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

Authors: Wu, Yaoqiang

Article Type: Research Article

Abstract: In this paper, we introduce the concept of weak partial-quasi k-metrics, which generalizes both k-metric and weak metric. Also, we present some examples to support our results. Furthermore, we obtain some fixed point theorems in weak partial-quasi k-metric spaces.

Keywords: weak metric, k-metric, partial-quasi k-metric, weak partial-quasi k-metric, fixed point theorem

DOI: 10.3233/JIFS-202768

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

Authors: Naeem, Muhammad | Ali Khan, Muhammad | Abdullah, Saleem | Qiyas, Muhammad | Khan, Saifullah

Article Type: Research Article

Abstract: Probabilistic hesitant fuzzy Set (PHFs) is the most powerful and comprehensive idea to support more complexity than developed fuzzy set (FS) frameworks. In this paper, it can explain a novel, improved TOPSIS-based method for multi-criteria group decision-making (MCGDM) problem through the Probabilistic hesitant fuzzy environment, in which the weights of both experts and criteria are completely unknown. Firstly, we discuss the concept of PHFs, score functions and the basic operating laws of PHFs. In fact, to compute the unknown weight information, the generalized distance measure for PHFs was defined based on the Probabilistic hesitant fuzzy entropy measure. Second, MCGDM will …be presented with the PHF information-based decision-making process. Show more

Keywords: Probabilistic hesitant fuzzy Set, extended TOPSIS method, application in decision making

DOI: 10.3233/JIFS-202700

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

Authors: Cao, Xin-Zi | Luo, Sheng-Zhou | Li, Jing-Cong | Pan, Jia-Hui

Article Type: Research Article

Abstract: The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately …compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method. Show more

Keywords: Bladder tumor segmentation, U-ResNet network, grade and stage, feature extraction, support vector machine

DOI: 10.3233/JIFS-210263

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

Authors: Jiang, Chunmao | Guo, Doudou | Sun, Lijuan

Article Type: Research Article

Abstract: The basic idea of the three-way decisions (3WD) is ‘thinking in threes.’ The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper’s principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a …method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers’ preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions. Show more

Keywords: Three-way decisions, change-based TAO model, interval set, utility measurement, dual expected utility

DOI: 10.3233/JIFS-202207

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

Authors: Li, Chunhua | Xu, Baogen | Huang, Huawei

Article Type: Research Article

Abstract: In this paper, the notion of a fuzzy *–ideal of a semigroup is introduced by exploiting generalized Green’s relations L * and R * , and some characterizations of fuzzy *–ideals on an arbitrary semigroup are obtained. Our main purpose is to establish the relationship between fuzzy *–ideals and abundance for an arbitrary semigroup. As an application of our results, we also give some new necessary and sufficient conditions for an arbitrary semigroup to be regular and inverse, respectively.

Keywords: fuzzy*–ideals, abundant semigroups, abundance, 20M20

DOI: 10.3233/JIFS-202759

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

Authors: Wang, Guan | Wu, Lingjiu | Liu, Yusheng | Ye, Xiaoping

Article Type: Research Article

Abstract: With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy …theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling. Show more

Keywords: Preference modeling, fuzzy preference modeling, fuzzy sets, group decision-making, decision-making modeling

DOI: 10.3233/JIFS-201529

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

Authors: Dai, Ziwei | Zhang, Zhiyong | Chen, Mingzhou

Article Type: Research Article

Abstract: Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for …determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. Show more

Keywords: Cloud manufacturing, task scheduling, multi-supply chain collaboration, new task arrival, biogeography-based optimization

DOI: 10.3233/JIFS-201066

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

Authors: Zhu, Jia-Nian | Liu, Xu-Chong | Liu, Chong

Article Type: Research Article

Abstract: Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k ) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k ) model, the NENGM (1,1, k ) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k ) model, and a NENGM (1,1, k ) model based on double optimization is established (abbreviated as FBNENGM (1,1, k ) …model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k ) model, the FBNENGM (1,1, k ) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k ) model based on double optimization is better than other prediction models. Show more

Keywords: Grey system theory, fractional-order accumulation, grey prediction model, FBNENGM (1, 1, k) model, ·whale optimization algorithm

DOI: 10.3233/JIFS-210023

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

Authors: Rajareega, S. | Vimala, J.

