Journal of Intelligent & Fuzzy Systems - Volume 44, issue 4

Authors: Dehghani, Alireza | Bagherifard, Karamolah | Nejatian, Samad | Parvin, Hamid

Article Type: Research Article

Abstract: Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the …impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t -test show that the proposed method performs significantly better than all the other compared methods. Show more

Keywords: K-means imputation, missing values imputation, kernel-based weighting, grey relation analysis, data pre-processing

DOI: 10.3233/JIFS-200774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5675-5697, 2023

Authors: Zhao, Peichen | Yue, Qi | Deng, Zhibin

Article Type: Research Article

Abstract: Probabilistic hesitant fuzzy set (PHFS), as a complex ambiguous information representation tool, has been widely used in decision making problem, but is rarely applied in a two-sided matching (TSM). Therefore, it is important and necessary to investigate the TSM problem with PHFS. This paper proposes a decision method for TSM with probabilistic hesitant fuzzy Numbers (PHFNs) and applies it to the person-job fit problem. Firstly, a novel TSM decision model on the basis of PHFNs is constructed. In order to solve this model, the TSM model is transformed into a two-goal TSM model by using linear weighted method. And then, …the two-goal TSM model with PHFN can be changed into a single-goal TSM model with scores through score function. The perfect alternative of TSM could be obtained through model solution. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method. Show more

Keywords: Two-sided matching decision making (TSMDM), Probabilistic hesitant fuzzy number (PHFN), Optimal model

DOI: 10.3233/JIFS-213010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5699-5709, 2023

Authors: Pathik, Babita | Pathik, Nikhlesh | Sharma, Meena

Article Type: Research Article

Abstract: The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a …hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate. Show more

Keywords: Clustering, fuzzy c-means, grey wolf optimizer, test case prioritization, test suite augmentation

DOI: 10.3233/JIFS-222433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5711-5718, 2023

Authors: Bilal, Ahmad | Munir, Muhammad Mobeen

Article Type: Research Article

Abstract: The largest absolute eigenvalue of a matrix A associated to the graph G is called the A -Spectral Radius of the graph G , and A -energy of the graph G is defined as the absolute sum of all its eigenvalues. In the present article, we compute Randic energies, Reciprocal Randic energies, Randic spectral radii and Reciprocal Randic radii of s -shadow and s -splitting graph of G . We actually relate these energies and Spectral Radii of new graphs with the energies and Spectral Radii of original graphs.

Keywords: Shadow graph, splitting graph, randic energy, randic spectral radius, reciprocal randic energy, reciprocal randic spectral radius, eigenvalues

DOI: 10.3233/JIFS-221938

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5719-5729, 2023

Authors: Ashok Kumar, L. | Karthika Renuka, D. | Saravana Kumar, S.

Article Type: Research Article

Abstract: Human-wildlife conflicts in the habitats along the forest fringes are a substantial issue. An automated monitoring system that can find animal breaches and deter them from foraging fields is essential to solve this conflict. However, automatically forefending the intruding animals is a challenging task. In this paper, we propose a deep learning model for elephant identification using YOLO lite with knowledge distillation which could be easily deployed in edge devices. We also propose an elephant re-identification system using Siamese network which is helpful in tracking the number of times the elephant tries to forage the field. This re-encounter information about …the same elephant can be used to decide the averting sound for the particular elephant. The proposed system is found to show an accuracy of 89%, which is provides good performance improvement when compared to the state of art models proposed for animal identification. Thus the proposed lite weight knowledge distillation based animal identification model and deep learning based animal re-identification model can be employed in edge devices for real time monitoring and animal deterring to safe guard the farm fields. Show more

Keywords: Neural networks, knowledge distillation, siamese neural network, classification, re-identification, computer vision

DOI: 10.3233/JIFS-222672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5731-5743, 2023

Authors: Zhang, Yangjingyu | Cai, Qiang | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: Based on the traditional TOPSIS method and 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), this paper builds a novel 2TLPF-TOPSIS method that combines cumulative prospect theory (CPT) to cope with the multiple attribute group decision-making (MAGDM). This new method takes into account the decision-makers’ mind and the uncertainty of decision-making, and is more in line with the real decision-making environment. First, this paper briefly reviews some necessary theories related to PFS, as well as the calculation rules and comparison methods of 2TLPFNs. Then, since there is often subjective randomness when determining the weight, the entropy method is utilized to objectively determine …the weight. After that, give the specific calculation steps of the new method. In order to show the effectiveness of the new method, apply it into a specific numerical example about evaluating airline business operations capability, and compare it with the other four different methods. The ranking results depict that the new method designed is effective and reasonable, and has good application value of MAGDM problems. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), TOPSIS method, cumulative prospect theory (CPT), airline business operations capability

DOI: 10.3233/JIFS-220776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5745-5758, 2023

Authors: Wu, Chun | Yang, Liu

Article Type: Research Article

Abstract: A unique approach for assessing the compressive strength (CS ) of high-performance concrete (HPC ) incorporating blast furnace slag (BFS ) and fly ash (FA ) has been created using support vector regression (SVR ) analytics. In order to identify crucial SVR methodology variables that could be adjusted for improved performance, the Henry gas solubility optimization (HGSO ) and Cuckoo search optimization (CSO ) algorithms were both employed in this study. The recommended methods were developed utilizing 1030 experiments and eight inputs, including the CS as the forecasting objective, admixtures, aggregates, and curing age as the main mix …design component. The results were then contrasted with those from related literature. The estimate results suggest that combined HGSO-SVR and CSO-SVR analysis might perform extraordinarily well in estimating. The Root mean square error value for the HGSO  - SVR decreased remarkably when compared to the CSO  - SVR . As can be seen from the comparisons, the HGSO  - SVR that was built beats anything previously published. In conclusion, the suggested HGSO  - SVR analysis might be determined as the proposed system for forecasting the CS of HPC improved with FA and BFS . Show more

Keywords: High-performance concrete, Compressive strength, fly ash, blast furnace slag, estimation, SVR, HGSO, COA

