Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Selvakumar, K. | SaiRamesh, L. | Ayyasamy, A. | Archana, M.

Article Type: Research Article

Abstract: This research work confronts a sender-based responsive and novel protocol named “Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)” protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of ‘n’ number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route …during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node’s trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols. Show more

Keywords: QoS, energy level, fuzzy rules, mobility prediction, optimal path, trustworthiness

DOI: 10.3233/JIFS-190050

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1-17, 2021

Authors: Aziz, Ahmed | Singh, Karan | Osamy, Walid | Khder, Ahmed M. | Tuan, Le Minh | Son, Le Hoang | Long, Hoang Viet | Rakhmonov, Dilshodjon

Article Type: Research Article

Abstract: Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M < <N. Through this paper, we propose a new scheme for data gathering in IoT based …heterogeneous WSN that includes a new effective Deterministic Clustering using CS technique (DCCS) to handle the data acquisition problem. DCCS reduces the total overhead computational cost needed to self-organize WSN using a simple approach and then uses CS at each sensor node to decrease the overall energy consumption and increase the network lifetime. The proposed scheme includes also an effective CS reconstruction algorithm called Random Selection Matching Pursuit (RSMP) to improve the recovery process at the base station (BS). RSMP adds a random selection process during the forward step to give the opportunity for more columns to be selected as an estimated solution in each iteration. The simulation results show that the proposed scheme succeeds to minimize the overall network power consumption and prolong the network lifetime beside provide better performance in CS data reconstruction. Show more

Keywords: Internet of things, clustering based wireless sensor networks, compressive sensing, routing techniques, data reconstruction, network lifetime

DOI: 10.3233/JIFS-190862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 19-35, 2021

Authors: Iqbal, Jawaid | Umar, Arif Iqbal | Ul Amin, Noor | Waheed, Abdul | Abdullah, Saleem | Zareei, Mahdi | Khattak, Muazzam Ali Khan

Article Type: Research Article

Abstract: In the last decade, due to wireless technology’s enhancement, the people’s interest is highly increased in Wireless Body Sensor Networks (WBSNs). WBSNs consist of many tiny biosensor nodes that are continuously monitoring diverse physiological signals such as BP (systolic and diastolic), ECG, EMG, SpO2, and activity recognition and transmit these sensed patients’ sensitive information to the central node, which is straight communicate with the controller. To disseminate this sensitive patient information from the controller to remote Medical Server (MS) needs to be prolonged high-speed wireless technology, i.e., LTE, UMTS, WiMAX, WiFi, and satellite communication. It is a challenging task for …the controller to choose the optimal network to disseminate various patient vital signs, i.e., emergency data, normal data, and delay-sensitive data. According to the nature of various biosensor nodes in WBSNs, monitor patient vital signs and provide complete intelligent treatment when any abnormality occurs in the human body, i.e., accurate insulin injection when patient sugar level increased. In this paper, first, we select the optimal network from accessible networks using four different fuzzy attribute-based decision-making techniques (Triangular Cubic Hesistent Fuzzy Weighted Averaging Operator, Neutrosophic Linguistic TOPSIS method, Trangualar Cubic Hesistent Fuzzy Hamacher Weighted Averaging Operator and Cubic Grey Relational Analysis) depending upon the quality of service requirement for various application of WBSNs to prolong the human life, enhanced the society’s medical treatment and indorse living qualities of people. Similarly, leakage and misuse of patient data can be a security threat to human life. Thus, confidential data transmission is of great importance. For this purpose, in our proposed scheme, we used HECC for secure key exchange and an AES algorithm to secure patient vital signs to protect patient information from illegal usage. Furthermore, MAC protocol is used for mutual authentication among sensor nodes and Base Stations (BS). Mathematical results show that our scheme is efficient for optimal network selection in such circumstances where conflict arises among diverse QoS requirements for different applications of WBSNs. Show more

Keywords: Wireless body sensor network, quality of service, security, fuzzy logic, decision making

DOI: 10.3233/JIFS-191104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 37-55, 2021

Authors: Ji, Junqing | Kong, Xiaojia | Zhang, Yajing | Xu, Tongle | Zhang, Jing

Article Type: Research Article

Abstract: The traditional blind source separation (BSS) algorithm is mainly used to deal with signal separation under the noiseless model, but it does not apply to data with the low signal to noise ratio (SNR). To solve the problem, an adaptive variable step size natural gradient BSS algorithm based on an improved wavelet threshold is proposed in this paper. Firstly, an improved wavelet threshold method is used to reduce the noise of the signal. Secondly, the wavelet coefficient layer with obvious periodicity is denoised using a morphological component analysis (MCA) algorithm, and the processed wavelet coefficients are recombined to obtain the …ideal model. Thirdly, the recombined signal is pre-whitened, and a new separation matrix update formula of natural gradient algorithm is constructed by defining a new separation degree estimation function. Finally, the adaptive variable step size natural gradient blind source algorithm is used to separate the noise reduction signal. The results show that the algorithm can not only adaptively adjust the step size according to different signals, but also improve the convergence speed, stability and separation accuracy. Show more

Keywords: Improved wavelet threshold function, noise reduction, blind source separation, natural gradient, adaptive variable step size

DOI: 10.3233/JIFS-200111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 57-68, 2021

Authors: Zhang, Yikai | Peng, Yong | Bian, Hongyu | Ge, Yuan | Qin, Feiwei | Kong, Wanzeng

Article Type: Research Article

Abstract: Concept factorization (CF) is an effective matrix factorization model which has been widely used in many applications. In CF, the linear combination of data points serves as the dictionary based on which CF can be performed in both the original feature space as well as the reproducible kernel Hilbert space (RKHS). The conventional CF treats each dimension of the feature vector equally during the data reconstruction process, which might violate the common sense that different features have different discriminative abilities and therefore contribute differently in pattern recognition. In this paper, we introduce an auto-weighting variable into the conventional CF objective …function to adaptively learn the corresponding contributions of different features and propose a new model termed Auto-Weighted Concept Factorization (AWCF). In AWCF, on one hand, the feature importance can be quantitatively measured by the auto-weighting variable in which the features with better discriminative abilities are assigned larger weights; on the other hand, we can obtain more efficient data representation to depict its semantic information. The detailed optimization procedure to AWCF objective function is derived whose complexity and convergence are also analyzed. Experiments are conducted on both synthetic and representative benchmark data sets and the clustering results demonstrate the effectiveness of AWCF in comparison with some related models. Show more