Article Type: Research Article

Abstract: This paper introduces some new operations on complex intuitionistic fuzzy lattice ordered groups such as sum, product, bounded product, bounded difference and disjoint sum, and verifying its pertinent properties. The research exhibits the CIFS-COPRAS algorithm in a complex intuitionistic fuzzy soft set environment. This method was furthermore applied for the equipment selection process.

Keywords: Complex intuitionistic fuzzy soft sets, complex intuitionistic fuzzy soft lattice ordered group, COPRAS, C-COPRAS, equipment selection process

DOI: 10.3233/JIFS-189890

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

Authors: Renugadevi, T. | Geetha, K.

Article Type: Research Article

Abstract: Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the …workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption. Show more

Keywords: Sustainable data centers, carbon footprint, brownout, migration, green energy, workload allocation

DOI: 10.3233/JIFS-189887

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

Authors: Li, Bingjie

Article Type: Research Article

Abstract: With the proposal of “One Belt One Road” initiative, more and more technology-driven enterprises in China are going abroad for foreign direct investment (FDI). Thus, how to correctly assess and reasonably prevent OFDI risk have become urgent tasks for enterprises. This paper takes 20 countries along the “Belt and Road” as the research object and combines the characteristics of technology-oriented enterprises to determine the risk assessment indicators at all levels from four aspects: politics and policy, economy and finance, society and culture, and technological risks. Using fuzzy cluster analysis, the countries along the “Belt and Road” are classified into four …groups: low, medium-low, medium-high, and high risks, and corresponding countermeasures are proposed: technology-oriented enterprises should raise their political risk awareness, comprehensively assess the economic and financial environment of host countries before making FDIs, implement “localized” operation and management, and pay attention to the protection of intellectual property rights. Show more

Keywords: “One Belt One Road” initiative, technology-based enterprises, OFDI, risk assessment

DOI: 10.3233/JIFS-189907

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2021

Authors: George, Sheeja P. | Isaac, Johney | Philip, Jacob

Article Type: Research Article

Abstract: A higher operating frequency is desirable for Surface Acoustic Wave (SAW) based sensors as they become more sensitive at high frequencies. The acoustic wave gets more confined near the surface at high frequencies and become more sensitive to the external stimulations. This makes SAW devices a suitable device for sensing gaseous state chemicals. SAW devices have become the basic building block of wireless sensor networks with its advantages enabling remote sensing. In this paper, a SAW based Hydrogen sensor is realized through the Finite Element Analysis tool ANSYS. Hydrogen even though has a significant role in many industries, its explosive …nature demands constant monitoring. SAW delay line made up of XY-LiNbO3 as substrate with a thin layer of Palladium coated along the delay length as the sensing element is modeled. Palladium with its high affinity for Hydrogen absorbs the same and undergoes changes in properties like density and stiffness. This disturbs the surface wave propagation and in turn, affects the operating frequency which is the sensor response parameter. The frequency shift of 1.91 MHz for Hydrogen concentration of 0.3 a.f. as compared to 0.49 MHz with YZ- LiNbO3. The operating frequency also shifts to a higher range as the acoustic velocity of the substrate increases. Show more

