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

Authors: Zheng, Qinghe | Yang, Mingqiang | Wang, Deqiang | Tian, Xinyu | Su, Huake

Article Type: Research Article

Abstract: Throughout the wireless communication network planning process, efficient signal reception power estimation is of great significance for accurate 5 G network deployment. The wireless propagation model predicts the radio wave propagation characteristics within the target communication coverage area, making it possible to estimate cell coverage, inter-cell network interference, and communication rates, etc. In this paper, we develop a series of features by considering various factors in the signal transmission process, including the shadow coefficient, absorption coefficient in test area and base station area, distance attenuation coefficient, density, azimuth angle, relative height and ground feature index coefficient. Then we design a …quantile regression neural network to predict reference signal receiving power (RSRP) by feeding the above features. The network structure is specially constructed to be generalized on various complex real environments. To prove the effectiveness of proposed features and deep learning model, extensive comparative ablation experiments are applied. Finally, we have achieved the precision rate (PR), recall rate (RR), and inadequate coverage recognition rate (PCRR) of 84.3%, 78.4%, and 81.2% on the public dataset, respectively. The comparison with a series of state-of-the-art machine learning methods illustrates the superiority of the proposed method. Show more

Keywords: RSRP prediction, neural network, correlation analysis, shadowing effect, information entropy

DOI: 10.3233/JIFS-202430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6067-6078, 2021

Authors: Revathy, K. | Thenmozhi, K. | Praveenkumar, Padmapriya | Amirtharajanr, Rengarajan

Article Type: Research Article

Abstract: In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; …(ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility. Show more

Keywords: Spectrum, cognitive, fuzzy, femtocell

DOI: 10.3233/JIFS-202540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6079-6088, 2021

Authors: Mi, Xiao | Wang, Xue-ping

Article Type: Research Article

Abstract: This paper investigates minimal solutions of fuzzy relation inequalities with addition-min composition. It first shows the conditions that an element is a minimal solution of the inequalities, and presents the conditions that the inequalities have a unique minimal solution. It then proves that every solution of the inequalities has a minimal one and proposes an algorithm to searching for a minimal solution with computational complexity O  (n 2 ) where n is the number of unknown variables of the inequalities. This paper finally describes all minimal solutions of the inequalities.

Keywords: Fuzzy relation inequality, addition-min composition, minimal solution, algorithm

DOI: 10.3233/JIFS-202590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6089-6095, 2021

Authors: Shi, Jiawen | Li, Hong | Wang, Chiyu | Pang, Zhicheng | Zhou, Jiale

Article Type: Research Article

Abstract: Short text matching is one of the fundamental technologies in natural language processing. In previous studies, most of the text matching networks are initially designed for English text. The common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, this method often results in word segmentation errors. Chinese short text matching faces the challenges of constructing effective features and understanding the semantic relationship between two sentences. In this work, we propose a novel lexicon-based pseudo-siamese model (CL2 N), which can fully mine the information expressed in Chinese text. Instead of …utilizing a character-sequence or a single word-sequence, CL2 N augments the text representation with multi-granularity information in characters and lexicons. Additionally, it integrates sentence-level features through single-sentence features as well as interactive features. Experimental studies on two Chinese text matching datasets show that our model has better performance than the state-of-the-art short text matching models, and the proposed method can solve the error propagation problem of Chinese word segmentation. Particularly, the incorporation of single-sentence features and interactive features allows the network to capture the contextual semantics and co-attentive lexical information, which contributes to our best result. Show more

Keywords: Short text matching, Chinese text, semantic relationship, pseudo-siamese model, multi-granularity information

DOI: 10.3233/JIFS-202592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6097-6109, 2021

Authors: Tilli, Tuomo | Espinosa-Leal, Leonardo

Article Type: Research Article

Abstract: Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on …two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs. Show more

Keywords: Bid shading, bid optimization, multi-armed bandits, reinforcement learning

DOI: 10.3233/JIFS-202665

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6111-6125, 2021

Authors: Surekha, S. | Zia Ur Rahman, Md.

