results are compared with the intuitionistic fuzzy weighted average and Development Index from Wangdaizhijia, that verify the validity and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper intends to develop a novel framework for text document clustering with the aid of a new improved meta-heuristic algorithm. Initially, the features are selected from the text document by subjecting each word under Term Frequency-Inverse Document Frequency (TF-IDF) computation. Subsequently, centroid selection plays a vital role in cluster formation, which is done using a new Improved Lion Algorithm (LA) termed as Cross over probability-based LA model (CP-LA). As a novelty, this paper introduced a new inter and intracluster similarity model. Moreover, this centroid selection is made in such a way that the proposed adaptive weighted similarity should be minimal. Based on the characteristics of the document, the weights are automatically adapted with the similarity measure. The proposed adaptive weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents. Finally, the superiority of the proposed over other models is proved under different performance measures. [ABSTRACT FROM AUTHOR]

Nowadays, many sequences of events are generated in areas as diverse as healthcare, finance, and social network. People have been studying these data for a long time. They hope to predict the type and occurrence time of the next event by using relationships among events in the data. recently, with the successful application of Recurrent Neural Network (RNN) in natural language processing, it has been introduced into point process. However, RNN cannot capture the long-term dependence among events well, and self-attention can partially mitigate this problem precisely. Transformer Hawkes Process (THP) using self-attention greatly improves the performance of the Hawkes Process, but THP cannot ignore the effect of irrelevant events, which will affect the computational complexity and prediction accuracy of the model. In this paper, we propose an Adaptively Sparse Transformers Hawkes Process (ASTHP). ASTHP considers the periodicity and nonlinearity of event time in the time encoding process. The sparsity of the ASTHP is achieved by substituting Softmax with α -entmax: α -entmax is a differentiable generalization of Softmax that allows unrelated events to gain exact zero weight. By optimizing the neural network parameters, different attention heads can adaptively select sparse modes (from Softmax to Sparsemax). Compared with the existing models, ASTHP model not only ensures the prediction performance but also improves the interpretability of the model. For example, the accuracy of ASTHP model on MIMIC-II dataset is improved by nearly 3 percentage points, and the model fitting degree and stability are also improved significantly. [ABSTRACT FROM AUTHOR]

During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination. [ABSTRACT FROM AUTHOR]

An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one's disposal. [ABSTRACT FROM AUTHOR]

In a power distribution system, faults occurring can cause voltage sag that can affect critical loads connected in the power network which can cause serious effects in the oil and gas industry. The objective of this paper is to design and implement an efficient and economical dynamic voltage restorer (DVR) to compensate for voltage sag conditions in the oil and gas industry. Due to the complexity and sensitivity of loads, a short voltage sag duration can still cause severe power quality problems to the entire system. Dynamic Voltage Restorer (DVR) is a static series compensating type custom power device. The overall efficiency of the DVR largely relies on the effectiveness of the control strategy governing the switching of the inverters. It can be said that the heart of the DVR control strategy is the derivation of reference currents. This paper deals with the extraction of reference current values using a controller based on a combination of probabilistic and fuzzy set theory. The basis of the proposed controller is that Gaussian Mixture Model (GMM) which is a probabilistic approach can be translated to an additive fuzzy interface system i.e. Generalized Fuzzy Model (GFM). The proposed controller (GMM-GFM) initially optimizes the membership functions using GMM and the final output is calculated using GFM in a single iteration i.e. with no recursions. In the control scheme two control loops are used: a feed-forward loop that uses the Proportional and Integral (PI) controller and the feedback loop uses GMM-GFM based controller. The controller is implemented and respective simulations are performed in the MATLAB SIMULINK environment for three-phase, three-wire distribution system with various issues. A comparative analysis is then done amongst all the three controllers which are based on the T-S, ML, and GMM-GFM modes respectively. The simulation results of this comparison rank the DVR with the GMM-GFM controller first, followed by the fuzzy logic Mamdani model and then with the fuzzy logic T-S model. [ABSTRACT FROM AUTHOR]