Keywords: SAW, gas sensor, wireless sensor, FEM, ANSYS

DOI: 10.3233/JIFS-189895

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

Authors: Jiali, Jiang | Jun, Wang

Article Type: Research Article

Abstract: With the accelerated urbanization progress, the governmental financial funds fail to satisfy the needs of highway project construction in some areas. The building-transfer (BT) model and public-private partnership (PPP) model have been carried out in succession in China to solve the shortage of financial funds. However, the emotion analysis method in Guide of Value for Money Evaluation for PPP Project (trial) enacted by the Ministry of Finance is of great subjectivity when applied to the calculation of risk quantification, which is the key to quantitative evaluation, so it is in urgent need of perfection through scientific methods to promote the …implementation of PPP projects. On this basis, under the research background of conversion of a highway stock project from BT model to PPP model in city Z, the fuzzy analytic hierarchy process (HAHP) and set-valued statistics method were applied to the method improvement for the risk quantification part of value for money (VFM) evaluation cases in this project. The results show that the VFM (calculated based on the improved risk quantification method) of the BT-to-PPP model for the highway stock project in city Z is elevated from the original 248,240,000 RMB to 271,643,500 RMB. The conclusions can provide a certain reference basis for further improving China’s VFM evaluation system, and boosting the development of PPP model in China. Show more

Keywords: Value for money, risk evaluation, quantification, PPP model

DOI: 10.3233/JIFS-189922

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

Authors: Sheng, Feng | Yang, Peng

Article Type: Research Article

Abstract: The mental health of adolescents not only affects their own healthy growth and success, but is also related to the future of the country and the development of the whole society. Based on the assessment and early warning of adolescents’ psychological problems, different ways to improve adolescents’ health literacy according to the psychological characteristics of adolescents at different ages were explored in this study. The methods of relative superior degree matrix evaluation, entropy value method, coefficient of variation method, subjective and objective weighting method and other methods were employed to establish the fuzzy comprehensive evaluation classification model. MATLAB, SPSS and …other software were used for programming to analyze the impact of risk preference, cognitive needs, and decision-making style on the risk of psychological problems of teenagers. The obtained evaluation criterion was used to evaluate the psychological status of respondents. Finally, this study analyzed and established the early-warning mechanism based on the structure model in the system engineering theory, and proposed reasonable solutions to alleviate the risk of psychological problems from the perspective of reality and combines with conclusions. Show more

Keywords: Fuzzy evaluation, psychological risk, adolescent, health literacy

DOI: 10.3233/JIFS-189903

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

Authors: Isaeva, Ekaterina | Rocha, Alvaro

Article Type: Research Article

DOI: 10.3233/JIFS-189896

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-4, 2021

Authors: Zhang, Yanqing | Jing, Hongjun | Dai, Jun

Article Type: Research Article

Abstract: High rockfill embankment is widely applied to high-grade highways in mountainous areas, and its stable operation can significantly promote traffic and economic development. In this study, a stability evaluation model of high rockfill embankment was constructed based on fuzzy mathematics theory to realize effective and rapid evaluation. Considering representativeness and easy operability, 5 level-1 factor indexes and 16 level-2 factor indexes were determined from different perspectives of embankment fillers, slope conditions, hydraulic conditions, slope protection, and other factors. Weights of different factor indexes were allocated using analytic hierarchy process and all factor indexes were quantified and classified using the fuzzy …statistical method according to stability level. Reliabilities of factor indexes were combined with reliability theory for analysis. Finally, the reasonability of the proposed fuzzy evaluation model was verified through application in the high rockfill embankment engineering on the secondary highway of the National Highway 316 from Xunyang to Ankang in Shaanxi Province, China. Results demonstrate that the reliability of this high rockfill embankment is 0.8671 and the final score of the evaluation system is 73.1619, indicating stability. Therefore, the proposed fuzzy evaluation model is reasonable and feasible to evaluate stability of high rockfill embankment, and can provide references for future design. Show more

Keywords: High rockfill embankment, stability, reliability, analytic hierarchy process, fuzzy synthetic evaluation

DOI: 10.3233/JIFS-189898

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

Authors: Liu, Wei | Feng, Wenlong | Huang, Mengxing | Han, Huirui | Han, Guilai | Lin, Jialun

