Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Zhan, Keqiang

Article Type: Research Article

Abstract: In recent years, the application of network technology has become popular, and the application of the Internet has occupied a large proportion in people’s daily life. The issue of network security has also begun to be paid attention to. In recent years, due to the rapid expansion of network applications, malicious events such as network threats have occurred from time to time. Many computers do not have a good protection structure so that they are often vulnerable to attacks during network interconnection. Traditional computer protection measures are border-based protection, but with the development of network technology, this protection system can …no longer guarantee computer security. Therefore, in order to keep computers safe in the current network environment, the role of artificial intelligence in the computer field should be given full play. The artificial intelligence analysis system can analyze and predict the situation of computer network security based on the situation of network security. This paper integrates convolutional neural network algorithms on the basis of traditional machine learning to establish a new network intrusion model. This paper verifies the feasibility of the model through experiments, and the experimental results show that the accuracy of the new model proposed in this paper can reach more than 90% for KDDCUP99 data detection. In addition, traditional computer protection systems have many errors when dealing with DNN attack detection. In order to reduce the occurrence of this situation, this paper proposes a standardized attack detection model based on deep nerves. The detection precision of this model is higher and the results obtained are more accurate. In addition, this new model can also synthesize the impact of different network attacks on the security situation, and construct attack situation predictions for computer systems. Show more

Keywords: Artificial intelligence, neural network, network security, defense system

DOI: 10.3233/JIFS-189794

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Feng, Lv

Article Type: Research Article

Abstract: In recent years, scientists have begun to introduce dynamic elements into wireless networks. With the introduction of mobile sink node, the phenomenon of “hot node” and “energy hole” can be effectively avoided, so as to realize more network connection and improve network flexibility. Therefore, it is imperative to design energy-saving algorithm with popular sink code. In this paper, a multi hop data forwarding algorithm is proposed for solar powered wireless sensor networks. The algorithm divides the monitoring area of the network and the communication area of the node. Through the sensor node, the next hop node is selected from the appropriate …area, thus forming the path from the data source point to the base station. At the same time, in order to reduce the energy consumption and delay in the network, a multi-objective programming model of the next hop data forwarding node is established. The reasonable area of static and dynamic area is calculated by mathematical analysis. Finally, the paper calculates the network’s life cycle, energy consumption and transmission time, and compares the static sink with the network using only mobile sink. Show more

Keywords: Wireless sensor, network, energy saving algorithm, simulation test

DOI: 10.3233/JIFS-189789

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Qiu, Chenye | Liu, Ning

Article Type: Research Article

Abstract: Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles …in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features. Show more

Keywords: Feature selection, particle swarm optimization, three layer structure, random exemplar selection, local search operator

DOI: 10.3233/JIFS-202647

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Sarin, Sumit | Mittal, Antriksh | Chugh, Anirudh | Srivastava, Smriti

Article Type: Research Article

Abstract: Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product …fusion rules work best for the data used in the experiments. Show more

Keywords: Multimodal, touchless, biometric system, gait recognition, speech recognition

DOI: 10.3233/JIFS-189765

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Gupta, Sangeeta | Varshney, Pragya | Srivastava, Smriti

Article Type: Research Article

Abstract: This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial …conditions. Show more

Keywords: Fractional order chaotic system (FOCS), fractional order financial chaotic system (FOFCS), whale optimization algorithm (WOA), particle swarm optimization (PSO), proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller

DOI: 10.3233/JIFS-189761

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sun, Zhuo | Zhang, Shuang | Liu, Mairu

Article Type: Research Article

Abstract: Horse racing is different from other competitive sports. It is an entertainment sport which combines commercialization and sports spirit. Therefore, horse racing has the characteristics of sports and commercial events. In addition, horse racing also has the nature of social welfare, and part of the funds for holding horse racing will be used for fund-raising. Now horse racing has become a complete industry and an important part of the tertiary industry. Horse racing, as a new industry, has developed rapidly. However, there are still some defects in the management of horse racing events. The main problem is the lack of …information management of horse racing industry. The introduction of information technology into the management of horse racing industry can realize the efficient management of industrial information, which is more professional and orderly for horse racing. Show more

Keywords: Machine learning, data mining, speed racing, industry information management

DOI: 10.3233/JIFS-189801

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Hasan, Mashhood | Alhazmi, Waleed Hassan | Zakri, Waleed

Article Type: Research Article

Abstract: In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original …value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. Show more

Keywords: Center of gravity, fuzzy logic controller, fuzzy rule, membership function, solar photovoltaic

DOI: 10.3233/JIFS-189767

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Meghana, Pulimamidi | Yammani, Chandrasekhar | Salkuti, Surender Reddy

Article Type: Research Article

Abstract: This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can …be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers. Show more

Keywords: Blockchain technologies, distributed generators, electric vehicles, green energy, microgrid, vehicle-to-grid

DOI: 10.3233/JIFS-189766

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Songyun, Wang

Article Type: Research Article

Abstract: With the development of social economy and the improvement of science and technology, digital video on the Internet is increasing rapidly, and it has become a new force to promote the development of the times. Most of these videos are stored in the memory, which poses a great challenge to the research and development of the system. The reader service system is an important part of library service. The library uses it to collect information resources, not just for service and work. The document combines the video of library service, the analysis of video recovery and video software requirements of …digital library, puts forward the design goal and conception of video search, and puts forward a foundation. From the video data of digital library, video retrieval experiments are gradually carried out. These experimental results show that the number of enhanced dynamic clustering algorithm increases to ensure the complexity of the image. Show more

Keywords: Artificial intelligence, machine learning, intelligent library, reader service system

DOI: 10.3233/JIFS-189800

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Tyagi, Shikhar | Chawla, Bhavya | Jain, Rupav | Srivastava, Smriti

Article Type: Research Article

Abstract: Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional …neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates. Show more

Keywords: Multimodal biometrics, face, finger vein, convolutional neural network, score level fusion

DOI: 10.3233/JIFS-189762

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Gan, Weichao | Ma, Zhengming | Liu, Shuyu

Article Type: Research Article

Abstract: Tensor data are becoming more and more common in machine learning. Compared with vector data, the curse of dimensionality of tensor data is more serious. The motivation of this paper is to combine Hilbert-Schmidt Independence Criterion (HSIC) and tensor algebra to create a new dimensionality reduction algorithm for tensor data. There are three contributions in this paper. (1) An HSIC-based algorithm is proposed in which the dimension-reduced tensor is determined by maximizing HSIC between the dimension-reduced and high-dimensional tensors. (2) A tensor algebra-based algorithm is proposed, in which the high-dimensional tensor are projected onto a subspace and the projection coordinate …is set to be the dimension-reduced tensor. The subspace is determined by minimizing the distance between the high-dimensional tensor data and their projection in the subspace. (3) By combining the above two algorithms, a new dimensionality reduction algorithm, called PDMHSIC, is proposed, in which the dimensionality reduction must satisfy two criteria at the same time: HSIC maximization and subspace projection distance minimization. The proposed algorithm is a new attempt to combine HSIC with other algorithms to create new algorithms and has achieved better experimental results on 8 commonly-used datasets than the other 7 well-known algorithms. Show more

Keywords: Dimensionality reduction, tensor mode product, hilbert-schmidt independence criterion, reproducing kernel hilbert space

DOI: 10.3233/JIFS-202582

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Malik, Shaily | Bansal, Poonam

Article Type: Research Article

Abstract: The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, …and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query. Show more

Keywords: Semantic autoencoder, hypergraph, twofold validation, cross model retrieval

DOI: 10.3233/JIFS-189759

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Bhati, Nitesh Singh | Khari, Manju

Article Type: Research Article

Abstract: With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and …ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. Show more

Keywords: Security, intrusion detection system, network security, ensemble, voting, machine learning

DOI: 10.3233/JIFS-189764

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Venkateswara Rao, B. | Devarapalli, Ramesh | Malik, H. | Bali, Sravana Kumar | Márquez, Fausto Pedro García | Chiranjeevi, Tirumalasetty

Article Type: Research Article

Abstract: The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with …emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved. Show more

Keywords: Bat algorithm, emission, optimal power flow, SVC, wind power

DOI: 10.3233/JIFS-189770

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Setiawan, Noor Akhmad | Nugroho, Hanung Adi | Persada, Anugerah Galang | Yuwono, Tito | Prasojo, Ipin | Rahmadi, Ridho | Wijaya, Adi

Article Type: Research Article

Abstract: Arrhythmia is a disease often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed, a method was prepared. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final …step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model. Show more

Keywords: Physionet, arrhythmia, cascade, transparent classifier

DOI: 10.3233/JIFS-189768

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Anees, Mohd. Anas | Mohammad, | Lodi, Kaif Ahmed | Alam, Mahetab | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time …emulator. Show more

Keywords: Model predictive control, packed-U-Cell, reactive power compensation, multilevel inverter

DOI: 10.3233/JIFS-189749

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Mirsadeghpour Zoghi, S.M. | Saneie, M. | Tohidi, G. | Banihashemi, Sh. | Modarresi, N.

