Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Li, Shugang | Lu, Hanyu | Dou, Qian | Wang, Ru | Yu, Zhaoxu

Article Type: Research Article

Abstract: In the social business platform, continuous marketing to consumers can fully explore the consumer purchasing potential. However, since consumers can be influenced by their social friends, their tastes often change, which resulting in the cold start problem of familiar users (CSPFU), and the traditional product recommendation methods are difficult to achieve satisfactory results because they focus on identifying the preferable new products instead of boring familiar products. Therefore, a consumer multi-stage compensation product evaluation model (CMCPEM) based on the multidimensional correlation of products and customers to identify the products that consumers may feel tired is proposed. Specifically, the multidimensional correlation …indexes are firstly proposed to depict the preferences of the consumer for the target product to be identified, other consumers who have social contagion and structural equivalence relationships with the consumer and other consumers of homogeneous products. After the direct linear, non-linear and indirect fusion of these multidimensional correlation indexes, the compensation indexes (CIs) are proposed to comprehensively describe the first stage of product evaluation process of consumers. Then, J test in the non-nested model is used to screen out the non-nested CIs that consumers focus on. Finally, in the third stage, the final decision result is given by comprehensively considering CIs that consumers focus on and the indexes that represent consumers’ favorite. Experiment results on YELP data confirm the effectiveness of CMCPEM in successfully launching the continuous marketing campaign. Show more

Keywords: Social business network, consumer boring products, J test, non-nested model, continuous marketing

DOI: 10.3233/JIFS-201980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5929-5941, 2021

Authors: Hamed, Nadir O. | Samak, Ahmed H. | Ahmad, Mostafa A.

Article Type: Research Article

Abstract: The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function …to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using “Spambase” and “SpamAssassin.” Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification. Show more

Keywords: Feature selection, e-mail, e-mail classification, differential evolution (DE), support vector machine (SVM)

DOI: 10.3233/JIFS-201990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5943-5955, 2021

Authors: Zhou, Lintao | Wu, Qinge | Chen, Hu | Hu, Tao

Article Type: Research Article

Abstract: Accurately diagnosing power transformer faults is critical to improving the operational reliability of power systems. Although some researchers have made great efforts to improve the accuracy of transformer fault diagnosis, accurate diagnosis of multiple faults is still a difficult problem. In order to improve the accuracy of transformer multiple faults diagnosis, a multiple fault diagnosis method based on interval fuzzy probability is proposed. Different from the previous methods which provide single-value probability, this method use probability interval to represent the occurrence degree of various possible faults, which can objectively predict the potential faults that occurring in a transformer and provide …a more reasonable explanation for the diagnosis results. In the proposed method, the interval fuzzy set is used to describe the evaluation of state variables and the interval fuzzy probability model based on interval weighted average is applied to integrate the fault information. The representative matrix of fault types based on fuzzy preference relationship is established to estimate the relative importance of each gas in the dissolved gases. The proposed method can provide the probability of probable faults in transformer, help engineers quickly determine the type and location of faults, and improve the accuracy of diagnosis and maintenance efficiency of transformer. The effectiveness of the method is verified with case studies. Show more

Keywords: Multiple faults diagnosis, Power transformer, Interval fuzzy probability, Interval weighted average

DOI: 10.3233/JIFS-202083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5957-5968, 2021

Authors: Nivedhitha, V. | Thirumurugan, P. | Gopi Saminathan, A. | Eswaramoorthy, V.

Article Type: Research Article

Abstract: A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this …paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced. Show more

Keywords: Harris’s hawk optimization, fuzzification, cluster head election, energy efficient routing

DOI: 10.3233/JIFS-202098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5969-5984, 2021

Authors: Wang, Y. | Chu, Y.M. | Khan, Y.A. | Khan, Z.Y. | Liu, Q. | Malik, M.Y. | Abbas, S.Z.