Keywords: Concept factorization, auto-weighting, feature map, data representation, clustering

DOI: 10.3233/JIFS-200298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 69-81, 2021

Authors: Hussain, Abid | Munawar, Saima | Naveed, Nasir

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) consist of various low-cost devices with limited battery power for surveillance of certain vicinity. The main concern was to prolong the network lifetime to save energy. The heterogeneous nodes are deployed in the given setting divided into two INSTANT-OFF and NEVER-OFF states. Then each one is further subdivided by a Fuzzy Inference System (FIS). The INSTANT-OFF (Good, Better, and Best) has three states active, idle, sleep, and always worked as Cluster Members (CMs) to sense the physical environment. The NEVER-OFF (Good, Better, and Best) has active and idle states. The first two most optimum NEVER-OFF selected …as Cluster Head (CH) and Data Collector (DC), and the remaining belonged to CMs. The cluster boundary was defined by parameter Distance from Base Station (DisBS) to meet the unequal clustering approach. The energy consumes during sensing, processing, and transmission phases by its appropriate nodes. The CMs worked reactively and saved energy by idle and sleep states, while the CH and DC worked in a proactive mode and saved energy in an idle state. The sensing job was done by CMs that consumed a minor amount of energy and transmitted packets of 200 bits length to DC. The DC received packets of 200 bits length from CMs and aggregated them into 6400 bits length packets, then delivered them to CH. The reactive and proactive mechanisms saved the energy as 85.1033% in 2000 rounds; increased lifetime up to 774 rounds, re-clustering setup took place after 1912 rounds, and enhanced the throughput as 100% and latency time 0.001123 by experiment evaluation. The result shows that most energy consumption job were communicated with BS performed by CH hop by hop through other CH. The unequal clustering approach maintained the consumption of energy levels throughout WSNs processing. Show more

Keywords: Wireless Sensor Networks, Fuzzy Inference System, Residual Energy, Sensor Node, energy constraint, clustering

DOI: 10.3233/JIFS-200382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 83-98, 2021

Authors: Cao, Lianglin | Ben, Kerong | Peng, Hu

Article Type: Research Article

Abstract: Firefly algorithm (FA) is one of most important nature-inspired algorithm based on swarm intelligence. Meanwhile, FA uses the full attraction model, which results too many unnecessary movements and reduces the efficiency of searching the optimal solution. To overcome these problems, this paper presents a new job, how the better fireflies move, which is always ignored. The novel algorithm is called multiple swarm strategy firefly algorithm (MSFFA), in which multiple swarm attraction model and status adaptively switch approach are proposed. It is characterized by employing the multiple swarm attraction model, which not only improves the efficiency of searching the optimal solution, …but also quickly finds the better fireflies that move in free status. In addition, the novel approach defines that the fireflies followed different rules in different status, and can adaptively switch the status of fireflies between the original status and the free status to balance the exploration and the exploitation. To verify the robustness of MSFFA, it is compared with other improved FA variants on CEC2013. In one case of 30 dimension on 28 test functions, the proposed algorithm is significantly better than FA, DFA, PaFA, MFA, NaFA,and NSRaFA on 24, 23, 23, 17, 15, and 24 functions, respectively. The experimental results prove that MSFFA has obvious advantages over other FA variants. Show more

Keywords: Fiefly algorithm, multiple swarm strategy, adaptively switch, global optimization

DOI: 10.3233/JIFS-200619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 99-112, 2021

Authors: Wang, Yan | Wang, Jianchun | Li, Yanju | Yu, Ming | Zhou, Yancong | Zhang, Bo

Article Type: Research Article

Abstract: Facial expression recognition (FER) has been an active research area in recent years, which plays a vital role in national security and human-computer interaction. Due to the lacking of sufficient expression features and facial images, it is challenging to automatically recognize facial expression with high accuracy. In this paper, we propose a fusion handcraft feature method to improve FER from images. Firstly, a new texture feature extraction method PD-LDN (Pixel Difference Local Directional Number pattern) is proposed, which can extract more local information, reduce noise disturbance and feature dimension. Secondly, the handcrafted features including PD-LDN texture features, geometric features, and …BOVW (Bag of Visual Words) semantic features are connected in parallel to an improved autoencoder network for fusion. Finally, the fused features are input into the softmax classifier for recognizing facial expression. We conduct extensive experiments on JAFFE and CK+datasets. Our proposed method shows superior performance than the state-of-the-art approaches on recognizing facial expressions. Show more

Keywords: Facial expression recognition, LDN, feature fusion, softmax

DOI: 10.3233/JIFS-200713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 113-123, 2021

Authors: Hussain, Muhammad Iftikhar | He, Jingsha | Zhu, Nafei | Ali Zardari, Zulfiqar | Razque, Fahad | Hussain, Saqib | Pathan, Muhammad Salman

Article Type: Research Article

Abstract: Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, …firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases. Show more

Keywords: Multi-cloud, next-generation firewall (NGFW), firewall security, cloud computing, single SLA

DOI: 10.3233/JIFS-200835

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 125-136, 2021

Authors: Zhang, Tao | Li, Shizheng | Wang, Jin

Article Type: Research Article

Abstract: China has proposed medical couplet body to alleviate residents’ difficulties in seeking medical treatment, and the future development ability of medical couplet body has gradually become a research interest. On the basis of prospect theory, this study constructs a comprehensive evaluation index system with qualitative and quantitative indexes, clear hierarchy, and diverse attribute characteristics. The development ability of medical couplet body is also comprehensively and systematically evaluated. In addition, the evidential reasoning method is proposed on the basis of the equivalent transformation of prospect value. Furthermore, the validity and feasibility of the model are proven through experiments, and the influence …of decision makers’ risk attitude on the evaluation results is discussed. Show more

Keywords: Medical couplet body, prospect theory, evidence reasoning

DOI: 10.3233/JIFS-200883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 137-154, 2021

Authors: Cosme, Luciana Balieiro | D’Angelo, Marcos Flávio Silveira Vasconcelos | Caminhas, Walmir Matos | Camargos, Murilo Osorio | Palhares, Reinaldo Martínez