Article Type: Research Article

Abstract: According to modern finance theory and increasing need for efficient investments, we evaluate the portfolio performance based on the data envelopment analysis method. By the fact that stock market’s return distributions usually exhibit skewness, kurtosis and heavy-tails, we consider some appropriate underlying distributions that affect the input and output of the model. In this regard, the multivariate skewed t and the multivariate generalized hyperbolic as the heavy-tailed distributions of Normal mean-variance mixture are applied. The models are inspired by the Range Directional Measure (RDM) model to deal with negative values. The value-at-risk (VaR) and conditional VaR (CVaR) as risk …measures are used in these optimization problems. We estimate the parameters of such distributions by Expectation Maximization algorithm. Then we present an empirical investigation to measure the relative efficiency of two sets of seven groups of companies from different industries of Iran stock exchange market. By comparing the results of introduced models with previous RDM approach, we show that how well the distribution of assets affect the performance evaluation. Show more

Keywords: Data envelopment analysis, normal mean-variance mixture distributions, portfolio optimization, VaR, CVaR

DOI: 10.3233/JIFS-202332

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Bisht, Vimal Singh | Hasan, Mashhood | Malik, Hasmat | Sunori, Sandeep

Article Type: Research Article

Abstract: For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used …of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time Show more

Keywords: Remaining-useful-life, health indicator, lithium-Ion battery, Box-Cox, data-driven

DOI: 10.3233/JIFS-189758

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Huang, Jinfang | Jin, Xin | Lee, Shin-Jye | Huang, Shanshan | Jiang, Qian

Article Type: Research Article

Abstract: Since the intuitionistic fuzzy set (IFS) was proposed by Atanassov, many explorations of this particular fuzzy set were conducted. One of the most important areas is the study of similarity and distance between IFSs, which can measure the degree of deviation of objects with uncertain and vague features, and this technique has great value and potential to solve the fuzzy and uncertain problems in the real world. Based on our previous similarity/distance measure model D JJ  (α , β ), a new method is proposed for improving the performance of similarity/distance measure model of IFSs, which is derived from …the sum of the areas of two triangles constructed by the transformed isosceles triangles of two IFSs. A great effort is made to prove the validity of the proposed method by mathematical derivation. In order to further demonstrate the performance of the proposed method, we apply this method to solve some practical problems such as pattern recognition, medical diagnosis, and cluster analysis. In addition, we also list a series of the existing methods which are used to compare with the proposed method to prove the effectiveness and superiority. The experimental results confirm that the performance of the proposed method exceeds most of the existing methods. Show more

Keywords: Intuitionistic fuzzy set, similarity/distance measure, transformed isosceles triangle fuzzy number, decision-making, cluster analysis

DOI: 10.3233/JIFS-201763

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Lijun, Wang | Yaqian, Pang | Mengdong, Chen

Article Type: Research Article

Abstract: Data envelopment analysis (DEA) was used to measure the comprehensive efficiency, pure technical efficiency and scale efficiency of science and technology business incubators in 11 provinces and cities of the Yangtze River economic belt from 2011 to 2017, and the situation of incubators in the Yangtze River economic belt was analyzed from the overall, horizontal and vertical perspectives. Results show that the overall operation efficiency of science and technology business incubators in the Yangtze River economic belt is relatively high, but it shows a downward trend in the sample period, and it is found that the development of science and …technology business incubators in the Yangtze River economic belt is unbalanced, there are regional differences, and some provinces and cities have serious redundancy of incubator personnel and incubation funds. On this basis, some suggestions are put forward, such as reducing the number of managers and tutors, adjusting the dominant position of government investment in science and technology business incubators, and creating resource input sharing enterprise output circulation chain. Show more

Keywords: DEA, operating efficiency, business incubator, yangtze river economic belt

DOI: 10.3233/JIFS-189916

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Srivastava, Vishal | Srivastava, Smriti

Article Type: Research Article

Abstract: Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the …performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. Show more

Keywords: Ball and beam, particle swarm optimization (PSO), gravitational search algorithm (GSA), grey wolf optimization (GWO), mean square error (MSE), robustness

DOI: 10.3233/JIFS-189760

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Alzubi, Omar A.

Article Type: Research Article

Abstract: Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising …the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time. Show more

Keywords: Deep learning, intrusion detection, industrial wireless sensor networks, IWSN security, Fréchet hyperbolic, statistical dirichlet distribution, machine learning, security

DOI: 10.3233/JIFS-189756

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Li, Yanling

Article Type: Research Article

Abstract: The teaching resource bank is very important for the education of linguistics. In the construction of teaching Chinese as a foreign language, we should first build a teaching resource library. Only in this way can we meet the requirements of the Ministry of education and achieve better teaching objectives. The purpose of the construction of teaching resources database is to make the teaching resources can be systematically and scientifically planned, stored in the computer in the form of data, and can be directly extracted from the computer when necessary, so as to realize the efficient utilization of resources. Based on …the existing problems of teaching Chinese as a foreign language, this paper puts forward some suggestions on the construction of teaching resource database. In recent years, the rise of artificial intelligence technology provides a new idea for the establishment of teaching resource database. According to the basic idea of artificial intelligence, scientists have established a new database of teaching Chinese as a foreign language, updated and improved the original database, and established a new standard for teaching database according to the characteristics of artificial intelligence technology. A teaching resource database system based on teaching resources and artificial intelligence is established. The system is divided into data layer, logic layer and presentation layer. Artificial intelligence is used as a bridge connecting different parts. In the process of establishing the multimedia database of TCFL, we should develop in all aspects, and pave the way for the future research after laying a solid foundation of data. Show more

Keywords: Artificial intelligence, chinese as a foreign language, teaching resource library, XML, net

DOI: 10.3233/JIFS-189793

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jing, Wu

Article Type: Research Article

Abstract: In year 2020, a large-scale outbreak of pneumonia caused by new coronavirus has affected the development of many industries and enterprises in China. Under the strong leadership of the Chinese government, the development of the epidemic situation in China has been well controlled. The development of various industries also began to show a good situation, many large-scale sports competitions also need to be restored. In order to ensure the normal development of large-scale sports events, we need to consider the development of epidemic situation to determine the time of sports events. Based on the study of FPGA theory, this paper …designs a specific scheme of programming and system debugging, which includes a variety of program operations. In order to better predict the situation of the epidemic situation, this paper also uses the basic knowledge of machine learning to establish a relevant model to evaluate the situation of large-scale sports events under the development of the epidemic situation, and provide feasible suggestions for the recovery of large-scale sports events under the epidemic situation. Show more

Keywords: FPGA system, machine learning, epidemic prevention and control, sports events

DOI: 10.3233/JIFS-189791

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Yang, Yang

Article Type: Research Article

Abstract: In order to improve the effect of sports movement training, this paper builds a sports movement training model based on artificial intelligence technology based on the generation of confrontation network model. Moreover, in order to achieve the combination of model and model-free deep reinforcement learning algorithm, this paper implements the model’s guidance and constraints on deep reinforcement learning algorithm from the perspective of reward value and behavior strategy and divides the model into two situations. In one case, the existing or manually established expert rules are used as model constraints, which is equivalent to online learning by experts. In another …case, expert samples are used as model constraints, and an imitation learning method based on generative adversarial networks is introduced. Moreover, using expert samples as training data, the mechanism that the model is guided by the reward value is combined with the model-free algorithm by generating a confrontation network structure. Finally, this paper studies the performance of the model through experimental research. The research results show that the model constructed in this paper has a certain effect. Show more

Keywords: Generative confrontation network, artificial intelligence, sports action, machine learning

DOI: 10.3233/JIFS-189799

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Subudhia, Jyotirmayee | Indumathi, P.

Article Type: Research Article

Abstract: Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering …and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum. Show more

Keywords: Energy efficiency, MIMO-NOMA, non-Orthogonal multiple access, PSO optimization, power allocation, layered transmission, user clustering

DOI: 10.3233/JIFS-201412

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Rababa, Salahaldeen | Al-Badarneh, Amer

Article Type: Research Article

Abstract: Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for processing large-scale data. However, it has some limitations in processing heterogeneous datasets. This is because of the large amount of redundant intermediate records that are transferred through the network. Several filtering techniques have been developed to improve the join performance, but they require multiple MapReduce jobs to process the input datasets. To address this issue, the adaptive filter-based join algorithms are presented in this paper. Specifically, three join algorithms are introduced to perform the processes of filters creation …and redundant records elimination within a single MapReduce job. A cost analysis of the introduced join algorithms shows that the I/O cost is reduced compared to the state-of-the-art filter-based join algorithms. The performance of the join algorithms was evaluated in terms of the total execution time and the total amount of I/O data transferred. The experimental results show that the adaptive Bloom join, semi-adaptive intersection Bloom join, and adaptive intersection Bloom join decrease the total execution time by 30%, 25%, and 35%, respectively; and reduce the total amount of I/O data transferred by 18%, 25%, and 50%, respectively. Show more

Keywords: Join algorithms, big data management, query optimization, MapReduce

DOI: 10.3233/JIFS-201220

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Du, Yuetao | Yang, Nana

Article Type: Research Article

Abstract: In order to further satisfy the needs of visual perception, a computer image segmentation algorithm based on visual characteristics is proposed. The computer image geometric features are linearly segmented to obtain multiple sub-panels and various geometric features of each sub-panel are extracted. The geometric features are input as low-level features into the deep neural network model to learn to generate high-level features. Finally, based on the high-level features, the clustering center is obtained by Gaussian mixture model, and the final segmentation result is obtained by using graph cut. The results of experiments on Princeton standard data set and COSEG dataset …show that the rand index RI value is the best value for each 3D model, which shows that the proposed method is better than the traditional segmentation method. It has good consistent segmentation results. The research showed that using a variety of geometric features compared to a single geometric feature, the obtained features have a more comprehensive geometric meaning, which can effectively make image segmentation meet the visual characteristics requirements. Show more

Keywords: Visual characteristics, computer image, segmentation algorithm

DOI: 10.3233/JIFS-189913

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Shan, Qinxing | Li, Zhiwei | Liu, Rong

