Journal of Intelligent & Fuzzy Systems - Volume 42, issue 4

Authors: Yang, Songyue | Yu, Guizhen | Meng, Zhijun | Wang, Zhangyu | Li, Han

Article Type: Research Article

Abstract: In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to …ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%. Show more

Keywords: UAV, obstacle avoidance, DQN, overestimation, convergence rate

DOI: 10.3233/JIFS-211192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3323-3335, 2022

Authors: Zhu, Wuqiang | Lu, Yang | Zhang, Yongliang | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: End-to-end deep learning has gained considerable interests in autonomous driving vehicles. End-to-end autonomous driving uses the deep convolutional neural network to establish input-to-output mapping. However, existing end-to-end driving models only predict steering angle with front-facing camera data and poorly extract spatial-temporal information. Based on deep learning and attention mechanism, we propose an end-to-end driving model which combines the multi-stream attention module with the multi-stream network. As a multimodal multitask model, the proposed end-to-end driving model not only fully extracts spatial-temporal information from multimodality, but also adopts the multitask learning method with hard parameter sharing to predict the steering angle and …speed. Furthermore, the proposed multi-stream attention module predicts the attention weights of streams based on the multimodal feature fusion, which encourages the proposed end-to-end driving model to pay attention to streams that positively impact the prediction result. We demonstrate the efficiency of the proposed driving model on the public Udacity dataset compared to existing models. Experimental results show that the proposed driving model has better performances than other existing methods. Show more

Keywords: End-to-end autonomous driving, attention mechanism, multimodal, multitask

DOI: 10.3233/JIFS-211206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3337-3348, 2022

Authors: Işık, Gürkan | Kaya, İhsan

Article Type: Research Article

Abstract: Although traditional acceptance sampling plans (ASPs) need certain mass quality characteristics, it is not easy to define them as crisp value in some real case problems. The fuzzy set theory (FST) is one of the popular techniques to model uncertainties of the process and therefore fuzzy ASPs have been offered in the literature. Fuzzy set extensions have been proposed recently for better modeling of the uncertainties having different sources and characteristics. One of these extensions named neutrosophic sets (NSs) can be used to increase the sensitiveness and flexibility of ASPs. The ASPs based on NSs can give ability to classify …the items as defective, non-defective and indeterminate. Since the operator can become indecisive for slightly defective items, these plans can provide a good representation of human evaluations under uncertainty. In this study, single and double ASPs are designed based on NSs by using binomial and poisson distributions that are also re-analyzed based on NSs. For this aim, some characteristics functions of ASPs such as probability of accepting a lot (P a ), average outgoing quality (AOQ ), average total inspection (ATI ) and average sample number (ASN ) have also been analyzed based on NSs. Numerical examples are presented to analyze the proposed plans. Show more

Keywords: Acceptance sampling plans, fuzzy sets, neutrosophic sets, neutrosophic poisson distribution

DOI: 10.3233/JIFS-211232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3349-3366, 2022

Authors: Khalil, Ahmed Mostafa

Article Type: Research Article

Abstract: In this article, we will define the new notions (e.g., semi-θ-neighbor-hood system of point, semi-θ-closure (interior) of a set and semi-θ-closed (open) set) based on fuzzy logic (i.e., fuzzifying topology). Then, we will explain the interesting properties of above five notions in detail. Several basic results (for instance, Definition 2.3, Theorem 2.5 (iii), (v) and (vi), Theorem 2.10, Theorem 2.14 and Theorem 4.6) in classical topology are generalized to the fuzzy case based on Łukasiewicz logic. In addition to, we will show that every fuzzifying semi-θ-closed set is fuzzifying semi-closed set (by Theorem 2.5 (vi)). Further, we will study the …notion of fuzzifying semi-θ-derived set and fuzzifying semi-θ-boundary set, and discuss several of their fundamental basic relations and properties. Also, we will present a new type of fuzzifying strongly semi-θ-continuous mapping between two fuzzifying topological spaces. Finally, several characterizations of fuzzifying strongly semi-θ-continuous mapping, fuzzifying strongly semi-θ-irresolute mapping, and fuzzifying weakly semi-θ-irresolute mapping along with different conditions for their existence are obtained. Show more

Keywords: Fuzzy logic, fuzzifying topology, fuzzifying semi-θ-closure of a set, fuzzifying semi-θ-closed sets, fuzzifying strongly semi-θ-continuous mapping

DOI: 10.3233/JIFS-211301

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3367-3379, 2022

Authors: Gong, Zengtai | Xiao, Zhiyong

Article Type: Research Article

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

DOI: 10.3233/JIFS-211306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3381-3391, 2022

Authors: Cai, Qian | Xiong, Xingliang | Gong, Weiqiang | Wang, Haixian

Article Type: Research Article

Abstract: BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-class classification. METHOD: To complete the multi-class classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metric. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding …is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-class classification of action intention understanding brain signals. Show more

Keywords: Action intention understanding, EEG, classification, feature extraction, graph metric

DOI: 10.3233/JIFS-211333

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3393-3403, 2022

Authors: Jiang, Zhiwei | Wei, Guiwu | Guo, Yanfeng

Article Type: Research Article

Abstract: In the garment manufacturing industry, purchasing management is an important link. The materials of making clothes often need high cost. In addition, customers put forward a request in the quality of clothes. Thus, choosing an optimal supplier is an essential part of job. Reaching cooperation with an optimal supplier not only can help garment manufacturer improve the quality of clothes but also is benefit to reduce the cost of producing. Most importantly, it can improve the competitiveness of manufacture enterprises. So, it is important for managers to find an optimal supplier and make a cooperation with it. In this paper, …we analysis an issue about choosing an optimal supplier during four different suppliers. With analyzing this problem, we can introduce an extended method under picture fuzzy environment to evaluate and choose an optimal supplier. In this article, we describe some basic knowledges about picture fuzzy sets (PFSs) and picture fuzzy numbers (PFNs). Then, we introduce the extension of MABAC method which is on the basis of prospect theory (PT) with picture fuzzy numbers (PF-PT-MABAC) and utilize the PF-PT-MABAC model to evaluate different suppliers to choose an optimal supplier. Finally, we compare the result of PF-PT-MABAC with the result of traditional MABAC, PFWG operators and traditional TODIM method to test the efficiency of PF-PT-MABAC model. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), MABAC method, prospect theory (PT), suppliers selection

DOI: 10.3233/JIFS-211359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3405-3415, 2022

Authors: Kashif, Agha | Rashid, Tabasam | Noor, Bibi | Sindhu, Muhammad Sarwar

Article Type: Research Article

Abstract: Motivated by intuitionistic fuzzy sets and soft sets, a novel concept of lattice ordered interval-valued intuitionistic fuzzy soft sets (LOIVIFSSs) is introduced in this article. Operational rules like union, intersection, complement and some properties of LOIVIFSSs are demonstrated with examples. In this regard, an algorithm is developed to solve the multiple criteria decision-making (MCDM) problems based on LOIVIFSSs. Further, a benchmark problem concerning medical diagnosis have been investigated and a comparative analysis with existing technique is furnished to strengthen our approach.