Article Type: Research Article

Abstract: The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approach based on the IMM filter and retention models to determine the TPM adaptively and automatically with relatively low computational cost and no need for complex operations or storing the measurement history. The proposed method is compared to the traditional IMM filter, IMM with Bayesian Network (BNs) and a state-of-the-art Adaptive TPM-based …parallel IMM (ATPM-PIMM) algorithm. The experiments were carried out in an artificial numerical example as well as in two real-world health monitoring applications: the PRONOSTIA platform and the Li-ion batteries data set provided by NASA. The Retention Interacting Multiple Model (R-IMM) results indicate that a better prediction performance can be obtained when the TPM is not properly adjusted or not precisely known. Show more

Keywords: Adaptive systems, dynamic systems, filtering techniques, markov models, system state estimation

DOI: 10.3233/JIFS-201129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 155-166, 2021

Authors: An, Jiangfeng | Wu, Jun | Zheng, Penghua | Pan, Ying | Zhou, Xuejie | Yang, Mingshu

Article Type: Research Article

Abstract: The environmental adaptabilities of low-density polyethylene (LDPE) play an important role for high-speed trains’ reliability and comfort. The weathering of LDPE depends on environment factors, while the complexity of the weathering processes inhibits the evaluation of environmental weathering risks. To elucidate the correlation between weathering and environmental factors, and to predict the weathering risk of target areas of interest, three-year-long natural weathering tests were conducted at twelve natural exposure stations in China. Properties of weathered LDPE were compared and analysed using factor analysis. The fuzzy recognition method based on analytic hierarchy process (AHP) was established and used to predict the …weathering risk based on environmental database. The results indicate that the factor scores can partitioned the atmospheric environments into five weathering risk grades. This article used the accumulated cumulative temperature of the daily maximum temperature for weathering risk evaluation, which is more scientific than the annual average temperature widely used and is useful for revealing the difference in LDPE weathering in Turpan and Korla. A comparative chart of LDPE’s weathering risk in China was established by this fuzzy recognition method for the first time, which caters to the continuous extension of high-speed railway to new regions. Show more

Keywords: Weathering risk, fuzzy recognition, factor analysis, accumulated temperature, low-density polyethylene

DOI: 10.3233/JIFS-201201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 167-179, 2021

Authors: Alli, P. | Dinesh Peter, J.

Article Type: Research Article

Abstract: The day-to-day progress in communication plays a vital role in transmitting millions and trillions of data through the unsecured network channels. It creates a way where the user’s data becomes the victim of various security threats. Among those users’ data, images act as primary data, and its encryption security methodologies are fascinating. The conventional encryption techniques don’t work well against the various other hidden security threats but require substantial computational time and cost with poor permutation performance. Hence to deal with this, an auto-encoder induced DNA (Deoxyribonucleic acid) sequence via chaotic image encryption framework is designed in our proposed work. …It integrates the properties of DNA encoding and the chaotic maps to handle the data losses effectively and resist several attacks such as statistical attacks, chosen-plaintext attacks, etc. Moreover, an auto-encoder is used to control the data noises, thereby ensuring a better encryption performance. Here, the auto-encoder is activated to generate a permuted image with less time complexity and noise. A secret key is then initialized with the aid of SHA-256. Finally, image encryption and decryption are achieved, followed by the successful transmission of data over a digital network. The performance of the proposed work is analyzed with varied metrics to strengthen its efficiency over the prior techniques. Show more

Keywords: Permuted image, SHA-256, DNA computing sequence, stacked auto-encoder, chaos based image encryption

DOI: 10.3233/JIFS-201224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 181-198, 2021

Authors: Shahri, Majid Mardani | Jahromi, Abdolhamid Eshraghniaye | Houshmand, Mahmoud

Article Type: Research Article

Abstract: The purpose of maintenance is to ensure the maximum efficiency and availability of production assets at optimal cost considering quality, safety, and environmental aspects. Assets criticality analysis is one of the main steps in many maintenance methodologies, including Reliability Centered Maintenance. The present study seeks to provide a solution for determining critical assets for more efficient maintenance management. In this regard, an integrated approach of the analytical hierarchy process and fuzzy inference system was proposed based on the concept of the risk matrix. According to the concept of the risk matrix, two main criteria of failure consequences and probability were …employed to determine assets criticality. Analytic Hierarchy Process (AHP) was used to consider all sub-criteria of failure consequences and probability. Finally, using two main criteria as inputs, a fuzzy inference system was developed to determine the criticality of the assets. The proposed approach was implemented in a gas refinery; the results showed its effectiveness and applicability in the process of prioritizing assets based on criticality criteria. The proposed approach has the advantages of multi-criteria decision-making techniques, modeling ambiguity and uncertainty in real issues, modeling the process of inference in the human mind, and storing the knowledge of the organization’s expert. Show more

Keywords: Assets criticality analysis, maintenance management, fuzzy inference system, risk matrix, analytical hierarchy process

DOI: 10.3233/JIFS-201407

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 199-217, 2021

Authors: Nishanthini, Radhakrishnan | Jeyabalan, Ramasamy | Balasundar, Samipillai | Kumar, Gurunathan

Article Type: Research Article

Abstract: The conception of magic labeling in fuzzy graphs elongates to fuzzy vertex magic labeling together with consecutive non-integer values in (0, 1] and the graph’s repercussion is named as fuzzy consecutive vertex magic labeling graphs (FCVM) along with the z -index. In this manuscript, we give some properties associated with FCVM labeling along with z -index as well as the presence of FCVM labeling with z -index in trees and some generalizations. Moreover, we examine the FCVM labeling along with z -index of both regular and irregular graphs. Finally, in real-time applications, we bestow an instance for fuzzy consecutive vertex …magic labeling graphs. Show more

Keywords: Fuzzy vertex magic, fuzzy consecutive vertex magic, comb graph, generalized butterfly graph, generalized peterson graph

DOI: 10.3233/JIFS-201489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 219-230, 2021

Authors: Fu, Wenqing | Khalil, Ahmed Mostafa | Zahran, Ahmed Mohamed | Basheer, Rehab

Article Type: Research Article

Abstract: The aim of this article is to present the concept of restricted union and extended intersection of belief interval-valued soft sets, along with its properties. In addition, we propose the concept of possibility belief interval-valued soft set theory and investigate their properties. For suitability of possible applications, there are seven kinds of operations (e.g., union, intersection, restricted union, extended intersection, complement, soft max-AND, and soft min-OR) on the possibility belief interval-valued soft sets are defined and their basic theoretical are given. Then, we construct two algorithms by using soft max-AND and soft min-OR operations of possibility interval-valued soft sets for …fuzzy decision-making problem. Lastly, we introduce an algorithm using a possibility interval-valued soft set to solve the decision-making problems and clarify its applicability by a numerical example. Show more