Article Type: Research Article

Abstract: At present, the diagnosis of breast tumors is affected by many factors, which leads to certain errors in the diagnosis results. Therefore, it is necessary to improve the diagnosis in combination with the actual situation. This study used the whole tumor ADC histogram to identify the heterogeneous features of benign and malignant breast lesions and used the diffusion characteristics of the whole tumor to construct a diagnostic model suitable for breast tumor image feature recognition. Simultaneously, this study combined the actual situation to construct a system framework of image enhancement algorithm based on Retinex theory, and combined image processing algorithms …to improve the model. In addition, this study converted the pixel data type of the grayscale image of each color channel into a double type and converted each color channel image into a logarithmic domain. Finally, in order to study the performance of the algorithm, this study designed a comparative test for performance analysis. The research shows that the algorithm has certain clinical effects and can provide theoretical reference for subsequent related research. Show more

Keywords: Image enhancement, breast neoplasms, image processing, tumor recognition, feature extraction

DOI: 10.3233/JIFS-189792

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jun, He

Article Type: Research Article

Abstract: When the new model is used to comprehensively study the dynamic influencing factors of inflation, the conclusions drawn can reflect economic conditions more truly, and can provide more practical policy recommendations for macroeconomic management. According to the machine learning algorithm and the current situation of inflation, this paper constructs an analysis model of the factors affecting inflation based on the M-F model. Moreover, by analyzing the linkage relationship between exchange rate and interest rate under the current economic background, based on the M-F-D model of machine learning, this paper conducts an empirical test on the linkage effect of interest rate …and exchange rate since my country’s exchange rate reform. In addition, this paper selects 10 macroeconomic indicators related to inflation from the macroeconomic data and uses the time-varying parameter state space model to conduct a comprehensive analysis. The research results show that the model constructed in this paper has a certain effect in the research of inflationary factors. Show more

Keywords: M-F model, inflation, influencing factors, machine learning

DOI: 10.3233/JIFS-189795

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Jamil, Faisal | Kim, DoHyeun

Article Type: Research Article

Abstract: In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. …The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach. Show more

Keywords: ANN, FIS based Kalman Filter, navigation system, inertial measurement unit, indoor navigation, sensors fusion

DOI: 10.3233/JIFS-201352

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Jin, Dan | Su, Xiaojuan | Wang, Yeqing | Shi, Dai | Xu, Liang

Article Type: Research Article

Abstract: Traditional brain imaging usually does not show anomalies. Based on this, this study used DTI to find evidence that the brain structure microstructure may be abnormal, and to study the BOLD signal changes of functional magnetic resonance imaging and the changes of DTI microstructure in patients with mild traumatic brain injury. At the same time, based on literature collection and actual data, the current status of nuclear magnetic resonance diagnosis of brain trauma was collected. Moreover, this study combines the problem to improve the algorithm and propose an image diagnosis method for brain trauma to improve the cluster quality and …stability. In addition, the experiment was designed to analyze the performance of the algorithm in this study. Finally, in this study, resting state functional magnetic resonance imaging was used to study the resting brain function in patients with mild cognitive impairment within one week after traumatic brain injury. The results show that the method proposed in this study has certain effects and can provide theoretical reference for related research. Show more

Keywords: DTI image, image processing, brain trauma, nuclear magnetic resonance, diagnostic analysis

DOI: 10.3233/JIFS-189797

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Kang, Keming | Tian, Shengwei | Yu, Long

Article Type: Research Article

Abstract: In the Chinese named entity recognition method based on deep learning, a named entity recognition method for drug adverse reactions based on the Adversarial Transfer Learning(ATR) is proposed to solve the problem that deep learning has insufficient ability to learn a small amount of data, and constructs a neural network model, ATR based self-Attention, independently recurrent neural network(IndRNN), Bi-directional long short-Term Memory(BiLSTM) and conditional random field(CRF)(ASAIBC). There are only a few publicly annotated data sets in the current task of Chinese named entity recognition(NER). Therefore, this article introduced ATR network, using it to make full use of Chinese word segmentation …tasks(CWS) and the boundary of the NER tasks share information, as well as filter the specific information in the CWS at the same time, combining with self-Attention mechanisms and IndRNN to improve the expression ability of feature, so that the model can pay attention to the important information of different entities from different levels to capture the dependency of long sentences better, and to improve the recognition ability of the model further. The results obtained by ASAIBC model on WeiBoNER and MSRA data sets are better than the previous research methods. Meanwhile, the accuracy, precision, recall and F-Score value of this model obtained by the experiment based on the data set of Xinjiang local named entity recognition of ADR(XJADRNER) labeled by hand are 98.97%, 91.01%, 90.21% and 90.57% respectively. The experimental results show that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang. Show more

Keywords: Transfer learning, self-Attention mechanism, IndRNN, named entity recognition of adverse drug reactions, deep learning

DOI: 10.3233/JIFS-201017

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Pervez, Imran | Sarwar, Adil | Alam, Afroz | Mohammad, | Chakrabortty, Ripon K. | Ryan, Michael J.

Article Type: Research Article

Abstract: Due to its clean and abundant availability, solar energy is popular as a source from which to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power …on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations. Show more

Keywords: Maximum power point tracking, metaheuristic algorithms, partial shading, photovoltaic

DOI: 10.3233/JIFS-189750

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Gautam, Abhinav K. | Tariq, Mohd | Verma, Kripa Shankar | Pandey, Jai Prakash

Article Type: Research Article

Abstract: A Maximum Power Tracking Technique (MPPT) for Photovoltaic Powered e-Vehicles via Black Widow Optimization Technique is introduced. The proposed system addresses the problems of conventional MPPT methods via a black widow spider-inspired optimization approach. As a result, the design would require fewer iterations to achieve prime conditions, thus increasing the complete efficiency of the proposed system. Field-oriented control (FOC) is used for speed control of the BLDC engine (e-vehicle). The proposed model was first designed, and then simulated in MATLAB environment. The simulink results run in parallel with the Typhoon HIL 402 setup. The results obtained the superior performance of …the BWO-based MPPT technique. Details of the modeling of a new MPPT used for PV-driven BLDC-based e-vehicles are also discussed in this paper. There are many factors involved in a real situation for poor efficiencies, such as shade, irregular sunlight, and weather conditions, which show the non-linear characteristics of PV. The MPPT approach discussed in this article may be used to increase overall productivity and minimize costs for the operation of e-vehicles based on the PV framework. Show more

Keywords: MPPT, BWO, electric vehicle, BLDC, battery, VSI, boost converter

DOI: 10.3233/JIFS-189747

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Huang, Yanan | Miao, Yuji | Da, Zhenjing

Article Type: Research Article

Abstract: The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further …make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated. Show more

Keywords: Transfer learning, English data, time detection, English recognition

DOI: 10.3233/JIFS-189798

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: (Sixue) Jia, Susan | Wu, Banggang

Article Type: Research Article

Abstract: In order to reach a compromise between adhering to the traditional culture and embracing the modern lifestyle, more and more Asian moms are heading towards postpartum care centres for postpartum recovery. However, research regarding the quality of care of these postpartum care centres is nearly missing from the literature. This paper investigated the status quo of the postpartum care centres in Shanghai, China from mothers’ perspectives by means of analysing the 34280 pairs of ratings and reviews posted by postpartum care centre customers on the internet with machine learning and text mining. Results show that the mothers are generally satisfied …with the studied care centres. Meanwhile, the 13 major topics in the customer online reviews were identified, which provide an overview of the interaction between a mother and a care centre. In addition, weight of topic analysis suggests that the studied care centres can further improve in the areas of support team, environment, and facility. Show more

Keywords: Postpartum care centre, text mining, user generated content

DOI: 10.3233/JIFS-189726

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Li, Zhaowen | Liao, Shimin | Qu, Liangdong | Song, Yan

Article Type: Research Article

Abstract: Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ -reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction …algorithms base on θ -discernibility matrix, θ -information entropy and θ -significance in an IIVIS are given. Show more

Keywords: Rough set theory, IIVIS, similarity degree, θ-reduction, θ-discernibility matrix, θ-information entropy, θ-significance, algorithm

DOI: 10.3233/JIFS-200394

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Zhang, Mo | Zhang, Qinghua | Gao, Man

Article Type: Research Article

Abstract: As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant …fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model. Show more

Keywords: Hesitant fuzzy sets, three-way decisions, shadowed sets, sequential strategy

DOI: 10.3233/JIFS-201524

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Malik, Hasmat | Ahmad, Waseem | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But …PMUs are not economical, so PSN operator needs to install minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus system. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared. Show more

Keywords: Condition monitoring, PMU, placement, wide area monitoring, smart grid

DOI: 10.3233/JIFS-189752

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Li, Xin | Li, Xiaoli | Wang, Kang

Article Type: Research Article

Abstract: In the past two decades, multi-objective evolutionary algorithms (MOEAs) have achieved great success in solving two or three multi-objective optimization problems. As pointed out in some recent studies, however, MOEAs face many difficulties when dealing with many-objective optimization problems(MaOPs) on account of the loss of the selection pressure of the non-dominant candidate solutions toward the Pareto front and the ineffective design of the diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm based on vector guidance. In this algorithm, the value of vector angle distance scaling(VADS) is applied to balance convergence and diversity in environmental selection. In addition, tournament …selection based on the aggregate fitness value of VADS is applied to generate a high quality offspring population. Besides, we adopt an adaptive strategy to adjust the reference vector dynamically according to the scales of the objective functions. Finally, the performance of the proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on 52 instances of 13 MaOPs with diverse characteristics. Experimental results show that the proposed algorithm performs competitively when dealing many-objective with different types of Pareto front. Show more

Keywords: Vector angle distance scaling, evolutionary algorithm, many-objective optimization problem

DOI: 10.3233/JIFS-202724

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2021

Authors: DongLi, Li | Sheng, Wen | Yu, Zou | Chong, Peng | Yan Jun, Jiao | Cao, Shipeng | Selim, Mahmoud M.

