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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
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: Stochastic configuration networks (SCNs), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCNs is affected by the assignation and selection of some network parameters significantly. Harris hawk optimizer (HHO) algorithm is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of Harris hawk. In this paper, a SCNs based on HHO algorithm is first introduced, termed as HHO-SCNs. As the performance of SCNs is related to regularization parameter r and scale factor lambda of weights and biases, then HHO is …employed to give better parameters for SCNs automatically. A numerical function and six benchmark datasets are used to verify the regression performance of the proposed model. Three benchmark datasets are introduced to illustrate the effectiveness of the proposed model for classification performance. Experimental results demonstrate the feasibility and validity of HHO-SCNs compared with incremental random vector functional link, SCNs, fast SCNs, and SCNs based on whale optimization algorithm. The proposed HHO-SCNs improves the generalization performance of standard SCNs, and provides a new idea for expanding the development and application of SCNs. Show more
Keywords: Stochastic configuration networks, Harris hawks optimizer, hyper-parameters, optimization
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 paper proposes an electric vehicle charging station (EVCS) network design in urban communities using hybrid technique. The proposed approach consists of Reptile Search Algorithm (RSA) and Honey Badger Algorithm (HBA), which is jointly called as RSA-HBA technique.In this paper, a modeling method is embedded in the proposed technique to control the optimal design and sorts of electric vehicle distribution equipment for the community, considering the heterogeneity on demand and driver behaviors.Distance from home, drivers’ arrival patterns, willingness to walk, parking location and traffic on weekdays and weekends are certain important random data parameters deemed under this technique. To asymptotically …coverage an optimal solution, the hybrid algorithm is used. A hybrid technique is proposed to direct the computational challenges for huge-scale phenomena. A detailed computational experiment is conducted to quantify the performance of the proposed technique. The performance of the proposed hybrid system is executed in the MATLAB platform and related with various methods. Show more
Keywords: Electric vehicles (EVs), electric charging stations, willingness to walk, drivers’ arrival patterns, parking location
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: Self-Attention based Generative Adversarial Capsule Network optimized with Atomic orbital search algorithm based Sentiment Analysis is proposed in this manuscript for Online Product Recommendation (SFA-AGCN-AOSA-SA-OPR). Here, Collaborative filtering (CF) and product-product (P-P) similarity method is utilized for designing the new recommendation system. CF is employed for predicting the best shops and P-P similarity method is employed to predict the better product. Initially, the datas are gathered via Amazon Product recommendation dataset. After that, the datas are given to pre-processing. During pre-processing, Markov chain random field (MCRF) co-simulation is used to remove the unwanted content and filtering relevant text. The preprocessing …output is fed to feature extraction. The features, like manufacturing date, Manufacturing price, discounts, offers, quality ratings, and suggestions or reviews are extracted using Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. Finally, Self-Attention based Generative Adversarial Capsule Network (SFA-AGCN) categorizes the product recommendation as excellent, good, very good, bad, very bad. Atomic orbital search algorithm optimizes the SFA-AGCN weight parameters. The performance metrics, like accuracy, precision, sensitivity, recall, F-measure, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) is examined. The efficiency of the proposed method provides higher mean absolute percentage error 98.23%, 88.34%, 90.35% and 78.96% and lower Mean squared error 92.15%, 90.25%, 89.64% and 92.48% compared to the existing methods, such as sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS (DLMNN-IANFIS-SA-OPR), intelligent sentiment analysis approach using edge computing based deep learning technique (DCNN-SA-OPR), sentiment analysis for online product reviews in Chinese depending on sentiment lexicon and deep learning (CNN-BiGRU-SA-OPR) and sentiment analysis on product reviews depending on weighted word embedding and deep neural networks (CNN-LSTM-SA-OPR) respectively. Show more