One of the main objectives of econometrics is to predict future values of important economics-related quantities, such as unemployment level, stock prices, currency exchange rates, etc. — and especially to predict how different possible economy-boosting measures will affect these quantities. To perform this prediction, we design a model of such effect and train it on the available data. Usually, the daily (or weekly) data are used for this training. However, economics-related quantities fluctuate all the time. So, for each moment of time (e.g., for each day) instead of a single value of the corresponding quantity, we have smallest and largest daily values — i.e., the interval range of daily values. At first glance, it may seem that using both endpoints of this interval for training will lead to more accurate predictions, but in reality, predictions become less accurate. Predictions become more accurate if we only use midpoints of the corresponding intervals. Several recent papers showed that even more accurate predictions are possible if we allow general convex combinations of the intervals' endpoints — and select the corresponding coefficients so as to best fit the data. In this paper, we provide a theoretical explanation for these results. [ABSTRACT FROM AUTHOR]

The problem of belief change is considered as a major issue in managing the dynamics of an information system. It consists in modifying an uncertainty distribution, representing agents' beliefs, in the light of a new information. In this paper, we focus on the so-called multiple iterated belief revision or C-revision, proposed for conditioning or revising uncertain distributions under uncertain inputs. Uncertainty distributions are represented in terms of ordinal conditional functions. We will use prioritized or weighted knowledge bases as a compact representation of uncertainty distributions. The input information leading to a revision of an uncertainty distribution is also represented by a set of consistent weighted formulas. This paper shows that C-revision, defined at a semantic level using ordinal conditional functions, has a very natural representation using weighted knowledge bases. We propose simple syntactic methods for revising weighted knowledge bases, that are semantically meaningful in the frameworks of possibility theory and ordinal conditional functions. In particular, we show that the space complexity of the proposed syntactic C-revision is linear with respect to the size of initial weighted knowledge bases. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study lacunary A-statistical convergence of complex uncertain sequences via Orlicz functions. We investigate some aspects of uncertainty such as mean, measure, almost surely, uniformly almost surely and distribution. Additionally, we make an effort to provide illustrative examples that elucidate the interrelations within the constructed sequence spaces. [ABSTRACT FROM AUTHOR]

Due to the limitations associated with the use of a single type of data during the recommendation process, recent research has focused on developing new fusion-based recommenders that make use of multiple heterogeneous sources of information to provide more accurate suggestions. However, the realistic and flexible methods available to users for expressing their preferences for products and services inherently generate uncertain, imperfect, and ambiguous data that feed recommenders and thus affect their accuracy. As a result, Recommender Systems (RS) make significant use of soft mathematical tools to deal with uncertainty. Among these tools is Dempster-Shafer Theory (DST), which has been shown to be effective at dealing with the inherent uncertainty in numerous applications. This article provides a survey of the use of DST in the RS field. Thus, after a brief introduction to recommender systems and the DST, this survey discusses recent DST applications in RS. It introduces a new taxonomy that encompasses the primary application context for DST-based RS solutions, as well as a comprehensive multi-criteria analysis of the peer-reviewed papers. The resulting comparisons are analyzed to draw conclusions, identify current study limitations, and define future research directions. This survey serves as a valuable resource for the entire research community that is interested in recommender systems and DST. [ABSTRACT FROM AUTHOR]

Symbolically representing the knowledge acquired by a neural network is a profound endeavor aimed at illuminating the latent information embedded within the network. The literature offers a multitude of algorithms dedicated to extracting symbolic classification rules from neural networks. While some excel in producing highly accurate rules, others specialize in generating rules that are easily comprehensible. Nevertheless, only a scant few algorithms manage to strike a harmonious balance between comprehensibility and accuracy. One such exemplary technique is the Rule Extraction from Neural Network Using Classified and Misclassified Data (RxNCM) algorithm, which adeptly generates straightforward and precise rules outlining input data ranges with commendable accuracy. This article endeavors to enhance the classification performance of the RxNCM algorithm by leveraging ensemble technique. Ensembles, a burgeoning field, focus on augmenting classifier performance by harnessing the strengths of individual classifiers. Extraction of rules through neural network ensembles is relatively underexplored, this paper bridges the gap by introducing the Rule extraction using Neural Network Ensembles (RENNE) algorithm. RENNE is designed to refine the classification rules derived from the RxNCM algorithm through ensemble strategy. Specifically, RENNE leverages patterns correctly predicted by an ensemble of neural networks during the rule generation process. The efficacy of the algorithm is validated using seven datasets sourced from the UCI repository. The outcomes indicate that the proposed RENNE algorithm outperforms the RxNCM algorithm in terms of performance. [ABSTRACT FROM AUTHOR]