Article Type: Research Article

Abstract: According to the actual needs, this paper designs and implements a power inspection system based on the Internet of things GIS, including server and mobile terminal. It mainly includes basic information management, patrol task management, statistical query management, and other functions, and describes its design method in detail. Finally, this paper summarizes the key technologies of the power inspection system based on the Internet of things GIS and describes the realization of each functional module of the power inspection system, and through a more detailed description of the implementation process of each functional module of the system, and an example …of operation to show the system. Show more

Keywords: Power inspection system, internet of things, GIS

DOI: 10.3233/JIFS-189790

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Wei, Xinyu

Article Type: Research Article

Abstract: The traditional English teaching mode mostly relies on rote memorization of textbooks, but it lacks the training of oral expression skills and lacks intelligent guidance for students. Taking machine learning algorithm as the system algorithm, this paper combines the CA-IAFSA algorithm to construct an English intelligent system based on artificial intelligence. The system uses image recognition technology, introduces population pheromone and tribal pheromone, and adopts multiple ant colony planning and dual pheromone feedback strategies. Moreover, this paper improves the heuristic information search strategy, pheromone update strategy, and state transition probability of the basic ant colony algorithm. In addition, this paper …proposes the MACDPA path planning algorithm to realize the intelligent analysis of English textbook images. Finally, after constructing the model, this paper conducts research and analysis on the performance of the model and uses controlled experimental methods and mathematical statistics to analyze the data. The research results show that the model constructed in this paper performs well in assisted teaching and intelligent translation and meets the expected requirements. Show more

Keywords: CA-IAFSA algorithm, machine learning, artificial intelligence, English

DOI: 10.3233/JIFS-189796

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Riyaz, Ahmed | Sadhu, Pradip Kumar | Iqbal, Atif | Tariq, Mohd

Article Type: Research Article

Abstract: The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, …selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). Show more

Keywords: Renewable energy sources (RES), packed E-Cell (PEC), genetic algorithm (GA), total harmonic distortion (THD), selective harmonic elimination (SHE)

DOI: 10.3233/JIFS-189751

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Alsaidan, Ibrahim | Rizwan, Mohammad | Alaraj, Muhannad

Article Type: Research Article

Abstract: The rapid advancements in the technology, increase in comfort levels, movement of population to urban areas, depletion of fossil fuels and increasing greenhouse gas emissions have invigorated the use of renewable energy resources for power generation in the last few years. The major renewable energy resources which have potential to fulfill the requirements includes solar energy, wind energy, small hydro and biomass etc. Among these major resources, solar energy-based technology is considered as one of the fastest growing technology because of its various advantages and ubiquitous availability of the resources. However, there are certain challenges in the utilization of solar …energy for power generation because of various uncertainties in the atmosphere. As a result, the power generated from solar based power plants is fluctuating in nature which is not desirable. Therefore, the utilities are adopting the smart grid approach which has ability to integrate the solar power plants efficiently and the solar energy forecasting is one of the essential tools for this new model. In this paper, AI based techniques are utilized to forecast solar energy using high quality measured solar irradiance data. The forecasting accuracy of the developed models is evaluated based on statistical indices such as absolute relative error and mean absolute percentage error. The results obtained from the developed models are compared to observe the forecasting ability and performance with the high-quality measured data and found accurate. Show more

Keywords: Artificial intelligence techniques, solar energy forecasting, smart energy management, intelligent systems, sustainable power generation

DOI: 10.3233/JIFS-189757

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Fatema, Nuzhat | Farkoush, Saeid Gholami | Hasan, Mashhood | Malik, H

Article Type: Research Article

Abstract: In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. …The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. Show more

Keywords: Gravitational search algorithm, back-propagation algorithm, neural network, machine learning, optimization, occupancy, smart building

DOI: 10.3233/JIFS-189748

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Tak, Nihat | Egrioglu, Erol | Bas, Eren | Yolcu, Ufuk

Article Type: Research Article

Abstract: Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the …optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE. Show more

Keywords: Forecast combination, meta-analysis, intuitionistic fuzzy c-means, meta fuzzy functions, meteorology

DOI: 10.3233/JIFS-202021

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Liu, Peide | Wang, Xiyu | Teng, Fei

Article Type: Research Article

Abstract: In today’s education industry, online teaching is increasingly becoming an important teaching way, and it is necessary to evaluate the quality of online teaching so as to improve the overall level of the education industry. The online teaching quality evaluation is a typical multi-attribute group decision-making (MAGDM) problem, and its evaluation index can be expressed by linguistic term sets (LTSs) by decision makers (DMs). Especially, multi-granularity probabilistic linguistic term sets (MGPLTSs) produced from many DMs are more suitable to express complex fuzzy evaluation information, and they can not only provide different linguistic term set for different DMs the give their …preferences, but also reflect the importance of each linguistic term. Based on the advantages of MGPLTSs, in this paper, we propose a transformation function of MGPLTSs based on proportional 2-tuple fuzzy linguistic representation model. On this basis, the operational laws and comparison rules of MGPLTSs are given. Then, we develop a new Choquet integral operator for MGPLTSs, which considers the relationship among attributes and does not need to consider the process of normalizing the probabilistic linguistic term sets (PLTSs), and can effectively avoid the loss of evaluation information. At the same time, the properties of the proposed operator are also proved. Furthermore, we propose a new MAGDM method based on the new operator, and analyze the effectiveness of the proposed method by online teaching quality evaluation. Finally, by comparing with some existing methods, the advantages of the proposed method are shown. Show more

Keywords: Multiple-attribute group decision-making, online teaching quality evaluation, multi-granularity probabilistic linguistic term sets, Choquet integral

DOI: 10.3233/JIFS-202543

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: kaur, Surinder | Chaudhary, Gopal | Dinesh kumar, Javalkar

Article Type: Research Article

Abstract: Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping …the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment. Show more

Keywords: Biometrics, deep learning, feature level fusion, fusion, score level fusion

DOI: 10.3233/JIFS-189753

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Jia, Zhifu | Liu, Xinsheng | Zhang, Yu

Article Type: Research Article

Abstract: Uncertain pantograph differential equation (UPDE for short) is a special unbounded uncertain delay differential equation. Stability in measure, stability almost surely and stability in p -th moment for uncertain pantograph differential equation have been investigated, which are not applicable for all situations, for the sake of completeness, this paper mainly gives the concept of stability in distribution, and proves the sufficient condition for uncertain pantograph differential equation being stable in distribution. In addition, the relationships among stability almost surely, stability in measure, stability in p -th moment, and stability in distribution for the uncertain pantograph differential equation are also discussed.

Keywords: uncertainty theory, uncertain pantograph differential equation, stability in distribution, the relationships among stabilities

DOI: 10.3233/JIFS-201864

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Iqbal, Naeem | Ahmad, Rashid | Jamil, Faisal | Kim, Do-Hyeun

Article Type: Research Article

Abstract: Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and …social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc. Show more

Keywords: Movie quality prediction, machine learning, data mining, business intelligence, predictive analytics

DOI: 10.3233/JIFS-201844

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2021

Authors: Emeç, Şeyma | Akkaya, Gökay

Article Type: Research Article

Abstract: Energy consumption increases due to technological developments, urbanization, industrialization and population. The fact that the constantly increasing energy demand is not exactly known is an important issue for countries. In addition, due to changing climate conditions, the amount of emission emitted and energy produced from energy sources are also not quite known. Therefore, determining the energy demand, protecting the environment, and minimizing the energy cost by using resources effectively has become one of the most important problems of countries. In this context, the present study developed a fuzzy optimal renewable energy model (F-OREM) to solve the energy problem involving fuzzy …parameters. Fuzzy linear programming (FLP) models provide the best decision by producing faster and more flexible solutions compared to classical linear programming (CLP) models in situations where there are uncertainties and a lack of information. The purpose of the developed model was to minimize the cost of generating electrical energy from different energy sources in an uncertain environment under potential, demand, emission and efficiency constraints. The developed F-OREM was operated using CPLEX decoder in the GAMS 24.2.3 package program and using the particle swarm optimization (PSO) for ∝ different values between 0-1. The results showed that the results of the metaheuristic method and the results of the GAMS package program were the same, and the results were consistent According to the results obtained, the emission level at which the objective function was minimum (when ∝=1) was at the lowest level. In this case, the total emitted amount was 1,06125E+14 g-CO2/kWh.. In this context, the developed model can be applied using metaheuristic or heuristic methods for larger test cases with thousands of variables. This study contributed to the practicality of FLP by offering decision-makers a wider solution area than the CLP approach. Show more

Keywords: Energy economics, energy policy, fuzzy programming, mathematical model, optimization

DOI: 10.3233/JIFS-201994

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Jun, Jia | Fu, Rui | Jian, Wang | Dong, Dai Yong | Xiang, Shen | Atassi, Reem

Article Type: Research Article

Abstract: This paper briefly introduces the background, significance, and development status of 3D radar technology at home and abroad, and then explains the concept, working principle, system composition, and workflow of the radar system. Combined with the current development trend of smart grids, it focuses on the application scope of this technology in the field of transmission line construction, operation, and maintenance. Then, through the specific implementation of the project cases, the daily operation and maintenance of four 500 kV transmission lines in Nanjing have played a certain guiding role. Finally, according to the development trend of smart grid and the actual …demand of power system production and business integration, this paper briefly prospects the function expansion of this technology in transmission line operation evaluation, fault analysis and diagnosis, emergency rescue plan formulation, and other fields. Show more

Keywords: Radar measurement, transmission line, 3D mapping

DOI: 10.3233/JIFS-189788

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Xiao, Yanjun | Yu, Anqi | Qi, Hao | Jiang, Yunfeng | Zhou, Wei | Gao, Nan