Keywords: Interval-valued fuzzy set, belief interval-valued soft set, possibility belief interval-valued soft set, decision-making

DOI: 10.3233/JIFS-201621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 231-249, 2021

Authors: Berenjian, Golnaz | Motameni, Homayun | Golsorkhtabaramiri, Mehdi | Ebrahimnejad, Ali

Article Type: Research Article

Abstract: Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph …(DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study. Show more

Keywords: Green service level agreement, hroughput, dynamic voltage and frequency scaling, energy-aware scheduling, slack-time distribution, petri nets

DOI: 10.3233/JIFS-201696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 251-272, 2021

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

Article Type: Research Article

Abstract: Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs ), the further extension of picture fuzzy sets (P c FSs ) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM …(S p FHM ) operator and weighted spherical fuzzy HM (WS p FHM ) operator to accumulate the spherical fuzzy (S p F ) information. Moreover, various properties and some particular cases of S p FHM and the WS p FHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WS p FHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed. Show more

Keywords: Spherical fuzzy sets, MCDM, linear programming model, Hamy mean operator

DOI: 10.3233/JIFS-201708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 273-298, 2021

Authors: Masood, Naveen | Farooq, Humera

Article Type: Research Article

Abstract: Most of the electroencephalography (EEG) based emotion recognition systems rely on single stimulus to evoke emotions. EEG data is mostly recorded with higher number of electrodes that can lead to data redundancy and longer experimental setup time. The question “whether the configuration with lesser number of electrodes is common amongst different stimuli presentation paradigms” remains unanswered. There are publicly available datasets for EEG based human emotional states recognition. Since this work is focused towards classifying emotions while subjects are experiencing different stimuli, therefore we need to perform new experiments. Keeping aforementioned issues in consideration, this work presents a novel experimental …study that records EEG data for three different human emotional states evoked with four different stimuli presentation paradigms. A methodology based on iterative Genetic Algorithm in combination with majority voting has been used to achieve configuration with reduced number of EEG electrodes keeping in consideration minimum loss of classification accuracy. The results obtained are comparable with recent studies. Stimulus independent configurations with lesser number of electrodes lead towards low computational complexity as well as reduced set up time for future EEG based smart systems for emotions recognition Show more

Keywords: Common spatial pattern (CSP), electrodes selection, electroencephalography (EEG), emotion recognition, feature extraction, genetic algorithm

DOI: 10.3233/JIFS-201779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 299-315, 2021

Authors: Li, Shugang | Wang, Ru | Zhang, Yuqi | Lu, Hanyu | Cai, Nannan | Yu, Zhaoxu

Article Type: Research Article

Abstract: Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands …the users’ AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter. Show more

Keywords: Concept marketing, social media influencers, attention concentration index, consensus smart discriminant algorithm

DOI: 10.3233/JIFS-201809

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 317-329, 2021

Authors: Wang, Tianxiong | Zhou, Meiyu

Article Type: Research Article

Abstract: When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the …significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need. Show more

Keywords: Rough set, semantic difference method, fuzzy set, customer satisfaction, kansei engineering

DOI: 10.3233/JIFS-201829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 331-353, 2021

Authors: Taimoor, Muhammad | Lu, Xiao | Shabbir, Wasif | Sheng, Chunyang | Samiuddin, Muhammad

Article Type: Research Article

Abstract: This research is concerned with the adaptive neural network observer based fault approximation and fault-tolerant control of time-varying nonlinear systems. A new strategy for adaptively updating the weights of neural network parameters is proposed to enhance fault detection accuracy. Lyapunov function theory (LFT) is applied for adaptively updating the learning parameters weights of multi-layer neural network (MLNN). The purpose of using adaptive learning rates to update the weight parameters of MLNN is to obtain the global minima for highly nonlinear functions without increasing the computational complexities and costs and increase the efficacy of fault detection. Results of the proposed adaptive …MLNN observer are compared with conventional MLNN observer and high gain observer. The effects of various faults or failures are studied in detail. The proposed strategy shows more robustness to disturbances, uncertainties, and unmodelled system dynamics compared to the conventional neural network, high gain observer and other existing techniques in literature. Fault tolerant control (FTC) schemes are also proposed to account for the presence of various faults and failures. Separate sliding mode control (SMC) based FTC schemes are designed for each observer to ensure stability of the faulty system. The suggested strategy is validated on Boeing 747 100/200 aircraft. Results demonstrate the effectiveness of both the proposed adaptive MLNN observer and the FTC based on the proposed adaptive MLNN compared to the conventional MLNN, high gain observer and other existing schemes in literature. Comparison of the performance of all the strategies validates the superiority of the proposed strategy and shows that the FTC based on proposed adaptive MLNN strategy provides better robustness to various situations such as disturbances and uncertainties. It is concluded that the proposed strategy can be integrated into the aircraft for the purpose of fault diagnosis, fault isolation and FTC scheme for increasing the performance of the system. Show more

Keywords: Sensors, fault detection, fault-tolerant control, neural networks, sliding mode control, observer

DOI: 10.3233/JIFS-201830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 355-386, 2021

Authors: Li, Li | Xie, Yongfang | Chen, Xiaofang

Article Type: Research Article

Abstract: Root cause diagnosis is of great significance to make efficient decisions in industrial production processes. It is a procedure of fusing knowledge, such as empirical knowledge, process knowledge, and mechanism knowledge. However, it is insufficient and low reliability of cause analysis methods by using crisp values or fuzzy numbers to represent uncertain knowledge. Therefore, a dynamic uncertain causality graph model (DUCG) based on picture fuzzy set (PFS) is proposed to address the problem of uncertain knowledge representation and reasoning. It combines the PFS with DUCG model to express expert doubtful ideas in a complex system. Then, a new PFS operator …is introduced to characterize the importance of factors and connections among various information. Moreover, an enhanced knowledge reasoning algorithm is developed based on the PFS operators to resolve causal inference problems. Finally, a numerical example illustrates the effectiveness of the method, and the results show that the proposed model is more reliable and flexible than the existing models. Show more

Keywords: Root cause diagnosis, uncertain knowledge, picture fuzzy set, dynamic uncertainty causality graph