The spatial land change of villages and towns can be analyzed through external spatio-temporal characteristics, which can realize the evaluation of historical development and assist the implementation of planning and design norms and the implementation of problem rectification. To explore the specific process and the overall pattern of land use change, it is necessary to analyze and predict all kinds of data in a unified way to grasp the law of development and the direction of future development. This paper takes the spatial development and planning of villages and towns as the research object, selects the remote sensing image data of regional historical development as the driving factor, combines with Gaussian function fitting to calculate the training samples, and constructs the regional spatial environment analysis system. According to the processing model of external characteristics of time series, the spatial distribution and evolution of Risk-Screening Environmental Indicators (RSEI) and its components are analyzed. The multi-dimensional remote sensing data is innovatively fused into the time series processing model. Besides, the curve which can characterize the spatial change law and is not completely symmetrical is obtained through the least square method fitting so that the data with unequal time series intervals can be flexibly processed. Finally, through the experimental analysis, the traditional Asymmetric Gaussian model (A-G) and Savitzky-Golay (S-G) are taken to compare and analyze the effect of various factors on the processing of time series characteristics. Tests show that the proposed algorithm is superior to the comparison method in noise removal of remote sensing data. The data fitting coefficient is 0.02, and the root mean square measurement error is more than 7% higher than the comparison method. It can predict the trend of land use change distribution, play a key role in assisting decision-making for different land use transformations and utilization in villages and towns, and make a scientific evaluation for the impact of social and economic development potential. [ABSTRACT FROM AUTHOR]

Pop-ups are a recently popular way of human-computer interaction, allowing viewers to actively participate in the discussion of a film video, but pop-ups can be deleted due to exceeding the size limit of the pop-up pool, and pop-up data is difficult to obtain in its entirety. To this end, this paper proposes a local convolutional neural network (CNN) based pop-up text sub-recognition algorithm. Finally, the fuzzed data is reconstructed into a data look-up table and the contents of the data look-up table are formatted for output. The results of this text classification and recognition algorithm are compared with those of four commonly used domestic and international search engines by subjective evaluation method, and the proposed text classification and recognition algorithm is found to be advanced. [ABSTRACT FROM AUTHOR]

Today's society has entered the pace of information development era. All kinds of information are digitized, while the loss of information data also directly affects the normal operation of the application system and has become the biggest obstacle to the development of information technology. The existing missing filling methods do not take into account the time-series information of the data set. Based on the neural network method for filling missing data in time series, an end-to-end missing data filling method for time series based on the residual of regression equation is proposed. Under the assumption that the activation function and the noise diffusion coefficient function are satisfied, the exponential stability of the complex-valued stochastic inertial neural network systems with additive time-varying delays is studied. The bipartite graph model of the time series data missing value filling method of the countermeasure network and the filling cyclic neural unit are generated. According to the end-to-end time series data missing value filling method of the self encoder, the corresponding low-dimensional feature vector can be automatically generated for each time series data. It avoids overfitting by using nonlinear methods directly in low-dimensional space. Experiments show that the method proposed in this paper uses Monte Carlo to fill data missing at different proportions. In data gap filling process, the coefficient variance is less than 0.05, which enhances the rationality of filling data. It is of great research value and practical significance to reasonably fill in the missing values of time series data. The research can accelerate the improvement of big data system, improve the level and effectiveness of database management, and is an important means of computing capacity of existing data centers. [ABSTRACT FROM AUTHOR]

Passive RFID has the advantages of rapid identification of multi-target objects and low implementation cost. It is the most critical technology in the Industrial Internet of Things information-gathering layer and is extensively applied in various industries, such as smart production, asset management, and monitoring. The signal collision caused by the communication between the reader/writer and tags sharing the same wireless channel has caused a series of problems, such as the reduction of the identification efficiency of the reader/writer and the increment of the missed reading rate, thus restricting the further development of RFID. At present, many hybrid anti-collision algorithms integrate the advantages of Aloha and TS algorithms to optimize RFID system performance, but these solutions also suffer from performance bottlenecks. In order to break through such performance bottleneck, based on the ISE-BS algorithm, we combined the sub-frame observation mechanism and the Q value adjustment strategy and proposed two hybrid anti-collision algorithms. The experimental results show that the two algorithms proposed in this paper have obvious advantages in system throughput, time efficiency and other metrics, surpassing existing UHF RFID anti-collision algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, enterprise digitalization, and Internet + application, the digitalization and improvement of the used car trading business and its management process improvement are facing many challenges. This paper attempts to use the CMMI process improvement model to analyze the factors affecting the digital construction, business process optimization, and digital management process improvement of the used car market through research on the digital management process and the related theories of CMMI, such as talent training, application of information technology, immature business development, irregular business process, and industry policy restriction. The FAHP analysis method is used to establish a comparison matrix to explore the construction of an evaluation system model for the improvement of the digital management process of the used car trading market based on the CMMI model. [ABSTRACT FROM AUTHOR]