Article Type: Research Article

Abstract: In the industrial field, the lithium battery industry has a long history and a large market scale. Lithium battery electrode strip rolling mill belongs to the high-end production equipment in the lithium battery industry. However, due to its complex structure, the tension of lithium battery electrode mill is prone to large fluctuation. This will lead to the phenomenon of wrinkle and looseness, which will affect the quality of the electrode strip. At present, the tension control method of lithium battery electrode mill mostly adopts traditional Proportional-Integral-Differential(PID) control. Under this control mode, the production speed and precision of lithium battery electrode …mill need to be improved. In this paper, the fuzzy PID tension control method of lithium battery electrode mill based on genetic optimization is studied. Based on fuzzy theory and PID control method, a tension fuzzy PID model is established for experimental verification, and the initial parameters and fuzzy rules of fuzzy PID are optimized by Genetic Algorithm(GA). This method has better stability, can improve the precision of strip tension control, make the tension more stable when the rolling mill is running, and help to improve the quality of electrode strip production. Show more

Keywords: Fuzzy theory, genetic algorithm, lithium battery electrode mill, PID, tension

DOI: 10.3233/JIFS-201675

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2021

Authors: Jin, Yilun | Liu, Yanan | Zhang, Wenyu | Zhang, Shuai | Lou, Yu

Article Type: Research Article

Abstract: With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a ne1 w feature-enhanced stacking method is proposed to construct an effective ensemble model by …composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models. Show more

Keywords: Credit scoring, ensemble model, imbalanced learning, K-means, stacking

DOI: 10.3233/JIFS-201954

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sarita, Kumari | Devarapalli, Ramesh | Kumar, Sanjeev | Malik, H. | Márquez, Fausto Pedro García | Rai, Pankaj

Article Type: Research Article

Abstract: Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also …for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis. Show more

Keywords: Machine learning, industry 4.0, PCA, condition monitoring, predictive maintenance, preprocessing

DOI: 10.3233/JIFS-189755

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Xiao, Hui-Min | Wang, Mei-Qi | Cao, Yan-Li | Guo, Yu-Jie

Article Type: Research Article

Abstract: In this paper, to improve the situation of singleness of selecting results in hesitant fuzzy set decision-making and expand the range of choices for decision makers, we construct a hesitant fuzzy set clustering algorithm combined with fuzzy matroid operation. The algorithm synthesizes the r-cut set, fuzzy shrinking matroids in the fuzzy matroids and the operational properties of the fuzzy derived matroids, the r value also is used to connect the two types of fuzzy matroids to form a clustering algorithm. Finally, we apply the algorithm to the hesitant fuzzy set decision-making of job seekers choosing recruitment websites, each recruitment website …as an optional scheme is divided into three categories of excellent to inferior schemes to provide job seekers with ideas and methods for favorably selecting recruitment websites. Show more

Keywords: Hesitant fuzzy set decision-making, fuzzy matroid, contraction matroid, derived matroid, clustering algorithm

DOI: 10.3233/JIFS-201476

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Fatema, Nuzhat | Malik, H | Abd Halim, Mutia Sobihah Binti

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this …procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics. Show more

Keywords: ARIMA model, explanatory feature, multi-step ahead, medical tourism forecasting, Monte Carlo simulation, feature extraction

DOI: 10.3233/JIFS-189785

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

Authors: Xu, Tongtong | Xiang, Zheng

Article Type: Research Article

Abstract: In this work, modified constant modulus algorithm based on bat algorithm is proposed for wireless sensor communications systems. The bat algorithm is a swarm intelligence optimization algorithm, which mainly used to solve optimization problems. The proposed algorithm focused on modified constant modulus algorithm, which is also applicable to the constant modulus algorithm. The error function of blind equalization algorithm is used as the evaluation function of the bat algorithm, and then the initial value of the weight vector is calculated adaptively by the bat algorithm. Theoretical analysis is provided to illustrate that the proposed algorithm has a faster convergence speed …than the original one and is suitable for almost all blind channel equalization algorithms. The simulation results support the newly proposed improved algorithm. The proposed algorithm could be applied to some more complex wireless channel environments to improve the reception performance of sensor communication systems. Show more

Keywords: Wireless sensor communications, blind equalization, bat algorithm, weight vector, modified constant modulus algorithm

DOI: 10.3233/JIFS-189709

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Chen, Yingjiao | Liang, Hu

Article Type: Research Article

Abstract: Rural revitalization is an attempt by China’s rural reform plan. Among them, the pastoral complex emphasizes the integration of agriculture and tourism, extends the agricultural chain, and integrates health care, technology, tourism, creativity, and leisure. However, the pastoral complex is faced with solving rural-urban integration in the context of the current rural revitalization strategy, involving complex factors such as land, ecological, cultural, and social issues. Therefore, this study analyzes the complex factors affecting the pastoral complex. Fuzzy calculation theory is introduced in the pastoral complex to discuss the pastoral complex system’s operational characteristics and further explore how to build an …adaptive system evaluation system at different system levels. Considering that the process needs to consider several Conflicting factors from qualitative to quantitative, to deal with the uncertainty of human judgment in the evaluation process, the process uses fuzzy analytical hierarchy process to obtain the weight of each factor and understand the degree of influence of each factor. The research results show that various factors have different degrees of influence on the pastoral complex. Therefore, in the complex pastoral process, more attention should be paid to the operation mechanism factors to make the complex pastoral system more scientific. Show more

Keywords: Fuzzy calculation, rural revitalization, pastoral complex, FAHP

DOI: 10.3233/JIFS-189727

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zong, Ke | Kong, Ye

Article Type: Research Article

Abstract: This research focus on the formation mechanism and intervention strategy of coal miners’ job burnout, based on a simulation study using system dynamics method. The simulation result indicates that, work assignment alienation has higher sensitivity to intervention strategies than other elements of coal miners’ job burnout, while health damage is least sensitive to intervention. The top three adoptable strategies shall be reasonable working hours, self-psychological adjustment, and psychological counseling program. As the impact of one intervention strategy weakens with time, it is necessary to constantly change intervention strategies or to adopt a strategy combination to intervene miners’ burnout. This study …explains the formation mechanism of coal miners’ job burnout and offers targeted advice for coal enterprises, aiming to effectively improve their safety management mechanism and to reduce casualties. Show more

Keywords: Coal miners, job burnout, intervention strategy, system dynamics, simulation

DOI: 10.3233/JIFS-189728

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Zhu, Wenye | Tan, Chengxiang | Xu, Qian | Xiao, Ya

Article Type: Research Article

Abstract: The cross-trust domain environment in which heterogeneous identity alliances are located often does not have a completely trusted centralized trust root, and different trust domains and entities also have specific security requirements. In view of the above problems, we believe that trust measurement of cross-domain identities based on risk assessment is an effective method to achieve decentralized proof of user identities in heterogeneous cyberspace. There are various risk assessment models. We choose the more mature attack graph theory in the existing research to apply to the new field of cross-trust domain management of heterogeneous identities. We propose an attribute attack …graph evaluation model to evaluate cross-domain identities through risk measurement of attributes. In addition, heterogeneous identity alliances also have architectural risks, especially the risk of decentralized underlying structures. In response to this problem, we identify the risk of the identity alliance infrastructure, and combine the risk assessment and presentation system design to verify the principle. Show more

Keywords: Heterogeneous identity alliance, attribute attack graph, proof of identity, cyber security assessment, trust management

DOI: 10.3233/JIFS-189729

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Malik, Hasmat | Gopal, | Srivastava, Smriti

Article Type: Editorial

Abstract: The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such …as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation based performance demonstration and finally conclusive summary of the developed tool/technique along with future scope. Show more

Keywords: Digital transformation, advancement, artificial intelligence, machine learning, application, data analytics, cybersecurity, condition monitoring, fault detection and diagnosis, prediction, forecasting, renewable energy, feature extraction, feature selection, healthcare, greater sustainability, resilient infrastructure, automation

DOI: 10.3233/JIFS-189787

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Zhao, Hu | Sayed, O.R. | El-Sanousy, E. | Ragheb Sayed, Y.H. | Chen, Gui-Xiu

Article Type: Research Article

Abstract: Different from the separation axioms in the framework of (L , M )-fuzzy convex spaces defined by Liang et al.(2019). In this paper, we give some new investigations on separation axioms in (L , M )-fuzzy convex structures by L -fuzzy hull operators and r -L -fuzzy biconvex. We introduce the concepts of r -LFS i spaces where i  = {0, 1, 2, 3, 4}, and obtain various properties. In particular, we discuss the invariance of these separation properties under subspace and product.