Article Type: Research Article

Abstract: This paper addressed the prediction of heart sicknesses from hazard elements through a decision-making tree. We introduced the facts mining technique in public fitness to extract high-degree knowledge from raw data, which facilitates predicting heart diseases from risk factors and their prevention. The existing work intends to introduce a new risk element in heart diseases using novel data mining strategies. Latest actual international affected person’s information (e.g., smoking, area of residence, age, weight, blood stress, chest pain, low-density lipoproteins (LDL), high-density lipoproteins (HDL), block arteries became accrued by way of the use of questionnaire through direct interview technique from patients. …Novel two-variable decision trees are constructed for coronary heart illness records primarily based on chance factors and ranking of risk elements. The results show a correct prediction of cardiovascular disease (CVD) from the risk factor if records on chance factors are available as direct results of this study, tobacco, loss of physical exercise, and weight-reduction plan play a vital role in predicting heart diseases, which is the most important reason for mortality in developing countries, especially in my country. Show more

Keywords: Machine learning, heart diseases, prevention, decision tree, risk factors, prediction, hybrid technique, low-density lipoproteins (LDL), high-density lipoproteins (HDL)

DOI: 10.3233/JIFS-202226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5985-6002, 2021

Authors: Liu, Jianyong | Cai, Yanhua | Zhang, Qinjian | Zhang, Haifeng | He, Hu | Gao, Xiaodong | Ding, Liantong

Article Type: Research Article

Abstract: A method that combines temperature field detection, adaptive FCM (Fuzzy c-means) clustering algorithm and RBF (Radial basis function network) neural network model is proposed. This method is used to analyze the thermal error of the spindle reference point of the tauren EDM (Electro-discharge machining) machine tool. The thermal imager is used to obtain the temperature field distribution of the machine tool while the machine tool simulates actual operating conditions. Based on this, the arrangement of temperature measurement points is determined, and the temperature data of the corresponding measurement points are got by temperature sensors. In actual engineering, too many temperature …measurement points can cause problems such as too high cost, too much wiring. And normal processing can be affected. In order to establish that the thermal error prediction model of the machine tool spindle reference point can meet the actual engineering needs, the adaptive FCM clustering algorithm is used to optimize the temperature measurement points. While collecting the temperatures of the optimized temperature measurement points, the displacement sensors are used to detect the thermal deformation data in X, Y, Z directions of the spindle reference position. Based on the test data, the RBF neural network thermal errors prediction model of the machine tool spindle reference point is established. Then, the test results are used to verify the accuracy of the thermal errors analysis model. The research method in this paper provides a system solution for thermal error analysis of the tauren EDM machine tool. And this builds a foundation for real-time compensation of the machine tool’s thermal errors. Show more

Keywords: The tauren EDM machine tool, adaptive fuzzy clustering algorithm, RBF neural network model, thermal errors

DOI: 10.3233/JIFS-202241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6003-6014, 2021

Authors: Wang, Xiangling

Article Type: Research Article

Abstract: The existing greenhouse monitoring algorithm has a long delay time, so it is unable to carry out effective remote greenhouse monitoring, therefore, a new wireless monitoring algorithm based on the fuzzy control technolog was put forward, which was able to remotely monitor the greenhouse temperature, humidity and illumination data in real time. Firstly, the overall framework of greenhouse monitoring algorithm was built, including fuzzy clustering algorithm and sensing layer devices. Secondly, the temperature-humidity sensors and light sensitivity sensors in the sensing layer devices were used to deeply mine and optimize the parameters of temperature, humidity and light intensity in current …greenhouse, so as to ensure the stability of subsequent transmission. Meanwhile, the corresponding perceptual recognition layer and broadband access method were designed, and GPRS technology was used to feed back the data information to the monitoring data layer through temperature-humidity sensors and light sensitivity sensors. Moreover, UDP protocol was taken as the data core transmission protocol, and the adaptive protection design algorithm was proposed to ensure the most reasonable transmission of monitoring data, get the current monitoring data of temperature, humidity and illuminance. The experimental results show that the maximum delay time of the algorithm is 46 s, which is far lower than the traditional algorithm, and the delay time of temperature monitoring is also lower than the traditional algorithm. It is results show that the response delay of remote intelligent greenhouse monitoring algorithm is low and the overall monitoring effect is ideal. The purpose of monitoring temperature, humidity and illumination can be achieved. Show more