Keywords: Fuzzy sets, intuitionistic fuzzy sets, soft sets, lattice

DOI: 10.3233/JIFS-211376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3417-3430, 2022

Authors: Jiang, Tianhua | Zhu, Huiqi | Gu, Jiuchun | Liu, Lu | Song, Haicao

Article Type: Research Article

Abstract: This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the …search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP. Show more

Keywords: Dual-resource constraint, energy-saving scheduling, flexible job shop, discrete animal migration optimization

DOI: 10.3233/JIFS-211399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3431-3444, 2022

Authors: Joshi, Pallavi | Raghuvanshi, Ajay Singh

Article Type: Research Article

Abstract: The abrupt changes in the sensor measurements indicating the occurrence of an event are the major factors in some monitoring applications of IoT networks. The prediction-based approach for data aggregation in wireless sensor networks plays a significant role in detecting such events. This paper introduces a prediction-based aggregation model for sensor selection named the Grey prediction model and the Kalman filter-based data aggregation model with rank-based mutual information (GMKFDA-MI) that has a dual synchronization mechanism for aggregating the data and selecting the nodes based on prediction and cumulative error thresholds. Furthermore, the nodes after deployment are clustered using K-medoids clustering …along with the Salp swarm optimization algorithm to obtain an optimized aggregator position concerning the base station. An efficient clustering promises energy efficiency and better connectivity. The experiments are accomplished on real-time datasets of air pollution monitoring applications and the results for the proposed method are compared with other similar state-of-the-art techniques. The proposed method promises high prediction accuracy, low energy consumption and enhances the throughput of the network. The energy-saving is recorded to be more than 10 to 30% for the proposed model when compared with other similar approaches. Also, the proposed method achieves 97.8% accuracy as compared to other methods. The method proves its best working efficiency in the applications like event reporting, target detection, and event monitoring. Show more

Keywords: IoT, wireless sensor networks, data aggregation, grey model, kalman filter, mutual information, K-medoids clustering, salp swarm optimization

DOI: 10.3233/JIFS-211436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3445-3464, 2022

Authors: Ghafour, Karzan Mahdi

Article Type: Research Article

Abstract: Demand forecasting is a fundamental element in industrial problems. Forecasts are crucial for accurately estimating intermittent demand to establish inventory measurements. The demand estimation by the Croston method gives less accurate values, which increases the standard deviation value. This increase indicates that the forecasted method is an inappropriate method of intermittent demand data because of the zero values. However, real data were adopted in an industrial sector for three years with constant lead-time. Furthermore, an integration of Bernoulli distribution and geometric distribution has been done to establish the new formulation, then extracting the mean equation and the standard deviation equation …of intermittent demand during lead-time. Relying on it, the optimal quantity of safety stock and reorder levels have been obtained. Furthermore, the proposed modified forecasting method was evaluated based on the criteria of CV and the results that obtained gives a less ratio dispersion of data thus accurate results. These procedures are very important to the industrial sectors in drawing future inventory policies. Show more

Keywords: Intermittent demand, forecasting, croston’s method, reorder level and safety stock

DOI: 10.3233/JIFS-211454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3465-3475, 2022

Authors: Prajna, Yellamelli | Nath, Malaya Kumar

Article Type: Research Article

Abstract: Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the …classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation. Show more

Keywords: Fundus image, U-Net, MSRI, accuracy, ROC curve

DOI: 10.3233/JIFS-211479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3477-3489, 2022

Authors: Farooq, Muhammad | Qamar-uz-zaman, | Ijaz, Muhammad

Article Type: Research Article

Abstract: The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of …FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. Show more

Keywords: FEW, MLE, statistical properties, applications, and simulation

DOI: 10.3233/JIFS-211519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3491-3499, 2022

Authors: Tripathy, Santosh Kumar | Sudhamsh, Repala | Srivastava, Subodh | Srivastava, Rajeev

Article Type: Research Article

Abstract: Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed …them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches. Show more

Keywords: Atrous (Dilated)-CNN, multiscale spatial-temporal features, dimension reduction, PCA, OC-SVM

DOI: 10.3233/JIFS-211556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3501-3516, 2022

Authors: Khan, Izaz Ullah | Aftab, Muhammad

Article Type: Research Article

Abstract: This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan …comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable. Show more

Keywords: Fuzzy Linear Programming (FLP), fuzzy mathematical programming, dynamic programming, education, fuzzy sets and fuzzy modeling, resource allocation

DOI: 10.3233/JIFS-211577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3517-3535, 2022

Authors: Sun, Quan | Yu, Xianghai | Li, Hongsheng | Peng, Fei | Sun, Guodong

Article Type: Research Article

Abstract: With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is …converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks. Show more

Keywords: Continuous wavelet transform, convolution neural network, fault detection, power electronic converter

DOI: 10.3233/JIFS-211632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3537-3549, 2022

Authors: Jin, LeSheng | Mesiar, Radko | Yager, Ronald | Kaya, Sema Kayapinar

Article Type: Research Article

Abstract: The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is …presented. Show more

Keywords: Aggregation operators, basic uncertain information, decision making, interval basic uncertain information, preference aggregation

DOI: 10.3233/JIFS-211635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3551-3558, 2022

Authors: Li, Feng

Article Type: Research Article

Abstract: Many experts and scholars focus on the Maclaurin symmetric mean (MSM) equation, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy data intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the security evaluation of Wireless Sensor Network (WSN). And the WSN security evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. In this paper, we expand the generalized Maclaurin symmetric mean (GMSM) equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy generalized MSM (FNIFGMSM) …equation and fuzzy number intuitionistic fuzzy weighted generalized MSM (FNIFWGMSM) equation in this study. A few MADM tools are developed with FNIFWGMSM equation. Finally, taking WSN security evaluation as an example, this paper illustrates effectiveness of the depicted approach. Moreover, by comparing and analyzing the existing methods, the effectiveness and superiority of the FNIFWGMSM method are further certified. Show more

Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy sets (FNIFSs), fuzzy number intuitionistic fuzzy GMSM (FNIFGMSM) equation, fuzzy number intuitionistic fuzzy weighted GMSM (FNIFWGMSM) equation, Wireless Sensor Network (WSN)

DOI: 10.3233/JIFS-211731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3559-3573, 2022