Traditional digital image processing techniques face problems such as complex feature extraction and weak robustness when dealing with surface defects of multiple categories of electronic components. Deep learning is widely used in industrial defect detection. However, the performance of electronic component defect detection at the pixel segmentation level needs to be improved. For pixel-level defect detection, this paper constructs a defect detection model (ECSDDNet) for electronic component surface defects in computer vision engineering management system. To improve the segmentation accuracy and detection effect, three stages of experiments are conducted to address mis-segmentation problems and the shortcomings of the Unet network structure. Firstly, a classification network that can perform weight transfer is used to replace the encoding structure in the Unet network. Secondly, a simplified version of the feature fusion is proposed and added to the skip connection of the Unet network. Finally, label smoothing is used to optimize the loss and improve the generalization of the network. After the optimization experiment, some noisy contours and small defect contours that are mis-segmented are removed. Experimental results show that ECSDDNet has good segmentation effects on electronic component surface defects and can meet the segmentation and detection needs of electronic component surface defects. [ABSTRACT FROM AUTHOR]

In recent years, heterogeneous network/graph representation learning/embedding (HNE) has drawn tremendous attentions from research communities in multiple disciplines. HNE has shown its outstanding performances in various networked data analysis and mining tasks. In fact, most of real-world information networks in multiple fields can be modelled as the heterogeneous information networks (HIN). Thus, the HNE-based techniques can sufficiently capture rich-structured and semantic latent features from the given information network in order to facilitate for different task-driven learning tasks. This is considered as fundamental success of HNE-based approach in comparing with previous traditional homogeneous network/graph based embedding techniques. However, there are recent studies have also demonstrated that the heterogeneous network/graph modelling and embedding through graph neural network (GNN) is not usually reliable. This challenge is original come from the fact that most of real-world heterogeneous networks are considered as incomplete and normally contain a large number of feature noises. Therefore, multiple attempts have proposed recently to overcome this limitation. Within this approach, the meta-path-based heterogeneous graph-structured latent features and GNN-based parameters are jointly learnt and optimized during the embedding process. However, this integrated GNN and heterogeneous graph structure (HGS) learning approach still suffered a challenge of effectively parameterizing and fusing different graph-structured latent features from both GNN- and HGS-based sides into better task-driven friendly and noise-reduced embedding spaces. Therefore, in this paper we proposed a novel attention-supplemented heterogeneous graph structure embedding approach, called as: AGSE. Our proposed AGSE model supports to not only achieve the combined rich heterogeneous structural and GNN-based aggregated node representations but also transform achieved node embeddings into noise-reduced and task-driven friendly embedding space. Extensive experiments in benchmark heterogeneous networked datasets for node classification task showed the effectiveness of our proposed AGSE model in comparing with state-of-the-art network embedding baselines. [ABSTRACT FROM AUTHOR]

Neuro-fuzzy techniques have been widely used in many applications due to their ability to generate interpretable fuzzy rules. Ensemble learning, on the other hand, is an emerging paradigm in artificial intelligence used to improve performance results by combining multiple single learners. This paper aims to develop and evaluate a set of homogeneous ensembles over four medical datasets using hyperparameter tuning of four neuro-fuzzy systems: adaptive neuro-fuzzy inference system (ANFIS), Dynamic evolving neuro-fuzzy system (DENFIS), Hybrid fuzzy inference system (HyFIS), and neuro-fuzzy classifier (NEFCLASS). To address the interpretability challenges and to reduce the complexity of high-dimensional data, the information gain filter was used to identify the most relevant features. After that, the performance of the neuro-fuzzy single learners and ensembles was evaluated using four performance metrics: accuracy, precision, recall, and f1 score. To decide which single learners/ensembles perform better, the Scott-Knott and Borda count techniques were used. The Scott-Knott first groups the models based on the accuracy to find the classifiers appearing in the best cluster, while the Borda count ranks the models based on all the four performance metrics without favoring any of the metrics. Results showed that: (1) The number of the combined single learners positively impacts the performance of the ensembles, (2) Single neuro-fuzzy classifiers demonstrate better or similar performance to the ensembles, but the ensembles still provide better stability of predictions, and (3) Among the ensembles of different models, ANFIS provided the best ensemble results. [ABSTRACT FROM AUTHOR]