DOI: 10.3233/JIFS-222348

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5759-5772, 2023

Authors: Zhang, Xiaolu | Wan, Jun | Luo, Ji

Article Type: Research Article

Abstract: Interval-valued q-rung orthopair fuzzy number (IVq-ROFN) is a popular tool for modeling complex uncertain information and has gained successful applications in the field of comprehensive evaluation. However, most of the existing studies are based on the absolute values of evaluation data but fail to take incentive effects into account. Reasonable and appropriate incentive can guide the evaluated objects to better achieve the decision goals. Therefore, this study develops an incentive mechanism-based interval-valued q-rung orthopair fuzzy dynamic comprehensive evaluation method. Firstly, new interval-valued q-rung orthopair fuzzy measures including deviation measure and correlation coefficient are proposed for managing IVq-ROFNs data. To overcome …the limitations of the existing aggregating operators that are not suitable for scenarios with need of many times of data aggregation, we introduce two new interval-valued q-rung orthopair fuzzy aggregating operators. Furthermore, a new interval-valued orthopair fuzzy CRITIC method is developed to objectively determine the importance of the evaluated criteria. More importantly, the horizontal incentive effects within a single period and the vertical incentive effects during multiple periods under IVq-ROFNs environments are proposed to reward (or punish) the evaluated objects in the evaluation process. The evaluated results are determined based on the full compensatory model and the multiplicative form model. The main advantage of the developed method is that the expectations of decision-makers and the dynamic characteristics during multiple periods are taken fully into account, which can make the evaluation results more reasonable and reliable. Finally, this developed comprehensive evaluation method is applied to evaluate the green development level of Jiangxi province within eleven cities from 2016 to 2020. We observe that the cities x 2 , x 3 , x 4 , x 5 , x 7 , x 8 are rewarded within positive incentive values and the cities x 1 , x 6 , x 9 , x 10 , x 11 are punished within negative incentive values. Especially, the positive incentive value for the city x 3 is the biggest and the negative incentive value for the city x 9 is the biggest. The best city in term of GDL is x 3 . The evaluated results with consideration of incentive effects are in line with the expectation of the decision-maker. Show more

Keywords: Interval-valued q-rung orthopair fuzzy number, Comprehensive evaluation, CRITIC, Incentive effect, Green development level

DOI: 10.3233/JIFS-222505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5773-5787, 2023

Authors: Pavithra, S. | Manimaran, A.

Article Type: Research Article

Abstract: Soft graphs are an interesting way to represent specific information. In this paper, a new form of graphs called Z-soft covering based rough graphs using soft adhesion is defined. Some important properties are explored for the newly constructed graphs. The aim of this study is to investigate the uncertainty in Z-soft covering based rough graphs. Uncertainty measures such as information entropy, rough entropy and granularity measures related to Z-soft covering-based rough graphs are discussed. In addition, we develop a novel Multiple Attribute Group Decision-Making (MAGDM) model using Z-soft covering based rough graphs in medical diagnosis to identify the patients at …high risk of chronic kidney disease using the collected data from the UCI Machine Learning Repository. Show more

Keywords: Soft graphs, soft covering rough set, uncertainty measures, decision making

DOI: 10.3233/JIFS-223678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5789-5802, 2023

Authors: Noor Mohamed, Sheerin Sitara | Srinivasan, Kavitha

Article Type: Research Article

Abstract: Recent technological developments and improvement in the medical domain demands advancement, to address the issue of early disease detection. Also, the current pandemic has resulted in considerable progress of improvement in the medical domain, through online consultation by physicians for different diseases using clinical reports and medical images. A similar process is adopted in developing a Visual Question Answering (VQA) system in the medical field. In this paper, existing medical VQA datasets, appropriate techniques, suitable quantitative metrics, real time challenges and, the implementation of one VQA approach with algorithms and performance evaluation are discussed. The medical VQA datasets collected from …multiple sources are represented in different perspectives (organwise, planewise, modality-type and abnormality-type) for a better understanding and visualization. Then the techniques used in VQA are subsequently grouped and explained, based on evolution, complexity in the dataset and the need for semantics in understanding the questions. In addition, the implementation of a VQA approach using VGGNet and LSTM is carried out for existing and improved datasets, and analyzed with accuracy and BLEU score metrics. The improved datasets, created through dataset reduction and augmentation approaches, resulted in better performance than the existing datasets. Finally, the challenges of the medical VQA domain are examined in terms of datasets, combining techniques, and modifying the parameters of existing performance metrics for future research. Show more

Keywords: Visual question answering, medical VQA, ImageCLEF, VQA-MED dataset, VQA-RAD dataset, VGGNet, LSTM, challenges of VQA

DOI: 10.3233/JIFS-222569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5803-5819, 2023

Authors: Chen, Bo | Cai, Qiang | Wei, Guiwu | Mo, Zhiwen

Article Type: Research Article

Abstract: This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC …method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective. Show more

Keywords: Multi-attribute group decision-making (MAGDM), linguistic Z-number (LZN), CRITIC method, MABAC method, green supplier selection, risk vector

DOI: 10.3233/JIFS-223447

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5821-5836, 2023

Authors: Chalasani, Rama Devi | Radhika, Y.

Article Type: Research Article

Abstract: ITK inhibitor is used for the treatment of asthma and activity of inhibitor prediction helps to provide better treatment. Few researches were carried out for the analysis and prediction of kinases activity. Existing methods applied for the inhibitor prediction have limitations of imbalance dataset and lower performance. In this research, the Posterior Probabilistic Weighted Average Based Ensemble voting (PPWAE)ensemble method is proposed with various classifier for effective prediction of kinases activity. The PPWAE model selects the most probable class from the classification method for prediction. The co-train model has two advantages: Features are trained together to increases the learning rate …of model and probability is measured for each model to select the efficient classifier. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Classification and Regression Tree (CART), and Nave Bayes were among the classifiers employed. The results suggest that the Probabilistic Co-train ensemble technique performs well in kinase activity prediction. In the prediction of ITK inhibitor activity, the suggested ensemble method has a 74.27 percent accuracy, while the conventional SVM method has a 60% accuracy. Show more

Keywords: Decision tree, ITK inhibitor, posterior probabilistic weighted average based ensemble voting, random forest, support vector machine

DOI: 10.3233/JIFS-221412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5837-5846, 2023

Authors: Qiao, Jian-min | Li, Wo-yuan

Article Type: Research Article

Abstract: The research work on two-sided matching decision problem considering interval triangular fuzzy information is very scarce, and it needs to be further studied. Based on this, this paper proposes a two-sided matching model based on interval triangular fuzzy sets, with the background of the two-sided matching problem in the interval triangular fuzzy set environment. Firstly, the theory of two-sided matching and interval triangular fuzzy sets is given; Secondly, the comprehensive mean value formula is defined, and the interval triangular fuzzy evaluation matrix is transformed into the comprehensive mean value matrix by using the comprehensive mean value formula; Thirdly, a two-sided …matching model is built with the goal of maximizing the satisfaction of each subject; Finally, the feasibility and effectiveness of the proposed method are verified by examples of investment fund institutions and financing enterprises. Show more