Authors: Varatharaj, Nagaraj | Ramalingam, Sumithira Thulasimani

Article Type: Research Article

Abstract: Most revolutionary applications extending far beyond smartphones and high configured mobile device use to the future generation wireless networks’ are high potential capabilities in recent days. One of the advanced wireless networks and mobile technology is 5G, where it provides high speed, better reliability, and amended capacity. 5 G offers complete coverage, which is accommodates any IoT device, connectivity, and intelligent edge algorithms. So that 5 G has a high demand in a wide range of commercial applications. Ambrosus is a commercial company that integrates block-chain security, IoT network, and supply chain management for medical and food enterprises. This paper proposed a …novel framework that integrates 5 G technology, Machine Learning (ML) algorithms, and block-chain security. The main idea of this work is to incorporate the 5 G technology into Machine learning architectures for the Ambrosus application. 5 G technology provides continuous connection among the network user/nodes, where choosing the right user, base station, and the controller is obtained by using for ML architecture. The proposed framework comprises 5 G technology incorporate, a novel network orchestration, Radio Access Network, and a centralized distributor, and a radio unit layer. The radio unit layer is used for integrating all the components of the framework. The ML algorithm is evaluated the dynamic condition of the base station, like as IoT nodes, Ambrosus users, channels, and the route to enhance the efficiency of the communication. The performance of the proposed framework is evaluated in terms of prediction by simulating the model in MATLAB software. From the performance comparison, it is noticed that the proposed unified architecture obtained 98.6% of accuracy which is higher than the accuracy of the existing decision tree algorithm 97.1%. Show more

Keywords: 5 G Technology, internet of things, block-chain security, wireless communication, machine learning models

DOI: 10.3233/JIFS-211745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3575-3590, 2022

Authors: Garg, Harish | Alodhaibi, Sultan S. | Khalifa, Hamiden Abd El-Wahed

Article Type: Research Article

Abstract: Rough set theory, introduced by Pawlak in 1981, is one of the important theories to express the vagueness not by means of membership but employing a boundary region of a set, i.e., an object is approximately determined based on some knowledge. In our real-life, there exists several parameters which impact simultaneously on each other and hence dealing with such different parameters and their conflictness create a multi-objective nonlinear programming problem (MONLPP). The objective of the paper is to deal with a MONLPP with rough parameters in the constraint set. The considered MONLPP with rough parameters are converted into the two-single …objective problems namely, lower and upper approximate problems by using the weighted averaging and the ɛ - constraints methods and hence discussed their efficient solutions. The Karush-Kuhn-Tucker’s optimality conditions are applied to solve these two lower and upper approximate problems. In addition, the rough weights and the rough parameter ɛ are determined by the lower and upper the approximations corresponding each efficient solution. Finally, two numerical examples are considered to demonstrate the stated approach and discuss their advantages over the existing ones. Show more

Keywords: Multiobjective nonlinear programming, rough set, lower approximation programming problem, upper approximation programming problem, weighting method, ɛ- constraints method, parametric analysis

DOI: 10.3233/JIFS-211747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3591-3604, 2022

Authors: Zhou, Wei | Jiang, Xing | Guo, Bingli | Meng, Lingyu

Article Type: Research Article

Abstract: Currently, Quality-of-Service (QoS)-aware routing is one of the crucial challenges in Software Defined Network (SDN). The QoS performances, e.g. latency, packet loss ratio and throughput, must be optimized to improve the performance of network. Traditional static routing algorithms based on Open Shortest Path First (OSPF) could not adapt to traffic fluctuation, which may cause severe network congestion and service degradation. Central intelligence of SDN controller and recent breakthroughs of Deep Reinforcement Learning (DRL) pose a promising solution to tackle this challenge. Thus, we propose an on-policy DRL mechanism, namely the PPO-based (Proximal Policy Optimization) QoS-aware Routing Optimization Mechanism (PQROM), to …achieve a general and re-customizable routing optimization. PQROM can dynamically update the routing calculation by adjusting the reward function according to different optimization objectives, and it is independent of any specific network pattern. Additionally, as a black-box one-step optimization, PQROM is qualified for both continuous and discrete action space with high-dimensional input and output. The OMNeT ++ simulation experiment results show that PQROM not only has good convergence, but also has better stability compared with OSPF, less training time and simpler hyper-parameters adjustment than Deep Deterministic Policy Gradient (DDPG) and less hardware consumption than Asynchronous Advantage Actor-Critic (A3C). Show more

Keywords: PQROM, SDN, QoS-aware routing

DOI: 10.3233/JIFS-211787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3605-3614, 2022

Authors: Chen, Lijun | Luo, Damei | Wang, Pei | Li, Zhaowen | Xie, Ningxin

Article Type: Research Article

Abstract:  An approximation space (A -space) is the base of rough set theory and a fuzzy approximation space (FA -space) can be seen as an A -space under the fuzzy environment. A fuzzy probability approximation space (FPA -space) is obtained by putting probability distribution into an FA -space. In this way, it combines three types of uncertainty (i.e., fuzziness, probability and roughness). This article is devoted to measuring the uncertainty for an FPA -space. A fuzzy relation matrix is first proposed by introducing the probability into a given fuzzy relation matrix, and on this basis, it is expanded to an FA …-space. Then, granularity measurement for an FPA -space is investigated. Next, information entropy measurement and rough entropy measurement for an FPA -space are proposed. Moreover, information amount in an FPA -space is considered. Finally, a numerical example is given to verify the feasibility of the proposed measures, and the effectiveness analysis is carried out from the point of view of statistics. Since three types of important theories (i.e., fuzzy set theory, probability theory and rough set theory) are clustered in an FPA -space, the obtained results may be useful for dealing with practice problems with a sort of uncertainty. Show more

Keywords: FPA-space, fuzzy relation, uncertainty, measure, information granulation, entropy, effectiveness

DOI: 10.3233/JIFS-211790

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3615-3638, 2022

Authors: Zhao, Mujie | Zhang, Tao | Wang, Di

Article Type: Research Article

Abstract: Aiming at the nonlinear filter problem in Ultra Wide Band (UWB) navigation and position, a high-order Unscented Kalman Filter (UKF) position method is proposed. On the one hand, the position and velocity are used as state variables to establish a nonlinear filtering model based on UWB position system. On the other hand, based on the fifth order cubature transform (CT), the analytical solution of the high-order unscented Kalman filter is obtained by introducing a free parameter δ . To verify the effectiveness of the proposed method, the Time of Arrival (TOA) location method, the least square method and fifth order …CKF method are introduced as comparison methods. The simulation and experimental results show that the proposed high-order UKF method has good positioning accuracy in both static and dynamic UWB positioning methods. Show more

Keywords: UWB positioning, UKF, TOA, least square method

DOI: 10.3233/JIFS-211810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3639-3652, 2022

