Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Senthilkumar, V.M. | Thenmozhi, S. | Kumudavalli, M.V. | Yedukondalu, U.

Article Type: Research Article

Abstract: The Severe Acute Respiratory Syndrome (SARS) are caused by the strain of the corona virus causes cold and influenza. In recent years, the covid pandemic spread throughout the world killing millions of people. The fatality rate has increased and it also leads to pneumonia for breathing problems. Several methods like wavelet filter banks, time series methods, Neural networks was developed for the diagnosis of severe acute respiratory syndrome coronavirus, still the accuracy can be improved. Less works is carried out for hardware implementation for syndrome detectors. This proposed work represents the FPGA (Field Programmable Gate Array) implementation of the hybrid …method using Convolutional Recurrent neural network and Independent Components Analysis (ICA). The architecture extracts the ccomplex features from ECG (Electrocardiogram) samples. The hybrid Statistical and Recurrent Neural Network (RNN) Architecture implementation in a real time hardware detects the Severe Acute Respiratory Syndrome presented. The proposed method can be implemented in MATLAB, Embedded and DSP (Digital Signal Processor). But, the FPGAs consume less power computationally efficient. Since, ICA is an efficient method due to its blind source separation property accumulate the extraction of features accurate described. The mathematical model for the analysis of ECG signal using RNN is analyzed and based on that the proposed model is selected. On investigation the hybrid method using the statistical and neural network model is efficient in the analysis of biomedical signal especially ECG. The proposed ICA based RNN model is mathematically evaluated and tested with real time data. For implementation, Quartus software is used for effectiveness of the proposed model. Show more

Keywords: Field programmable gate array, recurrent neural network, independent component analysis, electrocardiogram, severe acute respiratory syndrome

DOI: 10.3233/JIFS-224289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8803-8816, 2023

Authors: Wang, Hua | Wang, Zhi-Ming | Cui, Xiu-Tao | Li, Long

Article Type: Research Article

Abstract: Considering the heterogeneity, diffusive shape, and complex background of tumors, automatic segmentation of hepatic lesions in computed tomography (CT) images has been considered a challenging task. The performance of existing methods remains subject to segmentation uncertainties, especially in tumor boundary regions. The pixel information in these regions will be affected by both sides, thereby exposing the segmentation results to missing marks. To this end, a new network architecture named Two Direction Segmentation U-Net (TDS-U-Net) is hereby designed based on the classic Attention U-Net to tackle this problem. As the most important blocks of the Attention U-Net network, attention gates (AGs) …focus on the target structures of different shapes and sizes. In the last layer of TDS-U-Net, two dichotomous convolutional networks are applied to obtain the segmentation maps of the liver and the tumor respectively. Superimposing two segmented maps to obtain the final image addresses the above problems. The entire structure has been verified on two widely accepted public CT datasets, LiTS17 and KiTS19. Compared with the state of the art, this method exhibits superior performance and excellent shape extractions with high detection sensitivity, perfectly demonstrating its effectiveness in medical image segmentation. Show more

Keywords: Attention gates, CT, deep learning, liver tumor segmentation, kidney tumor segmentation, U-Net

DOI: 10.3233/JIFS-221111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8817-8825, 2023

Authors: Pavithra, R. | Ramachandran, Prakash

Article Type: Research Article

Abstract: A spectrum-image based representation of machine vibration signals with deep convolution neural network is proposed for machine fault classification in which the convolution layer is used for automatic feature extraction as an alternate to the conventional feature-based methods. Two different forms of spectrum representations are proposed, one based on the short time Fourier transform of the original signals and the other based on the short time Fourier transform of the intrinsic mode functions acquired by empirical mode decomposition. Empirical mode decomposition has its own merits in discriminating non stationary signals and the novelty of the work is to use the …short time Fourier transform of intrinsic mode functions with deep convolution neural network model. The classification and validation accuracy of the model are investigated with respect to epochs. It is demonstrated that both spectrum-based techniques perform good with 100% model accuracies in a numerical experiment of binary classification on a bearing dataset that comprises of normal and faulty signals. In another experiment using milling data set, short time Fourier transform of intrinsic mode functions representation performs better with 100% training accuracy, F1 score of 0.8933 which is better than that of using short time Fourier transform of raw signals whose training accuracy is 64% and F1 score of 0.7486. The numerical study shows that the empirical mode decomposition based spectrum representation delivers the highest accuracy in the learning model obviating the necessity for independent feature extraction, feature selection, and dimension reduction. The numerical experiment is extended using empirical mode decomposition based spectrums for multiple class classification problems in bearing dataset. The confusion matrix obtained for 10 classes, shows that validation accuracy is 100% for all classes. The performance comparison throws light on the merits of empirical mode decomposition spectrum method over other state of the art methods. Show more

Keywords: Convolutional neural network (CNN), empirical mode decomposition (EMD), intrinsic mode function (IMF), short-time Fourier transform (STFT)

DOI: 10.3233/JIFS-223012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8827-8840, 2023

Authors: Qiu, Guangying | Tao, Dan | Su, Housheng

Article Type: Research Article

Abstract: The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), …bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields. Show more

Keywords: Vessel, power equipment, named entity recognition, BERT

DOI: 10.3233/JIFS-223200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8841-8850, 2023

Authors: Sharma, Surya Prakash | Singh, Laxman | Tiwari, Rajdev

Article Type: Research Article

Abstract: In the current market scenario, online customer reviews had a significant impact on boosting the sale of online products. Recently, there has been exponential growth in e-commerce industry owning to the online customer reviews. Over the years, researchers has observed the importance of online consumer reviews for purchasing online products. Hence, in this study, authors made an attempt to develop an efficient convolutional neural network (CNN) based classification model that aims to predict the usefulness of product reviews with higher accuracy on two different types of data sets (i.e., search product and experienced product). In our proposed study, to determine …the usefulness of a review in terms of structural, linguistic, sentimental, lexical, and voting feature sets, we build a deep learning model to predict the review helpfulness as a binary classification problem. The performance of the proposed method is evaluated in terms of accuracy, precision, F1 score etc. and had been compared against the various leading machine learning (ML) state of art models viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian Naive Bays (GNB), Linear Discriminant Analysis (LDA) etc. The results demonstrate that CNN achieved better classification performance in comparison to other state of art models, with highest accuracy of 99.26% and 98.97%, precision of 99% and 99.01%, F1 score of 99% and 99.89%, AUC of 0.9999 and 0.9998, Average Precision (AP) of 0.9999 and 0.9997 and recall of 100% and 100% for two different amazon product datasets. Show more

Keywords: CNN, reviews helpfulness, online reviews, machine learning, binary classification, reviews feature set

DOI: 10.3233/JIFS-223546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8851-8868, 2023

Authors: Selvarajan, L. | Venkataramanan, K. | Rajavel, R. | Senthilkumar, T.S.