Keywords: Interval triangular fuzzy set, Integrated mean, Two-sided matching

DOI: 10.3233/JIFS-222108

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5847-5857, 2023

Authors: Zhang, Sumin | Ye, Jun

Article Type: Research Article

Abstract: Group decision-making is that individuals collectively make a choice from a set of alternatives. Then, in complex decision-making problems, the decision-making process is no longer subject to a single individual, but group decision-making. Hence, the decision reliability and credibility of the collective evaluation information become more critical. However, current decision-making methods lack the confidence level and credibility measure of group evaluation information. To ensure the confidence level and credibility measure of small-scale group decision-making problems, the aim of this paper is to propose a Multi-Attribute Group Decision-Making (MAGDM) approach using a hyperbolic sine similarity measure between Confidence Neutrosophic Number Credibility …Sets (CNNCSs) in the circumstance of Fuzzy Credibility Multi-Valued Sets (FCMVSs). To achieve this aim, this paper contains the following works. First, we present FCMVS to represent the mixed information of fuzzy sequences and credibility degree sequences with different and/or identical fuzzy values. Second, according to the normal distribution and confidence level of fuzzy values and credibility degrees in FCMVS, FCMVS is transformed into CNNCS to avoid the operational issue between different fuzzy sequence lengths in FCMVSs and to ensure the confidence neutrosophic numbers/confidence intervals of fuzzy values and credibility degrees. Third, a hyperbolic sine similarity measure of CNNCSs is established in the circumstance of FCMVSs. Fourth, a MAGDM approach is developed based on the weighted hyperbolic sine similarity measure in the circumstance of FCMVSs. Fifth, the proposed MAGDM approach is applied to an actual example of the equipment supplier choice problem to illustrate the efficiency and rationality of the proposed MAGDM approach in a FCMVS circumstance. In general, this study reveals new contributions in the representation, transformation method, and similarity measure of small-scale group assessment information, as well as the proposed MAGDM method subject to the normal distribution and confidence levels in small-scale MAGDM scenarios. Show more

Keywords: Fuzzy credibility multi-valued set, confidence neutrosophic number credibility set, hyperbolic sine similarity measure, group decision making

DOI: 10.3233/JIFS-223065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5859-5869, 2023

Authors: Hu, Danhui | Huang, Zeqi | Yin, Kan | Li, Feng

Article Type: Research Article

Abstract: Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that …the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times. Show more

Keywords: Multimodal deep learning, power transmission and transformation equipment, heterogeneous data, clustering, mining, similarity

DOI: 10.3233/JIFS-222924

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5871-5878, 2023

Authors: Hou, Yongchao | Fei, Jingtai | Xia, Xiaofang | Cui, Jiangtao

Article Type: Research Article

Abstract: As data collection increases, more and more sensitive data is being used to publish query results. This creates a significant risk of privacy disclosure. As a mathematically provable privacy theory, differential privacy (DP) provides a tool to resist background knowledge attacks. Fuzzy differential privacy (FDP) generalizes differential privacy by employing smaller sensitivity and supporting multiple similarity measures. Thus the output error can be reduced under FDP. Existing FDP mechanisms employ sliding window strategy, which perturb the true query value to an interval with this value as the midpoint to maintain similarity of outputs from neighboring datasets. It is still possible …for an attacker to infer some sensitive information based on the difference between the left and right endpoints of the output range. To address this issue, this article present two solutions: fixed interval perturbation and infinite interval perturbation. These strategies perturb the true query values of two neighboring datasets to the same interval and provide fuzzy differential privacy protection for the dataset. We apply the proposed method to the privacy-preserving problem of bipartite graph subgraph counting and verify the effectiveness by experiments. Show more

Keywords: Fuzzy differential privacy, privacy protection, subgraph counting, bicliques

DOI: 10.3233/JIFS-221505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5879-5892, 2023

Authors: Fan, Jiongjiong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219329 .

DOI: 10.3233/JIFS-220931

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5893-5919, 2023

Authors: Luo, Hongyun | Lin, Xiangyi | Yu, Yan

Article Type: Research Article

Abstract: This paper aims to analyze the coupling coordination degree of technology, economy, social responsibility, and ethic of technological innovation in high-tech enterprises, and provide basis for the optimization of technological innovation system structure in high-tech enterprises. Using data of high-tech enterprises in China Statistical Yearbook and China Statistical Yearbook of Science and Technology in 2018, the authors applied Cloud model to index transformation, consistent fuzzy preference relations to determine index weights, coupling degree model to measure the coupling degree of responsible innovation system of high-tech enterprises in China. Research results show that the responsible innovation system of China’s high-tech enterprises …in 2018 is in a low degree of coordination and coupling stage, and the high-tech enterprises in China invest relatively little in technical level, social development, and ethical innovation. This research contributes to the literature on responsible innovation, ethical responsibility in the high-tech enterprises, which is conducive to improving the quality of innovation activities. However, this research collected data from a single country at a single point in time. This paper studies from the perspective of responsible innovation and measures the coupling degree between innovation and ethical responsibility of high-tech enterprises. The establishment of coupling analysis model can not only effectively calculate the coupling degree of technological innovation system, but also deeply analyze the shortcomings of each subsystem of technological innovation system, and provide a basis for the formulation of promotion strategy. Show more

Keywords: Responsible innovation, high-tech enterprises, coupling synergy, ethical responsibility, cloud model

DOI: 10.3233/JIFS-221269

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5921-5936, 2023

Authors: Chen, Yun | Ma, Chongsen | Ou, Liang

Article Type: Research Article

Abstract: Collusion between governments and enterprises has occurred in many economies around the world in the context of government investment projects and tenders. Not only is collusion an illegal act, but it may also lead to learning and imitation by non-colluding parties. Therefore, to control collusion and ensure the quality of government investment projects, investigating the spread of collusion in the bidding process of such projects is important. This study presents a simulation of the diffusion process of collusion among multiple entities through NetLogo, drawing on a contagious disease model. The effectiveness of the hypothesised control tools is validated through the …changing trend of collusion in bidding in China. The findings provide a new approach to controlling collusion based on the perspective of the proliferation of bidding behaviour and have some reference value for the government to formulate policies. Show more

Keywords: Infectious disease model, collusive behaviour, diffusion mechanism, solicitation, simulation analysis

DOI: 10.3233/JIFS-222490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5937-5952, 2023

Authors: He, Qiang | Wang, Guanqun | Wang, Hengyou | Chen, Linlin

Article Type: Research Article

Abstract: Multivariate time series anomaly detection has been investigated extensively in recent years. Capturing long-term time series information is one of the challenges in this field. We propose a novel multivariate time series anomaly detection framework MTAD-TCGA comprising several modules that efficiently and accurately capture dependencies in long-term multivariate time series. The proposed model contains a temporal convolutional module and uses two parallel graph attention layers to learn the complex dependencies of time series in both the temporal and feature dimensions. A Gated Recurrent Unit layer, based on an improved attention mechanism, and an auto-regressive model is used for prediction, and …the prediction model and reconstruction model are jointly optimized. Finally, the threshold is selected by extreme value theory, and then anomalies are identified. The experimental results on three public datasets show our framework is superior to other state-of-the-art models, achieving F1 scores uniformly at levels above 0.9, verifying the effectiveness and feasibility of the MTAD-TCGA method. Show more