Article Type: Research Article

Abstract: In medical telemetry networks, cognitive radio technology is mostly used to avoid licensed spectrum underutilization and by providing access to unlicensed spectrum users without causing interference to primary users, this concept is widely used in development of smart hospitals and smart cities. In medical telemetry networks frequency spectrum concept is used for providing treatment to patients who are far away from hospitals. In cognitive radios, spectrum sensing concept is used in which energy detection method is mostly used because it is simple to implement. While measuring health care environments using cognitive radios probability detection, false alarm probability and threshold parameters …are calculated. In this paper for identifying spectrum holes in spectrum sensing using energy detection, distributed diffusion non-negative least mean square algorithm is proposed. It gives better results compared to energy detection concept alone in terms of probability detection converged earlier. If number of nodes are increasing probability detection is decreased from one and move towards left and its SNR is around 1.5-2 dB with proposed method. Hence simulation results give better results in terms of sensing ability while measuring patient condition. Show more

Keywords: Cognitive sensors, energy detection, spectrum holes, spectrum sensing, medical telemetry

DOI: 10.3233/JIFS-202673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6127-6136, 2021

Authors: Xiong, Pingping | Xiao, Lushuang | Liu, Yuchun | Yang, Zhuo | Zhou, Yifan | Cao, Shuren

Article Type: Research Article

Abstract: Faced with serious growing global warming problem, it is important to predict carbon emissions. As there are a lot of factors affecting carbon emissions, a novel multi-variable grey model (GM(1,N) model) based on linear time-varying parameters discrete grey model (TDGM(1,N)) has been established. In this model, linear time-varying function is introduced into the traditional model, and dynamic optimization of fixed parameters which can only be used for static analysis is carried out. In order to prove the applicability and effectiveness of the model, this paper compared the model with the traditional model and simulated the carbon emissions of Anhui Province …from 2005 to 2015. Carbon emissions in the next two years are also predicted. The results show that the TDGM(1,N) model has better simulation effect and higher prediction accuracy than the traditional GM(1,N) model and the multiple regression model(MRM) in practical application of carbon emissions prediction. In addition, the novel model of this paper is also used to predict the carbon emissions in 2018–2020 of Anhui Province. Show more

Keywords: Linear time-varying parameters, grey system theory, multi-variable model, carbon emissions, forecasting

DOI: 10.3233/JIFS-202711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6137-6148, 2021

Authors: Pan, Xingguang | Wang, Lin | Huang, Chengquan | Wang, Shitong | Chen, Haiqing

Article Type: Research Article

Abstract: In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order …to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented. Show more

Keywords: Feature weight learning, fuzzy c-means, priori knowledge, gradient descent

DOI: 10.3233/JIFS-202779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6149-6167, 2021

Authors: Yanjun, Xiao | Yameng, Zhang | Nan, Gao | Kai, Peng | Wei, Zhou | Weiling, Liu

Article Type: Research Article

Abstract: In the current industrial production process, waste heat of low quality is seriously wasted. In order to effectively recover low-quality waste heat, the research group developed a small energy conversion device –Roots power machine. On this basis, the research group designed a low-quality waste heat efficient utilization system with the equipment as the core and successfully applied it to low-quality waste heat recovery. However, in the actual operation process, the system can not run stably due to the occasional fluctuation of the input gas source. In view of this, after the study of waste heat recovery system, the fluctuation of …gas source can be controlled by different grades according to the degree of change. Fuzzy rules also divide variables into different grades to solve problems, and fuzzy control can convert continuous changes of airflow into discrete changes, which greatly reduces the complexity of the control system. Therefore, the research group proposed a control strategy based on fuzzy PID. The simulation results show that the adjustment time of fuzzy PID is within 7 s, and the adjustment effect is obviously better than that of traditional PID. The experimental results show that the speed deviation under the condition of air source fluctuation is within the speed fluctuation rate (±5%), and the speed deviation under the condition of sudden disturbance load is within the steady speed adjustment rate (±3.5%), both of which meet the requirements of indexes. Therefore, the fuzzy PID control strategy can further improve the stability of output speed, reduce airflow pulsation, and provide the possibility for grid-connected power generation. Show more

Keywords: Efficient utilization, fuzzy PID, low-quality waste heat, roots power machine, variable coupling

DOI: 10.3233/JIFS-202784

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6169-6179, 2021

Authors: Zhao, Yun-Tao | Gan, Lei | Li, Wei-Gang | Liu, Ao

Article Type: Research Article

Abstract: The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to …solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces. Show more

Keywords: Spot welding robot, multi-objective grey wolf optimization algorithm, density estimation, path planning

DOI: 10.3233/JIFS-202810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6181-6189, 2021

Authors: Raipurkar, Abhijeet R. | Chandak, Manoj B.

