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

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: 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