Keywords: Long-term time series, anomaly detection, time convolution network, graph attention network, gated recurrent unit

DOI: 10.3233/JIFS-222554

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5953-5962, 2023

Authors: Liu, Jiacheng | Chau, Gavin | Su, Pianpain

Article Type: Research Article

Abstract: Improving college students’ satisfaction with the teaching quality of ideological and political theory courses in colleges and universities is the need to promote college students to consciously fulfill their ideological and political quality, and it is also the need to further form a strong driving force for the teaching reform of ideological and political theory courses in colleges and universities. The requirements of teaching level are also the requirements to further enhance the competitiveness of colleges and universities. The ideological and political education quality (IPEQ) evaluation of college students is looked as multiple attribute group decision-making (MAGDM) problem. In this …paper, the 2-tuple linguistic neutrosophic TOPSIS (2TLN-TOPSIS) model is built based on the traditional TOPSIS and 2-tuple linguistic neutrosophic sets (2TLNSs). Firstly, the 2TLNSs is introduced. Then, combine the TOPSIS model with 2TLNSs, the 2TLN-TOPSIS model is established for MAGDM. Finally, a numerical example for IPEQ evaluation of College students have been given and some comparisons are also conducted to further illustrate advantages of the 2TLN-TOPSIS method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), TOPSIS method, ideological and political education quality (IPEQ)

DOI: 10.3233/JIFS-223387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5963-5975, 2023

Authors: Zhou, Ling | Zhang, Qian | Li, Haili | Zhao, Xuehan

Article Type: Research Article

Abstract: How to make good use of new network technology and design the classroom teaching of a course needs to be based on the teaching object, teaching content, and the teacher’s mastery of the technology and teaching platform. In teaching design, scholars also put forward different teaching links based on their own teaching experience. The cooperative learning links should be designed in college teaching. To build a positive and interdependent organizational structure and an equal and democratic learning atmosphere will help students to stimulate their learning motivation and sense of responsibility. The fuzzy evaluation of the teaching effect of the “micro-ideological …and political” model in medical colleges and universities is viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonomy method is designed for solving the MAGDM under interval-valued neutrosophic sets (IVNSs). First, the score function of IVNSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) method is built to deal with MAGDM problem. Finally, a numerical example for teaching effect evaluation of the “micro-ideological and political” model in medical colleges and universities is given to illustrate the built method. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval-valued neutrosophic sets (IVNSs), Taxonomy method, CRITIC method, teaching effect evaluation

DOI: 10.3233/JIFS-224186

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5977-5989, 2023

Authors: Das, Subhranil | Mishra, Sudhansu Kumar

Article Type: Research Article

Abstract: Planning a collision-free path while preserving processing time and minimizing cost function has been considered a significant challenge in developing an Autonomous Mobile Robot (AMR). Various optimization techniques for avoiding obstacles and path planning problems have been proposed recently. But, the computation time for executing these techniques is comparatively higher and has lesser accuracy. In this paper, the State Estimation Obstacle Avoidance (SEOA) algorithm has been proposed for estimating the position and velocity of both of the wheels of the AMR. Moreover, this algorithm has been also applied in path planning for reaching the destination point in minimum computational time. …Five different positions of static obstacle are demonstrated in a real time static environment where the proposed SEOA algorithm has been compared with state-of-the-art path planning algorithms such as A* and VFH. The simulation results demonstrate that the proposed algorithm takes lesser computational time to generate the collision free path when compared to other mentioned algorithms. Show more

Keywords: Autonomous mobile robot, static obstacle, optimization, state estimation, path planning

DOI: 10.3233/JIFS-221426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 5991-6002, 2023

Authors: Goel, Sachin | Agrawal, Rajeev | Bharti, R.K.

Article Type: Research Article

Abstract: Epilepsy is the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature …of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signals as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision. Show more

Keywords: Epilepsy, focal & non-focal classification, LSTM, entropy measures

DOI: 10.3233/JIFS-222745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6003-6020, 2023

Authors: Yi, Lingzhi | Long, Jiao | Huang, Jianxiong | Xu, Xunjian | Feng, Wenqing | She, Haixiang

Article Type: Research Article

Abstract: In order to improve the accuracy and reliability of fault diagnosis of oil-immersed power transformers, a fault diagnosis method based on the Modified Artificial Gorilla Troops Optimizer (MGTO) and the Stochastic Configuration Networks with Block Increments (BSCN) is proposed. First, the original artificial gorilla troop optimization algorithm is improved, which effectively improves the convergence speed and optimization accuracy of the algorithm. Secondly, the conventional Stochastic Configuration Networks (SCN) learning methodology is modified when the fault diagnosis model is constructed. The original SCN adopts point incremental approach to gradually add hidden nodes, while BSCN adopts block increment approach to learn features. …It significantly accelerates training. MGTO algorithm is used to jointly optimize regularization parameter and scale factor in BSCN model, and the fault diagnosis model with the highest accuracy is constructed. The experimental results show that the accuracy of MGTO-BSCN for transformer fault diagnosis reaches 95.9%, which is 3.5%, 9.9% and 11.7% higher than BSCN fault diagnosis models optimized by GTO, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) respectively, reflecting the superiority of MGTO algorithm. Meanwhile, the comparison with the traditional model shows that the proposed method has obvious advantages in diagnostic effect. Show more

Keywords: Transformer, Stochastic configuration networks, GTO optimization algorithm, Fault diagnosis

DOI: 10.3233/JIFS-223443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6021-6034, 2023

Authors: Jose, Merin | Mathew, Sunil C.

Article Type: Research Article

Abstract: This paper presents the idea of Alexandrov L -quasi-G-filter space and examines its relationship with L -fuzzy relations. It is proved that every Alexandrov L -quasi-G-filter induces an L -fuzzy relation and vice-versa. By identifying certain functors, the study has brought out the connections between the categories of Alexandrov L -quasi-G-filter spaces and Alexandrov L -fuzzy pre-uniform spaces. Further, the study has explored and thereby establishes the scope of Alexandrov L -quasi-G-filter spaces in mathematical modeling and decision-making processes.