DOI: 10.3233/JIFS-201837

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 387-397, 2021

Authors: Liu, Qianqian | Shi, Gang | Sheng, Yuhong

Article Type: Research Article

Abstract: In this paper, an uncertain SEIR rumor model driven by one uncertain process is formulated to investigate the influence of perturbation in the transmission of rumor. Firstly, the deduced process of the uncertain SEIR rumor model is presented. Then, we proposed the existence and uniqueness theorem for the solution of the model. Moreover, the study of the stability of the uncertain SEIR rumor model was carried out, and then we came to the conclusion that the model stable in mean. In addition, computer algorithm and numerical simulation is used to verify the accuracy of the theoretical results. The simulation results …show that the proposed model can explain the trend of rumor propagation correctly and describe the rumor propagation accurately. Finally, we have compared the propagation process of the uncertain rumor model and the deterministic model according to the numerical algorithm, and drew the conclusion that the model with uncertain perturbation fluctuates around the deterministic model. Show more

Keywords: Uncertain differential equation, Liu process, Uncertain SEIR model, Existence and uniqueness, Stability

DOI: 10.3233/JIFS-201865

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 399-412, 2021

Authors: Trisal, Sushil Kumar | Kaul, Ajay

Article Type: Research Article

Abstract: Stress has become a household word which generates emotional distress, physical diseases, dysfunction and social ills. An abundant evidence is present in the literature that makes the stress research and theory high profile and important for physiological, psychological and social health. It can be legitimately said that due to the advent of social media, it has opened up inputs for the exploration of stress. The social media has become very prominent as it has touched daily lives. It has changed the way we are looking at the things, it has changed the life style, it has changed the way we …are consuming the information. It has created a bridge of trust among the people of different professional’s. Social media has become undeniably a global phenomenon in the last decade or so, since the founding of social media sites like Twitter and Facebook. It is of significant importance to detect and manage the stress from theses interactions at early stage otherwise it wreaks havoc on your emotional equilibrium and your physical health. It narrows your ability to think clearly, function effectively and enjoy life. In this work our endeavor is that to present a novel method to detect the different stress levels from the social media interactions using fuzzy and factor graph methods. A correlation analysis between stressed, non-stressed and emotion tweets is carried out for social engagement correlation and behavior correlation analysis of the social media users. The proposed method performs better when results are compared with the other state of art machine learning methods. Show more

Keywords: Stress, social engagement, correlation, psychological stress, machine learning, social media

DOI: 10.3233/JIFS-202035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 413-430, 2021

Authors: Palraj, K. | Kalaivani, V.

Article Type: Research Article

Abstract: In modern times, digital medical images play a significant progression in clinical diagnosis to treat the populace earlier to hoard their lives. Magnetic Resonance Imaging (MRI) is one of the most advanced medical imaging modalities that facilitate scanning various parts of the human body like the head, chest, abdomen, and pelvis and identify the diseases. Numerous studies on the same discipline have proposed different algorithms, techniques, and methods for analyzing medical digital images, especially MRI. Most of them have mainly focused on identifying and classifying the images as either normal or abnormal. Computing brainpower is essential to understand and handle …various brain diseases efficiently in critical situations. This paper knuckles down to design and implement a computer-aided framework, enhancing the identification of humans’ cognitive power from their MRI. Images. The proposed framework converts the 3D DICOM images into 2D medical images, preprocessing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain’s cognitive power. This study widens the efficient use of machine learning methods, Voxel Residual Network (VRN), with multimodality fusion architecture to learn and analyze the image to classify and predict cognitive power. The experimental results denote that the proposed framework demonstrates better performance than the existing approaches. Show more

Keywords: Medical image processing, MRI, deep learning, 3D MRI, 2D brain segmentation, classification, cognitive power of brain

DOI: 10.3233/JIFS-202069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 431-449, 2021

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: Granular computing is a relatively new platform for constructing, describing and processing information or knowledge. For crisp information granulation, the universe is decomposed into granules by binary relations on the universe, say, preorder, tolerance and equivalence relations. A knowledge structure is composed of all information granules induced by a relation that corresponds to the granulation. This paper establishes a novel theoretical framework for the measurement of information granularity of knowledge structures. First, two new relations between knowledge structures are introduced through the use of their respective Boolean relation matrices, where the granular equality relation is defined based on an orthogonal …transformation with the transformation matrix being a permutation matrix, and the granularly finer relation is presented by combining the classical finer relation and the orthogonal transformation. Then, it is demonstrated that the simplified knowledge structure base with the granularly finer relation is a partially ordered set, which can be represented by a Hasse diagram. Subsequently, an axiomatic definition of information granularity is proposed to satisfy the constraints regarding these two relations. Moreover, a general form of the information granularity is given, and some existing measures are proved to be its special cases. Finally, as an application of the proposed measure, the attribute significance measure is developed based on the information granularity. Show more

Keywords: Granular computing, information granularity, knowledge structure, granular equality relation, granularly finer relation

DOI: 10.3233/JIFS-202086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 451-466, 2021

Authors: Liu, Jia | Wang, Shuwei

Article Type: Research Article

Abstract: It is impossible for agents on both sides to achieve complete rationality in the decision-making process of two-sided matching (TSM). The TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method considering the psychological behavior of decision-makers is well applied in the multiple criteria decision making (MCDM) problems. The TSM is a MCDM problem. Therefore, in this paper, a method based on TODIM technique is introduced to solve the TSM problem, in which the intuitionistic linguistic numbers are utilized to describe the mutual evaluation between candidates and hiring managers. The focus of this paper is to develop a method …for the multi-criteria TSM problem under intuitionistic linguistic environment. First, the evaluation matrices of each agent with respect to each criterion are provided by agents on the opposite side, and the weight assigned to each criterion is determined according to the importance of the evaluation criterion to the matching agent. Then, the dominance measurement of each agent over another one can be calculated based on the intuitionistic linguistic TODIM method. Next, a bi-objective optimization model which aims to maximize the overall satisfaction degree of agents on both sides is constructed to attain the optimal matching pair. Furthermore, the feasibility of the solution method is verified by a case study of person-position matching (PPM), and the matching result demonstrates that the proposed method is effective in dealing with multi-criteria PPM problem. Finally, the sensitivity of parameters and some comparative studies are discussed. Show more

Keywords: Two-sided matching, TODIM method, intuitionistic linguistic set, optimization model