Authors: Ali, Zeeshan | Mahmood, Tahir | Ullah, Kifayat | Chinram, Ronnason

Article Type: Research Article

Abstract: The major contribution of this analysis is to analyze the confidence complex q-rung orthopair fuzzy weighted averaging (CCQROFWA) operator, confidence complex q-rung orthopair fuzzy ordered weighted averaging (CCQROFOWA) operator, confidence complex q-rung orthopair fuzzy weighted geometric (CCQROFWG) operator, and confidence complex q-rung orthopair fuzzy ordered weighted geometric (CCQROFOWG) operator and invented their feasible properties and related results. Future more, under the invented operators, we diagnosed the best crystalline solid from the family of crystalline solids with the help of the opinion of different experts in the environment of decision-making strategy. Finally, to demonstrate the feasibility and flexibility of the invented …works, we explored the sensitivity analysis and graphically shown of the initiated works. Show more

Keywords: Complex q-rung orthopair fuzzy sets, aggregation operators, confidence levels, decision-making methods

DOI: 10.3233/JIFS-211840

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3653-3675, 2022

Authors: Gupta, Punit | Bhagat, Sanjit | Rawat, Pradeep

Article Type: Research Article

Abstract: The evolution of cloud computing is increasing exponentially which provides everything as a service. Clouds made it possible to move a huge amount of data over the networks on-demand. It removed the physical necessity of resources as resources are available virtually over the networks. Emerge of new technologies improvising the cloud system and trying to overcome cloud computing challenges like resource optimization, securities etc. Proper utilization of resources is still a primary target for the cloud system as it will increase the cost and time efficiency. Cloud is a pay-per-uses basis model which needs to perform in a flexible manner …with the increase and decrease in demand on every level. In general, cloud is assumed to be non-faulty but faulty is a part of any system. This article focuses on the hybridization of Neural networks with the harmony Search Algorithm (HSA). The hybrid approach achieves a better optimal solution in a feasible time duration in the faulty environment to improve the task failure and improve reliability. The harmony Search approach is inspired from the music improvisation technique, where notes are adjusted until perfect harmony is matched. HS (Harmony search) is chosen, as it is capable to provide an optimal solution in a feasible time, even for complex optimization problems. An ANN-HS model is introduced to achieve optimal resource allocation. The presented model is inspired by Harmony Search and ANN. The proposed model considers multi-objective criteria. The performance criteria include execution time, task failure count and power consumption(Kwh). Show more

Keywords: Cloud infrastructure, HS (Harmony Search), metaheuristic, neural network, task scheduling

DOI: 10.3233/JIFS-211846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3677-3689, 2022

Authors: Chen, Junying | Liu, Shipeng | Zhao, Liang | Chen, Dengfeng | Zhang, Weihua

Article Type: Research Article

Abstract: Since small objects occupy less pixels in the image and are difficult to recognize. Small object detection has always been a research difficulty in the field of computer vision. Aiming at the problems of low sensitivity and poor detection performance of YOLOv3 for small objects. AFYOLO, which is more sensitive to small objects detection was proposed in this paper. Firstly, the DenseNet module is introduced into the low-level layers of backbone to enhance the transmission ability of objects information. At the same time, a new mechanism combining channel attention and spatial attention is introduced to improve the feature extraction ability …of the backbone. Secondly, a new feature pyramid network (FPN) is proposed to better obtain the features of small objects. Finally, ablation studies on ImageNet classification task and MS-COCO object detection task verify the effectiveness of the proposed attention module and FPN. The results on Wider Face datasets show that the AP of the proposed method is 11.89%higher than that of YOLOv3 and 8.59%higher than that of YOLOv4. All of results show that AFYOLO has better ability for small object detection. Show more

Keywords: Small object detection, YOLOv3, DenseNet, attention mechanism, FPN

DOI: 10.3233/JIFS-211905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3691-3703, 2022

Authors: Talebi, A.A. | Muhiuddin, G. | Sadati, S.H. | Rashmanlou, Hossein

Article Type: Research Article

Abstract: Fuzzy graphs have a prominent place in the mathematical modelling of the problems due to the simplicity of representing the relationships between topics. Gradually, with the development of science and in encountering with complex problems and the existence of multiple relationships between variables, the need to consider fuzzy graphs with multiple relationships was felt. With the introduction of the graph structures, there was better flexibility than the graph in dealing with problems. By combining a graph structure with a fuzzy graph, a fuzzy graph structure was introduced that increased the decision-making power of complex problems based on uncertainties. The previous …definitions restrictions in fuzzy graphs have made us present new definitions in the fuzzy graph structure. The domination of fuzzy graphs has many applications in other sciences including computer science, intelligent systems, psychology, and medical sciences. Hence, in this paper, first we study the dominating set in a fuzzy graph structure from the perspective of the domination number of its fuzzy relationships. Likewise, we determine the domination in terms of neighborhood, degree, and capacity of vertices with some examples. Finally, applications of domination are introduced in fuzzy graph structure. Show more

Keywords: Fuzzy graph structure, μi-dominating set, μi-neighborhood, full-dominating set

DOI: 10.3233/JIFS-211923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3705-3718, 2022

Authors: Yang, Luting | Li, Yan

Article Type: Research Article

Abstract: Online shopping has gradually become an important way of consumption, and consumers are paying more and more attention to negative reviews. In order to avoid the massive amount of negative review information leading to loss of useful information, this paper proposes a method for evaluating the usefulness of negative online reviews. Firstly, the method constructs an evaluation index system for the usefulness of negative online reviews from three aspects: the form feature, text feature, and reviewer feature of negative reviews, and uses a combination weighting method based on fuzzy analytic hierarchy process (FAHP) and entropy method to determine the weight …of each index. Secondly, the usefulness ranking results of negative online reviews are obtained through the improved TOPSIS method based on the combined weighting method. Finally, the empirical analysis of the proposed model is carried out by crawling the negative online reviews of JD.com Fresh Food platform, and the improved model is compared with the traditional TOPSIS model, which proves the feasibility and effectiveness of the model. Show more

Keywords: Negative reviews, usefulness ranking, FAHP, entropy method, improved TOPSIS

DOI: 10.3233/JIFS-211928

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3719-3736, 2022

Authors: Cui, ChunSheng | Cao, YanLi

Article Type: Research Article

Abstract: In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between …the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative. Show more

Keywords: Vague sets, evidence theory, multi-attribute group decision-making, score function

DOI: 10.3233/JIFS-211937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3737-3747, 2022