Keywords: Lattice, category, functor, fuzzy relation

DOI: 10.3233/JIFS-223832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6035-6046, 2023

Authors: Gao, Lu | Yao, Bingxue | Li, Lingqiang

Article Type: Research Article

Abstract:  Approximate accuracy is an important concept in rough set theory, which is defined by upper and lower approximations. Generally speaking, the higher precision means the better application performance. The approximation accuracy can be improved by minimizing the upper approximation and maximizing the lower approximation. Recently, Zhou [52 ] introduced two types of fuzzy-covering based rough set models by using inclusion relation between fuzzy sets. In this paper, by replacing inclusion relation with implication degree, we investigate two new fuzzy covering-based rough set models. Compared with inclusion relationship, the inclusion degree can describe the contained relation between fuzzy sets in more …detail. This makes our upper approximation smaller than Zhou’s upper approximation, while the lower approximation is larger than Zhou’s. Therefore, the approximate accuracy of our model is higher than that of Zhou. Furthermore, we apply the new model to the study of multi-attribute decision-making (MADM). Combined with the car buying problem, we demonstrate the effectiveness of our model and compare it with other methods. The results show that we can get the same optimal choice as other methods. However, according to Zhou’s model, we cannot get the optimal choice. Show more

Keywords: Rough set, fuzzy set, approximation operator, covering, inclusion degree

DOI: 10.3233/JIFS-221097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6047-6063, 2023

Authors: Balan, Elakkiya | Saraniya, O.

Article Type: Research Article

Abstract: COVID-19 is a rapidly proliferating transmissible virus that substantially impacts the world population. Consequently, there is an increasing demand for fast testing, diagnosis, and treatment. However, there is a growing need for quick testing, diagnosis, and treatment. In order to treat infected individuals, stop the spread of the disease, and cure severe pneumonia, early covid-19 detection is crucial. Along with covid-19, various pneumonia etiologies, including tuberculosis, provide additional difficulties for the medical system. In this study, covid-19, pneumonia, tuberculosis, and other specific diseases are categorized using Sharpened Cosine Similarity Network (SCS-Net) rather than dot products in neural networks. In order …to benchmark the SCS-Net, the model’s performance is evaluated on binary class (covid-19 and normal), and four-class (tuberculosis, covid-19, pneumonia, and normal) based X-ray images. The proposed SCS-Net for distinguishing various lung disorders has been successfully validated. In multiclass classification, the proposed SCS-Net succeeded with an accuracy of 94.05% and a Cohen’s kappa score of 90.70%; in binary class, it achieved an accuracy of 96.67% and its Cohen’s kappa score of 93.70%. According to our investigation, SCS in deep neural networks significantly lowers the test error with lower divergence. SCS significantly increases classification accuracy in neural networks and speeds up training. Show more

Keywords: COVID-19, chest X-ray, cosine normalization, tuberculosis, pneumonia

DOI: 10.3233/JIFS-222840

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6065-6078, 2023

Authors: Zhang, Jinhua | Zhang, Qishan | Zhang, Jinxin

Article Type: Research Article

Abstract: This paper discusses how to deal with the greyness problem in the system from the perspective of “result”. Aiming at the greyness problem of the traditional grey relational analysis result, an information fusion grey relational analysis method based on D-S evidence theory and multi solution information fusion is proposed, which mends the traditional grey relational analysis method. The results show that the method proposed in the study has better effect than the traditional grey relational analysis method, and has higher accuracy in the wear particle identification, which indicates that it can further expand the application scope of the grey relational …analysis method. Show more

Keywords: Grey relational analysis, greyness of grey relational analysis, D-S evidence theory, information fusion

DOI: 10.3233/JIFS-223323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6079-6088, 2023

Authors: Wang, Hongli | Fei, Liguo | Feng, Yuqiang

Article Type: Research Article

Abstract: In order to overcome the weakness of subjectivity of variable and subjectivity of membership function in fuzzy probability the cloud probability model and its algorithm are proposed. Firstly, the representation model of cloud probability is given based on the fusion of cloud model and fuzzy probability. Then the cloud probability algorithm of continuous random variable based on slice method is proposed. Then the relationship between slice number and cloud probability is discussed. And the cloud probability algorithm of discrete random variable is given. Finally, through the application case of e-commerce intelligent decision-making based on cloud probability the effectiveness of the …proposed cloud probability algorithm is verified. The research in this paper has good reference significance for dealing with the events represented by uncertain variables. Show more

Keywords: Cloud probability, fuzzy probability, cloud model, double cloud model, slicing method, fuzziness-randomness

DOI: 10.3233/JIFS-222518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6089-6102, 2023

Authors: Dou, Fei | Wei, Yun | Huang, Yakun | Ning, Yao | Wang, Li

Article Type: Research Article

Abstract: In the condition of large passenger flow, subway station managers take measures of passenger flow control organization for reducing high safety operation risks at subway stations. The volume of passenger flow in urban railway network operation continues to increase and the Congestion of passenger flow is very high. Passenger flow control measures can greatly give birth to the pressure of transportation and ensure an urban rail transit system’s safe operation. In this paper, we develop a cloud model-based method for passenger flow control, which extends the four-level risk-control grade of a large passenger flow at facilities by considering its fuzzy …and stochastic characteristics. Then, an efficient passenger flow control strategy for subway stations is made, where the control time and locations are simultaneously determined. Finally, a station in the Beijing subway is studied to test the validity of the proposed approach. The results show that the time of maximum queuing length is much shorter and the density of passenger flow is lower than existing methods in practice. With the in-depth study of complex network controllability, many studies have applied to control judgment and real network optimization. This paper analyzes the cloud-model-based method for passenger flow control at subway stations and therefore a new method can be incorporated for developing and optimizing control strategies. A few researchers have attempted to find the solution to the problem of crowding risk classification and the passenger flow control strategy. The focus of some studies simultaneously solves the passenger flow control with multiple stations. Show more

Keywords: Subway stations, passenger flow control, risk-control grade identification, cloud model, crowding, control problem, normal cloud

DOI: 10.3233/JIFS-223110

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6103-6115, 2023

Authors: Yang, Can

Article Type: Research Article

Abstract: In the context of sharing economy, logistics companies have begun to adopt a new collection and distribution model based on external vehicles, and external vehicles are used to provide customers with collection/distribution services. A kind of Multi-Vehicle Split Pickup and Delivery Problem with Time Windows (MVSPDPTW) is studied in the paper. The minimum total length of the vehicle’s travel path is the objective function, and a mixed-integer linear programming model is established. A high-efficiency Tabu Simulated Annealing (TSA) algorithm is proposed. Two new neighborhood search operators are designed in the algorithm, they are used to repair the violation of capacity …constraints and the operation of car replacement. In the method, the neighborhood search range is expanded and the algorithm is avoided from falling into a local optimum. In addition, a taboo mechanism and a penalty mechanism for violation of constraints are added to the algorithm, the effective tailoring of the search space is realized and the algorithm’s global optimization ability is improved. Finally, a large number of simulation experiments were performed on the algorithm based on the Solomon data set and the constructed simulation data set, and the effectiveness of the algorithm is verified in the experiments. Show more