Article Type: Research Article

Abstract: Electro discharge machining (EDM) is a cycle for molding tough materials and framing profound contour formed openings by warm disintegration in all sort of electrically conductive materials. The goal of the venture to be concentrating because of working parameters of EDM for machining of silicon nitride-titanium nitride in the machining qualities with copper electrode, for example input Spark on time (Son ), current (Ip ), Spark off time (Soff ), spark gap and dielectric pressure on the metal removal rate (MRR) and Electrode Wear Rate (EWR) were analyzed. Subsequently, using Taguchi analysis of various plots like Mean effect plots, Interaction …plots, and contour plots, performance characteristics are looked at in relation to multiple process factors. Fuzzy logic and Regression analysis is utilized to combine various reactions into a solitary trademark record known as the Multi Response Performance Index (MRPI).The trial and anticipated qualities were in a decent programming instrument for discovering the MRPI esteem. For numerous performance aspects, such as material removal rate, electrode wear rate and so on, the optimal process parameter combination was established using fuzzy logic analysis. The key process factors, which included spark off time and current, were found using an ANOVA based on a fuzzy algorithm. Topography on machined surface and cross-sectional view of conductive Si3 N4 -TiN composite and surface characteristics of machined electrode is examined by SEM analysis and identified the best hole surface and worst hole surface. Sensitivity analysis is being utilized to determine how much the input values, such as Ip, Son and Soff , will need to alter in order to get the desired, optimal result. In the complexity analysis, each constraint of the machine, composite and process is addressed. Future researches might look into various electrodes to assess geometrical tolerances including angularity, parallelism, total run out, flatness, straightness, concentricity, and line profile employing other optimization methodologies to achieve the best outcome. The findings of the confirmatory experiment have been established, indicating that it may be feasible to successfully strengthen the spark eroding technique. Show more

Keywords: EDM, Si3N4–TiN, fuzzy logic optimization, MRPI, surface texture

DOI: 10.3233/JIFS-223650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8869-8888, 2023

Authors: Rajeswary, C. | Thirumaran, M.

Article Type: Research Article

Abstract: Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development …of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments. Show more

Keywords: Phishing detection, attacks, tor network, random forest, CNN, LSTM

DOI: 10.3233/JIFS-224142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8889-8903, 2023

Authors: Pei, Mengjiao | Liu, Shuli | Wen, Haolan | Wang, Weizhong

Article Type: Research Article

Abstract: Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict …the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation. Show more

Keywords: Failure mode and effect analysis, GLDS, Fermatean fuzzy set, C-OWA operator

DOI: 10.3233/JIFS-222692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8905-8923, 2023

Authors: Wang, W.-C. | Yeh, Y.-W. | Chen, R.

Article Type: Research Article

Abstract: In the cooperative multi-agent pathfinding and motion planning, given a unique start position and a unique goal position for each agent, all agents are able to pursue their own goals without colliding with each other. To aim at realizing the collision-free motion of the agents within the tractable time, this work proposes a polynomial-time solver, called the HBD-AOI, hybridizing centralized and decentralized schemes. Firstly, an algorithm of centralized pathfinding is utilized to plan the optimal paths of all agents. Afterwards, each of the agents updates the local motion pattern to tracks its own planned waypoints with the obstacle avoidance in …a decentralized manner. Furthermore, to resolve unavoidable egoistic conflicts occurring in the decentralized scheme, a centralized intervener with the route replanning is invoked to coach the involved agents to abort the existing deadlocks. Bounded by an amount of time, the performances of the proposed and benchmarked algorithms are simulated on the same instance, from the evaluated testbeds that consists of various maps and scenarios. In the simulations, it is proved that this work outperforms other benchmarked algorithms for all presented instances in the term of the success rate. The experimental results are also demonstrated to verify the feasibility of the proposed methodology. Show more

Keywords: Multi-agent, pathfinding, motion navigation, hybrid approach, deadlock

DOI: 10.3233/JIFS-223157

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8925-8941, 2023

Authors: He, Xiaoxu

Article Type: Research Article

Abstract: Complex time series appear in numerous applications and are related to some essential physiological and natural systems. Their comparison faces big challenges: 1) with different complexity; 2) with significant phase shift in one series or shift∖on the time axis. Existing methods work well for periodic time-series data, but fail to produce satisfactory results in complex time-series. In this paper, we introduce a novel distance function based on the evolution rule for complex time series comparison. Here, the evolution rule, as the innate generative mechanism of time series, is creatively used to characterize complicated dynamics from complex time series. The comparison …includes different level comparisons: the coarse level is to compare the difference in complexity, and the fine level is to compare the difference in actual evolution behavior. The proposed method is inspired by the observation that similar sequences come from the same source, e.g. a person’s heart, in the case of ECG, thus two similar series will have the same innate generative mechanism. The performance has been verified by the conducting experiments, and the experiment results show that the proposed method is superior to the previously existing methods in clustering and classification on some real data sets. Show more

Keywords: Complex time series, evolution rule, complex system, data mining, non-parametric

DOI: 10.3233/JIFS-223338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8943-8955, 2023

Authors: Yu, Ming | Lin, Xiaoqing | Liu, Yi | Guo, Yingchun

Article Type: Research Article

Abstract: Existing saliency detection methods have achieved great progress in extracting multi-level features, however it is a challenging problem to catch accurate long-range dependencies that can enhance the accuracy of semantic information. To address this, a Transformer-based multi-scale attention and boundary enhancement with long-range dependency (MSBE) network is proposed in this paper. A multi-scale attention enhancement module (MSAEM) is designed to reduce the redundant or noisy features and generate a high-quality feature representation by integrating multiple attentional features with diverse perspectives. The high-quality features are then fed into the triple Transformer encoder embedding module (TEM) to enhance high-level semantic features by …learning long-range dependencies across layers. In the decoder part, a cross-layer feature fusion module (CLFFM) and boundary enhancement module (BEM) are designed to improve the effect of feature fusion and get accurate prediction results. Extensive experiments on six challenging public datasets demonstrate that the proposed method achieves competitive performance. Show more

Keywords: Salient object detection, long-range dependencies, transformer encoder, cross-layer feature fusion, boundary enhancement module

DOI: 10.3233/JIFS-223726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8957-8969, 2023

Authors: Belal, Mohamad Mulham | Sundaram, Divya Meena

Article Type: Research Article

Abstract: The security defenses that are not comparable to sophisticated adversary tools, let the cloud as an open environment for attacks and intrusions. In this paper, an intelligent protection framework for intrusion detection in a cloud computing environment based on a covariance matrix self-adaptation evolution strategy (CMSA-ES) and multi-criteria decision-making (MCDM) is proposed. The proposed framework constructs an optimal intrusion detector by using CMSA-ES algorithm which adjusts the best parameter set for the attack detector. Moreover, the proposed framework uses a MEREC-VIKOR, a hybrid standardized evaluation technique. MEREC-VIKOR generates the own performance metrics (S, R, and Q) of the proposed framework …which is a combination of multi-conflicting criteria. The proposed framework is evaluated for attack detection by using CICIDS 2017 dataset. The experiments show that the proposed framework can detect cloud attacks accurately with low S (utility), R (regret), and Q (integration between S and R). The proposed framework is analyzed with respect to several evolutionary algorithms such as GA, IGASAA, and CMA-ES. The performance analysis demonstrates that the proposed framework that depends on CMSA-ES converges faster than the other evolutionary algorithms such as GA, IGASAA, and CMA-ES. The outcomes also demonstrate that the proposed model is comparable to the state-of-the-art techniques. Show more