Article Type: Research Article

Abstract: A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To …improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique. Show more

Keywords: Simulated annealing, EAQO-ESA, materialized view selection, OLAP queries

DOI: 10.3233/JIFS-202821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6191-6205, 2021

Authors: Alsayadi, Hamzah A. | Abdelhamid, Abdelaziz A. | Hegazy, Islam | Fayed, Zaki T.

Article Type: Research Article

Abstract: Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, …the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%. Show more

Keywords: Arabic speech recognition, Arabic diacritics, End-to-End deep learning, CNN-LSTM

DOI: 10.3233/JIFS-202841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6207-6219, 2021

Authors: Wei, Yanxia | Wang, Qinghai

Article Type: Research Article

Abstract: Compared to hesitant fuzzy sets and intuitionistic fuzzy sets, dual hesitant fuzzy sets can model problems in the real world more comprehensively. Dual hesitant fuzzy sets explicitly show a set of membership degrees and a set of non-membership degrees, which also imply a set of important data: hesitant degrees.The traditional definition of distance between dual hesitant fuzzy sets only considers membership degree and non-membership degree, but hesitant degree should also be taken into account. To this end, using these three important data sets (membership degree, non-membership degree and hesitant degree), we first propose a variety of new distance measurements (the …generalized normalized distance, generalized normalized Hausdorff distance and generalized normalized hybrid distance) for dual hesitant fuzzy sets in this paper, based on which the corresponding similarity measurements can be obtained. In these distance definitions, membership degree, non-membership-degree and hesitant degree are of equal importance. Second, we propose a clustering algorithm by using these distances in dual hesitant fuzzy information system. Finally, a numerical example is used to illustrate the performance and effectiveness of the clustering algorithm. Accordingly, the results of clustering in dual hesitant fuzzy information system are compared using the distance measurements mentioned in the paper, which verifies the utility and advantage of our proposed distances. Our work provides a new way to improve the performance of clustering algorithms in dual hesitant fuzzy information systems. Show more

Keywords: Dual hesitant fuzzy set, distance measures, similarity measures, clustering algorithm

DOI: 10.3233/JIFS-202846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6221-6232, 2021

Authors: Al Tahan, Madeline | Hoskova-Mayerova, Sarka | Davvaz, Bijan

Article Type: Research Article

Abstract: In recent years, fuzzy multisets have become a subject of great interest for researchers and have been widely applied to algebraic structures including groups, rings, and many other algebraic structures. In this paper, we introduce the algebraic structure of fuzzy multisets as fuzzy multi-subnear rings (multi-ideals) of near rings. In this regard, we define different operations on fuzzy multi-ideals of near rings and we generalize some results known for fuzzy ideals of near rings to fuzzy multi-ideals of near rings.

Keywords: Near ring, multiset, fuzzy multiset, fuzzy multi-ideal, fuzzy multi-subnear ring

DOI: 10.3233/JIFS-202914

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6233-6243, 2021

Authors: Park, Choonkil | Ashraf, Shahzaib | Rehman, Noor | Abdullah, Saleem | Aslam, Muhammad

Article Type: Research Article

Abstract: As a generalization of Pythagorean fuzzy sets and picture fuzzy sets, spherical fuzzy sets provide decision makers more flexible space in expressing their opinions. Preference relations have received widespread acceptance as an efficient tool in representing decision makers’ preference over alternatives in the decision-making process. In this paper, some new preference relations are investigated based on the spherical fuzzy sets. Firstly, the deficiency of the existing operating laws is elaborated in detail and three cases are described to identify the accuracy of the proposed operating laws in the context of t-spherical fuzzy environment. Also, a novel score function is proposed …to obtain the consistent value in ranking of the alternatives. The backbone of this research, t-spherical fuzzy preference relation, consistent t-spherical fuzzy preference relations, incomplete t-spherical fuzzy preference relations, consistent incomplete t-spherical fuzzy preference relations, and acceptable incomplete t-spherical fuzzy preference relations are established. Additionally, some ranking and selection algorithms are established using the proposed novel score function and preference relations to tackle the uncertainty in real-life decision-making problems. Finally, evaluation of the product quality of the online shopping platform problem is demonstrated to show the applicability and reliability of proposed technique. Show more

Keywords: Spherical fuzzy Sets, t-spherical fuzzy set, Improved operational laws, Improved score function, preference relations, incomplete preference relations.