DOI: 10.3233/JIFS-202087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 467-480, 2021

Authors: Poonia, Mahima | Bajaj, Rakesh Kumar

Article Type: Research Article

Abstract: In the present work, the adjacency matrix, the energy and the Laplacian energy for a picture fuzzy graph/directed graph have been introduced along with their lower and the upper bounds. Further, in the selection problem of decision making, a methodology for the ranking of the available alternatives has been presented by utilizing the picture fuzzy graph and its energy/Laplacian energy. For the shake of demonstrating the implementation of the introduced methodology, the task of site selection for the hydropower plant has been carried out as an application. The originality of the introduced approach, comparative remarks, advantageous features and limitations have …also been studied in contrast with intuitionistic fuzzy and Pythagorean fuzzy information. Show more

Keywords: Picture fuzzy graph, score function, energy of graph, Laplacian energy, adjacency matrix, spectrum

DOI: 10.3233/JIFS-202131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 481-498, 2021

Authors: Brás, Glender | Silva, Alisson Marques | Wanner, Elizabeth Fialho

Article Type: Research Article

Abstract: This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by …a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks. Show more

Keywords: Neo-fuzzy-neuron, genetic programming, multi-gene, NFN-MG-GP, forecasting, non-linear system identification

DOI: 10.3233/JIFS-202146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 499-516, 2021

Authors: Ali, Riaz | Abdullah, Saleem | Muhammad, Shakoor | Naeem, Muhammad | Chinram, Ronnason

Article Type: Research Article

Abstract: Due to the indeterminacy and uncertainty of the decision-makers (DM) in the complex decision making problems of daily life, evaluation and aggregation of the information usually becomes a complicated task. In literature many theories and fuzzy sets (FS) are presented for the evaluation of these decision tasks, but most of these theories and fuzzy sets have failed to explain the uncertainty and vagueness in the decision making issues. Therefore, we use complex intuitionistic fuzzy set (CIFS) instead of fuzzy set and intuitionistic fuzzy set (IFS). A new type of aggregation operation is also developed by the use of complex intuitionistic …fuzzy numbers (CIFNs), their accuracy and the score functions are also discussed in detail. Moreover, we utilized the Maclaurin symmetric mean (MSM) operator, which have the ability to capture the relationship among multi-input arguments, as a result, CIF Maclarurin symmetric mean (CIFMSM) operator and CIF dual Maclaurin symmetric mean (CIFDMSM) operator are presented and their characteristics are discussed in detail. On the basis of these operators, a MAGDM method is presented for the solution of group decision making problems. Finally, the validation of the propounded approach is proved by evaluating a numerical example, and by the comparison with the previously researched results. Show more

Keywords: Intuitionistic fuzzy set, complex intuitionistic fuzzy set, multi-attribute group decision making, emergency management program evaluation, Maclaurin symmetric mean operator

DOI: 10.3233/JIFS-202254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 517-538, 2021

Authors: Deng, Xue | Huang, Cuirong

Article Type: Research Article

Abstract: In the previous uncertain portfolio literature on background risk and mental account, only a general background risk and a few kinds of mental accounts were considered. Based on the above limitations, on the one hand, the multiple background risks are defined by linear weighting of different background asset risks in this paper; on the other hand, the total nine kinds of mental accounts are comprehensively considered. Especially, the risk curve is regarded as the risk measurement of different mental accounts for the first time. Under the framework of uncertainty theory, a novel mean-entropy portfolio model with risk curve and total …mental accounts under multiple background risks is constructed. In addition, transaction fees, chance constraint, upper and lower limits and initial wealth constraints are also considered in our proposed model. In theory, the equivalent forms of the models with different uncertainty distributions (general, normal and zigzag) are presented by three theorems. Simultaneously, the corresponding concrete expressions of risk curves are obtained by another three theorems. In practice, two numerical examples verify the feasibility and effectiveness of our proposed model. Finally, we can obtain the following unique and meaningful findings: (1) investors will underestimate the potential risk if they ignore the existence of multiple background risks; (2) with the increase of the return threshold, the return of the sub-portfolio will inevitably increase, but investors also bear the risk that the risk curve is higher than the confidence curve at this time. Show more

Keywords: Uncertainty theory, mental account, background risk, entropy, risk curve

DOI: 10.3233/JIFS-202256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 539-561, 2021

Authors: Fu, Yingwen | Lin, Nankai | Lin, Xiaotian | Jiang, Shengyi

Article Type: Research Article

Abstract: Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) …comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets. Show more

Keywords: Indonesian, named entity recognition, named entity corpus, structured-attention, residual gated convolution neural network

DOI: 10.3233/JIFS-202286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 563-574, 2021

Authors: Zhu, Yan | Fan, Chuanhao | Xiao, Jing | Liu, Shenghua

Article Type: Research Article

Abstract: Quality function deployment (QFD) is a quality management tool that aims to improve customer satisfaction by transforming customer requirements into technical characteristics. It is a crucial procedure to obtain the prioritization of technical characteristics for the products or services in QFD. Traditional QFDs are often implemented by a small number of QFD members. However, with the increasing complexity of product and service design, QFD requires the participation of more QFD members from dispersed departments or institutions. Additionally, the evaluation information given by QFD members may widely differ due to their different knowledge and background. Furthermore, the psychological behaviours of QFD …members also greatly influence the final prioritization of technical characteristics. Hence, this paper proposes a novel QFD framework to prioritize technical characteristics using a consensus-reaching process and prospect theory when large numbers of QFD members are involved. In the large-scale QFD framework, prospect theory is generally utilized to depict the psychological behaviours of QFD members. Then, QFD members are divided into several clusters. Eventually, a consensus-reaching process is established to assist QFD members in reaching a consensus. To verify the practicability of the presented framework, this paper applies it to the evaluation of contingency plan to determine the critical measures. Show more

Keywords: Quality function deployment, consensus-reaching process, prospect theory, large-scale group decision making, contingency plan

DOI: 10.3233/JIFS-202326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 575-594, 2021

Authors: Jiang, Le | Liu, Hongbin

Article Type: Research Article

Abstract: The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step …being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility. Show more

Keywords: Green supplier selection, interval type-2 fuzzy set, lower membership function, probabilistic linguistic term set, upper membership function

DOI: 10.3233/JIFS-202386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 595-612, 2021