Keywords: Multi-criteria decision-making, MEREC, VIKOR, CMSA-ES, intrusion detection system, security

DOI: 10.3233/JIFS-224135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8971-9001, 2023

Authors: Zhang, Haibo

Article Type: Research Article

Abstract: For a long time, the level of endurance quality of our male basketball athletes is not high, and there is a gap with the strongest countries in Europe and America. The former head coach of Chinese men’s basketball team diagnosed the biggest problem of Chinese men’s basketball team and Chinese youth men’s basketball team is the poor quality of endurance. It is especially important to strengthen the endurance training of our basketball players and improve their endurance level. However, from the current situation, the teams in the training due to the lack of standards for endurance quality training has led …to a great blindness in endurance quality training. The endurance quality level evaluation of young male basketball players is a classic multiple attribute group decision making (MAGDM) issue with vague, inconsistent, and indeterminate information. The 2-tuple linguistic neutrosophic sets (2TLNSs) is an appropriate form to express the indeterminate decision-making information in the endurance quality level evaluation of young male basketball players. Therefore, in this paper, the 2-tuple linguistic neutrosophic numbers CLVA (2TLNN-CLVA) is built based on traditional close value (CLVA) method and applies it to evaluate the endurance quality level of young male basketball players. Finally, a numerical example for evaluating the endurance quality level of young male basketball players has been given and some decision comparisons are also conducted to further illustrate the advantages of the 2TLNN-CLVA method. Show more

Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), CLVA method, endurance quality level, basketball players

DOI: 10.3233/JIFS-224327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9003-9014, 2023

Authors: Yao, Zhigang | Ran, Hui

Article Type: Research Article

Abstract: At present, the basic pension insurance system for urban and rural residents in China has played a positive role in guaranteeing the basic life of the elderly in urban and rural areas. At present, the basic pension insurance system for urban and rural residents is not perfect, and there is still a great lag in the formulation of cross-system and cross-regional policies. There are differences in treatment between groups, between regions and between urban and rural areas. The coverage is not comprehensive enough and there are still some people who are not included in the basic protection system, etc. People …urgently need a social pension insurance system that can provide reliable and sustainable protection in their old age. The operational efficiency evaluation of urban and rural residents’ basic pension insurance systems is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers grey relational analysis (TFNN-GRA) method is built based on the traditional grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs). Finally, a numerical example for operational efficiency evaluation of urban and rural residents’ basic pension insurance systems has been given and some comparisons are used to illustrate advantages of 2TLNN-GRA method. Show more

Keywords: Multiple attribute decision making (MAGDM) problems, triangular fuzzy neutrosophic sets (TFNSs), GRA method, operational efficiency evaluation

DOI: 10.3233/JIFS-221631

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9015-9026, 2023

Authors: Premananthan, G. | Nagaraj, B. | Jaya, J.

Article Type: Research Article

Abstract: In recent times, ML algorithms that plays a significant role right from drug discovery to clinical decision making. The recent advances in DL technologies contribute towards improved performance for carrying out computer aided medical image analysis and disease diagnosis. The key benefit of AI in processing of medical big data offers spectacular insights into the hierarchal relationships that exist among data which can be algorithmically explored thus replacing the tedious manual processes to extract and localize specific areas of interests in medical images thus considerably changing the way medicine has been practiced so far. In bio medical related clinical applications, …there is a constant demand pertaining the research and development with respect to deploying AI as a mainstream tool to perform several medical imaging activities like analysis, diagnosis, segmentation as well as classification. The increased usage of electronic health records and medical images being its integral component the need for appropriate and efficient AI assisted medical image analysis system that takes care of accurate and automated decision making could be of great help to radiologists and medical practitioners. Molecular image analysis is a dynamic field that makes use of ML and DL algorithms that utilizes labeled and structured information which also proves to be helpful to the patients as they serve as an initial interface before further diagnosis and treatments. Thus our research aims to offer a novel and efficient AI based medical analysis system that can assist clinical practitioners to focus on enhancing the disease diagnosis through DL based medical image analysis and decision making. In addition, we also address specific challenges related to disease diagnosis and propose novel GAN model for improved diagnosis and implementation. Our proposed technique can also be generalized to generate synthetic data for further issues related to molecular image analysis in the field of medicine and help towards building a better disease diagnosis model. Show more

Keywords: Artificial Intelligence (AI), Deep learning (DL), electronic health records (EHR), Generative Adversarial networks (GAN), medical image analysis, Machine learning (ML).

DOI: 10.3233/JIFS-223354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9027-9037, 2023

Authors: He, Qiang | Wang, Guanqun | Huo, Lianzhi | Wang, Hengyou | Zhang, Changlun

Article Type: Research Article

Abstract: Multivariate time series anomaly detection has made significant progress and has been studied in many fields. One of the difficulties in time-series data analysis is the complex nonlinear dependencies between multiple time steps and multiple variables. Therefore, detecting anomalies in these data is challenging. Although many studies used classical attention mechanisms to model the temporal patterns of data, few have combined multiple attention mechanisms and analyzed the data’s temporal characteristics and feature correlations. Therefore, we propose an autocorrelation and attention mechanism-based anomaly detection (ACAM-AD) framework that combines an autocorrelation model based on the Autoformer model, which is superior to the …self-attention mechanism, a multi-head graph attention network, and a dot-product attention mechanism to model the complex dependencies of data considering temporal and feature dimensions. The autoregressive model is parallelized with the neural network, and a sparse autocorrelation mechanism and sparse graph attention network are used to reduce model complexity. Experiments on public datasets show that the model is effective and performs better than the baseline model. Show more

Keywords: Multivariate time series, anomaly detection, autocorrelation, multi-head graph attention network

DOI: 10.3233/JIFS-224416

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9039-9051, 2023

Authors: Yang, Guangfen | Zhang, Hui

Article Type: Research Article

Abstract: Owing to the lack of information, it is more realistic that the sum of probabilities is less than or equal to one in the probabilistic hesitant fuzzy elements (P-HFEs). Probabilistic-normalization method and cardinal-normalization method are common processing methods for the P-HFEs with incomplete information. However, the existed probabilistic-normalization method of sharing the remaining probabilities will lose information and change the information integrity of the P-HFEs. The first existed cardinal-normalization method of adding maximum or minimum membership degree with probability zero are influenced by the subjectivity of the decision makers. And the second existed cardinal-normalization method named as reconciliation method only …applicable to the P-HFEs with complete information. Aiming at solving those shortcomings, we propose a possibility degree method based on a novel cardinal-normalization method for the sake of comparing the P-HFEs in pairs. In the process of comparison, the information integrity remains unchanged. Then, we propose a multi-criteria decision making (MCDM) problem, where the attribute weight is determined by entropy measures of the integration results. Finally, an application case in green logistics area is given for the sake of illustrating the efficiency of the proposed method, where the evaluation values are given in the P-HFEs form with incomplete information. Numerical and theoretical results show that a MCDM problem based on the proposed cardinal-normalization method and possibility degree method have a wide range of application. Show more

Keywords: Probabilistic hesitant fuzzy element, possibility degree method, entropy measures, reconciliation method, the identical membership method