Keywords: Fuzzy control, intelligent greenhouse, wireless monitoring, temperature and humidity, illumination

DOI: 10.3233/JIFS-202300

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6015-6023, 2021

Authors: Zhao, Yifan | Li, Kai

Article Type: Research Article

Abstract: In the recent years, several new construction methods of fuzzy implications have been proposed. However, these construction methods actually care about that the new implication could preserve more properties. In this paper, we introduce a new method for constructing fuzzy implications based on an aggregation function with F  (1,  0) =1, a fuzzy implication I and a non-decreasing function φ , called FI φ -construction. Specifically, some logical properties of fuzzy implications preserved by this construction are studied. Moreover, it is studied how to use the FI φ -construction to produce a new implication satisfying a specific property. Furthermore, we …produce two new subclasses of fuzzy implications such as UI φ -implications and G p I φ -implications by this method and discuss some additional properties. Finally, we provide a way to generate fuzzy subsethood measures by means of FI φ -implications. Show more

Keywords: Fuzzy implication, aggregation function, uninorm, nullnorm, fuzzy subsethood measure

DOI: 10.3233/JIFS-202385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6025-6038, 2021

Authors: Lu, Zhen-Yu | Wang, Xiao-Kang | Wang, Jian-Qiang | Cheng, Peng-Fei | Li, Lin

Article Type: Research Article

Abstract: The wireless propagation model is important for accurate 5 G network deployment. However, the traditional wireless propagation model is faced with the problems of limited application scenarios, unstable prediction results and high marginal cost of improving accuracy. In order to solve these problems, this paper constructs new features from the original data from different angles, and uses the random forest model to select the core features, which are used to train the fusion model based on the linear weighted summation of regression models such as KNN, LightGBM, and Bagging. After training, the final fusion model is obtained, it solves the …problems faced by traditional wireless propagation models. The results and analysis show that the fusion model outperforms the traditional wireless propagation models and the single models that constitutes the fusion model in terms of prediction accuracy and stability, and is not limited by scenarios and easy to deploy. Show more

Keywords: 5 G, wireless propagation model, feature engineering, fusion model

DOI: 10.3233/JIFS-202388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6039-6052, 2021

Authors: Malik, Manisha | Gupta, S. K. | Ahmad, I.

Article Type: Research Article

Abstract: In many real-world problems, one may encounter uncertainty in the input data. The fuzzy set theory fits well to handle such situations. However, it is not always possible to determine with full satisfaction the membership and non-membership degrees associated with an element of the fuzzy set. The intuitionistic fuzzy sets play a key role in dealing with the hesitation factor along-with the uncertainty involved in the problem and hence, provides more flexibility in the decision-making process. In this article, we introduce a new ordering on the set of intuitionistic fuzzy numbers and propose a simple approach for solving the fully …intuitionistic fuzzy linear programming problems with mixed constraints and unrestricted variables where the parameters and decision variables of the problem are represented by intuitionistic fuzzy numbers. The proposed method converts the problem into a crisp non-linear programming problem and further finds the intuitionistic fuzzy optimal solution to the problem. Some of the key significance of the proposed study are also pointed out along-with the limitations of the existing studies. The approach is illustrated step-by-step with the help of a numerical example and further, a production planning problem is also demonstrated to show the applicability of the study in practical situations. Finally, the efficiency of the proposed algorithm is analyzed with the existing studies based on various computational parameters. Show more

Keywords: Intuitionistic fuzzy linear programming problem, accuracy function, crisp non-linear programming problem, triangular intuitionistic fuzzy number

DOI: 10.3233/JIFS-202398

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6053-6066, 2021