The uncertain vector autoregressive model is able to model the interrelationships between different variables, which is more advantageous compared to the traditional autoregressive model, when modeling real-life objects and where the observed values are imprecise. In this paper, the parameters of the uncertain vector autoregressive model are estimated by using least absolute deviation estimation (LAD) to obtain a fitted uncertain vector autoregressive model, and residual analysis is performed to obtain estimates of expected values and variances of the residuals. In addition, future values are modeled by using forecasting methods, i.e., point estimation and interval estimation. The order of the uncertain vector autoregressive model is also determined by the indicator summation of test errors (STE) in the cross-validation, and we also analyze that the least absolute deviation estimation outperforms the least squares estimation method in the presence of outliers. [ABSTRACT FROM AUTHOR]

Fluid - Level measurement is required in recognizing the state variable of a Level processing plant for monitoring the level deviations in most the industrial plants. This issue has mostly been addressed by conventional methods using level sensors and level transmitters. However, the cost associated with these mechanisms can be reduced. It has been identified the chances with the applications machine learning approach in this scenario. Such a solution first of its kind is proposed and tried in this article. Gabor feature selection is established with SMOTE (Synthetic Minority Oversampling Technique) to overcome the imbalanced data set to improve the accuracy performance using Weka software. This paper aims to construct a level predictor based on machine learning algorithms and to demonstrate the training and testing performance validated with more accuracy. At this juncture, we consider the fluid level images for analyzing and predicting the level measurement by building supervised models with selected classifiers like IBk, JRip, J48, and Random Forest machine learning algorithms. The java-implemented tool Weka is subjected to get the maximum accuracy of 76.4286% and weighted average ROC (Receiver Operator Characteristics) values of 0.903. Hence such smart measurements using Machine learning algorithms, part of Artificial intelligence provide us a vital role in measuring the level parameter in less time using image processing filters. [ABSTRACT FROM AUTHOR]

Introduce a new dynamic classifying algorithm in this paper to recognize and monitor fish activity to simultaneously better comprehend their synapomorphies. The pre-trained Mask Regional Convolutional Neural Network (Mask-R-CNN) is trained using a set of test models extracted from recorded video recording. The approach suggested subsequently yields well-enhanced feature vectors. The system's automatic fish detection and tracking capabilities are improved, enhancing underwater investigation for supervising ecological biodiversity. The publicly accessible field-truth dataset assesses recall, F1-score, and classification and tracking precision. Utilizing current tracking R-CNN algorithms like Lowest Output Sum of Siamese Mask (SiamMask), Sequential Non-Maximum Suppression (Seq-NMS), and Squared Errors (MOSSE), comparative performance testing is conducted. In comparison to Siam-Mask (84%), Seq-NMS (78%) and MOSSE (75%), more than 120 of 170 specific bream were detected using the pre-trained Mask-R-CNN of the proposed algorithm (87%). This pre-trained Mask R-CNN system was used in the evaluation, and it was discovered that detection and tracking accuracy had increased significantly. This suggests that the ocean ecosystem could benefit from applying the proposed approach for management of ecology. [ABSTRACT FROM AUTHOR]

Currently, the entire world is fighting against the Corona Virus (COVID-19). As of now, more than thirty lacs of people all over the world were died due to the COVID-19 till April 2021. A recent study conducted by China suggests that Chest CT and X-ray images can be used as a preliminary test for COVID detection. This paper propose a transfer learning-based mathematical COVID detection model, which integrates a pre-trained model with the Random Forest Tree (RFT) classifier. As the available COVID dataset is noisy and imbalanced so Principal Component Analysis (PCA) and Generative Adversarial Networks (GANs) is used to extract most prominent features and balance the dataset respectively. The Bayesian Cross-Entropy Loss function is used to penalize the false detection differently according to the class sensitivity (i.e., COVID patient should not be classified as Normal or Pneumonia class). Due to the small dataset, a pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2 were chosen to extract features and then trained them over the RFT for the classification task. The experiment results showed that ResNet50 gives the maximum accuracy of 99.51%, 98.21%, and 97.2% for training, validation, and testing phases, respectively, and none of the COVID Chest X-ray images were classified as Normal or Pneumonia classes. [ABSTRACT FROM AUTHOR]