DOI: 10.3233/JIFS-222733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9053-9072, 2023

Authors: Ding, Ji-Feng | Weng, Ju-Hui | Chou, Chien-Chang

Article Type: Research Article

Abstract: Evaluating the factors affecting customer value in department stores will shed light on the motivations of customers when choosing department stores, which will help department stores to improve their business performance and competitiveness. This paper applies the fuzzy Analytic Hierarchy Process (AHP) method to empirically analyze the determinants of customer value at department stores in Taiwan. This study first found the major factors influencing customer value at department stores in Taiwan through a review of the literature and expert interviews, and these factors consisted of four evaluation dimensions and 20 evaluation criteria. An empirical investigation was then conducted through an …AHP expert questionnaire survey. The main findings of this paper were as follows: (1) “Physical environment” was the most important evaluation dimension for customer value at department stores in Taiwan. (2) The four leading factors influencing customer value in department stores were “roomy and comfortable space,” “responsive customer service,” “planning of lines of movement at counters,” and “parking area and facilities.” This study also performed further discussion of the four evaluation criteria as a reference for department stores that wish to raise their competitiveness. Show more

Keywords: Customer value, determinant, department store, fuzzy, analytic hierarchy process (AHP)

DOI: 10.3233/JIFS-222175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9073-9089, 2023

Authors: Lian, Lian

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-222395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9091-9107, 2023

Authors: Karthigha, M. | Latha, L.

Article Type: Research Article

Abstract: Industrial Control Systems (ICS) are susceptible to threats or attacks, and even minor changes or manipulation could cause major damage to industrial operations. Industrial control system cybersecurity is vital owing to the severe negative effects it could have on the economy, the environment, people, and politics. Therefore, it’s also crucial to design intrusion detection systems for industrial control systems. In this paper, an efficient intrusion detection system with clustered ensemble feature selection and a Multi-Level Modified Gated Recurrent Unit (M-GRU) classification model is proposed. This intrusion detection system with a general framework for clustered ensemble feature ranking approach is proposed …to effectively find the best feature subset in network packet traffic data. The features designated are fed into a multi class classification algorithm Multi-Level Modified Gated Recurrent Unit (M-GRU) to efficiently detect the cyberattacks. Evaluation criteria including precision, accuracy, recall and F1 score are assessed and compared to other cutting-edge algorithms to assess the performance of the proposed model. The proposed model attained an average accuracy of 98.21 %. Results show that the suggested model increased the attack detection accuracy by an average of 5.935% and 0.116% when compared to the Gated Recurrent Unit, Long Short Term Memory, random forest and naïve bayes models. Show more

Keywords: Industrial control system, intrusion detection, ensemble feature selection, classification, gated recurrent unit

DOI: 10.3233/JIFS-222643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9109-9127, 2023

Authors: Nurhidayat, Irfan | Pimpunchat, Busayamas | Klomsungcharoen, Wiriyabhorn

Article Type: Research Article

Abstract: This study aims to present the modified SVM polynomial method in order to evaluate insurance data. The research methodology discusses classical and modified SVM polynomial methods by R programming, and uses performance profiles to create the most preferable methods. It offers a new algorithm called an accurate evaluating algorithm as the way to construct the modified SVM polynomial method. The classical SVM polynomial method is also represented as the main idea in finding the modified polynomial SVM method. Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROCs) Curve, Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds are …further named an accurate evaluating algorithm, employed to build the modified SVM polynomial method. The research paper also presents the best performance profiles based on the computing time and the number of iterations of both classical and modified SVM polynomial methods. Performance profiles show numerical comparisons based on both methods involving insurance data also displayed in this paper. It can be concluded that applying an accurate evaluating algorithm on the modified SVM polynomial method will improve the data accuracy up to 86% via computing time and iterations compared to the classical SVM polynomial method, which is only 79%. This accurate evaluating algorithm can be applied to various large-sized data by utilizing R programming with changing any suitable kernels for that data. This vital discovery will offer solutions for faster and more accurate data analysis that can benefit researchers, the private sector, or governments struggling with data. Show more

Keywords: Modified SVM polynomial method, classical SVM polynomial method, accurate evaluating algorithm, insurance data, simulation

DOI: 10.3233/JIFS-222879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9129-9141, 2023

Authors: Feng, Xiangqian | Zibibula, Minawaer | Wei, Cuiping

Article Type: Research Article

Abstract: With the rapid development of science and technology, high-tech enterprises need to constantly carry out technological innovation to adapt to the changes in the external environment, and maintain their competitive advantages. However, the current research on technological innovation of high-tech enterprises is carried out from a static perspective, which is difficult to understand the dynamic evolution process of continuous technological innovation of high-tech enterprises in a turbulent environment. Therefore, this paper studies high-tech enterprises’ dynamic technological innovation ability from a dynamic perspective, through literature reading and the investigation of the technological innovation status of high-tech enterprises, the evaluation index system …of 12 indicators under three dimensions is constructed. The multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA) –Level-based weight assessment (LBWA) comprehensive evaluation model based on Pythagorean fuzzy number (PFN) is proposed to evaluate the dynamic technological innovation ability of high-tech enterprises. Finally, the accuracy and reliability of the model are verified by case analysis. The result of this study shows that the ability to identify new technological knowledge and information outside the enterprise, the ability to obtain technological innovation resources, and the ability to strengthen the input of innovation resources are important factors for the dynamic technological innovation capability of enterprises, so enterprises should pay more attention from these aspects. This study provides a new comprehensive evaluation model and evaluation results can help the decision-makers find their strengths and weaknesses in time and improve them, to promote the sustainable development of high-tech enterprises. Show more

Keywords: Dynamic capability, Pythagorean fuzzy set, LBWA, MULTIMOORA, high-tech enterprises innovation

DOI: 10.3233/JIFS-222965

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9143-9165, 2023

Authors: Jin, Feifei | Li, Danning | Guo, Shuyan | Zhou, Ligang | Chen, Yi | Zhu, Jiaming

Article Type: Research Article

Abstract: Under the Pythagorean fuzzy environment, this paper presents a multi-attribute decision-making (MADM) model based on exponential entropy measure and exponential similarity measure to evaluate new energy battery supplier’s performance. In this method, the notion of Pythagorean fuzzy linguistic sets (PFLSs) is first introduced by combining the linguistic fuzzy sets (LFSs) and the Pythagorean fuzzy sets (PFSs). Then, the axiomatic definitions of Pythagorean fuzzy entropy and Pythagorean fuzzy similarity measure are developed to measure the degree of uncertainty and similarity between two Pythagorean fuzzy linguistic values (PFLVs). The PFLVs can be expressed by the linguistic membership degree (LMD) and linguistic non-membership …degree (LNMD). In addition, we construct two new information measure formulas based on exponential function. Through a series of proofs, we verify that they satisfy the axiomatic conditions of entropy and similarity measure of Pythagorean fuzzy language respectively. On this basis, we research the relationship between the two information measures. Finally, we present a novel Pythagorean fuzzy linguistic MADM model. An example for evaluating performance of new energy battery supplier is given to explain the effectiveness of the newly-developed approach. The stability and validity of the newly-developed approach is performed by sensitivity analysis and comparative analysis. Show more