Keywords: Atomic orbital search algorithm, gray level co-occurrence matrix, markov chain random field, online product recommendation, self-attention generative adversarial capsule network, sentiment analysis
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: In a recent survey conducted in the year of 2022, it came to know that India contains around 50 percentages of its population is occupied by young people belonging to the age group of 25 and falls under a category of student. Guiding this young mass people in a right guidelines and strengthening the future of this students are a huge responsibility. The power of students are elder citizens of India such as their parent’s teachers, professors, entrepreneur etc. The only way to strengthen the future of the young students is through educating them. In order to analyze how effectively …the student can compete with the modern world and what kind of teaching methodology are needed to be adopted to each student, which is very important to every country peoples. Therefore, we are provided to be interpret the effective education to the children’s and students. To monitor student’s academic record there is a need of building a new model which can predict the performance and risk factor associated with the students for the next academic year. This is done by gathering the student’s previous academic record to analysis with different classifier techniques. The proposed work is aim to build a model, that can predict the student’s competency level and the risk factors or students are improve himself with all fields effort to both academic and non-academic activity. This proposed model also helps the parents, teachers, and educational institutes. This entire analysis is done by using different Machine Learning (ML) bifurcations algorithm. Also we aim to find the best classifier which can emerge with a highest predicting accuracy among all other classifiers to the above said problems. Show more
Keywords: Decision tree, random forest, support vector, logistic regression, classifier
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
Authors: Zhu, Yakun | Gong, Weiqiang | Lu, Xuesong | Wang, Haixian
Article Type: Research Article
Abstract: Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global …integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing. Show more
Keywords: Effective connectivity, EEG, mathematically gifted adolescents, information flow, graph theory
DOI: 10.3233/JIFS-223819
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9615-9626, 2023
Authors: Varish, Naushad | Hasan, Mohammad Kamrul | Khan, Asif | Zamani, Abu Taha | Ayyasamy, Vadivel | Islam, Shayla | Alam, Rizwan
Article Type: Research Article
Abstract: This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial …relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods. Show more
Keywords: Radar remote sensing image retrieval, adaptive tetrolet transform, gray level Co-occurrence matrix, prediction learning, statistical parameters
DOI: 10.3233/JIFS-224083
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9627-9650, 2023
Authors: Du, Baigang | Zha, Dahu | Guo, Jun | Yu, Xiaobing
Article Type: Research Article
Abstract: The water transmission and distribution process of the water supply pump station of the water purification plant plays a key role in the entire urban water supply system. When the requirements of water supply quantity and water pressure are satisfied, the reduction of operating energy consumption of the pump set and improvement of its service life are urgent problems. Therefore, to reduce the cost of water supply pump station, a mathematical model is established to minimize the energy consumption of pump group, the number of pump switches and the load balancing in this paper. In order to solve the pump …scheduling problem, a two-stage strategy based on genetic algorithm is proposed. In stage one, the frequency conversion ratio and the number of pumps needed to be turned on at the lowest energy consumption are calculated. In stage two, through the improved genetic algorithm and iterative way to reduce the number of pump switches and load balancing. Finally, a case study from a real waterworks in Suzhou, China is used to verify the validity of the proposed model. Numerical results reveal that the improved genetic algorithm outperforms the competing algorithms. In addition, a proper sensitivity analysis allows assessing the effects under different pump operating conditions. Show more
Keywords: Pump scheduling, load balancing, improved genetic algorithm
DOI: 10.3233/JIFS-224245
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9651-9669, 2023
Authors: Hashmi, Mohammad Farukh | Naik, Banoth Thulasya | Keskar, Avinash G.