This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking. [ABSTRACT FROM AUTHOR]

This paper discusses a nonlinear optimization problem with the system of max-Archimedean bipolar fuzzy relation equations as constraints. Some results related to the structure of the solution set of max-Archimedean bipolar fuzzy relation equations are proved. Using these results, a genetic algorithm is proposed to solve the problem for obtaining optimal or converging solutions. The effectiveness of the algorithm is also compared with other methods found in the literature. The previous methods require conversion of the problem into 0-1 mixed integer optimization problem solvable by some nonlinear optimization solvers and thereby, the computational work may increase with the size of the problem. Some test problems are developed to evaluate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In social media, the data-sharing activities have turned out to be more pervasive; individuals and companies have comprehended the significance of promoting info by social media network. However, these individuals and companies face more challenges with the issue of "how to obtain the full benefit that the platforms provide". Therefore, social media policies to improve the online promotion are turning out to be more significant. The popularization of social media contents are related to public attention and interest of users, thus the popularity fore cast of online contents has considered being the major task in social media analytic and it facilitates several appliances in diverse domain as well. This paper intends to introduce a popularity forecast approach that derives and combines the richest data of "text content encoder, user encoder, time series encoder, and user sentiment analysis". The extracted features are then predicted via Long Short Term Memory (LSTM). Particularly, to enhance the prediction accuracy of the LSTM, the weights are fine-tuned via Self Adaptive Rain optimization (SA-RO). [ABSTRACT FROM AUTHOR]

In this paper we study some geometrical properties of the polytope of 3-tolerant fuzzy measures. To achieve this task, we profit that this polytope is an order polytope and hence many properties can be extracted from the subjacent poset. The main result in the paper is a straightforward procedure for obtaining a random 3-tolerant fuzzy measure. We also compute the volume and obtain some other properties of this polytope. These results can be also applied by duality to the polytope of 3-intolerant measures and they can also be easily extended to other subfamilies of fuzzy measures. [ABSTRACT FROM AUTHOR]

In this paper, for sparse multi-label data, based on inter-instance relations and inter-label correlation, a Sparse Multi-Label Kernel Gaussian Neural Network (SMLKGNN) framework is proposed. Double insurance for the sparse multi-label datasets is constructed with bidirectional relations such as inter-instance and inter-label. When instance features or label sets are too sparse to be extracted effectively, we argument that the inter-instance relations and inter-label correlation can supplement and deduce the relevant information. Meanwhile, to enhance the explainable of neural network, Gaussian process is adopted to simulate the real underlying distribution of multi-label dataset. Besides, this paper also considers that contributions of different features have different effects on the experimental results, thus self-attention is leveraged to balance various features. Finally, the applicability of the algorithm is verified in three sparse datasets, and the generalization performance is also validated in three groups of benchmark datasets. [ABSTRACT FROM AUTHOR]

In this paper, we study uni-nullnorms and null-uninorms on bounded lattices, which are generalizations of uninorms, nullnorms, t-norms and t-conorms. We construct two uni-nullnorms and two null-uninorms on a bounded lattice L. We determine the smallest-greatest uni-nullnorms with 2-neutral element { e , 1 } a and the smallest-greatest null-uninorms with 2-neutral element { 0 , e } a by using these construction methods. [ABSTRACT FROM AUTHOR]

Parallel clustering serves as a platform for handling big data. The literature displays a number of clustering algorithms using a map-reduce framework, but they did not assure the effective clusters such that knowledge extraction becomes tough. With the aim to render a better and effective data clustering method to analyze the big data arriving from distributed systems, this paper uses a new clustering method. The proposed method named as BatDolphin-based Sparse Fuzzy C-Means (BatDol-Sparse FCM) clustering algorithm is proposed that paves way for the optimal selection of the cluster centroids. The distributed big data is managed using the Map-Reduce framework that is inbuilt with the BatDolphin-based Sparse Fuzzy C-Means algorithm such that the local and global clustering is executed. The implementation of the proposed BatDol-Sparse FCM algorithm is done by using data from the E-khool Learning Management System (LMS) and three medical datasets from the UCI repository. The analysis is performed using the metrics, such as clustering accuracy (CA) and dice coefficient (DC). From the simulation results, it is evident that the proposed parallel clustering scheme provided better results than the existing algorithms with the values of 0.96 and 0.9667 for CA and DC respectively. [ABSTRACT FROM AUTHOR]