Article Type: Research Article

Abstract: The Hainan Island has a generally high biological diversity with a wide variety of plant species, some of which are listed as endemic to the island. It is time-consuming and difficult, even for the botanist experts to determine the name of species based on observations. Automated plant identification enables experts to process significantly greater numbers of plants with higher efficiencies in shorter periods of time. However, plant recognition is a kind of fine-grained visual recognition problem, which is relatively harder than conventional image recognition. In this paper, we employ a Deep Convolutional Neural Network (DCNN) trained on the ImageNet database, …which contains millions of images, and then transfer the learning information for automated plant identification based on flower and fruit images. First, we modify the last three layers of the pre-trained network in order to adapt ResNet-50 model to our classification task, and replace the fully connected layer in the original pre-trained network with another fully connected layers, in which the output size represents the class of plants. Secondly, we use transfer experience and fine-tuned pre-trained DCNN for experiments using flower and fruit images. Finally, we evaluate the proposed network on two available botanical datasets: the Oxford flowers dataset with 102 classes and the HNPlant flowers and fruits dataset with 20 classes, and determine the optimal values of the associated hyperparameters to improve the overall performance. Experiment results demonstrate that the highest classification accuracies exhibited by the proposed model on the Oxford-102 and HNPlant-20 datasets are 92.4% and 95.0%, respectively, thus establishing their effectiveness and superiority. Show more

Keywords: Deep convolutional neural network, transfer learning, plant identification, flower and fruit classification

DOI: 10.3233/JIFS-189905

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

Authors: Jing, Hongjun | Gao, Meng | Song, Lichen | Liu, Qian

Article Type: Research Article

Abstract: Cold recycling of asphalt pavement is important in realizing sustainable development of highway transportation. Understanding change laws of cold recycled asphalt pavement (RAP) performance is important in the correct evaluation of pavement quality and scientific formulation of maintenance strategies. Various performance indexes were analytically demonstrated to predict and evaluate change laws of the cold RAP performance. The proposed cold recycled pavement evaluation indexes were divided into three fuzzy grades of evaluation indexes and subindexes. Integral algorithms from four indexes, namely, pavement surface condition index, riding quality index, rutting depth index, and pavement structure strength index (PSSI), were combined on the …basis of the traditional gray prediction model GM (1,1). Index weights were determined according to improved analytic hierarchy process, and a performance index database system based on historical data was established for the cold RAP. Finally, an evaluation system was set up on the basis of the prediction model GM (1,1), and prediction and evaluation results were analyzed with existing data. Results showed the excellent performance of the proposed method with the maximum weight of PSSI and a cold recycled pavement evaluation index score of 77.45. Goodness of fit between the prediction curve and original data is favorable with minimal relative errors. The curve analysis of evaluation indexes demonstrated the satisfactory performance of the pavement with the overall slow declining trend of pavement performance indexes. The research results of this study can provide a reference for evaluating performance variation trends of road network-level cold RAPs. Show more

Keywords: Road engineering, cold recycled asphalt pavement, big data, fuzzy prediction, fuzzy evaluation

DOI: 10.3233/JIFS-189899

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

Authors: Liu, Peide | Mahmood, Tahir | Ali, Zeeshan

Article Type: Research Article

Abstract: Complex q-rung orthopair fuzzy set (CQROFS) is a proficient technique to describe awkward and complicated information by the truth and falsity grades with a condition that the sum of the q-powers of the real part and imaginary part is in unit interval. Further, Schweizer–Sklar (SS) operations are more flexible to aggregate the information, and the Muirhead mean (MM) operator can examine the interrelationships among the attributes, and it is more proficient and more generalized than many aggregation operators to cope with awkward and inconsistence information in realistic decision issues. The objectives of this manuscript are to explore the SS operators …based on CQROFS and to study their score function, accuracy function, and their relationships. Further, based on these operators, some MM operators based on PFS, called complex q-rung orthopair fuzzy MM (CQROFMM) operator, complex q-rung orthopair fuzzy weighted MM (CQROFWMM) operator, and their special cases are presented. Additionally, the multi-criteria decision making (MCDM) approach is developed by using the explored operators based on CQROFS. Finally, the advantages and comparative analysis are also discussed. Show more