Article Type: Research Article
Abstract: Computer vision algorithms based on deep learning have evolved to play a major role in sports analytics. Nevertheless, in sports like table tennis, detecting the ball is a challenge as the ball travels at a high velocity. However, the events in table tennis games can be detected and classified by obtaining the locations of the ball. Therefore, existing methodologies predict the trajectories of the ball but do not detect and classify the in-game events. This paper, therefore, proposes a ball detection and trajectory analysis (BDTA) approach to detect the location of the ball and predict the trajectory to classify events …in a table tennis game. The proposed methodology is composed of two parts: i) Scaled-YOLOv4 which can detect the precise position of the ball ii) Analysis of trajectory based on ball coordinates to detect and classify the events. The dataset was prepared and labeled as a ball after enhancing the frame resolution with a super-resolution technique to get the accurate position of the ball. The proposed approach demonstrates 97.8% precision and 98.1% f1-score in detecting the location of the ball and 97.47% precision and achieved 97.8% f-score in classifying in-game events. Show more
Keywords: Table tennis sport, ball detection, event classification, Scaled-YOLOv4, Trajectory analysis
DOI: 10.3233/JIFS-224300
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9671-9684, 2023
Authors: Huang, Juan | Zhang, Chaoren
Article Type: Research Article
Abstract: Generally speaking, the evaluation of mental health education (MHE) in colleges is an activity and process of evaluating the elements, processes and effects of MHE in schools by systematically collecting relevant information, following reasonable evaluation principles and applying specialized evaluation methods and techniques according to certain evaluation index systems and value judgment systems. The fundamental goal of MHE evaluation in colleges is to promote and regulate the scientific, healthy and smooth development of MHE in colleges and universities, improve the quality of MHE, promote the reform of MHE, build a good psychological atmosphere in colleges and universities, and effectively improve …the psychological quality and mental health of college students. The MHE evaluation of college students is looked as multiple attribute group decision-making (MAGDM). In this paper, the 2-tuple linguistic neutrosophic number cross-entropy (2TLNN-CE) method is defined based on the traditional cross-entropy and 2-tuple linguistic neutrosophic sets (2TLNSs). Then, 2TLNN-CE method is established for MAGDM. Finally, a numerical example for MHE evaluation of college students was given and some comparisons are also conducted to further illustrate advantages of the built method. Show more
Keywords: Multiple attribute group decisionmaking (MAGDM) problems, 2-tuple linguistic neutrosophic sets(2TLNSs), 2TLNN-CE method, MHE evaluation
DOI: 10.3233/JIFS-230572
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9685-9696, 2023
Authors: Shanmuga Priya, K. | Vasanthi, S.
Article Type: Research Article
Abstract: An emotion is a conscious logical response that varies for different situations in women’s life. These mental responses are caused by physiological, cognitive, and behavioral changes. Gender-based violence undermines the participation of women in decision-making, resulting in a decline in their quality of life. More accurate and automatic classification of women’s emotions can enhance human-computer interfaces and security in real time. There are some wearable technologies and mobile applications that claim to ensure the safety of women. However, they rely on limited social action and are ineffective at ensuring women’s safety when and where it is needed. In this work, …a novel CDB-LSTM network has been proposed to accurately classify the emotions of women in seven different classes. The electroencephalogram (EEG) offers non-radioactive methods of identifying emotions. Initially, the EEG signals are preprocessed and they are converted into images via Time-Frequency Representation (TPR). A smoothed pseudo-Wigner-Ville distribution (SPWVD) is employed to convert the EEG time-domain signals into input images. Consequently, these converted images are given as input to the Convolutional Deep Belief Network (CDBN) for extracting the most relevant features. Finally, Bi-directional LSTM is used for classifying the emotions of women into seven classes namely: happy, relax, sad, fear, anxiety, anger, and stress. The proposed CDB-LSTM network preserves the high accuracy range of 97.27% in the validation phase. The proposed CDB-LSTM network improves the overall accuracy by 6.20% 32.98% 6.85% and 3.30% better than CNN-LSTM, Multi-domain feature fusion model, GCNN-LSTM and CNN with SVM and DT respectively. Show more
Keywords: Women safety, EEG signals, emotion classification, smoothed pseudo-wigner-ville distribution, convolutional deep belief network, bi-directional LSTM
DOI: 10.3233/JIFS-221825
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9697-9707, 2023
Authors: Chitra, R. | Prabakaran, K.
