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

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

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: This article has been retracted.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

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

Authors: Kalaivani, K. | Kshirsagarr, Pravin R. | Sirisha Devi, J. | Bandela, Surekha Reddy | Colak, Ilhami | Nageswara Rao, J. | Rajaram, A.

Article Type: Research Article

Abstract: The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much …training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals. Show more

Keywords: Electrocardiography (ECG), electroencephalography (EEG), electromyographic (EMG), deeplearning techniques, prediction, heart attack, emotion recognition, neuromuscular disease, R-CNN

DOI: 10.3233/JIFS-230399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9769-9782, 2023

Authors: Kianifar, Mohammad Ali | Motallebi, Hassan | Bardsiri, Vahid Khatibi

Article Type: Research Article

Abstract: Dynamic Classifier Selection (DCS) techniques aim to select the most competent classifiers from an ensemble per test sample. For each test sample, only a subset of the most competent classifiers is used to estimate its target value. The performance of the DCS highly depends on how we define the local region of competence, which is a local region in the feature space around the test sample. In this paper, we propose a new definition of region of competence based on a new proximity measure. We exploit the observed similarities between traffic profiles at different links, days and hours to obtain …similarities between different values. Furthermore, long-term traffic pattern prediction is a complex problem and most of the traffic prediction literature are based on time-series and regression approaches and their prediction time is limited to next few hours or days. We tackle the long-term traffic pattern prediction as a classification of discretized traffic indicators to improve the accuracy of urban traffic pattern forecasting of next weeks by using DCS. We also employ two different link clustering methods, for grouping traffic links. For each cluster, we train a dynamic classifier system for predicting the traffic variables (flow, speed and journey time). Our results on strategic road network data shows that the proposed method outperforms the existing ensemble and baseline models in long-term traffic prediction. Show more

Keywords: Long-term traffic prediction, monthly SRN data set, traffic link clustering, dynamic classifier selection, region of competence

DOI: 10.3233/JIFS-220759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9783-9797, 2023

Authors: Ramesh, Pinapilli | Yadaiah, Narri

Article Type: Research Article

Abstract: This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such …as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600. Show more

Keywords: Type-2 fuzzy logic controller, brain emotional learning, adaptive memberships functions

DOI: 10.3233/JIFS-222143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9799-9820, 2023

Authors: Wang, Yuan | Yu, Xiaobing | Wang, Xuming

Article Type: Research Article

Abstract: Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy …of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train. Show more

Keywords: Multi-verse optimizer, queuing searching algorithm, metropolis rule

DOI: 10.3233/JIFS-223369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9821-9845, 2023

Authors: Zhu, Xingchen | Wu, Xiaohong | Wu, Bin | Zhou, Haoxiang

Article Type: Research Article

Abstract: The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are …run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties. Show more

Keywords: Fuzzy clustering, FCM, PCM, Euclidean distance, distance function

DOI: 10.3233/JIFS-223576

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9847-9862, 2023

Authors: Bolourchi, Pouya | Ghasemzadeh, Aman

Article Type: Research Article

Abstract: In bioinformatics studies, many modeling tasks are characterized by high dimensionality, leading to the widespread use of feature selection techniques to reduce dimensionality. There are a multitude of feature selection techniques that have been proposed in the literature, each relying on a single measurement method to select candidate features. This has an impact on the classification performance. To address this issue, we propose a majority voting method that uses five different feature ranking techniques: entropy score, Pearson’s correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and t -test. By using a majority voting approach, only the features that appear in …all five ranking methods are selected. This selection process has three key advantages over traditional techniques. Firstly, it is independent of any particular feature ranking method. Secondly, the feature space dimension is significantly reduced compared to other ranking methods. Finally, the performance is improved as the most discriminatory and informative features are selected via the majority voting process. The performance of the proposed method was evaluated using an SVM, and the results were assessed using accuracy, sensitivity, specificity, and AUC on various biomedical datasets. The results demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods in the literature. Show more

Keywords: Classification, correlation coefficient, feature selection, feature ranking, gene data, majority

DOI: 10.3233/JIFS-224029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9863-9877, 2023