Keywords: r-LFS0 space, r-LFS1 space, r-LFS2 space, r-LFS3 space, r-LFS4 space

DOI: 10.3233/JIFS-200340

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Ding, Xiaobing | Yang, Kaihe | Hu, Hua | Liu, Zhigang

Article Type: Research Article

Abstract: There is a close relationship between the operation safety and the application of training equipment. If the relationship is not handled well, it will lead to serious problems, even the Conflicts. So designing training equipment management information system is extremely urgent. First, the main training categories are carded, such as drivers training, construction and maintenance, daily safety management, canteen safety, etc., and the basic flow chart of the 4 types of training are drawn; Second, the training equipment management database Train_Database is constructed based on training process, equipment involved, trainers, contingency plans of the 4 kinds of raw data, which …lay the foundation for the follow-up of the management information system design and development; Third, the training equipment declaration and management system is developed, which is called Training_Equipment_MS, and the main modules are: equipment resource information management module, equipment declaration module, equipment audit module, safety check form filling module, equipment declaration results publicity module, etc. Finally, the functions of each module are shown in details. It has good practical guidance to the application and operation of rail transit, which can reduce accidents and hidden danger in the course of training. Show more

Keywords: Rail transit training, safety management of equipment, resource declaration and audit, design and development of system

DOI: 10.3233/JIFS-189725

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Alzubi, Jafar A. | Jain, Rachna | Alzubi, Omar | Thareja, Anuj | Upadhyay, Yash

Article Type: Research Article

Abstract: The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver’s negligence towards safety a possible solution is to monitor the driver and …driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. Show more

Keywords: Machine learning, deep learning, convolutional neural network, CNN, distraction detection, model compression, pruning, quantization, deep compression

DOI: 10.3233/JIFS-189786

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Yan, Zheping | Zhang, Jinzhong | Zeng, Jia | Tang, Jialing

Article Type: Research Article

Abstract: In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and …higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust. Show more

Keywords: Water wave optimization (WWO), autonomous underwater vehicle (AUV), path planning, randomly generated threat areas, generated fixed threat areas

DOI: 10.3233/JIFS-201544

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Wei, Guangcun | Rong, Wansheng | Liang, Yongquan | Xiao, Xinguang | Liu, Xiang

Article Type: Research Article

Abstract: Aiming at the problem that the traditional OCR processing method ignores the inherent connection between the text detection task and the text recognition task, This paper propose a novel end-to-end text spotting framework. The framework includes three parts: shared convolutional feature network, text detector and text recognizer. By sharing convolutional feature network, the text detection network and the text recognition network can be jointly optimized at the same time. On the one hand, it can reduce the computational burden; on the other hand, it can effectively use the inherent connection between text detection and text recognition. This model add the …TCM (Text Context Module) on the basis of Mask RCNN, which can effectively solve the negative sample problem in text detection tasks. This paper propose a text recognition model based on the SAM-BiLSTM (spatial attention mechanism with BiLSTM), which can more effectively extract the semantic information between characters. This model significantly surpasses state-of-the-art methods on a number of text detection and text spotting benchmarks, including ICDAR 2015, Total-Text. Show more

Keywords: Scene text spotting, End-to-end, Joint optimization, TCM, SAM-BiLSTM

DOI: 10.3233/JIFS-200903

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Wang, Jian | Zhu, Yuanguo

Article Type: Research Article

Abstract: Uncertain delay differential equation is a class of functional differential equations driven by Liu process. It is an important model to describe the evolution process of uncertain dynamical system. In this paper, on the one hand, the analytic expression of a class of linear uncertain delay differential equations are investigated. On the other hand, the new sufficient conditions for uncertain delay differential equations being stable in measure and in mean are presented by using retarded-type Gronwall inequality. Several examples show that our stability conditions are superior to the existing results.

Keywords: Uncertainty theory, uncertain delay differential equation, analytic solution, stability

DOI: 10.3233/JIFS-202507

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Li, Chengzheng | Peng, Ying | Peng, Peng | Cao, Lei

Article Type: Research Article

Abstract: Investigating the factors influencing the performance of social conditioning in the network environment is the core issue for improving academic performance. Through the search of existing literature, the paper analyzes the main factors that influence social conditioning learning in current research, and through the questionnaire survey and in-depth processing of the raw data, the advanced behavioral indicators related to learning are obtained and analyzed by Spearman correlation coefficient and fuzzy modeling in machine learning. The results showed that the twelve dimensions of motivation regulation, trust building, efficacy management, cognitive strategy, time management, goal setting, task strategy, peer support, team assessment, …help seeking, environment construction, and team supervision were significantly related to group performance, with team supervision having a significant negative relationship with group performance. In addition, trust building, team supervision and environment construction were the main factors for online social learning, effectiveness management, task strategy, peer support and help-seeking were the secondary factors, while motivation regulation, cognitive strategies, goal setting and team assessment had little impact on the final performance. The findings have some implications for the optimization of social conditioning learning support services and the improvement of social conditioning learning performance. Show more

Keywords: Learning analysis, online collaborative learning, socially modulated learning, machine learning

DOI: 10.3233/JIFS-189724

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Liu, Fang | Liu, Yi | Abdullah, Saleem

Article Type: Research Article

Abstract: Based on decision theory rough sets (DTRSs), three-way decisions (TWDs) provide a risk decision method for solving multi-attribute decision making (MADM) problems. The loss function matrix of DTRS is the basis of this method. In order to better solve the uncertainty and ambiguity of the decision problem, we introduce the q-rung orthopair fuzzy numbers (q-ROFNs) into the loss function. Firstly, we introduce concepts of q-rung orthopair fuzzy β -covering (q-ROF β -covering) and q-rung orthopair fuzzy β -neighborhood (q-ROF β -neighborhood). We combine covering-based q-rung orthopair fuzzy rough set (Cq-ROFRS) with the loss function matrix of DTRS in the q-rung …orthopair fuzzy environment. Secondly, we propose a new model of q-ROF β -covering DTRSs (q-ROFCDTRSs) and elaborate its relevant properties. Then, by using membership and non-membership degrees of q-ROFNs, five methods for solving expected losses based on q-ROFNs are given and corresponding TWDs are also derived. On this basis, we present an algorithm based on q-ROFCDTRSs for MADM. Then, the feasibility of these five methods in solving the MADM problems is verified by an example. Finally, the sensitivity of each parameter and the stability and effectiveness of these five methods are compared and analyzed. Show more

Keywords: Covering-based q-rung orthopair fuzzy rough sets, q-ROF β-covering decision-theoretic rough sets, q-ROF β-neighborhood, MADM, DTRSs

DOI: 10.3233/JIFS-202291

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Liu, Shulin

Article Type: Research Article

Abstract: Under the background of the national fitness craze, the demand space for social sports professionals is constantly expanding. However, according to the author’s investigation, the overall situation shows that the number of high-quality social sports professionals in Chinese colleges and universities is relatively small. Among them, the unsound teaching quality evaluation system of social sports major is one of the important reasons affecting the cultivation of high-quality talents, so it is imperative to construct a sound teaching quality evaluation system of social sports major. At the same time, the perfect social physical education teaching quality evaluation system is an important …basis for teachers’ teaching job evaluation and strengthening teachers’ management. And it is frequently considered as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS method and intuitionistic fuzzy sets (IFSs), this essay designs a novel intuitive distance based IF-TOPSIS method for teaching quality evaluation of physical education. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each alternative. Eventually, an application about teaching quality evaluation of physical education and some comparative analysis have been given. The results think that the designed method is useful for teaching quality evaluation of physical education. Show more

Keywords: Multi-attribute group decision-making (MAGDM), intuitionistic fuzzy sets (IFSs), TOPSIS method, CRITIC method, teaching quality evaluation, physical education

DOI: 10.3233/JIFS-201672

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Wang, Zhiru | Wang, Min | He, Ruyu | Bhamra, Ran S. | Yang, Lili

Article Type: Research Article

Abstract: In order to better achieve active defense in the escalator risk management, this study based on the vulnerability theory, task driven theory, management error theory, proposed a Gray Relational Analysis (GRA) based fuzzy assessment of escalator accident risk approach. The risk assessment index system of subway station escalator accident was constructed based on the commonness and essence of management defects; the weight of risk index was calculated scientifically and reasonably by using Analytic Hierarchy Process (AHP); escalator accident risk was evaluated by the combination of GRA and Fuzzy approach. The results show that escalator equipment, environment, safety knowledge of riders …are all in good condition in the station. However, ‘Maintenance’ of escalator in the Beijing subway station is in an extremely high risk level. The contributions of this studies are: (1) general risk elements analysis model for escalator accidents which enable to compose any risk factor possible to induce escalator accident in subway station; (2) GRA based risk assessment approach can avoid the problem when expend the range to left and right. It can also judge whether the continuous improvement effect of the object is significant by the difference degree of each risk level before and after. Show more

Keywords: Subway escalator incident, risk assessment, gray relational analysis (GRA), gray clustering, fuzzy method

DOI: 10.3233/JIFS-189722

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Li, Jingyuan

Article Type: Research Article

Abstract: In 2020, under the major impact of the global COVID-19 epidemic, people are paying more and more attention to safety issues including health and personal safety, and relevant supervision and testing methods are also constantly updated across different countries. In industrial enterprises, both spread of diseases and occurrence of accidents are largely caused by unsafe state of things and unsafe behaviors of people, among which, human factor is the most important factor. In recent years, many scholars have conducted theoretical and practical research on individual behavior from the perspective of individual psychological characteristics. Where, individual initiative as an important individual …psychological trait is increasingly incorporated in the research category of safety behavior. A close correlation exists between individual initiative and evolution of safety production behavior. According to constraint conditions and replicator dynamics equation, this paper uses evolutionary game method and computer fuzzy system Matlab simulation software to conduct numerical experiment analysis on the ideal state of the game between organizations and individuals, thereby studying the behavior evolution trend. The basic idea of fuzzy control is to use computer to realize human’s qualitative control experience. Research is found that whether an individual adopts safety obedience behavior will be directly affected by whether the organization adopts regulatory safety production management model. And if the organization adopts regulatory safety production management model and the individual does not implement safety obedience behavior, it is impossible to achieve a stable state. The evolution process of the two to the ideal state is affected by multiple factors. Show more

Keywords: Safety management, safety behavior, fuzzy system simulation, evolutionary game

DOI: 10.3233/JIFS-189723

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Hua, Xing | Yue, Qi

Article Type: Research Article

Abstract: Based on a survey database of cross-border acquisitions by Chinese private firms, this study uses a fuzzy-set qualitative comparative analysis (fsQCA) to explore the holistic impact of acquisition ownership, organizational factors and environmental factors on acquisition performance in cross-border acquisitions. It is found that the cross-border acquisitions taken by Chinese private enterprises have four kinds of acquisition ownership strategies leading to high acquisition performance under different internal and external conditions. This study points out that ownership strategy is a key decision affecting cross-border acquisition performance and provides a variety of paths leading to the same outcome rather than just finding …the linear relationship between corporate activity and performance. This study supports the assumption of equivalence, and reveals a variety of scenarios in which cross-border acquisition ownership contributes to the outcome of high cross-border acquisition performance, and further confirms the view of causal asymmetry between condition and outcome. This study reveals whether the proportion of cross-border acquisition ownership affects cross-border acquisition performance and under what circumstances is conducive to the realization of expected cross-border acquisition performance. Show more

Keywords: Fuzzy-set qualitative comparative analysis (fsQCA), acquisition ownership, acquisition performance, cross-border acquisition

DOI: 10.3233/JIFS-189720

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Li, Dong | Sun, Xin | Gao, Furong | Liu, Shulin

Article Type: Research Article

Abstract: Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce false alarm rate. We …present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases. Show more

Keywords: Negative selection algorithm, anomaly detection, artificial immune algorithms, machine learning

DOI: 10.3233/JIFS-200405

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Yin, Tingting | Yang, Zhong | Wu, Youlong | Jia, Fangxiu

Article Type: Research Article

Abstract: The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation …method has the advantages of the high accuracy and good real-time performance. Show more

Keywords: Ordnance science and technology, high precision, roll attitude estimation, PMSG, hall-effect sensor, relative roll dynamic model

DOI: 10.3233/JIFS-189718

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Mohanta, Bhabendu Kumar | Jena, Debasish | Mohapatra, Niva | Ramasubbareddy, Somula | Rawal, Bharat S.