Keywords: Pythagorean fuzzy linguistic sets, information entropy, similarity measure, new energy battery supplier evaluation

DOI: 10.3233/JIFS-223088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9167-9182, 2023

Authors: Choudhary, Ashutosh Kumar | Rahamatkar, Surendra

Article Type: Research Article

Abstract: DoS, GH, Sybil, Masquerading, Spoofing, Man in the Middle, etc. constantly attack IoT networks. Internal or external attacks reduce end-to-end delay, throughput, energy use, and other metrics. To counter these attacks, researchers have proposed a number of security & privacy mechanisms with varying computational complexity and security levels. Immutability, traceability, transparency, and distributed nature make blockchain-based models secure. QoS depends on blockchain length, so these models aren’t scalable. Researchers say sidechaining improves QoS while remaining secure. Splitting or merging complex sidechains requires machine learning. Low-power IoT networks can’t use models. This text suggests a lightweight MGWO Model that helps establish …initial routes by choosing high-trust nodes, reducing sidechaining power consumption, and incorporating fault-aware trust establishment. MGWO Model determines blockchain piece count for high QoS. MGWO Model uses Q-Learning to detect network faults. Fault identification is controlled by a stochastically modelled and activated Intrinsic Genetic Algorithm (IGA). Q-Learning, MGWO, and IGA can mitigate Sybil, Masquerading, Grey Hole, DDoS, and MITM attacks. Even when attacked, the proposed model maintains high QoS, improving real-time deployment efficiency. The proposed model improves energy efficiency by 15.9%, throughput by 10.6%, communication speed by 8.3%, and packet delivery by 0.8% for different network scenarios. Show more

Keywords: Trust, wireless, IoT, blockchain, sidechain, MITM, MGWO, Q learning, IGA, DDoS, Sybil, QoS

DOI: 10.3233/JIFS-223316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9183-9201, 2023

Authors: Xiao, Yanjun | Zhao, Yue | Li, Zeyu | Wan, Feng

Article Type: Research Article

Abstract: Fault diagnosis of rapier loom is an inevitable requirement to meet the demand of intelligent manufacturing. Facing the strong noise interference caused by complex working environment, accurate and reliable vibration signal detection of blade loom spindle is the key to realize the rapier loom fault diagnosis. This paper proposes a method to extract the spindle vibration signal of the rapier loom by Adaptive Piecewise Hybrid Stochastic Resonance (APHSR) after the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Firstly, ICEEMDAN is used to pre-process the weak vibration signal containing noise, decompose the signal into multiple IMF components and …display the high and low frequency signal characteristics of the original signal. Then, the energy density method and the correlation coefficient method are used to remove high and low noise, respectively, to filter the optimal IMF components, and then the signal containing valid information is reconstructed. Finally, the reconstructed signal is input to APHSR for noise-assisted enhancement after scale transformation to restore the faint vibration signal feature frequencies and achieve effective feature extraction. Through the simulation experiment and the engineering fault experiment analysis, comparing ICEEMDAN-APHSR with CEEMDAN-SR, ICEEMDAN-SR, CEEMDAN-APHSR methods. The difference between the spectrum amplitude, the spectrum amplitude and the maximum noise and the maximum signal to noise ratio (SNR) of the fault feature frequency of the rapier loom spindle bearing increased by 3.3668 dB,1.7205 dB,2.3952 dB, respectively. The results show that ICEEMDAN-APHSR method can accurately extract the fault feature frequency of the spindle bearing of rapier loom, and effectively solves the problem of extracting the weak vibration signal feature of rapier loom in the background of strong noise. This method is beneficial to the future research of rapier loom fault diagnosis, and is of great significance to promote the maintenance of loom equipment and production safety and quality. Show more

Keywords: Weak signal detection, ICEEMDAN, APHSR, feature extraction, rapier loom, fault diagnosis

DOI: 10.3233/JIFS-223664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9203-9230, 2023

Authors: Ma, Haishu | Ma, Zongzheng

Article Type: Research Article

Abstract: Unexpected failure of production equipment may lead to fatal accidents and economic losses of the enterprise. It is important to find out the cause and reason as soon as possible and take appropriate maintenance measures. Condition monitoring is often applied to predict equipment failures based on certain parameters. Moreover, when the parts of the rotating machinery fail, the vibration signals collected by the sensors are often mixed with a large amount of noise, which will cause difficulties for the accuracy and generalization of traditional fault diagnosis models. How to extract more effective feature information from complex vibration signals is of …indescribable importance for optimizing fault diagnosis models. In order to improve the accuracy of fault diagnosis in manufacturing system, a deep neural network model was proposed, which was validated on a blower. First, the vibration signal was collected using the sensors mounted on the blower. Then, wavelet packet decomposition and fast fourier transform were applied for feature extraction. Deep learning model was built using keras to diagnose the blower. The stacked Autoencoder is adopted in the DNN for dimension reduction. The extracted features are fed into the Multilayer Perceptron for fault diagnosis. Experimental results show that the proposed deep neural network model is able to predict the degradation of the mechanical equipment with high accuracy. Show more

Keywords: Deep neural network, wavelet packet decomposition, Fourier transform, feature extraction, fault diagnosis

DOI: 10.3233/JIFS-224077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9231-9239, 2023

Authors: Shao, Yubo | Zhang, Bangcheng | Yin, Xiaojing | Gao, Zhi | Li, Jing

Article Type: Research Article

Abstract: The anomaly detection research of drive end bearings (DEBs) is of great significance to the safe and reliable operation of hoist. This paper proposes an anomaly detection method of DEBs based on the linear weighted sum combines with the belief rule base. First, in order to improve the accuracy of anomaly detection, the time-domain features and frequency-domain features are integrated by linear weighted sum (LWS) respectively. Then, belief rule base (BRB) method is provided for anomaly detection using fused features. Meanwhile, the covariance matrix adaption evolution strategy (CMA-ES) is utilized to optimize the parameters of belief rule base model. Finally, …the validity of the proposed method is verified by the vibration data, which are acquired from the condition monitoring system of hoist in body-in-white (BIW) welding production line. The proposed method achieves a high detection accuracy. It is proved that the proposed method is suitable for anomaly detection of DEBs in the actual BIW welding production line. Show more

Keywords: Anomaly detection, linear weighted sum, belief rule base, drive end bearing

DOI: 10.3233/JIFS-224102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9241-9255, 2023

Authors: Liu, Peng | Geng, Xiaonan

Article Type: Research Article

Abstract: Coal is a vital basic energy source for any economy in the world, and our country is no exception. Our coal resources are abundant, with high production and demand, not comparable to oil and natural gas. The coal supply chain plays an equally important role in economic production, but unfortunately, the current coal supply chain is not focused on greening while creating profits. Unfortunately, the current coal supply chain does not focus on green production and energy conservation and emission reduction while creating profits, which has caused irreversible harm and loss to resources and environment. This has caused irreversible damage …and loss to resources and the environment. The green supplier selection for coal enterprises is affirmed as multiple attribute decision making (MADM). In such paper, motivated by the idea of cosine similarity measure (CSM), the CSMs are extended to DVNSs and four CSMs are created under DVNSs. Then, two weighted CSMs are built for MADM under DVNSs. Finally, a numerical example for Green supplier selection for coal enterprises is affirmed and some comparative algorithms are produced to affirm the built method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), cosine similarity measure (CSM), green supplier selection