DOI: 10.3233/JIFS-202930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6245-6262, 2021

Authors: Dong, Shi | Zhou, Wengang

Article Type: Research Article

Abstract: Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show …that our method outperforms other methods on performance. Show more

Keywords: Influential nodes, complex networks, K-shell, page rank

DOI: 10.3233/JIFS-202943

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6263-6271, 2021

Authors: Sheng, JinFang | Zuo, Huaiyu | Wang, Bin | Li, Qiong

Article Type: Research Article

Abstract:  In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network …nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high. Show more

Keywords: Complex network, community detection, network embedding, density clustering

DOI: 10.3233/JIFS-202961

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6273-6284, 2021

Authors: Du, Yuqin | Ren, Weijia | Du, Yuhong | Hou, Fujun

Article Type: Research Article

Abstract: A Hamacher operator in a q-rung orthopair trapezoidal fuzzy linguistic environment is studied based on the definition of the q-rung orthopair fuzzy set and the Hamacher aggregation operator. First, we define a new fuzzy variable called q-rung orthopair trapezoidal fuzzy linguistic sets, and the operational laws, score function, accuracy function, comparison rules, and distance measures of the IVPFLVS are defined. Second, based on the Hamacher operator and the q-rung orthopair trapezoidal fuzzy linguistic sets, we propose several q-rung trapezoidal fuzzy linguistic Hamacher operator information aggregation operators, such as the generalized q-rung orthopair trapezoidal fuzzy linguistic Hamacher weighted averaging (q-GROTrFLHWA) operator, …and the generalized q-rung orthopair trapezoidal fuzzy linguistic Hamacher weighted geometric (q-GROTrFLHWG) operator. Third, some desirable properties of the correlation operators, such as idempotency, boundedness, and monotonicity are discussed. Finally, there are two group decision schemes based on q-rung orthopair trapezoidal fuzzy information with known attribute weights. The decision-making scheme is applied to the evaluation of school teaching quality, and the practicability and effectiveness of the scheme are demonstrated by different methods. Show more

Keywords: The q-rung trapezoidal fuzzy linguistic set, multi-attribute decision making, Hamacher operator, application

DOI: 10.3233/JIFS-210056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6285-6302, 2021

Authors: Xing, Shixiong | Chen, Guohua | Yu, Guoming | Chen, Xiaolan | Sun, Chuan

Article Type: Research Article

Abstract: According to the characteristics of NC milling, an approach for optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm is proposed. Taking the carbon emission and processing time as the objectives, the cutting rate, feed per tooth, and cutting width as the optimization variables. A multi-objective optimization model of NC milling parameters is established. An non-dominated sorting gravity search algorithm (NSGSA) is used to solve the multi-objective model, and the position update backoff operation is introduced. Finally, taking NC machining process as an example, the multi-objective optimization results and the single objective optimization results …are compared respectively, the actual data show that when the optimization objective is high efficiency and low carbon, the processing time and carbon emissions are 173 and 192 respectively. The comparison results show that the combination of processing parameters obtained by multi-objective optimization is the best, the optimal parameter combination obtained by NSGSA algorithm is verified by grey correlation analysis, and the grey correlation degree of the optimal solution set is 0.81, which is the largest in all solution sets. This approach can help the decision-makers flexibly select the corresponding milling parameters, and provide decision-makers with flexible selection decisions suitable for various scenarios. Show more

Keywords: NC milling, multi-objective model, milling parameter optimization, NSGSA, Grey relational analysis

DOI: 10.3233/JIFS-210059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6303-6321, 2021

Authors: Guan, Li | Zhang, Jinping | Zhou, Jieming

Article Type: Research Article

Abstract: This work proposes the concept of uncorrelation for fuzzy random variables, which is weaker than independence. For the sequence of uncorrelated fuzzy random variables, weak and strong laws of large numbers are studied under the uniform Hausdorff metric d H ∞ . The results generalize the law of large numbers for independent fuzzy random variables.