Article Type: Research Article

Abstract: Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random …Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices. Show more

Keywords: Intelligent data analytics, machine learning, intelligent transportation system, secure communication, internet of things

DOI: 10.3233/JIFS-189743

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Bai, Luyi | Li, Nan | Liu, Lishuang | Hao, Xuesong

Article Type: Research Article

Abstract: With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to …RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall , precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper. Show more

Keywords: Data integration, fuzzy query, fuzzy spatiotemporal data, RDF semantic model

DOI: 10.3233/JIFS-202357

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Tabrez, Md | Iqbal, Atif | Sadhu, Pradip Kumar | Husain, Mohammed Aslam | Bakhsh, Farhad Ilahi | Singh, S. P.

Article Type: Research Article

Abstract: Impedance mismatching between different phases of a multiphase transformer is generally observed e.g., in a three-phase to seven-phase transformer, due to an unequal number of turns in different coils. This mismatching introduces error in the study of per phase equivalent circuit diagrams as well as induces an imbalance in output voltages and currents. Therefore, it is a challenging task to develop a per-phase equivalent circuit for the secondary and primary sides (In some cases) too. This paper proposes an artificial intelligence optimization technique like PSO based modeling of the per-phase equivalent circuit of the secondary side. This paper deals with …the modeling and simulation of a three-phase to seven-phase power transformer using Artificial Intelligence technique like particle swarm optimization (PSO) and Genetic Algorithm (GA). The proposed model is optimized through PSO and GA algorithms and tested for minimum voltage error in each phase. The proposed model is designed and the objective function is optimized by PSO & GA in MATLAB environment. It is found that the optimized model can be effectively implemented as a per-phase equivalent circuit for the secondary side. Show more

Keywords: Genetic algorithm, multiphase, particle swarm optimization, transformer, seven-phase

DOI: 10.3233/JIFS-189741

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Xia, Yingchun | Xie, Zhiqiang | Xin, Yu | Zhang, Xiaowei

Article Type: Research Article

Abstract: The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed …to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible. Show more

Keywords: Customized products, integrated scheduling, multiple workshop, fixed output, branch and bound

DOI: 10.3233/JIFS-189721

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Li, Chongchong | Xiong, Jiangyong | Liu, Tingshan | Zhang, Ziang

Article Type: Research Article

Abstract: In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal …damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering. Show more

Keywords: Semi-active suspension, controlled damping, dynamic monitoring feedback iteration, analytic hierarchy process, genetic algorithm, ride performance

DOI: 10.3233/JIFS-189719

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: YE, Lv | Yang, Yue | Zeng, Jian-Xu

Article Type: Research Article

Abstract: The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The …captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent. Show more

Keywords: Intelligent recommendation, interpretability, XGBoost, attention mechanism, cross feature

DOI: 10.3233/JIFS-202308

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zhang, Pengdan | Liu, Qing | Kang, Bingyi

Article Type: Research Article

Abstract: Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA …weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model. Show more

Keywords: Fuzzy analytic hierarchy process(Fuzzy AHP), ordered weighted average (OWA), analytic hierarchy process (AHP), uncertain preferences, multi-attribute decision-making (MADM)

DOI: 10.3233/JIFS-202168

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Zhang, Haowen | Dong, Yabo | Xu, Duanqing

Article Type: Research Article

Abstract: Time series classification is a fundamental problem in the time series mining community. Recently, many sophisticated methods which can produce state-of-the-art classification accuracy on the UCR archive have been proposed. Unfortunately, most of them are parameter-laden methods and require fine-tune for different datasets. Besides, training these classifiers is very computationally demanding, which makes them difficult to use in many real-time applications and previously unseen datasets. In this paper, we propose a novel parameter-light algorithm, MDTW, to classify time series. MDTW has a few parameters which do not require any fine-tune and can be chosen arbitrarily because …the classification accuracy is largely insensitive to the parameters. MDTW has no training step; thus, it can be directly applied to unseen datasets. MDTW is based on a popular method, namely the nearest neighbor classifier with Dynamic Time Warping (NN-DTW). However, MDTW performs much faster than NN-DTW by representing time series in different resolutions and using filters-and-refine framework to find the nearest neighbor. The experimental results demonstrate that MDTW performs faster than the state-of-the-art, with small losses (<3%) in average classification accuracy. Besides, we embed a technique, prunedDTW, into the MDTW procedure to make MDTW even faster, and show by experiments that this combination can speed up the MDTW from one to five times. Show more

Keywords: Time series classification, Dynamic Time Warping, nearest neighbor, multilevel representations, filters-and-refine

DOI: 10.3233/JIFS-201281

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Guo, Shunsheng | Gao, Yuji | Guo, Jun | Yang, Zhijie | Du, Baigang | Li, Yibing

Article Type: Research Article

Abstract: With the aggravation of market competition, strategic supplier is becoming more and more critical for the success of manufacturing enterprises. Suppler selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the long-term development of organizations. Hence, strategic supplier selection must be restructures considering the long-term relationships and prospects for sustainable cooperation. This paper proposes a novel multi-stage multi-attribute group decision making method under an interval-valued q-rung orthopair fuzzy linguistic set (IVq-ROFLS) environment considering the decision makers’ (DMs) psychological state in the group decision-making process. First, the initial comprehensive fuzzy evaluations of DMs are …represented as IVq-ROFLS. Subsequently, two new operators are proposed for aggregating different stages and DMs’ preferences respectively by extending generalized weighted averaging (GWA) to IVq-ROFLS context. Later, a new hamming distance based linear programming method based on entropy measure and score function is introduced to evaluate the unknown criteria weights. Additionally, the Euclidean distance is employed to compute the gain and loss matrix, and objects are prioritized by extending the popular Prospect theory (PT) method to the IVq-ROFLS context. Finally, the practical use of the proposed decision framework is validated by using a strategic supplier selection problem, as well as the effectiveness and applicability of the framework are discussed by using comparative analysis with other methods. Show more

Keywords: Strategic supplier selection, multi-stage multi-attribute group decision making, interval-valued q-rung orthopair fuzzy linguistic set, hamming distance based linear programming, prospect theory

DOI: 10.3233/JIFS-202415

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

Authors: Sharma, Ajit Kumar | Bhushan, Bharat

Article Type: Research Article

Abstract: The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy …system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed. Show more

Keywords: Sliding mode control (SMC), inverted pendulum, adaptive control, fuzzy control, fuzzy sliding mode control (FSMC)

DOI: 10.3233/JIFS-189740

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhang, Wei Min | Zhang, Long | Zhang, Zheyu | Sun, Mingjun

Article Type: Research Article

Abstract: With the many varieties of AI hardware prevailing on the market, it is often hard to decide which one is the most suitable to use but not only with the best performance. As there is an industry-wide trend demand for deep learning deployment, the inference benchmark for the effectiveness of DNN processor becomes important and is of great help to select and optimize AI hardware. To systematically benchmark deep learning deployment platforms, and give more objective and useful metrics comparison. In this paper, an end to end benchmark evaluation system was brought up called IBD, it combined 4 steps include …three components with 6 metrics. The performance comparison results are obtained from the chipsets from Qualcomm, HiSilicon, and NVIDIA, which can provide hardware acceleration for AI inference. To comprehensively reflect the current status of the DNN processor deploying performance, we chose six devices from three kinds of deployment scenarios which are cloud, desktop and mobile, ten models from three different kinds of applications with diverse characteristics are selected, and all these models are trained from three major training frameworks. Several important observations were made by using our methodologies. Experimental results showed that workload diversity should focus on the difference came from training frameworks, inference frameworks with specific processors, input size and precision (floating and quantized). Show more

Keywords: AI, deep neural network processor, benchmark, end to end, inference

DOI: 10.3233/JIFS-202552

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Shi, Honghua | Ni, Yaodong

Article Type: Research Article

Abstract: Today’s supply chains have a greater likelihood of disruption risks than ever before. Sometimes, a lengthy recovery period is needed for supply chains to return to regular operation after being disrupted. During the recovery time window, how to increase the performance of supply chains is not sufficiently studied. Furthermore, the works considering parameter uncertainty arising from the lack of historical data are also limited. To address these problems, we formulate the recovery scheduling of supply chains under major disruption as mixed-integer linear programming models. In the presented models, outsourcing strategy and capacity expansion strategy are introduced to increase the service …level of the supply chain after the disruption. The effects of disruption risks on supply chain performance are quantified using uncertainty theory in the absence of historical data. A set of computational examples illustrate that cost may increase markedly when more facilities are disrupted simultaneously. Thus, decision-makers have to pay close attention to supply chain disruption management and plan for disruption in advance. Moreover, the results suggest that outsourcing strategy is more useful to reduce cost when a higher service level is required. Show more