Keywords: Complex q-rung orthopair fuzzy sets, Schweizer-Sklar Muirhead means operators, multi-criteria decision making

DOI: 10.3233/JIFS-202440

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2021

Authors: Chen, Guoyi | Zhang, Jiansheng | Zhang, Shangmin | Yan, Bangquan

Article Type: Research Article

Abstract: With the fierce competition under the background of knowledge-based economy, tourism enterprises are increasingly aware that they must turn their focus from traditional products to customers for the sake of survival and development. Integrating the customer relationship management and knowledge management, the customer knowledge management (CKM) has aroused higher attention from the tourism enterprises. As for how to determine the factors influencing the Customer knowledge management competence (CKMC) of tourism enterprises and their weights, an index system was established for evaluating CKMC of tourism enterprises based on the balanced score card (BSC) and knowledge management process, the weight design and …consistency check of the indexes were implemented using the analytic hierarchy process (AHP), and the overall evaluation value and concrete index scores at all levels were obtained via the fuzzy comprehensive evaluation model. In the end, the scientificity and operability of the evaluation model were verified through an empirical analysis of China Youth Travel Service (CYTS). The results show that: (1) The business process, customer communication, system support, and market performance are important level I indexes used to measure the CKMC; (2) The key Level II factors influencing the CKMC of enterprises include customer knowledge sharing mechanism, timeliness of customer communication, degree of importance attached by senior leadership, and customer acquisition rate; (3) The evaluation model based on AHP and fuzzy evaluation method can objectively describe the overall up-to-standard degree of enterprises’ CKMC, and clearly identify the strengths and weaknesses. This research shows that the combination of AHP and fuzzy evaluation-based method is capable of more scientific and complete evaluation of CKMC, compensates for the deficiencies of single evaluation model, and provides a new method for the effective improvement of enterprises’ CKMC. Show more

Keywords: Fuzzy comprehensive evaluation method, analytic hierarchy process, customer knowledge management competence

DOI: 10.3233/JIFS-189902

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

Authors: xu, Zhixuan | Chen, Caikou | Han, Guojiang | Gao, Jun

Article Type: Research Article

Abstract: As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces …Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods. Show more

Keywords: Subspace clustering, low rank representation, latent low rank representation, non-negative sparse laplacian constraints

DOI: 10.3233/JIFS-210274

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

Authors: Wang, Tianxiong | Zhou, Meiyu

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-201829

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2021

Authors: Liu, Yang | Yan, Xin | Shiwei, Xiao

Article Type: Research Article

Abstract: Nowadays, innovation and entrepreneurial development is increasingly accepted as the source of wealth, competitiveness, and advancement of modern society, thereby becoming a component of political agenda of developed and emerging countries. In China, intellectual property education gains unprecedented development during the construction of considerable power in intellectual property and innovation-driven strategy. However, integration is rare for interdisciplinary knowledge –a training system integrating intellectual property education and practical abilities of innovation and entrepreneurship in universities and colleges in China. As a result, college students have significantly lower practical ability in solving problems with intellectual properties compared with their understanding of theoretical …knowledge. Innovation and entrepreneurship observe not only a certain validity assumption, but also a structured method. On this basis, this study proposes a talent training system of “setting up a goal, integrating a framework, designing a method and establishing a system”. Based on fuzzy analysis, the intellectual property education of universities and colleges in China and improvement of abilities among Chinese university students in innovation and entrepreneurship are taken as the research objective. Through theory-driven practice intellectual property education is included into the innovation and entrepreneurship framework to stimulate innovative thinking and introduce a scientific evaluation system to drive development. Show more

Keywords: Intellectual property, integration of disciplines, university entrepreneurship, innovative thinking, fuzzy analytical method

DOI: 10.3233/JIFS-189909

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

Authors: Nagaballi, Srinivas | Kale, Vijay S.