Keywords: Vehicle routing, simulated annealing, intelligent optimization, split demand, pickup and delivery

DOI: 10.3233/JIFS-223708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6117-6129, 2023

Authors: Liang, Jun | Huang, Keyi | Qiu, Shaojian | Lin, Hai | Lian, Keng

Article Type: Research Article

Abstract: Trend following strategies have a wide-ranging role in quantitative trading fields, which can capture important unilateral market trends for large gains, while this is vulnerable to losses in the period of consolidation. In this paper, we explored the trend trading system in the Chinese futures market based on machine learning techniques and statistical methods. This research utilized the Long-Short-Term Memory network to extract features of time series then predicted the price movements by Machine Learning classifiers. Moreover, based on rebar futures data, the results reveal that the annualized return improved from 6.39% to 15.68% after the trading signals generated in …the trading strategy were filtered using the XGBoost model. Also, futures on gold and soybean were used to further test the integrated strategy and the results of the experiment show the effectiveness of the model in filtering false trading signals. Show more

Keywords: Machine learning, LSTM, time series forecasting, trend following strategies, deep learning

DOI: 10.3233/JIFS-223873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6131-6149, 2023

Authors: Li, Li

Article Type: Research Article

Abstract: Eliminating poverty, improving people’s livelihood, building a well-off society in all aspects and achieving common prosperity are the essential requirements of socialism. The 19th Party Congress formally put forward the strategy of rural revitalization and made it the focus of the direction of rural reform and development in the new period. In this context, how to make the basic strategy of precise poverty alleviation implemented and put into practice, and how to realize the work of precise poverty alleviation to better contribute to rural revitalization are both practical and academic questions that need to be further explored. The efficiency evaluation …of integrated development of agriculture and tourism to promote rural revitalization under the policy of precise poverty alleviation is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is built based on the traditional cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE method is established for MADM. Finally, a numerical example for efficiency evaluation of integrated development of agriculture and tourism to promote rural revitalization under the policy of precise poverty alleviation has been given to further illustrate advantages of the built method. Show more

Keywords: Multiple attribute decision making (MADM) problems, triangular fuzzy neutrosophic sets (TFNSs), cross-entropy method, 2TLNN-CE method, efficiency evaluation

DOI: 10.3233/JIFS-224126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6151-6161, 2023

Authors: Zeng, Guoqiang | Zhou, Huan | Tang, Jianrong

Article Type: Research Article

Abstract: COVID-19, as a public health emergency, poses a major challenge to the economic and social development of numerous countries, impacting not only the psychology and behavior of residents at the individual level, but also the stability of society at the societal level, typified by the increased difficulty of smooth economic operation due to residents’ irrational consumption behavior. In the present study, we have drawn on the psychological stimuli-organism-response (SOR) and mediating effect theories to explore the causes of irrational consumption behavior. Finally, based on the actual situation in the questionnaire survey, we propose the system for assessing the effectiveness of …the government’s response to the epidemic and measures under the influence of irrational consumption. In the results of our study, we found that the positive and negative irrational consumption adjustment coefficients of the government under the influence of its inherent image were 6.72% and 17.64%, respectively; the government intervention indices under the positive and negative perceptions output according to QFD theory were 0.14 and 0.02, respectively; and the positive and negative measure effectiveness indices of the government response programs were 2.28 and 2.10, respectively. Thus, through our study, we concluded that residents’ positive perception of government image would reduce the occurrence of irrational consumption behavior, while the improvement of irrational consumption behavior by perfect psychological services under residents’ negative perception of government image is more obvious. On the basis of summarizing the experience of this COVID-19, this study can serve the prediction and regulation of residents’ irrational consumption behavior under the government response to public health emergencies, and also enrich the research literature on irrational consumption behavior in related consumption behavior studies, and more importantly, provide theoretical and empirical support for similar academic cases in the international community. Show more

Keywords: Consumer behavior research, irrational consumer behavior, QFD theory, SOR model, mediating effects

DOI: 10.3233/JIFS-223505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6163-6182, 2023

Authors: Navin, K.S. | Nehemiah, H. Khanna | Nancy Jane, Y. | Veena Saroji, H.

Article Type: Research Article

Abstract: Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while …the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor. Show more

Keywords: Henry gas solubility optimization, atom search optimization algorithm, XGBoost, adaboost, catboost, LightGBM

DOI: 10.3233/JIFS-221348

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6183-6218, 2023

Authors: Jansi Rani, J. | Dhanasekar, S. | Micheal, David Raj | Manivannan, A.

Article Type: Research Article

Abstract: In dealing with real world transportation problems, several issues are frequently encountered as a result of uncontrollable factors. To deal this uncertainties, many authors have suggested transportation problems with intuitionistic fuzzy parameters. In this article, fully intuitionistic fuzzy transportation problem (InFTP) is considered in which the parameters are triangular intuitionistic fuzzy numbers. To solve this, initially intuitionistic fuzzy branch and bound technique are applied to get the initial basic feasible solution and then intuitionistic fuzzy modified distribution (InFMODI) method is applied to acquire the optimal solution of the fully InFTP. Also, a new ordering is developed here in which some …properties of compensation, linearity, additive and partial order relations are satisfied. The optimal solution obtained from the proposed method satisfies the condition of optimality and feasibility. Finally, two numerical examples are provided to show the effectiveness of the proposed method. Show more

Keywords: Intuitionistic fuzzy number, intuitionistic fuzzy transportation problem, intuitionistic fuzzy initial basic feasible solution, intuitionistic fuzzy optimal solution

DOI: 10.3233/JIFS-221345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6219-6229, 2023

Authors: Dong, Lihui | Yuan, Weijin | Deng, Yunfeng

Article Type: Research Article

Abstract: This paper proposes a new model for characterizing the emergency evacuation process of people during a disaster. This model considers the change of visual field based on a cellular automata model combined with a behavioral heuristic model. Using the behavioral heuristic model, the dynamic field parameters related to the change of visual field are first established. Then, new judgment rules are developed for personnel encountering obstacles by combining the characteristics of the new field of view. Finally, an analytical comparison is made between the proposed model and the traditional evacuation model in terms of the changes in the fields of …view and the number of evacuees. The results show that the level of path service determines the efficiency of evacuation. It is also seen that herd mentality acts as a hindrance in cases where the personnel are dependent while otherwise acting as a facilitator. It is also shown that the evacuation time increases by the number of evacuees up to a certain threshold. Beyond that threshold the evacuation time fluctuates within a certain range by increasing the number of evacuees is not affected by changes in the field of view. The new model is also faster than the social force model, easier to calculate on a large scale, and more realistic than the traditional cellular model. Show more