Keywords: Fuzzy random variable, uncorrelated, law of large numbers

DOI: 10.3233/JIFS-210099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6323-6330, 2021

Authors: Qin, Bin

Article Type: Research Article

Abstract: In reality there are always a large number of complex massive databases. The notion of homomorphism may be a mathematical tool for studying data compression in knowledge bases. This paper investigates a knowledge base in dynamic environments and its data compression with homomorphism, where “dynamic” refers to the fact that the involved information systems need to be updated with time due to the inflow of new information. First, the relationships among knowledge bases, information systems and relation information systems are illustrated. Next, the idea of non-incremental algorithm for data compression with homomorphism and the concept of dynamic knowledge base are …introduced. Two incremental algorithms for data compression with homomorphism in dynamic knowledge bases are presented. Finally, an experimental analysis is employed to demonstrate the applications of the non-incremental algorithm and the incremental algorithms for data compression when calculating the knowledge reduction of dynamic knowledge bases. Show more

Keywords: Dynamic knowledge base, knowledge reduction, data compression, homomorphism

DOI: 10.3233/JIFS-210136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6331-6341, 2021

Authors: He, Yanling | Yao, Chunji

Article Type: Research Article

Abstract: An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution. A FPIS as an IS with fuzzy relations includes three types of uncertainties (i.e., roughness, fuzziness and probability). This paper studies information structures in a FPIS from the perspective of granular computing (GrC). Firstly, two types of information structures in a FPIS are defined by set vectors. Then, equality, dependence and independence between information structures in a FPIS are …proposed, and they are depicted by means of the inclusion degree. Next, information distance between information structures in a FPIS is presented. Finally, entropy measurement for a FPIS is investigated based on the proposed information structures. These results may be helpful for understanding the nature of structures and uncertainty in a FPIS. Show more

Keywords: Fuzzy relation, FPIS, GrC, information structure, dependence, distance, uncertainty, measurement, entropy

DOI: 10.3233/JIFS-210149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6343-6361, 2021

Authors: Mahmood, Asma | Abbas, Mujahid

Article Type: Research Article

Abstract: A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also …taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix. Show more

Keywords: Group decision making, opinion dynamics, trust score evaluations, influence model

DOI: 10.3233/JIFS-210161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6363-6373, 2021

Authors: Maya, Mario | Yu, Wen | Telesca, Luciano

Article Type: Research Article

Abstract: Neural networks have been successfully applied for modeling time series. However, the results of long-term prediction are not satisfied. In this paper, the modified Meta-Learning is applied to the neural model. The normal Meta-Learning is modified by time-varying learning rates and adding a momentum term to improve convergence speed and robustness property. The stability of the learning process is proven. Finally, two experiments are presented to evaluate the proposed method. The first one shows an improvement in earthquakes prediction in the long-term, and the second one is a classical Benchmark problem. In both experiments, the modified Meta-Learning technique minimizes remarkably …the mean square error index. Show more

Keywords: Meta-learning, neural networks, long-term earthquake prediction

DOI: 10.3233/JIFS-210173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6375-6388, 2021

Authors: Bhatia, Tanveen Kaur | Kumar, Amit | Appadoo, S.S.

Article Type: Research Article

Abstract: Enayattabr et al. (Journal of Intelligent and Fuzzy Systems 37 (2019) 6865– 6877) claimed that till now no one has proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems (all-pairs shortest path problems in which distance between every two nodes is represented by an interval-valued trapezoidal fuzzy number). Also, to fill this gap, Enayattabr et al. proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems. In this paper, an interval-valued trapezoidal fuzzy shortest path problem is considered to point out that Enayattabr et al.’s approach fails to find correct shortest distance between two fixed nodes. Hence, it …is inappropriate to use Enayattabr et al.’s approach in its present from. Also, the required modifications are suggested to resolve this inappropriateness of Enayattabr et al.’s approach. Show more

Keywords: Interval-valued trapezoidal fuzzy all-pairs shortest path problem, interval-valued trapezoidal fuzzy numbers, signed distance ranking

DOI: 10.3233/JIFS-210182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6389-6406, 2021

Authors: Narendiranath Babu, T. | Singh, Prabhu Pal | Somesh, M. | Jha, Harshit Kumar | Rama Prabha, D. | Venkatesan, S. | Ramesh Babu, V.