Authors: Yang, Zuoxi | Dong, Shoubin

Article Type: Research Article

Abstract: Modeling user’s fine-grained preferences and dynamic preference evolution from their chronological behaviors are challenging and crucial for sequential recommendation. In this paper, we develop a Hierarchical Self-Attention Incorporating Knowledge Graph for Sequential Recommendation (HSRec). HSRec models not only the user’s intrinsic preferences but also the user’s external potential interests to capture the user’s fine-grained preferences. Specifically, the intrinsic interest module and potential interest module are designed to capture these two preferences respectively. In the intrinsic interest module, user’s sequential patterns are characterized from their behaviors via the self-attention mechanism. As for the potential interest module, high-order paths can be generated …with the help of the knowledge graph. Therefore, a hierarchical self-attention mechanism is designed to aggregate the semantic information of user interaction from these paths. Specifically, an entity-level self-attention mechanism is applied to capture the sequential patterns contained in the high-order paths while an interaction-level self-attention mechanism is designed to further capture the semantic information from user interactions. Moreover, according to the high-order semantic relevance, HSRec can explore the user’s dynamic preferences at each time, thus describing the user’s dynamic preference evolution. Finally, experiments conducted on three real world datasets demonstrate the state-of-the-art performance of the HSRec. Show more

Keywords: Sequential recommendation, knowledge graph, hierarchical self-attention, fine-grained preferences, dynamic preference evolution

DOI: 10.3233/JIFS-211953

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3749-3760, 2022

Authors: Lu, Fengli | Fu, Chengcai | Zhang, Guoying | Shi, Jie

Article Type: Research Article

Abstract: Accurate segmentation of fractures in coal rock CT images is important for the development of coalbed methane. However, due to the large variation of fracture scale and the similarity of gray values between weak fractures and the surrounding matrix, it remains a challenging task. And there is no published dataset of coal rock, which make the task even harder. In this paper, a novel adaptive multi-scale feature fusion method based on U-net (AMSFF-U-net) is proposed for fracture segmentation in coal rock CT images. Specifically, encoder and decoder path consist of residual blocks (ReBlock), respectively. The attention skip concatenation (ASC) module …is proposed to capture more representative and distinguishing features by combining the high-level and low-level features of adjacent layers. The adaptive multi-scale feature fusion (AMSFF) module is presented to adaptively fuse different scale feature maps of encoder path; it can effectively capture rich multi-scale features. In response to the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. These extensive experiments are conducted via seven state-of-the-art methods (i.e., FCEM, U-net, Res-Unet, Unet++, MSN-Net, WRAU-Net and ours). The experiment results demonstrate that the proposed AMSFF-U-net can achieve better segmentation performance in our works, particularly for weak fractures and tiny scale fractures. Show more

Keywords: Multi-scale feature fusion, U-net, fracture segmentation in coal rock CT image, dilation convolutions, residual U-net

DOI: 10.3233/JIFS-211968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3761-3774, 2022

Authors: Liu, Yicong | Chu, Junfeng | Wang, Yanyan | Wang, Yingming

Article Type: Research Article

Abstract: To obtain the suitable alternative(s) for the organization, this paper proposes a more practical method to solve the decision-making problems in society. That is combined with the TODIM (TOmada de decisão interativa multicrit e ´ rio). The maximizing dominance degree model to reach consensus is proposed with two following components: (1) constructing the complete trust relationships network; (2) the maximizing dominance degree feedback mechanism to reach group consensus. Therefore, firstly owing to the complexity of the trust relationships network, judging the direct and indirect trust propagation paths among the decision makers (DMs) to construct …the complete trust relationships network and identifying the highest value of Trust Score (TS) as the leader is possible. Then identify the inconsistent DM based on the established consensus index. During the feedback process, inconsistent DMs adopt the feedback mechanism based on the dominance degree of the leader until the group consensus is reached. Later, the corresponding ranking result is calculated by the TODIM method. Finally, a numerical example is applied to illustrate the effectiveness and feasibility of the optimal model. Show more

Keywords: Trust relationship, leader, TODIM, dominance degree, deviation degree

DOI: 10.3233/JIFS-211979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3775-3788, 2022

Authors: Zhang, Huaige | Bai, Xuyang | Hong, Xianpei

Article Type: Research Article

Abstract: With the rise in the global aging population, selecting sites for nursing homes for old-age care has become critical and challenging. The site selection of a nursing home can be considered as a multicriteria decision-making. Because of the increasing complexity and uncertainty of the socioeconomic environment, standard assessments cannot handle this multicriteria decision-making. Therefore, this study provides a multi-criteria decision-making method based on Interval 2 Fuzzy Sets (IT2FS). It obtains comprehensive weights through the AHP method and the CRITIC method. Compared with the traditional TOPSIS, the improved TOPSIS method reduces the difference between the evaluation results. This method is suitable …for the site selection of nursing homes in a certain area. We use the data of nursing homes to show the application of these methods. By comparing with traditional methods, we find that the integrated approach can consider more uncertainties. Show more

Keywords: Multicriteria decision-making, nursing home, site selection, interval type-2 fuzzy sets, AHP method, improved TOPSIS method

DOI: 10.3233/JIFS-212010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3789-3804, 2022

Authors: Huang, Xin | Chen, Hong-zhuan

Article Type: Research Article

Abstract: Combine complex equipment collaborative development in military-civilian integration context not only fulfils actual development requirement, but also beneficial to the national economy. Design procedure as first stage of complex equipment military-civilian collaborative development process, select suitable design supplier is significant to whole development process of complex equipment. In order to select suitable design supplier for complex equipment, two aspects done in this paper. One is comprehensive analysis of evaluated influencing factors that affect complex equipment military-civilian collaborative design process, corresponding evaluation indicator constructed and a combination of grey correlation, entropy, DEMATEL (Decision-making Trial and Evaluation Laboratory) and VIKOR analysis theory …to obtain grey entropy-DEMATEL-VIKOR, then the combined method is utilized to acquire matching attributes for followed research content. Meanwhile, satisfaction degree for matching side obtained with the help of information aggregation based on power generalized Heronian mean which on the basis of fuzzy preference information. Then, through constructed matching model, suitable design supplier obtained. Finally, a corresponding illustrative example given. Show more

Keywords: Complex equipment, military-civilian collaborative design, power generalized Heronian mean, matching model, design supplier

DOI: 10.3233/JIFS-212025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3805-3825, 2022

Authors: Jiang, Bin | Wang, Xinyu | Huang, Li | Xiao, Jian

Article Type: Research Article

Abstract:  Graph Convolutional Networks are able to characterize non-Euclidean spaces effectively compared with traditional Convolutional Neural Networks, which can extract the local features of the point cloud using deep neural networks, but it cannot make full use of the global features of the point cloud for semantic segmentation. To solve this problem, this paper proposes a novel network structure called DeepGCNs-Att that enables deep Graph Convolutional Network to aggregate global context features efficiently. Moreover, to speed up the computation, we add an Attention layer after the Graph Convolutional Network Backbone Block to mutually enhance the connection between the distant points of …the non-Euclidean space. Our model is tested on the standard benchmark S3DIS. By comparing with other deep Graph Convolutional Networks, our DeepGCNs-Att’s mIoU has at least two percent higher than that of all other models and even shows excellent results in space complexity and computational complexity under the same number of Graph Convolutional Network layers. Show more