Article Type: Research Article
Abstract: Accumulation of q ˜ -rung picture fuzzy information plays an essential part in decision-making situations. q ˜ -rung picture fuzzy sets can handle the uncertain information more precisely and flexibly because of the presence of parameter q ˜ . Also the Frank t-norm and t-conorm operations perform suitably for the data accumulation with the operational parameter. In this paper, we introduce q ˜ -Rung picture fuzzy Frank weighted averaging operator and q ˜ -rung picture …fuzzy Frank weighted geometric operator by extending q ˜ -rung orthopair fuzzy Frank arithmetic and geometric aggregation operators respectively. We establish an algorithm to address the tedious decision-making problems using these operators. Eventually, we discuss a multiple attribute decision-making problem to demonstrate the utility and efficacy of the proposed method. A comparison of existing methods is made to reveal the supremacy and benefits of our proposed method. Show more
Keywords: Accumulation, q-rung picture fuzzy, Frank weighted averaging, Frank weighted geometric, decision-making
DOI: 10.3233/JIFS-221889
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9709-9721, 2023
Authors: Nguyen, Tuan Anh
Article Type: Research Article
Abstract: This article studies the instability of automobiles when steering at high speeds. In this article, the model of spatial dynamics is used to simulate vehicle oscillation. Besides, the model of nonlinear double-track dynamics is also combined to determine the effects of the wheel when steering. To limit the instability when steering, the hydraulic stabilizer bar is suggested. The performance of the system depends on the previously designed controller. The FLC algorithm with two inputs is used to control the operation of the system. The membership function and fuzzy rules are determined based on the designer’s experience. The simulation is performed …by MATLAB software with three specific steering cases. In each case, the speed of the vehicle will be increased gradually. The results of the article show that the value of the roll angle is greatest in the third case, corresponding to the speed of v3 = 100 (km/h). If the vehicle does not have a stabilizer bar, the vehicle can roll over at any time. In contrast, when the active stabilizer bar was combined with the proposed FLC algorithm, the vehicle’s stability was significantly improved. The vehicle’s roll angle and the difference in vertical force at the wheels were also significantly reduced when using this algorithm for the stabilizer bar model. This result should be further verified through the experimental process. Show more
Keywords: Rollover, active stabilizer bar, nonlinear dynamics model, fuzzy control
DOI: 10.3233/JIFS-222780
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9723-9741, 2023
Authors: Huang, Shuangliu | Chen, Huazai
Article Type: Research Article
Abstract: As one of the teaching models that promote the orderly development of vocational education in China, the integration of industry and education has been recognized by all sectors of society in China’s many years of practice. In recent years, with the strong advocacy of the education sector in China, its development speed has been rapidly improved. Rural vocational education in China has also actively implemented and innovated the teaching mode of integration of industry and education, which has trained more excellent talents for agricultural development in various regions. The quality evaluation of industry-education integration for rural vocational education in the …perspective of rural revitalization is viewed as the multiple attribute group decision making (MAGDM). In this paper, the probabilistic linguistic Mixed Aggregation by Comprehensive Normalization Technique (PL-MACONT) method is built for MAGDM. At last, to verify the validity of the extended method, a numerical example to further account for quality evaluation of industry-education integration for rural vocational education in the perspective of rural revitalization is put into use. Show more
Keywords: Multiple attribute group decision making (MAGDM), Probabilistic linguistic term sets (PLTSs), Mixed Aggregation by Comprehensive Normalization Technique (MACONT), entropy weight method (EWM), quality evaluation of industry-education integration
DOI: 10.3233/JIFS-223856
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9743-9755, 2023
Authors: Wei, Chun | Shi, Haiyan | Liu, Baoliang | Zhang, Zhiqiang | Wen, Yanqing
Article Type: Research Article
Abstract: A system undergoes a failure process in which the internal degradation and external loads are independent and compete with each other. In the reliability model of competitive failure, the threshold of failure is often a dynamic process that changes with time. Based on this, this paper constructs a model in which the failure threshold is an uncertain Liu process and applies it to the competitive failure reliability process, where soft and hard failure thresholds are modeled by different Liu processes. In the absence of a large amount of failure data, uncertainty theory is used as a tool to analyze the …belief reliability and mean time to failure of the system. Taking gas-insulated substation (GIS) as an example, the effect of parameters on belief reliability is analyzed, and the reliability under Liu process threshold and constants threshold is compared, which shows the validity of the prosed model. Show more
Keywords: Belief reliability, uncertain failure threshold, uncertain liu process, uncertainty theory
DOI: 10.3233/JIFS-224057
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9757-9767, 2023
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