Authors: Dong, Yan Yan | Wang, Jun Tao

Article Type: Research Article

Abstract: In this paper, we first point out some mistakes in [12 ]. Especially the Theorem 3.9 [12 ] showed that: Let A be residuated lattice and ∅ ≠ X  ⊆ A , then the least ideal containing X can be expressed as: 〈X 〉 = {a  ∈ A |a  ≤ (·· · ((x 1  ⊕ x 2 ) ⊕ x 3 ) ⊕ ·· ·) ⊕ x n , x i  ∈ X , i  = 1, 2 ·· · , n }. But we present an example to illustrate the ideal generation formula may not hold on residuated lattices. Further we give the correct ideal generation formula on residuated lattices. Moreover, we extend the concepts of annihilators …and α -ideals to MTL-algebras and focus on studying the relations between them. Furthermore, we show that the set I α  (M ) of all α -ideals on a linear MTL-algebra M only contains two trivial α -ideals {0} and M . However, the authors [24 ] studied the structure of I α  (M ) in a linear BL-algebra M , which means some results with respect to I α  (M ) given in [24 ] are trivial. Unlike that, we investigate the lattice structure of I α  (M ) on general MTL -algebras. Show more

Keywords: Residuated lattice, MTL-algebra, Ideal generation formula, Annihilator, α-ideal

DOI: 10.3233/JIFS-202417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 613-623, 2021

Authors: Silmi Juman, Zeinul Abdeen M. | Masoud, Mahmoud | Elhenawy, Mohammed | Bhuiyan, Hanif | Komol, Md Mostafizur Rahman | Battaïa, Olga

Article Type: Research Article

Abstract: The uncapacitated transportation problem (UTP) deals with minimizing the transportation costs related to the delivery of a homogeneous product from multi-suppliers to multi-consumers. The application of the UTP can be extended to other areas of operations research, including inventory control, personnel assignment, signature matching, product distribution with uncertainty, multi-period production and inventory planning, employment scheduling, and cash management. Such a UTP with interval-defined demands and suppliers capacities (UTPIDS) is investigated in this paper. In UTPIDS, the demands and suppliers capacities may not be known exactly but vary within an interval due to variation in the economic conditions of the global …economy. Following the variation, the minimal total cost of the transportation can also be varied within an interval and thus, the cost bounds can be obtained. Here, although the lower bound solution can be attained methodologically, the correct estimation of the worst case realization (the exact upper bound) on the minimal total transportation cost of the UTPIDS is an NP-hard problem. So, the decision-makers seek for minimizing the transportation costs and they are interested in the estimation of the worst case realization on these minimal costs for better decision making especially, for proper investment and return. In literature very few approaches are available to find this estimation of the worst case realization with some shortcomings. First, we demonstrate that the available heuristic methods fail to obtain the correct estimation of the worst case realization always. In this situation, development of a better heuristic method to find the better near optimal estimation of the worst case realization on the minimal total costs of the UTPIDS is desirable. Then this paper provides a new polynomial time algorithm that runs in O (N2) time (N, higher of the numbers of source and destination nodes) for better estimation. A comparative assessment on solutions of available benchmark instances, some randomly generated numerical example problems and a real-world application shows promising performance of the current technique. So, our new finding would definitely be benefited to practitioners, academics and decision makers who deal with such type of decision making instances. Show more

Keywords: Heuristics, upper bound, least aggregated expense, NP-hard problem

DOI: 10.3233/JIFS-202436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 625-637, 2021

Authors: Pak, Sunae | Choe, Huichol | Sin, Kinam | Kwon, Sunghyok

Article Type: Research Article

Abstract: In this paper, we investigate the necessary and sufficient conditions for existence of solutions for initial value problem of fuzzy Bagley-Torvik equation and the solution representation by using the multivariate Mittag-Leffler function. First we convert fuzzy initial value problem into the cut problem (system of fractional differential equations with inequality constraints) and obtain existence results for the solution of the cut problem under (1,1)- differentiability. Next we study the conditions for the solutions of the cut problem to constitute the solution of a fuzzy initial value problem and suggest a necessary and sufficient condition for the (1,1)-solution. Also, some examples …are given to verify the effectiveness of our proposed method. The necessary and sufficient condition, solution representation for (1,2)-solution of initial value problem of fuzzy fractional Bagley-Torvik equation are shown in Appendix. Show more

Keywords: Fuzzy Fuzzy Bagley-Torvik equation, generalized Hukuhara differentiability, multivariate Mittag-Leffler function

DOI: 10.3233/JIFS-202453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 639-654, 2021

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | Al-Durgham, Lamees | Albashabsheh, Nibal T.

Article Type: Research Article

Abstract: A new integrated modelling architecture based on the concept of the fuzzy logic is presented to represent the turning process. Such an architecture consists of two stages. In the first stage, fuzzy logic systems (FLSs) having various topologies are employed to extract rule bases using perhaps limited amount of sparse data. In the second stage, the fuzzy rules extracted are assessed and integrated using the singular value decomposition-QR factorization (SVD-QR) paradigm in order to minimize the computational efforts. Such a step leads to reducing the number of fuzzy rules and results in a reduced FLS model. Such a reduced model …is then employed to represent the turning process and predict both the cutting force and the surface roughness. In addition, it provides a comprehensive understanding of the turning process presented linguistically in the form of If/Then rules. The proposed structure has been validated using a set of laboratory experiments. It has been noticed that it can predict both the cutting force and the surface roughness successfully. In addition, such an integrated architecture outperforms the artificial neural network, the well-known FLS, the radial basis functions and the multilinear regression model, where the overall improvement is of approximately 19%, 13%, 14% and 270%, respectively. Show more

Keywords: Fuzzy logic system, singular value decomposition-QR factorization (SVD-QR) algorithm, turning process

DOI: 10.3233/JIFS-202457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 655-667, 2021

Authors: Hikal, Noha A. | Shams, Mahmoud Y. | Salem, Hanaa | Eid, Marwa M.

Article Type: Research Article

Abstract: Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to …MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value. Show more

Keywords: Ada-boost, support vector machine, AOMDV, black-hole attack, and MANET

DOI: 10.3233/JIFS-202471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 669-682, 2021

Authors: Dhamija, Ashutosh | Dubey, R. B.