DOI: 10.3233/JIFS-224123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9257-9265, 2023

Authors: Chang, Kuei-Hu

Article Type: Research Article

Abstract: Risk prediction, assessment, and control are key parts of the successful operation and sustainable development of any enterprise. During the process of product failure risk assessment, evaluated risk factors belong to the group of multiple-criteria decision-making (MCDM) problems, including severity, occurrence, and detection when failure occurs. However, the traditional risk ranking method does not consider the subjective and objective weights of the assessment factors, and during risk prediction, assessment, and control, some unknown information in many practical situations is included. These reasons may cause the risk assessment results to be biased. In order to effectively deal with the problem of …risk assessment, this paper proposes a D numbers risk ranking method by considering subjective and objective weights between assessment factors under incomplete linguistic information. An illustrative example of screening unit failure risk assessment is used to explain and prove the rationality and correctness of the proposed method. Some risk ranking methods are compared with the proposed D numbers risk ranking method, and the simulation results present that the proposed ranking method handles the issue of incomplete information and provides more reasonable risk ranking results. Show more

Keywords: D numbers, risk ranking method, subjective weights and objective weights, multiple-criteria decision-making

DOI: 10.3233/JIFS-224139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9267-9280, 2023

Authors: Abdul Lathif, Syed Ismail | Cruz Antony, J. | Noel Jeygar Robert, V. | Aishwarya, D.

Article Type: Research Article

Abstract: A failure risk assessment must be carried out and potential drilling equipment failure risks must be promptly addressed in order to prevent drilling fluid pollution during offshore oil drilling. The qualitative, comprehensive, and quantitative failure risks for Drilling Permanent Magnetic Synchronous Motors (DPMSM) are examined in this article using a hybrid methodology. First, the Drilling PMSM using Failure Mode Analysis (FMA) method is combined with the Risk Matrix (RM) approach to analyse the risk levels of risk factors individually. Next, the Borda number is introduced to compare the risk levels exactly. To execute a Fuzzy Comprehensive Evaluation (FCE) of the …system failure risk, a fuzzy relation matrix of risk factors is generated, and the weight of each risk component is calculated using importance analysis. The failure rate is then determined using fuzzy inference, and the Fault Tree (FT) is then built based on the risk variables. Fault tree analysis is used to compute the system failure rate, and the significance of the bottom event is evaluated. The Bayesian network (BN) is used to depict the Fuzzy Fault Tree (FFT) analysis. By utilizing Bayesian forward causal inference and reverse diagnostic inference to calculate the leaf node failure rate and root node posterior probability, the system’s weak points and potential failure causes are determined. Show more

Keywords: Risk matrix, fuzzy comprehensive evaluation, fault tree, bayesian network, failure mode analysis

DOI: 10.3233/JIFS-224462

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9281-9295, 2023

Authors: Jebin Bose, S. | Kalaiselvi, R.

Article Type: Research Article

Abstract:  The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some …limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented. Show more

Keywords: Android, smartphones, datasets, malware, detection, classification, deep learning neural network, benign, preprocessing, feature selection, meta-heuristic artificial jellyfish search optimizer, dense dilated ResNet101

DOI: 10.3233/JIFS-230186

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9297-9310, 2023

Authors: Jiang, Lin | Chen, Biyun

Article Type: Research Article

Abstract: To study the bilateral matching problem of new R&D institution-talent teams based on uncertain linguistic assessment information and multiple indicators-multiple talents, a cloud model regret theory-based information gathering method is proposed, and a bi-objective bilateral matching model based on single-indicator utility maximization and overall indicator utility maximization is constructed.. The method firstly constructs the demand indicators of new R&D institutions for talent teams, uses cloud data to characterize uncertain group linguistic assessment information, and converts cloud data into cloud perceived utility based on power function; secondly, calculates the indicator weights of each expert based on entropy power method, and secondly …uses entropy power method to calculate comprehensive indicator weights, optimally solves objective expert weights based on the minimum variance of assessment information among experts, and integrates with subjective expert Again, based on regret theory, the cloud perceived utility of each talent under each index is converted into regret cloud perceived utility, and set with the index weights and expert weights into comprehensive cloud perceived utility; finally, a local-whole dual-objective bilateral matching model is constructed to obtain the matched talent team, and example analysis and method comparison are used to show that the method has feasibility and effectiveness. Show more

Keywords: New R&D institution, talent team, cloud model, regret theory, bilateral matching

DOI: 10.3233/JIFS-221944

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9311-9325, 2023

Authors: Muthuvinayagam, M. | Vengadachalam, N. | Subha Seethalakshmi, V. | Rajani, B.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-221820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9327-9345, 2023

Authors: Periakaruppan, Sudhakaran | Shanmugapriya, N. | Sivan, Rajeswari

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-222537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9347-9362, 2023

Authors: Senthamil Selvi, M. | Ranjeeth Kumar, C. | Jansi Rani, S.

Article Type: Research Article

Abstract: A smart city is a phenomenon that combines information technology with physical and social infrastructure to regulate a city’s cooperative intelligence. Wireless sensor networks (WSN) are the fundamental technology that smart cities use to administer and sustain their service offerings. To decrease the network’s energy consumption, clustering and multihop routing algorithms have been suggested, verified, and put into practice in the literature. This inspiration led to the development of the “energy-aware clustered route approach” in the current study, which is suggested for WSNs in smart cities. The presented method focuses on choosing the right cluster heads (CHs) and the best …pathways in a WSN. The presented model includes a fitness value-based clustering scheme for efficient CH selection to achieve this. The Deep Neural Network (DNN) algorithm is then used to carry out the routing operation. The suggested approach technique calculates a fitness function (FF) that consists of three variables, including node degree, base station distance, and residual energy. This fitness function aids in the WSN’s best route selection. Simulations were run to verify the presented model’s superiority in terms of network lifespan and energy efficiency, and the results demonstrated the model’s outstanding performance. Show more

Keywords: Wireless sensor networks, cluster based routing, deep neural networks, genetic algorithm, and fitness function based route

DOI: 10.3233/JIFS-222615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9363-9377, 2023

Authors: Gomes, Daiana | Serra, Ginalber

Article Type: Research Article

Abstract: In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral …Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability. Show more

Keywords: Systems identification, Kalman filter, interval type-2 fuzzy model, singular spectral analysis, evolving fuzzy systems

DOI: 10.3233/JIFS-222919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9379-9394, 2023

Authors: Liu, Jinpei | Bao, Anxing | Jin, Feifei | Zhou, Ligang | Shao, Longlong

Article Type: Research Article

Abstract: Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, …we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach. Show more

Keywords: Group decision-making approach, multiplicative probabilistic linguistic preference relations, consistency adjustment, pessimistic criterion, DEA cross-efficiency

DOI: 10.3233/JIFS-223117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9395-9410, 2023