Keywords: Point cloud processing, semantic segmentation, graph convolutional network, attention module, deep learning

DOI: 10.3233/JIFS-212030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3827-3836, 2022

Authors: Yan, Bing | Wang, Yanjun | Xia, Wei | Hu, Xiaoxuan | Ma, Huawei | Jin, Peng

Article Type: Research Article

Abstract: Satellite emergency mission scheduling scheme group decision making (SEMSSGDM) is a key part of satellite mission scheduling research. An appropriate evaluation model can provide a dependable and sustainable improvement and guide the functioning of emergency mission scheduling. Consequently, this research is devoted to proposing a novel decision-making method that employs a novel consensus model with hesitant fuzzy 2-tuple linguistic sets (HF2TLSs) to eliminate disagreements among satellite dispatchers and reach consensus in scheme decision-making. Within the novel method, it proposes a distance measurement function based on Hausdorff distance with HF2TLS to gauge the fit and similarity across satellite dispatchers. Additionally, a …consensus reaching process (CRP) is designed to adjust the judgement of satellite dispatchers taking into account the trust degree to improve consensus. Within the selection process, a combination of the particle swarm optimization (PSO) algorithm and the MULTIplicative MOORA (MULTIMOORA) method is applied, where PSO is performed to improve the accuracy of information aggregation, and the MULTIMOORA method is used to develop the robustness of the selection results. Lastly, an applicative example validates the effectiveness of the method based on a mission scheduling intelligent decision simulation system. Show more

Keywords: Satellite emergency mission scheduling scheme group decision-making, hesitant fuzzy 2-tuple linguistic set, consensus reaching processes(CRPs), particle swarm optimization(PSO), MULTIplicative MOORA (MULTIMOORA)

DOI: 10.3233/JIFS-212034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3837-3853, 2022

Authors: Khababa, Ghizlane | Seghir, Fateh | Bessou, Sadik

Article Type: Research Article

Abstract:  In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation …of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset. Show more

Keywords: Web service composition, Quality of Service (QoS), interval number, skyline operator, artificial bee colony

DOI: 10.3233/JIFS-212045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3855-3870, 2022

Authors: Nguyen, Binh | Le, Binh | Nguyen, Long H.B. | Dinh, Dien

Article Type: Research Article

Abstract:  Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive …results over the scaled Transformer with two times lower parameters. Show more

Keywords: Neural Machine Translation, Phrase Representation, Capsule Network

DOI: 10.3233/JIFS-212101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3871-3878, 2022

Authors: Varghese, Prathibha | Arockia Selva Saroja, G.

Article Type: Research Article

Abstract: Nature-inspired computing has been a real source of motivation for the development of many meta-heuristic algorithms. The biological optic system can be patterned as a cascade of sub-filters from the photoreceptors over the ganglion cells in the fovea to some simple cells in the visual cortex. This spark has inspired many researchers to examine the biological retina in order to learn more about information processing capabilities. The photoreceptor cones and rods in the human fovea resemble hexagon more than a rectangular structure. However, the hexagonal meshes provide higher packing density, consistent neighborhood connectivity, and better angular correction compared to the …rectilinear square mesh. In this paper, a novel 2-D interpolation hexagonal lattice conversion algorithm has been proposed to develop an efficient hexagonal mesh framework for computer vision applications. The proposed algorithm comprises effective pseudo-hexagonal structures which guarantee to keep align with our human visual system. It provides the hexagonal simulated images to visually verify without using any hexagonal capture or display device. The simulation results manifest that the proposed algorithm achieves a higher Peak Signal-to-Noise Ratio of 98.45 and offers a high-resolution image with a lesser mean square error of 0.59. Show more

Keywords: Computer vision, Nature Inspired Computing, hexagonal image, color image, Human Visual System

DOI: 10.3233/JIFS-212111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3879-3892, 2022

Authors: Peng, Zhenxing | He, Lina | Xie, Yushi | Song, Wenyan | Liu, Jue | Ming, Xinguo | Goh, Mark

Article Type: Research Article

Abstract: A sustainable supply chain (SSC) is vital for company’s sustainability success, so it is imperative to identify and prioritize SSC’s design requirements (DRs) for better SSC planning. For customer-centric markets, the customer requirements (CRs) need to be integrated into SSC’s DRs. This paper thus proposes a customer-centric approach based on Analytic Network Process (ANP), Quality Function Deployment (QFD), Grey Relational Analysis (GRA), and Pythagorean Fuzzy Set (PFS) to rank SSC’s DRs, considering CRs and information ambiguity. The PFS is combined with ANP, QFD, and GRA to better handle uncertainty in the SSC. The Pythagorean fuzzy ANP is applied to analyze …the correlations among the sustainable CRs and determine the corresponding weights. The sustainable CRs are transformed into the DRs using the Pythagorean fuzzy QFD. The relationships among the resulting DRs are analyzed through Pythagorean fuzzy GRA to prioritize DRs. The approach is validated through a case study. The results obtained in this paper shows that the proposed method is efficient to prioritize DRs of SSC with the consideration of sustainable CRs under uncertain environment. The novelties of proposed method are that it not only offers a customer-oriented SSC planning method through the integration of ANP, QFD and GRA, but also can reflect the uncertain information with a broader membership representation space via PFSs. Based on the proposed method, the decision-maker can conduct comprehensive analysis to prioritize DRs and design appropriate SSC to fulfill CRs under uncertain environment. Show more

Keywords: Sustainable supply chain, design requirements, analytic network process, pythagorean fuzzy set, quality function deployment, grey relational analysis

DOI: 10.3233/JIFS-212131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3893-3907, 2022

Authors: Zhu, Xiaowei | Han, Yu | Li, Shichong | Wang, Xinyin

Article Type: Research Article

Abstract: With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information is introduced to focus on bursty topics for further weakening the semantic sparsity …of social network context. Besides, we introduced the “Spike and Slab” prior to decouple the sparseness and smoothness. Simultaneously, we realized the automatic discovery of social network bursts by introducing the burstiness of words as the prior and binary switching variables. We constructed multiple sets of comparative experiments to verify the performance of ST-SRTM by leveraging different evaluation indicators on real Sina Weibo data sets. The experimental results confirm the superiority of our ST-SRTM. Show more

Keywords: Social network, bursty topic, topic model, RNN, sparse prior

DOI: 10.3233/JIFS-212135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3909-3922, 2022

Authors: Rashwan, Rashwan A. | Hammad, Hasanen A. | Nafea, A.

Article Type: Research Article

Abstract: In this manuscript, the concept of a cyclic tripled type fuzzy cone contraction mapping in the setting of fuzzy cone metric spaces is introduced. Also, some theoretical results concerned with tripled fixed points are given without a mixed monotone property in the mentioned space. Moreover, under this concept, some strong tripled fixed point results are obtained. Ultimately, to support the theoretical results non-trivial examples are listed and the existence of a unique solution to a system of integral equations is presented as an application.