Keywords: Cellular automata, Pedestrian dynamics, view, pedestrian evacuation

DOI: 10.3233/JIFS-222587

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6231-6247, 2023

Authors: Yavuz, Akif | Sen, Osman Taha

Article Type: Research Article

Abstract: This study aims to investigate the predictability of a friction-induced nonlinear dynamic behavior on a simplified yet controlled laboratory experiment through the fuzzy logic approach. First, a mass-sliding belt experiment is built to observe the effects of several operating parameters on the occurrence of nonlinear dynamic behavior. Second, experiments are performed at various levels of these operating parameter, and the data are recorded. Third, fuzzy logic model architectures with different membership functions are built, where these operating parameters are assumed as the input parameters. The output of the fuzzy logic model architecture is defined as a new parameter called squeal …index. Finally, a fuzzy logic model with a 96.97% prediction accuracy is obtained. Hence, it is shown that the proposed model can provide insight about the dynamic behavior of the system of interest without solving the nonlinear governing equations. Furthermore, the proposed model allows the prediction of the system state at operating conditions where experimentation is not possible, and it can be used for the determination of the critical operating parameters at which the system behavior switches from one state to another. Show more

Keywords: Fuzzy logic modelling, dynamic instability, friction induced vibration, mass-sliding belt experiment, disc brake squeal

DOI: 10.3233/JIFS-223177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6249-6264, 2023

Authors: Hasan, Mohammad Kamrul | Ali, Md. Yasin | Sultana, Abeda | Mitra, Nirmal Kanti

Article Type: Research Article

Abstract: Picture fuzzy set (PFS), is a newly developed apparatus to treaty with uncertainties in problems where the opinions are yes, no, neutral, and refusal types. Extension principle is one of the key tools for describing uncertainties. It provides a general method for existing classical mathematical concepts to address fuzzy quantities. It has numerous applications in various arena of our real life. However, there are less works on extension principle for picture fuzzy sets. In this article, new extension principles namely minimal extension principle and average extension principle are proposed for picture fuzzy sets. Various properties of the minimal extension principle …and the average extension principle for PFSs are also established. We also prove some properties of Zadeh’s extension principle for PFSs. Finally, arithmetic operations for PFSs based on the average extension principle are developed with numerical illustrations. Show more

Keywords: Picture fuzzy set, Zadeh’s extension principle, minimal extension principle, average extension principle, arithmetic operations

DOI: 10.3233/JIFS-220616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6265-6275, 2023

Authors: Zhang, Yang | Zhou, Wentao | Ma, Lina

Article Type: Research Article

Abstract: The success of technological innovation is related to the future and destiny of enterprises, but because of its uncertainty and high risk, the risk of failure of technological innovation exists objectively. This paper uses grounded theory to code the typical cases of technological innovation failure at home and abroad and explores the causes of technological innovation failure. It is found that policies and regulations, institutional environment, and market environment are the important external factors that cause the failure of enterprise technological innovation, while the defects of enterprise technological innovation products, enterprise system, internal management, technological resources, and managers are the …important internal factors that cause the failure of enterprise technological innovation. By constructing the evolution model of enterprise technological innovation failure, it is found that the failure of enterprise technological innovation is the result of the joint action of enterprise management operation mechanism, technology, capital, and other restraint mechanisms, as well as market and policy system guidance mechanism. Show more

Keywords: Failure of technological innovation, influence factors, formation mechanism, grounded theory, multicase

DOI: 10.3233/JIFS-221756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6277-6291, 2023

Authors: Cui, Chunsheng | Che, Libin | Wei, Meng

Article Type: Research Article

Abstract: The steady development of commercial banks plays a key role in maintaining the healthy development of the economy. The ability to judge financial risks is a reflection of the comprehensive risk management level of a commercial bank, and it is also an important criterion for measuring its competitiveness and operational stability. Based on the analysis of economic development laws and reference to relevant literature, this paper screened out the eight most representative risk evaluation measurement indicators of commercial banks, ranked these indicators in preference according to expert opinions, established a group decision-making model, and then obtained the consensus ranking by …using the least divergence method. The PCbHA method was used to check the consistency of the results, modify the expert opinions, iteratively calculate, and finally construct the importance ranking of commercial bank risk indicators. This paper discusses the construction of an evaluation system based on the perspective of risk management to enrich and improve the risk management content of commercial banks, enhance the risk prevention and control ability, and provide suggestions for the prevention and management of risks in commercial banks. Show more

Keywords: Iteration, group decision-making, PCbHA, commercial bank, risk monitoring

DOI: 10.3233/JIFS-222508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6293-6302, 2023

Authors: Shukla, Poorva | Patel, Ravindra | Varma, Sunita

Article Type: Research Article

Abstract: Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion …when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication. Show more

Keywords: Edge computing, blockchain system, DNN, Q-learning, SDN

DOI: 10.3233/JIFS-223073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6303-6326, 2023

Authors: Yang, Hong | Wang, Fan | Wang, Lina

Article Type: Research Article

Abstract: In this paper, the second-order fuzzy homogeneous differential equation is transformed into a more special simplest form under the condition that the solution of the boundary value problem of the equation exists and is unique. Then the eigenvalues of the boundary value problem of the second-order simplest fuzzy homogeneous differential equation are studied and the theorems that make the eigenvalues exist are proposed and then illustrated with examples. Finally, it is proved that when the second-order fuzzy coefficient p ˜ ( t ) in the second-order fuzzy homogeneous differential equation is a fuzzy number, …the solution set of its corresponding second-order granular homogeneous differential equation becomes larger, that is, the solution set of fuzzy differential equations with real numbers is a subset of the solution set with fuzzy coefficients as fuzzy numbers. Show more

Keywords: Fuzzy numbers, fuzzy differential equations(FDEs), granular differentiability, the horizontal membership function, fuzzy eigenvalues

DOI: 10.3233/JIFS-223003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6327-6340, 2023