Article Type: Research Article

Abstract: Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress in accuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face …recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%. Show more

Keywords: Age-invariant face recognition, feature extraction, PCA and QSVM

DOI: 10.3233/JIFS-202485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 683-697, 2021

Authors: Yang, Yulei | Zhang, Jin | Sun, Wenjie | Pu, Yun

Article Type: Research Article

Abstract: Under the background of the new medical reform, the pharmaceutical industry is in constant transformation and upgrading, and the establishment of a rational and efficient pharmaceutical logistics system is imminent. Carbon emission, cost and time are set as the target to construct the model of location-routing-inventory optimization of highway, rail and air transport hubs with capacity limits. Then the warehouse of pharmaceutical logistics hub is selected, and the distribution path of pharmaceutical logistics and the inventory strategy are planned to realize the scientific decision of the system. The NSGA-III algorithm is used to solve the problem. The diversity of the …population is maintained by the well-distributed reference points, and the optimal solution set of nondominant Pareto is obtained. Spacing, HRS, PR and GD are used to measure the performance of the algorithm. The example analysis shows that the number of Pareto optimal solutions solved by the algorithm is large and evenly distributed, and convergence and operation efficiency of algorithm is good. The sensitivity analysis of three kinds of freight rates shows that the influence of the freight rates on the objective function value should be fully considered when making decisions. The method focuses on the problem of optimizing the layout of multi-modal transport hubs and improves the existing theories of it. Show more

Keywords: Pharmaceutical warehouse, carbon emission, location-routing-inventory problem, NSGA-III

DOI: 10.3233/JIFS-202508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 699-713, 2021

Authors: Mousa, A.A. | Higazy, M. | Abdel-Khalek, S. | Hussein, Mohamed A. | Farouk, Ahmed

Article Type: Research Article

Abstract: The application and analysis of effective blood supply chain network under natural disaster imposed many critical challenges which addressed through an optimization of multiple objectives functions. In this article, relies on reference point algorithm, a user-preference based enriched swarm optimization algorithm is proposed where, inner reference points were produced depending on the perturbed reference point. For each inner reference point, weakly/ɛ -properly Pareto optimal solution was generated using augmented achievement function. All the generated solutions (points) are presented as potential positions for particles in the particle swarm optimization PSO. The proposed algorithm has been reinforced with a novel chaotic contraction …operator to retain the feasibility of the particles. To prove the validity of our algorithm, the obtained results are compared with true Pareto optimal front and three of the most salient evolutionary algorithms using inverted generational distance metric IGD. In addition it was implement to detect the most cost and time efficient blood supply chain to provide the required blood types demand on the blood transfusion center in emergence situation, where, it is required to solve this real life application with predefined supply time and predefined supply cost, which is considered as reference point to get the nearby Pareto optimal solution. By the experimental outcomes, we proved that the proposed algorithm is capable to find the set of Paetro optimal solutions nearby the predefined reference points. Show more

Keywords: Particle swam optimization, reference point, multi-objective optimization, blood supply chain

DOI: 10.3233/JIFS-202529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 715-733, 2021

Authors: Ranjith Pillai, R. | Murali, Ganesan

Article Type: Research Article

Abstract: Miniature flexible parallel robots, popularly used for micro positioning application demands the use of non conventional actuators. Shape memory alloys (SMA) are popular smart actuators because of its light weight, integration compatibility, ease of actuation and high power density. Inclusion of shape memory alloy actuators to the parallel robot brings in control challenges due to its nonlinearity, coupling effects and cocontraction of antagonistic pair of actuators in the mechanism in order to achieve bi directional motion. In this paper, a PID like fuzzy controller is designed and applied to a nonlinear SMA spring actuator connected to a symmetric 2 DOF …miniature parallel robot. The fuzzy rules are designed from the general response plot and modified to be applied to a parallel mechanism which involves cocontraction of antagonistic actuators. The paper has also presented the control and electrical circuit design used in the experimental set up. The fuzzy control is implemented in the hardware controller with model based position feedback and tested for the trajectory tracking characteristics of the end effector with disturbances. Experimental results are presented with quantitative analysis to show the effectiveness of the proposed controller in handling nonlinearities and disturbances compared to the conventional PID control and nonlinear Sliding mode control (NSMC). The test results has demonstrated the superior nature of proposed control over other controllers in the trajectory tracking with disturbances and also linearizing the hysteresis of controlled system. Show more

Keywords: SMA actuated parallel robot, PID like fuzzy control, control of cocontraction of actuators, flexible robot, SMA spring control

DOI: 10.3233/JIFS-202572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 735-755, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Dela Tattrah, Victor | Adu Ansere, James | Tashi, Nyima

Article Type: Research Article

Abstract: The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic …routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time. Show more

Keywords: Artificial intelligence, capsule network, convolutional neural network, deep learning, pneumonia, x-ray imaging

DOI: 10.3233/JIFS-202638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 757-781, 2021

Authors: Huang, Danyang | Zhou, Zhiheng | Deng, Ming | Li, Zhihao

Article Type: Research Article

Abstract: Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads …into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object is the next step. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art methods by at least 0.31% and 0.34%. Show more

Keywords: Nighttime vehicle detection, advanced driver-assistance systems, attention mechanism, deep learning

DOI: 10.3233/JIFS-202676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 783-801, 2021

Authors: Noorullah, R.M. | Mohammed, Moulana

Article Type: Research Article

Abstract: Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing the optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and the quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an …extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose the optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices. Show more

Keywords: Interactive visualization, visual non-negative matrix factorization model, an optimal number of topics, cluster validity indices, twitter data clustering

DOI: 10.3233/JIFS-202707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 803-817, 2021

Authors: Shabir, Muhammad | Mushtaq, Rimsha | Naz, Munazza

Article Type: Research Article

Abstract: In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of …these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop. Show more

Keywords: N-soft set, algebraic structure, top complement, bottom complement, TOPSIS

DOI: 10.3233/JIFS-202717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 819-839, 2021

Authors: Cheng, Haodong | Han, Meng | Zhang, Ni | Li, Xiaojuan | Wang, Le

Article Type: Research Article

Abstract: Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing …algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining. Show more

Keywords: Survey, pattern mining, incremental mining, high-utility patterns, frequent itemsets

DOI: 10.3233/JIFS-202745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 841-866, 2021

Authors: Yang, Zhan | Li, Chengliang | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment …classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM. Show more

Keywords: Aspect-based sentiment analysis, sentiment classification, dependency-relationship, attention mechanism

DOI: 10.3233/JIFS-202747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 867-877, 2021