Keywords: Strong tripled fixed point, fuzzy cone metric space, contraction condition, integral equation, cyclic tripled type fuzzy cone contraction mapping

DOI: 10.3233/JIFS-212188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3923-3943, 2022

Authors: Wang, Yini | Wang, Sichun

Article Type: Research Article

Abstract: Fuzzy relation is one of the main research contents of fuzzy set theory. This paper obtains some results on fuzzy relations by studying relationships between fuzzy relations and their uncertainty measurement. The concepts of equality, dependence, partial dependence and independence between fuzzy relations are first introduced. Then, uncertainty measurement for a fuzzy relation is investigated by using dependence between fuzzy relations. Moreover, the basic properties of uncertainty measurement are obtained. Next, effectiveness analysis is carried out. Finally, an application of the proposed measures in attribute reduction for heterogeneous data is given. These results will be helpful for understanding the essence …of a fuzzy relation. Show more

Keywords: Fuzzy relation, dependence, uncertainty, measurement, attribute reduction, heterogeneous data

DOI: 10.3233/JIFS-212215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3945-3961, 2022

Authors: Su, Nan | Lin, Zhishuo | You, Wenlong | Zheng, Nan | Ma, Kun

Article Type: Research Article

Abstract: Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, …and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient. Show more

Keywords: Garbage classification, multi-modality, picture recognition, real-time video recognition

DOI: 10.3233/JIFS-212225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3963-3973, 2022

Authors: Li, Dong | Gong, Lanlan | Liu, Shulin | Sun, Xin | Gu, Ming | Qian, Kun

Article Type: Research Article

Abstract: The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory …cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%. Show more

Keywords: Classification, continual learning, biological immune system, machine learning, artificial immune algorithm

DOI: 10.3233/JIFS-212226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3975-3991, 2022

Authors: Yang, Jie | Luo, Tian | Zeng, Lijuan | Jin, Xin

Article Type: Research Article

Abstract: Neighborhood rough sets (NRS) are the extended model of the classical rough sets. The NRS describe the target concept by upper and lower neighborhood approximation boundaries. However, the method of approximately describing the uncertain target concept with existed neighborhood information granules is not given. To solve this problem, the cost-sensitive approximation model of the NRS is proposed in this paper, and its related properties are analyzed. To obtain the optimal approximation granular layer, the cost-sensitive progressive mechanism is proposed by considering user requirements. The case study shows that the reasonable granular layer and its approximation can be obtained under certain …constraints, which is suitable for cost-sensitive application scenarios. The experimental results show that the advantage of the proposed approximation model, moreover, the decision cost of the NRS approximation model will monotonically decrease with granularity being finer. Show more

Keywords: Neighborhood rough sets, approximation model, cost-sensitive, Granular layer selection

DOI: 10.3233/JIFS-212234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3993-4003, 2022

Authors: Yu, Zhiqiang | Huang, Yuxin | Guo, Junjun

Article Type: Research Article

Abstract: It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder …into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works. Show more

Keywords: Neural machine translation, Thai-Lao, linguistic similarity, structure improving, lexicon

DOI: 10.3233/JIFS-212236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4005-4014, 2022

Authors: Binh, Nguyen Thanh | Hien, Nguyen Mong | Tin, Dang Thanh

Article Type: Research Article

Abstract: The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy …because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset. Show more

Keywords: Arterioles, venules, U-Net architecture, graph cuts, retinal blood vessels

DOI: 10.3233/JIFS-212259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4015-4026, 2022

Authors: Zhong, Xianyou | Xia, Tianyi | Zhao, Yankun | Zhao, Xiao

Article Type: Research Article

Abstract: The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. …Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method. Show more

Keywords: Swarm decomposition, frequency-weighted energy operator, fault diagnosis, rolling bearing

DOI: 10.3233/JIFS-212305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4027-4039, 2022

Authors: Guo, Wenbin | Zhang, Juan

Article Type: Research Article

Abstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network …and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network. Show more

Keywords: Rain removal, raindrop detection, semi-supervised learning, image restoration, shared weight

DOI: 10.3233/JIFS-212342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4041-4049, 2022

Authors: Kalimuthu, Raj Kumar | Thomas, Brindha

Article Type: Research Article

Abstract: In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource …allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms. Show more

Keywords: Metaheuristic algorithm, Internet of Things, cloud computing, resource optimization, scheduling algorithms

DOI: 10.3233/JIFS-212370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4051-4063, 2022

Authors: Huang, Xiaoqing | Wang, Zhilong | Liu, Shihao

Article Type: Research Article

Abstract: In order to solve the problem of health evaluation of CNC machine tools, an evaluation method based on grey clustering analysis and fuzzy comprehensive evaluation was proposed. The health status grade of in-service CNC machine tools was divided, and the performance indicator system of CNC machine tools was constructed. On the above basis, the relative importance of each performance and its indicators were combined, and grey clustering analysis and fuzzy comprehensive evaluation was utilized to evaluate the health status of in-service CNC machine tools to determine their health grade. The proposed health status evaluation method was applied to evaluate the …health level of an in-service gantry CNC machine that can be used for the machining propellers, and the results shown that the health status of the whole gantry CNC machine tool is healthy. The proposed evaluation method provides useful references for further in-depth research on the health status analysis and optimization of CNC machine tools. Show more

Keywords: CNC machine tools, grey clustering, fuzzy comprehensive evaluation, health evaluation, green performance

DOI: 10.3233/JIFS-212406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4065-4082, 2022

Authors: Javid, Irfan | Zager Alsaedi, Ahmed Khalaf | Ghazali, Rozaida | Mohmad Hassim, Yana Mazwin | Zulqarnain, Muhammad

Article Type: Research Article

Abstract: In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when …searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17%. In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease. Show more

Keywords: Gated recurrent unit, heart disease, overfitting, underfitting, feature selection

DOI: 10.3233/JIFS-212438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4083-4094, 2022

Authors: Ulusu, Uğur | Gülle, Esra

Article Type: Research Article

Abstract: The main purpose of this paper is introduced the concept of deferred Cesàro mean in the Wijsman sense for double sequences of sets and then presented the concepts of strongly deferred Cesàro summability and deferred statistical convergence in the Wijsman sense for double sequences of sets. Also, investigate the relationships between these concepts and then to prove some theorems associated with the concepts of deferred statistical convergence in the Wijsman sense for double sequences of sets is purposed.