Article Type: Research Article

Abstract: The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, …where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions. Show more

Keywords: Planetary gearbox, neural networks, deep learning

DOI: 10.3233/JIFS-210229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6407-6427, 2021

Authors: Alagarsamy, Ramachandran | Arunpraksh, R. | Ganapathy, Sannasi | Rajagopal, Aghila | Kavitha, R.J.

Article Type: Research Article

Abstract: Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm …(TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy. Show more

Keywords: Fuzzy logic, content ranking, clustering, content recommendation, semantic analysis, fuzzy rules and annova-T

DOI: 10.3233/JIFS-210246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6429-6441, 2021

Authors: Zhu, Yunwen | Zhang, Wenjun | Zhang, Meixian | Zhang, Ke | Zhu, Yonghua

Article Type: Research Article

Abstract: With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that …extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images. Show more

Keywords: Image emotion recognition, emotion distribution learning, fuzzy emotional membership, sparse learning

DOI: 10.3233/JIFS-210251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6443-6460, 2021

Authors: Guo, Dugang | Liu, Jun | Wang, Xuewei

Article Type: Research Article

Abstract: Plant disease is one of the major threats to food security. Accurate diagnosis of plant diseases can benefit the agricultural production. For the purpose of real-time plant disease diagnostics, the deep learning models are employed. In this study, we present an accurate identification method for common diseases of tomatoes based on deep-learning methods. The devising of multi-resolution detector, in line with bounding box generating and assigning, facilitates the feature extracting process of detection. The employment of an dropout and ADAMW (Adaptive moment estimation with decoupled weight decay) optimizer further resolve the overfitting problem. Using the collected images of healthy and …diseased tomatoes, our detector is trained to identify 10 different diseases. Experimental results showed that the disease identification method proposed in this study could accurately and rapidly identify common diseases of tomato with an average accuracy of 85.03%and a recognition speed of 61 frames per second, which was superior to other models under the same conditions and was beneficial for tomato disease control work. Show more

Keywords: Plant diseases, deep learning model, multi-resolution detection layers, bounding box, single shot multibox detector

DOI: 10.3233/JIFS-210262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6461-6471, 2021

Authors: Mehmood, Arif | Al Ghour, Samer | Ishfaq, Muhammad | Afzal, Farkhanda

Article Type: Research Article

Abstract: In this article, new definition of neutrosophic soft ** b -open set is introduced with the help of neutrosophic soft α -open set and neutrosophic soft β-open set. With the application of this new definition some neutrosophic soft separation axioms and neutrosophic soft other separation axioms are addressed with respect to soft points of the spaces. Suitable examples are provided for the clarification of different results. Soft countability results and its engagements with different other neutrosophic soft results are studied. In continuation, characterization of Bolzano Weirstrass Property with respect to neutrosophic soft results and neutrosophic soft compactness results are inaugurated.

Keywords: Neutrosophic soft set (NSS), neutrosophic soft point, neutrosophic soft ** b-open set and neutrosophic ** b-separation axioms

DOI: 10.3233/JIFS-210306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6473-6494, 2021

Authors: Li, Chenliang | Yu, Xiaobing | Zhao, Wen-Xuan

Article Type: Research Article

Abstract: In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment …factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. Show more

Keywords: Big data center, PROMETHEE, MCGP model, MCDM method

DOI: 10.3233/JIFS-210319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6495-6515, 2021

Authors: Li, Qiaoyang | Chen, Guiming | Li, Ziqi | Zhang, Yi | Xu, Lingliang

Article Type: Research Article

Abstract: To solve the problems of strong infrared radiation, poor continuous combat capability of the system, serious ablation of the launching device, and environmental pollution of the existing missile launching system, electromagnetic launch system (EMLS) has been studied for missile launch system. Combining the situation that the current research on missile electromagnetic launch system (MEMLS) mainly focuses on the key technical points and the deficiencies in the previous research on MEMLS, this paper establishes an effectiveness prediction model based on GRA-PCA-LSSVM, and discusses the investment efficiency of the system based on DEA. The experimental results prove that the established model is …reasonable, effective and superior, and provides a reference for the further improvement and development of MEMLS. Show more