Keywords: Supply chain, facility disruptions, recovery strategies, uncertainty

DOI: 10.3233/JIFS-202176

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Jain, Achin | Jain, Vanita

Article Type: Research Article

Abstract: This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, …we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T -Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time. Show more

Keywords: Classification, sentiment analysis, genetic algorithm, support vector machine, machine learning

DOI: 10.3233/JIFS-189738

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Sanaullah, Asif | Fatema, Nuzhat | Malik, H | Sanaullah, Arif | Ather, Muhammad

Article Type: Research Article

Abstract: The purpose of this study was to examine the relationship of relationship benefit and commitment in developing customer loyalty first and then to develop the intelligent model to predict the customer loyalty. Survey methodology was used to gather data from three different service sector based on the classification by Bowen. A sample of 600 customers and responses were collected randomly from the front desk of services. Regression analysis by Using SPSS 20 was applied to analyze the data collected. The finding of the study revealed that relationship benefit and commitment had direct positive influence on customer loyalty. Furthermore the commitment …of customer towards an organization is instrumental in developing loyalty. After performing the advance data analytics, ANN model was developed to predict the loyalty, which can be utilized to prepare the further directions and road map for service industry. Obtained results reveals that proposed machine intelligence approach is very useful for service industry for short-term as well long-term future planning. Show more

Keywords: Relationship benefit (RB), customer loyalty (CL), confidence benefit (CB), special treatment benefit (STB), social benefit (SB), affective commitment (AC), normative commitment (NC) and calculative commitment CC, ANN

DOI: 10.3233/JIFS-189742

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Ray, Papia | Salkuti, Surender Reddy | Biswal, Monalisa

Article Type: Research Article

Abstract: In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target Distributed Generation (DG) location of the distribution network under study. In one of the schemes, Discrete Wavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of …loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9 MW wind farm driven by six 1.5 MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based approach provides a better resolution and quick detection of islanding than the wavelet transform approach. Show more

Keywords: Artificial neural network, islanding detection, wavelet transforms, distributed generation, S-transform

DOI: 10.3233/JIFS-189746

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Wang, Caichuan | Li, Jiajun

Article Type: Research Article

Abstract: The decision on the investment project is to analyze the feasibility and rationality of the project plan from multiple angles. However, due to the limitations of the actual project investment decision-making, this paper proposes a group decision making method based multifunctional intuitively fuzzy VIKOR interval sets. Firstly, according to the established investment decision-making model, the first round of preliminary candidate project schemes is selected. According to the definition of interval intuitionistic fuzzy sets and the traditional VIKOR method, established the research method of this article, and the project investment decision-making model based on VIKOR interval intuitionistic fuzzy sets is established. …Finally, the project schemes are sorted according to the closeness degree of schemes. The results show that when sorting each candidate by Qi value, A4 >  A3 >  A2 >  A1 can be obtained. Because Q4 = 0, Q3 = 0.31, the condition q3-q4 >  0.25 is satisfied. It is concluded that the method can not only meet the needs of actual decision-making, but also has strong operability and practicability. The research results have reference value and guiding significance for project investment decision-making, and can promote the sustainable development of the project. Show more

Keywords: Project investment decision, break intuitively vague sets, VIKOR method, multi-attribute group decision making method

DOI: 10.3233/JIFS-189735

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Jia, Heming | Lang, Chunbo

Article Type: Research Article

Abstract: Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the …proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way. Show more

Keywords: Salp swarm algorithm, crossover scheme, Lévy flight, functions optimization

DOI: 10.3233/JIFS-201737

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Xu, Tingting | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method …is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses. Show more

Keywords: Pythagorean fuzzy set, 2-tuple probability weight, real-value 2-tuple, TOPSIS method

DOI: 10.3233/JIFS-201533

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Pirozmand, Poria | Ebrahimnejad, Ali | Motameni, Homayun | Kalantari, Kimia Rezaee

Article Type: Research Article

Abstract: Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature …according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods. Show more

Keywords: Quality evaluation, machine vision, rough theory, fuzzy logic, image processing

DOI: 10.3233/JIFS-202147

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Azeem, Abdul | Malik, Hasmat | Jamil, Majid

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), artificial neural network (ANN) and J48 algorithm of machine learning for real-time harmonics analysis of digital substation’s equipment based on IEC-61850 using explanatory input variables based on laboratory proto-type real-time recorded database. In the proposed hybrid model, these variables are first extracted then diagnostic of power transformer harmonics of digital substation is evaluated/analyzed to perform the long term as well as the short term goal and planning in the electrical power network. In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using …FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). Then, features are extracted from experimental dataset using EMD (empirical mode decomposition) method. The IMFs (intrinsic mode functions) have generated from EMD, which are used as an input variable to the two different diagnostic models, i.e., ANN and J48 algorithm. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using ANN and J48 method (with and without EMD method) and the results are compared. Obtained results shows that the proposed hybrid diagnostics approach for harmonics analysis has outperformance characteristics. Show more

Keywords: ANN, explanatory feature, J48 algorithm, EMD, IEC-61850, feature extraction, digital substation, real-time, harmonics, power transformer, diagnosis, incipient level

DOI: 10.3233/JIFS-189745

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Malik, Hasmat | Almutairi, Abdulaziz | Alotaibi, Majed A.

Article Type: Research Article

Abstract: In the modern electrical power system network (EPSN), the power quality disturbances (PSDs) are the serious issue for the power engineer to maintain the uninterrupted and reliable power supply. Generally, PQDs are generated due to non-linear loading condition, perturb loading and other occurrences such as transient, harmonics, sag, swell and interruptions. These problems of PQDs effect the power demand mapping problem, which effect the reliability and stability of the EPSN operating condition. In this study, a novel approach for PQDs diagnosis (PQDD) is proposed, which includes real-time data generation, data pre-processing, feature extraction, feature selection, intelligent model development for PQDD. …Data decomposition approach of EMD is utilized to generate the feature vector of IMFs. These features are utilized as an input variable to the intelligent classifiers. In this study PQDD is analyzed based on SVM method and obtained results are compared with conventional AI method of LVQ-NN. The results represent the higher acceptability of the proposed approach with diagnosis accuracy of 99.98% (training phase), 93.11% (testing phase) for SVM and 92.56% (training phase) and 91.0% (testing phase) for LVQ-NN based PQDD method. Show more

Keywords: Data pre-processing, diagnosis, EMD, LVQ, feature extraction, SVM

DOI: 10.3233/JIFS-189739

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Akram, Muhammad | Shahzadi, Gulfam | Butt, Muhammad Arif | Karaaslan, Faruk

Article Type: Research Article

Abstract: Soft set (S f S ) theory is a basic tool to handle vague information with parameterized study during the process as compared to fuzzy as well as q -rung orthopair fuzzy theory. This research article is devoted to establish some general aggregation operators (AOs), based on Yager’s norm operations, to cumulate the q -rung orthopair fuzzy soft data in decision making environments. In this article, the valuable properties of q -rung orthopair fuzzy soft set (q  - ROFS f S ) are merged with the Yager operator to propose four new operators, namely, q -rung orthopair fuzzy soft …Yager weighted average (q  - ROFS f YWA ), q -rung orthopair fuzzy soft Yager ordered weighted average (q  - ROFS f YOWA ), q -rung orthopair fuzzy soft Yager weighted geometric (q  - ROFS f YWG ) and q -rung orthopair fuzzy soft Yager ordered weighted geometric (q  - ROFS f YOWG ) operators. The dominant properties of proposed operators are elaborated. To emphasize the importance of proposed operators, a multi-attribute group decision making (MAGDM) strategy is presented along with an application in medical diagnosis. The comparative study shows superiorities of the proposed operators and limitations of the existing operators. The comparison with Pythagorean fuzzy TOPSIS (PF-TOSIS) method shows that PF-TOPSIS method cannot deal with data involving parametric study but developed operators have the ability to deal with decision making problems using parameterized information. Show more

Keywords: q-rung orthopair fuzzy soft numbers, yager operators, aggregation operators, TOPSIS method

DOI: 10.3233/JIFS-202336

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Liu, Peide | Pan, Qian | Xu, Hongxue

Article Type: Research Article

Abstract: The normal intuitionistic fuzzy number (NIFN), which membership function and non-membership function are expressed by normal fuzzy numbers (NFNs), can better describe the normal distribution phenomenon in the real world, but it cannot deal with the situation where the sum of membership function and non-membership function is greater than 1. In order to make up for this defect, based on the idea of q-rung orthopair fuzzy numbers (q-ROFNs), we put forward the concept of normal q-rung orthopair fuzzy numbers (q-RONFNs), and its remarkable characteristic is that the sum of the qth power of membership function and the qth …power of non-membership function is less than or equal to 1, so it can increase the width of expressing uncertain information for decision makers (DMs). In this paper, firstly, we give the basic definition and operational laws of q-RONFNs, propose two related operators to aggregate evaluation information from DMs, and develop an extended indifference threshold-based attribute ratio analysis (ITARA) method to calculate attribute weights. Then considering the multi-attributive border approximation area comparison (MABAC) method has strong stability, we combine MABAC with q-RONFNs, put forward the q-RONFNs-MABAC method, and give the concrete decision steps. Finally, we apply the q-RONFNs-MABAC method to solve two examples, and prove the effectiveness and practicability of our proposed method through comparative analysis. Show more

Keywords: Normal q-rung orthopair fuzzy numbers, multi-attributive border approximation area comparison, the q-RONFNs-MABAC method, indifference threshold-based attribute ratio analysis

DOI: 10.3233/JIFS-201526

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2021