Authors: Jin, LeSheng | Chen, Zhen-Song | Yager, Ronald R. | Langari, Reza

Article Type: Research Article

Abstract: This letter reports a new type of uncertain information that is different from some well known existing uncertain information, such as probability information, fuzzy information, interval information and basic uncertain information. This type of uncertain information allows some specified compromise in interacting decision environments and gives some acceptance area when facing with uncertainties. We firstly introduce the cognitive interval information and then naturally propose the cognitive uncertain information as an extension. The featured acceptance area provides more flexibility in uncertain information handling and it can be regarded as some specified uncertain range (versus the certainty degree in basic uncertain information). …The new proposals have advantages in some uncertain decision making scenarios where intersubjectivity and interaction of decision makers play important roles. Besides, some basic structural properties are briefly discussed. Moreover, some motivational examples are presented to show its usage in group decision making to help automatically obtain consistency or consensus in aggregating the different individual evaluations. Show more

Keywords: Cognitive interval information, cognitive uncertain information, decision making, group decision making, information fusion, uncertain information

DOI: 10.3233/JIFS-223119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9411-9418, 2023

Authors: Sangeetha, M. | Thiagarajan, Meera Devi

Article Type: Research Article

Abstract: A recommendation System (RS) is an emerging technology to figure out the user’s interests and intentions. As the amount of data increases exponentially, it is hard to analyze the user intentions and trigger the recommendation accordingly. In this research work, a novel recommendation system called the Deep Knowledge Graph based Attribute Preserving Recommendation (DKG-APR) is presented to analyze massive data and provide personalized recommendations to users. The Deep Knowledge Graph for Recommendation System (DKG-RS) uses Deep Convolutional Neural Network (DCNN) and attention mechanism to explicitly model high-order connections in knowledge graphs. According to empirical findings, Knowledge Graph Attention Network (KGAT) …performs better than other state-of-the-art recommendation techniques like RippleNet and Neural FM. Additional research demonstrates the effectiveness of embedding propagation for high-order relation modeling and the advantages of the attention mechanism for interpretability.The results also show that user information is crucial in the recommendation system, as seen from the optimal node-drop-out ratio of 0.2, which led to the best recall value of 0.2 for all datasets. Show more

Keywords: Knowledge graph, DCNN, DKG, recommendation system

DOI: 10.3233/JIFS-223775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9419-9430, 2023

Authors: Osman, H. Saber | El-Sheikh, S.A. | Radwan, Abdelaziz E. | El-Atik, Abdelfattah A.

Article Type: Research Article

Abstract: In this paper, the generalization of pre-topological spaces called bipretopological spaces (briefly, π-pre-topology) depending on two pre-topologies on an arbitrary universal set has been introduced. New kinds of separations axioms on π-pre-topological spaces are established and some of their properties are investigated. A comparison between four separation axioms on π-pre-topological spaces and pre-topological spaces with different sorts of counterexamples are presented. The topological property for some π-pre-separation axioms are satisfied and its relation with disubgraphs are discussed. A human heart will be studied through it is generated digraph. It is noted that all separation axioms for human heart are not …all satisfied. Show more

Keywords: Pre-topology, π-pre-topology, separation axioms, human heart, regularity, normality, hereditary, pre-topological property

DOI: 10.3233/JIFS-223891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9431-9439, 2023

Authors: Madhavi, S. | Santhosh, N.C. | Rajkumar, S. | Praveen, R.

Article Type: Research Article

Abstract: In Wireless Sensor Networks (WSNs), resource depletion attacks that focusses on the compromization of routing protocol layer is identified to facilitate a major influence over the network. These resource depletion attacks drain the batter power of the sensor nodes drastically with persistent network disruption. Several protocols were established for handling the impact of Denial of Service (DoS) attack, but majority of them was not able to handle it perfectly. In specific, thwarting resource depletion attack, a specific class of DoS attack was a herculean task. At this juncture, Multicriteria Decision Making Model (MCDM) is identified as the ideal candidate for …evaluating the impact introduced by each energy depletion compromised sensor nodes towards the process of cooperation into the network. In this paper, A Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model (PFSVT-MCDM) is proposed for counteracting with the impacts of resource depletion attacks to improve Quality of Service (QoS) in the network. This PFSVT-MCDM used the merits of Pythagorean Fuzzy Sets information for handling uncertainty and vagueness of information exchanged in the network during the process of data routing. It utilized VIKOR and TOPSIS for exploring the trust of each sensor nodes through the exploration of possible dimensions that aids in detecting resource depletion attacks. The experimental results of PFSVT-MCDM confirmed better throughput of 21.29%, enhanced packet delivery fraction of 22.38%, minimized energy consumptions 18.92%, and reduced end-to-end delay of 21.84%, compared to the comparative resource depletion attack thwarting strategies used for evaluation. Show more

Keywords: Wireless sensor networks, resource depletion attacks, pythagorean fuzzy sets, TOPSIS (Technique For Order Performance By Similarity To Ideal Solution), quality of service, VIKOR (VlseKriterijumska Optimizacija Kompromisno Resenje)

DOI: 10.3233/JIFS-224141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9441-9459, 2023

Authors: Cao, Maojun | Hu, Yingda | Yue, Lizhu

Article Type: Research Article

Abstract: The uncertainty of weight makes the weight density between samples not fixed. Aiming at the problem that the existing CLIQUE clustering algorithm does not consider the weight of object features, which leads to low accuracy, an improved weighted method combined with the thought of posets is proposed. In addition, this method does not need accurate weight assignment, only the weight order can run efficiently. First, the weight order of object features is obtained, and then the partial order weight is applied to the original data to obtain weighted data with weights. Then the traditional CLIQUE algorithm is used to cluster …according to weighted data, and finally the partial order weighted CLIQUE model is obtained. Through the experiment of six groups of data, the results show that: under the given weight sequence constraints, the clustering quality of the weighted CLIQUE model is significantly higher than that of the unweighted model, and the clustering accuracy and other aspects are significantly improved. In this method model, weight information is effectively integrated into the algorithm when only the feature weight order is obtained, and the function of feature weight is fully played to enhance the robustness of clustering results. At the same time, the idea of poset can effectively integrate expert information, and the representation of the nearest neighbor elements in Hasse graph can show the effect intuitively. It is an effective improvement method of CLIQUE clustering algorithm. Show more

Keywords: Clustering, proposed, CLIQUE algorithm, feature weight, Hasse graph

DOI: 10.3233/JIFS-224214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9461-9473, 2023

Authors: Peng, Peng | Wu, Danping | Han, Fei-Chi | Huang, Li-Jun | Wei, Zhenlin | Wang, Jie | Jiang, Yizhang | Xia, Kaijian