Keywords: MEMLS, Grey relation analysis (GRA), Principal component analysis (PCA), Least square support vector machine (LSSVM), Data Envelopment Analysis (DEA)

DOI: 10.3233/JIFS-210353

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6517-6526, 2021

Authors: Li, Longmei | Zheng, Tingting | Yin, Wenjing | Wu, Qiuyue

Article Type: Research Article

Abstract:  Entropy and cross-entropy are very vital for information discrimination under complicated Pythagorean fuzzy environment. Firstly, the novel score factors and indeterminacy factors of intuitionistic fuzzy sets (IFSs) are proposed, which are linear transformations of membership functions and non-membership functions. Based on them, the novel entropy measures and cross-entropy measures of an IFS are introduced using Jensen Shannon-divergence (J -divergence). They are more in line with actual fuzzy situations. Then the cases of Pythagorean fuzzy sets (PFSs) are extended. Pythagorean fuzzy entropy, parameterized Pythagorean fuzzy entropy, Pythagorean fuzzy cross-entropy, and weighted Pythagorean fuzzy cross-entropy measures are introduced consecutively based on the …novel score factors, indeterminacy factors and J -divergence. Then some comparative experiments prove the rationality and effectiveness of the novel entropy measures and cross-entropy measures. Additionally, the Pythagorean fuzzy entropy and cross-entropy measures are designed to solve pattern recognition and multiple criteria decision making (MCDM) problems. The numerical examples, by comparing with the existing ones, demonstrate the applicability and efficiency of the newly proposed models. Show more

Keywords: Pythagorean fuzzy entropy, Pythagorean fuzzy cross-entropy, parameterized Pythagorean fuzzy entropy, weighted Pythagorean fuzzy cross-entropy, score factor, indeterminacy factor

DOI: 10.3233/JIFS-210365

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6527-6546, 2021

Authors: Guo, Huijuan | Yao, Ruipu

Article Type: Research Article

Abstract: The symmetry between fuzzy evaluations and crisp numbers provides an effective solution to multiple attribute decision making (MADM) problems under fuzzy environments. Considering the effect of information distribution on decision making, a novel approach to MADM problems under the interval-valued q-rung orthopair fuzzy (Iq-ROF) environments is put forward. Firstly, the clustering method of interval-valued q-rung orthopair fuzzy numbers (Iq-ROFNs) is defined. Secondly, Iq-ROF density weighted arithmetic (Iq-ROFDWA) intermediate operator and Iq-ROF density weighted geometric average (Iq-ROFDWGA) intermediate operator are developed based on the density weighted intermediate operators for crisp numbers. Thirdly, combining the density weighted intermediate operators with the Iq-ROF …weighted aggregation operators, Iq-ROF density aggregation operators including Iq-ROF density weighted arithmetic (Iq-ROFDWAA) aggregation operator and Iq-ROF density weighted geometric (Iq-ROFDWGG) aggregation operator are proposed. Finally, effectiveness of the proposed method is verified through a numerical example. Show more

Keywords: Multiple attribute decision making (MADM), clustering, Iq-ROFDWAA aggregation operator, Iq-ROFDWGG aggregation operator

DOI: 10.3233/JIFS-210376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6547-6560, 2021

Authors: Muhiuddin, G. | Mahboob, A. | Khan, N. M. | Al-Kadi, D.

Article Type: Research Article

Abstract: In this paper, we introduce new types of fuzzy (m , n )-ideals in ordered semigroups. In fact, the notion of (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals of the ordered semigroups is introduced. Further, we present the characterzations of this notion in different ways. Then the (κ * , κ )-lower part of the (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals is defined and its associated properties are investigated. After that, (m , n )-regular ordered semigroups are characterized in terms of its (∈ , ∈ ∨ (κ * , q κ …))-fuzzy (m , n )-ideals and their (κ * , κ )-lower parts. Show more

Keywords: Ordered semigroups, fuzzy sets, (∈ , ∈ ∨ (κ*, qκ))-fuzzy (m, n)-ideals, (m, n)-regular ordered semigroups

DOI: 10.3233/JIFS-210378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6561-6574, 2021

Authors: Shi, Shuo | Huo, Changwei | Guo, Yingchun | Lean, Stephen | Yan, Gang | Yu, Ming