Authors: Mecheri, Karima | Klai, Sihem | Souici-Meslati, Labiba

Article Type: Research Article

Abstract: Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web …services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques. Show more

Keywords: Deep learning, recommendation systems, web services, mashup, quality of service, performance evaluation metrics

DOI: 10.3233/JIFS-224565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9879-9899, 2023

Authors: Qu, Ying | Chen, Hong

Article Type: Research Article

Abstract: During an emergency, the negative Internet public opinion in colleges and universities, especially the negative endogenous public opinion, will have a serious impact on the reputation of colleges and universities. It is of great significance to find out the negative influencing factors of endogenous public opinion and explore the mechanism of public opinion dissemination for resolving the crisis of public opinion in universities. The existing research does not distinguish the endogenous Internet public opinion in colleges and universities from the general Internet public opinion in colleges and universities, and the SIR model adopted fails to fully reflect the difference between …students and other dissemination subjects of endogenous public opinion in campus. In addition, various research methods and models currently used focus on the static expression of dissemination results, and the explanation of results is insufficient. The reason is that they do not well express the dynamic interaction mechanism between influencing factors and the dynamic conversion rate between roles. In this study, based on the improved infectious disease model and system dynamics theory, AnyLogic software is used to simulate the improved SNIDR model of infectious disease, to analyze the sensitivity of school supervision, school intervention, school response time and information transparency and to study the dynamic conversion rate between different roles. The SNIDR model effectively simulates the process of endogenous public opinion dissemination in colleges and universities after emergencies. The results show that, what has the greatest impact on the dissemination of public opinion is the school’s supervision and intervention efforts, which can suppress the dissemination from the source. Information transparency is an auxiliary variable and cannot function independently. During the dissemination period, the timelier the school responds, the faster the spreaders will drop to zero, and the better it will be to control the secondary dissemination of public opinion. Show more

Keywords: SNIDR model, governance strategies, internet public opinion, dissemination mechanism, emergency

DOI: 10.3233/JIFS-230002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9901-9917, 2023

Authors: Liu, Biyu | Chen, Ting | Yang, Haidong | Segerstedt, Anders

Article Type: Research Article

Abstract: Suppliers significantly affect the effectiveness of sustainable supply chain management. Hence, it is extremely important to evaluate and select suppliers scientifically and objectively. Based on the theory of triple bottom line (economic, social, and environmental dimension) and a balanced scorecard, a measureable supplier evaluation framework in a sustainable supply chain is first formulated. Second, to reduce the defects of the single weight method, the subjective and objective weights of evaluation indicators are determined by combining the fuzzy best-worst method (BWM) and the entropy method, and then the combination weights are obtained through linear weighting. Third, the grey relational technique for …order performance by similarity to ideal solution (TOPSIS) method is further adopted to evaluate and rank the suppliers. Finally, a case study illustrates and demonstrates the availability of the proposed supplier evaluation index system and evaluation method. Subsequently, some suggestions are proposed according to the results. Show more

Keywords: Sustainable supply chain management, supplier evaluation, the fuzzy BWM, grey relational, TOPSIS

DOI: 10.3233/JIFS-212996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9919-9932, 2023

Authors: Gao, Wang | Ni, Mingyuan | Deng, Hongtao | Zhu, Xun | Zeng, Peng | Hu, Xi

Article Type: Research Article

Abstract:  As people increasingly use social media to read news, fake news has become a major problem for the public and government. One of the main challenges in fake news detection is how to identify them in the early stage of propagation. Another challenge is that detection model training requires large amounts of labeled data, which are often unavailable or expensive to acquire. To address these challenges, we propose a novel Fake News Detection model based on Prompt Tuning (FNDPT). FNDPT first designs a prompt-based template for early fake news detection. This mechanism incorporates contextual information into textual content and extracts …relevant knowledge from pre-trained language models. Furthermore, our model utilizes prompt-based tuning to enhance the performance in a few-shot setting. Experimental results on two real-world datasets verify the effectiveness of FNDPT. Show more

Keywords: Fake news detection, few-shot, prompt-based tuning, pre-trained language model

DOI: 10.3233/JIFS-221647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9933-9942, 2023