Article Type: Research Article

Abstract: Currently, breast cancer is one of the most common cancers among women. To aid clinicians in diagnosis, lesion regions in mammography pictures can be segmented using an artificial intelligence system. This has significant clinical implications. Clustering algorithms, as unsupervised models, are widely used in medical image segmentation. However, due to the different sizes and shapes of lesions in mammography images and the low contrast between lesion areas and the surrounding pixels, it is difficult to use traditional unsupervised clustering methods for image segmentation. In this study, we try to apply the semisupervised fuzzy clustering algorithm to lesion segmentation in mammography …molybdenum target images and propose semisupervised fuzzy clustering based on the cluster centres of labelled samples (called SFCM_V, where V stands for cluster centre). The algorithm refers to the cluster centre of the labelled sample dataset during the clustering process and uses the information of the labelled samples to guide the unlabelled samples during clustering to improve the clustering performance. We compare the SFCM_V algorithm with the current popular semisupervised clustering algorithm and an unsupervised clustering algorithm and perform experiments on real patient mammogram images using DICE and IoU as evaluation metrics; SFCM_V has the highest evaluation metric coefficient. Experiments demonstrate that SFCM_V has higher segmentation accuracy not only for larger lesion regions, such as tumours, but also for smaller lesion regions, such as calcified spots, compared with existing clustering algorithms. Show more

Keywords: Medical image segmentation, semisupervised, fuzzy clustering algorithm, mammogram

DOI: 10.3233/JIFS-224458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9475-9493, 2023

Authors: Wang, Zeyuan | Cai, Qiang | Lu, Jianping | Wei, Guiwu

Article Type: Research Article

Abstract: With the development of globalization, companies from all over the world are now more closely connected, and they all play different roles in the industry in which they are located. There are more and more companies in a complete supply chain, which can greatly influence the stability of the supply chain, presents certain challenges. Therefore, choosing suppliers with sustainable development capabilities, especially in the event of interruption, can ensure the stability of the entire supply chain, thereby enhancing the company’s image and competitive advantage in a large-scale competition. The sustainable supplier selection is a classical multiple attribute group decision making …(MAGDM) issues. In this study, the dual probabilistic linguistic EDAS (DPL-EDAS) method is built based on the traditional EDAS method and dual probabilistic linguistic term sets (DPLTSs). Firstly, the DPLTSs is introduced. Then, combine the traditional EDAS method with DPLTSs information, the DPL-EDAS method is established and the computing steps for MAGDM are built. Finally, there are a numerical case involving sustainable supplier selection and some comparisons in this paper. The comparisons are used to illustrate advantages of DPL-EDAS method. Show more

Keywords: Multiple attribute group decision making (MAGDM), dual probabilistic linguistic term sets (DPLTSs), EDAS method, ITARA method, sustainable supplier selection

DOI: 10.3233/JIFS-230117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9495-9512, 2023

Authors: Wang, Encheng | Liu, Xiufeng | Wan, Jiyin

Article Type: Research Article

Abstract: Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter …(KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN. Show more

Keywords: Indoor localization, RSSI, Kalman filtering, DBSCAN-KF, DE-PSO-BP

DOI: 10.3233/JIFS-230178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9513-9525, 2023

Authors: Zhang, Xianyong | Wang, Qian | Fan, Yunrui

Article Type: Research Article

Abstract: Feature selection facilitates classification learning and can resort to uncertainty measurement of rough set theory. By fuzzy neighborhood rough sets, the fuzzy-neighborhood relative decision entropy (FNRDE) motivates a recent algorithm of feature selection, called AFNRDE. However, FNRDE has fusion defects for interaction priority and hierarchy deepening, and such fusion limitations can be resolved by operational commutativity; furthermore, subsequent AFNRDE has advancement space for effective recognition. For the measurement reinforcement, an improved measure (called IFNRDE) is proposed to pursue class-level priority fusion; for the algorithm promotion, the corresponding selection algorithm (called AIFNRDE) is designed to improve AFNRDE. Concretely, multiplication fusion of …algebraic and informational measures is preferentially implemented at the class level, and the hierarchical summation generates classification-level IFNRDE. IFNRDE improves FNRDE, and its construction algorithm and granulation monotonicity are acquired. Then, IFNRDE motivates a heuristic algorithm of feature selection, i.e., AIFNRDE. Finally, relevant measures and algorithms are validated by table examples and data experiments, and new AIFNRDE outperforms current AFNRDE and relevant algorithms FSMRDE, FNRS, FNGRS for classification performances. Show more

Keywords: Feature selection, fuzzy neighborhood rough set, uncertainty measure, relative decision entropy, hierarchical fusion

DOI: 10.3233/JIFS-223384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9527-9544, 2023

Authors: Shen, Hanhan | Pan, Xiaodong | Peng, Xiaoyu | Dan, Yexing | Qiao, Junsheng

Article Type: Research Article

Abstract: This paper focuses on simplifying the structure of fuzzy systems and improving the precision. By regarding the fuzzy rule base as a mapping from the vague partition on the input universe to the vague partition on the output universe, we first design a new type of fuzzy system using the complete and continuous fuzzy rule base in terms of vague partitions. We then exploit Weierstrass’s approximation theorem to show that this new type of fuzzy system can approximate any real continuous function on a closed interval to arbitrary accuracy and provide the corresponding approximation accuracy with respect to infinite norms. …We also provide two numerical examples to illustrate the effectiveness of this new type of fuzzy system. Both theoretical and numerical results show that this new type of fuzzy system achieves the quite approximation effect with a few fuzzy rules. Show more

Keywords: Vague partition, Fuzzy system, Fuzzy rule base, Approximation

DOI: 10.3233/JIFS-223542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9545-9563, 2023

Authors: Sathya, V. | Mahendra Babu, G.R. | Ashok, J. | Lakkshmanan, Ajanthaa

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-224586

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9565-9579, 2023

Authors: Yu, Bin | Zhu, Qing | Fu, Yu | Cai, Mingjie

Article Type: Research Article

Abstract: Forecasting is making predictions about what will happen or how things will change. This can help people avoid blindness and losses and play a significant role in their lives. In multi-attribute prediction problems, the correlation between attributes is often ignored, which affects prediction accuracy. Based on fuzzy rough sets and logistic regression, this paper proposes a new logistic regression method that fully considers attribute correlation, namely a twin logistic regression method based on attribute-oriented fuzzy rough sets. Firstly, attribute-oriented fuzzy rough sets are studied and analyzed. Then, the optimistic and pessimistic predictions are achieved by fuzzy rough sets and logistic …regression, and the final result is obtained by fusing the optimistic and pessimistic predictions. Finally, the effectiveness of the twin logistic regression method is verified. Show more

Keywords: Attribute-oriented fuzzy rough set, logistic regression, twin logistic regression based on attribute-oriented fuzzy rough set, multi-attribute prediction

DOI: 10.3233/JIFS-222986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9581-9597, 2023

Authors: Li, Bing | Cao, Yuwei | Li, Yongkun

Article Type: Research Article

Abstract: In this paper, the existence, uniqueness and global exponential stability of pseudo almost periodic solutions for a class of octonion-valued neutral type high-order Hopfield neural network models with D operator are established by using the Banach fixed point theorem and differential inequality techniques. Compared with most existing models, in this class of networks, all connection weights and activation functions are assumed to be octonion-valued functions except for time delays. And unlike most of the existing methods of studying octonion-valued neural networks, our method is a non-decomposition method, that is, the method of directly studying octonion-valued systems. The results and …methods in this paper are new. In addition, an example and its numerical simulation are given to illustrate the feasibility of our results. Show more

Keywords: Octonion, neutral type neural network, D operator, pseudo almost periodic solution, global exponential stability

DOI: 10.3233/JIFS-223766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9599-9613, 2023