Article Type: Research Article

Abstract: Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of …the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms. Show more

Keywords: Person re-identification, cross-modal, natural language description, cascade loss function, truncated attention mechanism

DOI: 10.3233/JIFS-210382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6575-6587, 2021

Authors: Rai, Ashok Kumar | Senthilkumar, Radha | Aruputharaj, Kannan

Article Type: Research Article

Abstract: Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. …The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods. Show more

Keywords: Face recognition, Framed Recurrent Neural Network(FRNN), Mutated Dragonfly Search Optimization (MDSO), Mean Square Error (MSE)

DOI: 10.3233/JIFS-210441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6589-6599, 2021

Authors: Yin, Tao | Mao, Xiaojuan | Wu, Xingtan | Ju, Hengrong | Ding, Weiping | Yang, Xibei

Article Type: Research Article

Abstract: Neighborhood classifier, a common classification method, is applied in pattern recognition and data mining. The neighborhood classifier mainly relies on the majority voting strategy to judge each category. This strategy only considers the number of samples in the neighborhood but ignores the distribution of samples, which leads to a decreased classification accuracy. To overcome the shortcomings and improve the classification performance, D-S evidence theory is applied to represent the evidence information support of other samples in the neighborhood, and the distance between samples in the neighborhood is taken into account. In this paper, a novel attribute reduction method of neighborhood …rough set with a dynamic updating strategy is developed. Different from the traditional heuristic algorithm, the termination threshold of the proposed reduction algorithm is dynamically optimized. Therefore, when the attribute significance is not monotonic, this method can retrieve a better value, in contrast to the traditional method. Moreover, a new classification approach based on D-S evidence theory is proposed. Compared with the classical neighborhood classifier, this method considers the distribution of samples in the neighborhood, and evidence theory is applied to describe the closeness between samples. Finally, datasets from the UCI database are used to indicate that the improved reduction can achieve a lower neighborhood decision error rate than classical heuristic reduction. In addition, the improved classifier acquires higher classification performance in contrast to the traditional neighborhood classifier. This research provides a new direction for improving the accuracy of neighborhood classification. Show more

Keywords: Attribute reduction, D-S evidence theory, neighborhood classification, rough set

DOI: 10.3233/JIFS-210462

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6601-6613, 2021

Authors: Sekar, Aravindkumar | Perumal, Varalakshmi

Article Type: Research Article

Abstract: Automatic road crack detection is a prominent challenging task, in view of that, a novel approach is proposed using multi-tasking Faster-RCNN to detect and classify road cracks. In this present study, we have collected the road images (a dataset of 19300 images) from the Outer Ring Road of Chennai, Tamil Nadu, India. The collected road images were pre-processed using various conventional image processing techniques to identify the ground-truth label of the bounding boxes for the cracks. We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road …cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods. Show more

Keywords: Multi-tasking faster-RCNN, RoI align, road crack detection, road crack classification

DOI: 10.3233/JIFS-210475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6615-6628, 2021

Authors: Li, Bin | He, Qiyu | Liu, Xiaopeng | Jiang, Yajun | Hu, Zhigang

Article Type: Research Article

Abstract: Person re-identification problem is a valuable computer vision task, which aims at matching pedestrian images of different cameras in a non-overlapping surveillance network. Existing metric learning based methods address this problem by learning a robust distance function. These methods learn a mapping subspace by forcing the distance of the positive pair much smaller than the negative pair by a strict constraint. The metric model is over-fitting to the training dataset. Due to drastic appearance variations, the handcrafted features are weak of representation for person re-identification. To address these problems, we propose a joint distance measure based approach, which learns a …Mahalanobis distance and a Euclidean distance with a novel feature jointly. The novel feature is represented with a dictionary representation based method which considers pedestrian images of different camera views with the same dictionary. The joint distance combine the Mahalanobis distance based on metric learning method with the Euclidean distance based on the novel feature to measure the similarity between matching pairs. Extensive experiments are conducted on the publicly available bench marking datasets VIPeR and CUHK01. The identification results show that the proposed method reaches a good performance than the comparison methods. Show more

Keywords: Person re-identification, metric learning, multi-distance, dictionary representation, Mahalanobis distance

DOI: 10.3233/JIFS-210505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6629-6639, 2021