Authors: Cao, Yunrui | Ma, Jinlin | Hao, Chaohua | Yan, Qi

Article Type: Research Article

Abstract: Tangut characters were created by the Tangut of the Western Xia (Xi Xia) Dynasty in ancient China and are over 1000 years old. In deep-learning-based recognition studies on Tangut characters, the lack of category-complete datasets has been problematic. Data augmentation cannot augment the character categories of unknown styles, whereas the use of image generation can effectively solve the problem. In this study, we consider the generation of antique book calligraphy styles of Tangut characters as a problem of learning to map from existing printed styles to personalized antique book calligraphy styles. We present M-ResNet, a multi-scale feature extraction residual unit, …and Tangut-CycleGAN, a model for generation Tangut characters that combine M-ResNet and a cycle-consistent adversarial network (CycleGAN). This method uses unpaired data to generate Tangut character images in the calligraphy style of ancient books. To enhance the response of the model to significant channels, a squeezing-and-excitation (SE) module is introduced based on Tangut-CycleGAN to design the Tangut-CycleGAN+SE method for generating images of Tangut characters. This method is not only suitable for Tangut character image generation, but also can effectively generate calligraphy with aesthetic value. In addition, we propose an overall quality discrepancy evaluation metric, FA (Fréchet inception distance + Accuracy), to evaluate the quality of character image generation, which combines style discrepancy and content accuracy metrics. Show more

Keywords: Tangut character, CycleGAN, unpaired data, image generation, evaluation metric

DOI: 10.3233/JIFS-221892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6341-6358, 2023

Authors: Zhao, Dongfang | Chen, Yesheng | Liu, Shulin | Shen, Jiayi | Miao, Zhonghua

Article Type: Research Article

Abstract: Fault diagnosis is of great significance for industrial equipment maintenance, and feature extraction is a key step of the entire diagnosis scheme. The symbolic aggregate approximation (SAX) is a popular feature extraction approach with great potential recently. In spite of the achievements the SAX has made, the adverse information aliasing still exists in its calculation procedure, and it may make the SAX fail to guarantee the information correctness. This work focuses on analyzing the information aliasing phenomenon of the SAX, followed by developing a novel alternative method, i.e. parallel symbolic aggregate approximation (PSAX). In the proposed PSAX, the information aliasing …is suppressed by designing anti-aliasing procedure, and the average of the symbolic results of several intermediate sequence is adopted to replace the final symbolic result. The Case Western Reserve University (CWRU) rolling bearing data together with the gas valve data of an actual reciprocating compressor assist in verifying the superiority exhibited by the suggested method. The experimental results show that, compared with other methods, the accuracy advantage of the PSAX on the 2 datasets can reach 1% –5%, indicating it is capable of providing high-quality feature vector for intelligent fault diagnosis. Show more

Keywords: Fault diagnosis, feature extraction, symbolic aggregate approximation, parallel symbolic aggregate approximation

DOI: 10.3233/JIFS-223575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6359-6374, 2023

Authors: Pichaimani, Venkateswari | Kalava, Manjula Ramakrishama

Article Type: Research Article

Abstract: Wireless localization or positioning is essential for delivering location-based services for designing location tracking systems. Traditional indoor floor planning system employs wireless signals for accurate position estimation. But these positioning schemes failed to perform position estimation effectively and accurately through many obstacles or objects. The novel technique called Linear Features Projective Geometric Damped Convolutional Deep Belief Network (LFPGDCDBN) is introduced to improve the position estimation accuracy with minimum error. The proposed LFPGDCDBN technique includes two major processes namely dimensionality reduction and position estimation. First, the dimensionality reduction process is performed by projecting the principle features using Linear Helmert–Wolf blocked Sammon …projection. After the feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network is employed to estimate the position of the devices with higher accuracy and minimum error. The Convolutional deep belief network uses the triangulation geometric method to identify the position of the device in an indoor positioning system. Then the Levenberg–Marquardt function is a Damped least square method to minimize the squares of the deviations between the expected and observed results at the output unit. As a result, the LFPGDCDBN increases the positioning accuracy and minimizes the error rate. Experimental MATLAB assessment is carried out with various factors such as computational time, Computational space, positioning accuracy, and positing error. The experimental results and discussion indicate that the proposed LFPGDCDBN provides improved performance in terms of achieving higher positioning accuracy and minimum error as well as computational time when compared to the existing methods. The experimental results and discussion indicate that the proposed LFPGDCDBN increases the positioning accuracy by 47% and computational time, computational complexity, and reduces the positioning error by 45%, 29%, and 74% as compared to state-of-the-art works. Show more

Keywords: Indoor floor planning, linear Helmert–Wolf blocked, Sammon projection based feature selection, Geometric Levenberg–Marquardt Convolutional deep belief network, damped least square method

DOI: 10.3233/JIFS-223618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6375-6386, 2023

Authors: Sandhu, Muhammad Abdullah | Amin, Asjad

Article Type: Research Article

Abstract:  During the last decade, dengue fever has emerged as a life-threatening disease. Dengue fever is caused by the bite of the dengue mosquito, and it spreads rapidly especially in the rainy season due to the availability of water carriers inside and outside the living vicinity. In this work, we propose an automated model for dengue larvae detection and tracking using Convolutional Neural Network (CNN) and Kalman filters. Despite substantial literature available on object tracking, no model has been proposed for dengue larvae. We started our work by collecting water areas and dengue larvae datasets as no public datasets were available. …Our water areas dataset has 30 videos of different containers and environments. The dengue larvae dataset has 50 short videos of dengue larvae having different locations, backgrounds, and textures. In the first step, we used CNN to detect water areas, and the detected water area is then processed for the detection and tracking of larvae. Next, we propose a Kalman filter-based workflow for dengue larvae detection and tracking. A Gaussian Mixer Model with background subtraction is applied for foreground and object detection. Then we used Kalman filters to track the moving larvae in the experimental videos. The proposed model shows excellent results considering the small size of larvae and the challenging dataset. Subjective and objective experimental results clearly show the superior performance of the proposed model. The feedback received from the health authorities has been encouraging and the work is expected to facilitate the health department in eliminating the dengue. Show more

Keywords: Dengue larvae, Detection, Tracking, CNN, Kalman Filtering

DOI: 10.3233/JIFS-223660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6387-6401, 2023

Authors: Li, Ze | Liu, Xiaoze | Ji, Lin | He, Guanglong | Sun, Liang

Article Type: Research Article

Abstract: The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use …the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. Show more

Keywords: Improved random forest algorithm, power system, operation and maintenance big data, mixed attributes, BP neural network, support vector machine

DOI: 10.3233/JIFS-223852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6403-6412, 2023

Authors: Gobinath, C. | Gopinath, M.P.

Article Type: Research Article

Abstract: Recent reports indicate a rise in retinal issues, and automatic artery vein categorization offers data that is particularly instructive for the medical evaluation of serious retinal disorders including glaucoma and diabetic retinopathy. This work presents a competent and precise deep-learning model designed for vessel segmentation in retinal fundus imaging. This article aims to segment the retinal images using an attention-based dense fully convolutional neural network (A-DFCNN) after removing uncertainty. The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via …individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases. Show more

Keywords: Deep learning, fundus image, fully-convolutional neural networks, blood vessel segmentation, artery vein classification

DOI: 10.3233/JIFS-224229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6413-6423, 2023