Keywords: Deferred Cesàro summability, deferred statistical convergence, double sequences of sets, convergence in the Wijsman sense

DOI: 10.3233/JIFS-212486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4095-4103, 2022

Authors: Zhang, Qinghui | Wu, Meng | Lv, Pengtao | Zhang, Mengya | Yang, Hongwei

Article Type: Research Article

Abstract: In the medical field, Named Entity Recognition (NER) plays a crucial role in the process of information extraction through electronic medical records and medical texts. To address the problems of long distance entity, entity confusion, and difficulty in boundary division in the Chinese electronic medical record NER task, we propose a Chinese electronic medical record NER method based on the multi-head attention mechanism and character-word fusion. This method uses a new character-word joint feature representation based on the pre-training model BERT and self-constructed domain dictionary, which can accurately divide the entity boundary and solve the impact of unregistered words. Subsequently, …on the basis of the BiLSTM-CRF model, a multi-head attention mechanism is introduced to learn the dependency relationship between remote entities and entity information in different semantic spaces, which effectively improves the performance of the model. Experiments show that our models have better performance and achieves significant improvement compared to baselines. The specific performance is that the F1 value on the Chinese electronic medical record data set reaches 95.22%, which is 2.67%higher than the F1 value of the baseline model. Show more

Keywords: Chinese electronic medical records, name entity recognition, character-word information fusion, multi-head attention

DOI: 10.3233/JIFS-212495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4105-4116, 2022

Authors: Albert, Johny Renoald | Sharma, Aditi | Rajani, B. | Mishra, Ashish | Saxena, Ankur | Nandagopal, C. | Mewada, Shivlal

Article Type: Research Article

Abstract: A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Modified Firefly Algorithm (MFA). It’s determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar …fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model. Show more

Keywords: Symmetric solar fed inverter, particle swarm optimization, genetic algorithm, modified firefly algorithm, power quality

DOI: 10.3233/JIFS-212559

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4117-4133, 2022

Authors: Do Xuan, Cho | Duong, Duc

Article Type: Research Article

Abstract: Nowadays, early detecting and warning Advanced Persistent Threat (APT) attacks is a major challenge for intrusion monitoring and prevention systems. Current studies and proposals for APT attack detection often focus on combining machine-learning techniques and APT malware behavior analysis techniques based on network traffic. To improve the efficiency of APT attack detection, this paper proposes a new approach based on a combination of deep learning networks and ATTENTION networks. The proposed process for APT attack detection in this study is as follows: Firstly, all data of network traffic is pre-processed, and analyzed by the CNN-LSTM deep learning network, which is …a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Then, instead of being used directly for classification, this data is analyzed and evaluated by the ATTENTION network. Finally, the output data of the ATTENTION network is classified to identify APT attacks. The optimization proposal for detecting APT attacks in this study is a novel proposal. It hasn’t been proposed and applied by any research. Some scenarios for comparing and evaluating the method proposed in this study with other approaches (implemented in section 4.4) show the superior effectiveness of our proposed approach. The results prove that the proposed method not only has scientific significance but also has practical significance because the model combining deep learning with ATTENTION network has helped improve the efficiency of analyzing and detecting APT malware based on network traffic. Show more

Keywords: APT, APT attack detection, Network traffic, Abnormal behavior, Deep Learning, attention

DOI: 10.3233/JIFS-212570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4135-4151, 2022

Authors: Adline Priya, G. | Sundar, C. | Pavalarajan, S.

Article Type: Research Article

Abstract: The adoption of a new transmission line is extremely complex because of its socio-economic problems such as environmental clearances. Thus, there is a prominence of better utility over available transmission infrastructure. The Flexible Alternating Current Transmission System (FACTS) devices can offer transmission capability enhancement, power compensation, and stability as well as voltage improvement. However, the FACTS devices have a higher penetration impact of wind generation for the dynamic stability of power networks. In this work, an efficient Intellectual Control system has been proposed to stabilize the FACTS devices placement. The Squirrel Search Optimization is adapted with an intellectual control system …to enhance the steady-state voltage stability of FACTS devices. The proposed system has been evaluated with the assist of IEEE 14 and 26 standard bus systems to handle the multi-objective functions like cost, reduction in power loss, reducing risks, and maximizing user’s benefit. These multi-objective functions facilitate to attain the optimal placement and load flows at various sites. The simulation can be carried out with MATLAB/SIMULINK environment and the results manifest that the proposed system outperforms well when compared with existing approaches. Show more

Keywords: FACTS devices, squirrel search optimization, voltage stability, multi-objective, optimal load flow

DOI: 10.3233/JIFS-212573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4153-4171, 2022

Authors: Sen, Rikta | Goswami, Saptarsi | Mandal, Ashis Kumar | Chakraborty, Basabi

Article Type: Research Article

Abstract: Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM …distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost. Show more

Keywords: Feature selection, JM distance multiclass extension, feature ranking and subset selection

DOI: 10.3233/JIFS-202796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4173-4190, 2022

Authors: Karimi, Saeed | Mirzamohammadi, Saeed | Pishvaee, MirSaman

Article Type: Research Article

Abstract: As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy …parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers. Show more

Keywords: Project portfolio selection, project interdependencies, possibilistic robustness, fuzzy uncertainty

DOI: 10.3233/JIFS-210144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4191-4204, 2022

Authors: Sun, Kexin | Xin, Yuelan | Ma, Yide | Lou, Meng | Qi, Yunliang | Zhu, Jie

Article Type: Research Article

Abstract: U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM …is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively. Show more

Keywords: Mammograms, mass segmentation, convolutional neural network, adaptive scale module, feature refinement module

DOI: 10.3233/JIFS-210393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4205-4220, 2022

Authors: Liu, Chaojie | Lu, Jie | Fu, Wenjing | Zhou, Zhuoyi

Article Type: Research Article

Abstract: How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier …Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model. Show more

Keywords: Housing price, big data, MGWR, GTWR, BLAAP, batch evaluation model

DOI: 10.3233/JIFS-210917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4221-4240, 2022

Authors: Xiang, Chen | Xing, Wang | Hubiao, Zhang | Yuheng, Xu | You, Chen | Xiaotian, Wu

Article Type: Research Article

Abstract: Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, …which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making. Show more

Keywords: Interval number, multiple attribute decision making (MADM), interval TOPSIS, comprehensive weight, threat evaluation

DOI: 10.3233/JIFS-210945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4241-4257, 2022

Authors: Zhou, Weibin | Chen, Tao | Huang, Huafang | Sheng, Chang | Wang, Yangfeng | Wang, Yang | Zhang, Daqiang

Article Type: Research Article

Abstract: Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the …last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms. Show more

Keywords: Iris segmentation, iris recognition, CNN, U-net, low complexity

DOI: 10.3233/JIFS-211396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4259-4275, 2022

Authors: Sworna Kokila, M.L. | Gomathi, V.

Article Type: Research Article

Abstract: Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible …to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities. Show more

Keywords: Auto track regression framework, auto wiley detection, originated pick virtual classifier

DOI: 10.3233/JIFS-201977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4277-4294, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4295-4295, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4297-4297, 2022