Article Type: Research Article

Abstract: The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with …suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy. Show more

Keywords: Artificial intelligence techniques, distributed generation, DSTATCOM, power losses, reconfiguration, voltage profile, stability, techno-economic benefits

DOI: 10.3233/JIFS-189891

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

Authors: Dat, Nguyen Quang | Ngoc Anh, Nguyen Thi | Nhat Anh, Nguyen | Solanki, Vijender Kumar

Article Type: Research Article

Abstract: Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle …seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series. Show more

Keywords: Hybrid online, RNN online, multi time series, multi seasonal decompose, Electricity forecasting

DOI: 10.3233/JIFS-189884

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

Authors: Wu, Yang

Article Type: Research Article

Abstract: In the non-medical model physiological parameter monitoring system, learning the monitoring parameters can improve the diagnostic and prediction accuracy. Aiming at the problems of insufficient information mining and low prediction accuracy in multi-task time series, the supervised and semi-supervised learning methods in machine learning are combined to predict the physiological status of remote health monitoring objects. This method uses the K-means algorithm to cluster the same type of data and use the Multitasking Least Squares Support Vector Machine (MTLS-SVM) to train historical data for trend prediction. In order to evaluate the effectiveness of the method, the MTLS-SVM method is compared …with the K-means and MTLS-SVM methods. It can be seen from the experimental results that the body temperature data measured by the GY-MCU90615 is close to that of the digital thermometer. Moreover, the body temperature speed collected by the GY-MCU90615 can reach the millisecond level, which can well meet the needs of the system. The research shows that the method has higher prediction accuracy and has a breakthrough significance for the monitoring of athletes’ physiological parameters. Show more

Keywords: K-means algorithm, MTLS-SVM algorithm, athlete, physiological parameters, monitoring

DOI: 10.3233/JIFS-189915

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

Authors: Wang, Man | Zhou, Jia | Lin, Huazhi

Article Type: Research Article

Abstract: User generated content on web serves as a valuable source of information for both companies and consumers. Scholars have analyzed emotional polarity of the reviews to study customer satisfaction, but the dominant factors are not explained accurately by numerical ratings solo and the simplistic-categories of emotional polarity. This paper investigates the service attributes and detailed emotions effecting consumer satisfaction using deep learning, to explore how consumption satisfaction is influenced by emotions and what factors arouse the certain emotion. First, more than 120,000 online hotel reviews related were retrieved. Second, a novel and dataset-based seven-dimensional evaluation system, applying the BERT model …was proposed. This solves the problem of polysemous words, and can more accurately reflect the service attributes consumers really care about. In particular, the analysis reveals that the overall consumer satisfaction is affected by key service attributes including service, cleanliness, equipment, price, location, internet and catering, among which the cleanliness attributes has the greatest impact. Lastly, the latest Kismet emotional recognition method was adopted to effectively identify the emotional polarity and 11 detailed emotions. The regression relationship between emotion and overall satisfaction was also verified, which enabled a more accurate analysis for consumption emotions and satisfaction. Show more

Keywords: User generated content, service attributes, hotels, sentiment analysis, consumer satisfaction

DOI: 10.3233/JIFS-201207

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

Authors: Zhang, Hong

Article Type: Research Article

Abstract: In content-based clothing image retrieval, color features can best reflect the basic characteristics of clothing, and also the most stable visual features. Compared with other image features, color features have smaller size, orientation and visual dependence. This paper studies the application of dominant color extraction algorithm in clothing image retrieval, and proposes a clothing classification method based on dominant color ratio. Clothing image is divided into color clothing and non color clothing. On this basis, a main color extraction algorithm of clothing image color feature extraction is proposed. Taking the clothing color features as an example, the image features are …analyzed, and then the SVM image classification algorithm is designed to analyze the image features. Then an improved scheme based on data mining technology is proposed, and the analysis model based on association rules is established. Finally, a method of standard man hour correction based on association rules is proposed. The experimental results show that, compared with the existing algorithms, the recall rate and accuracy rate are significantly improved for the clothing with simple or complex background, pattern and non pattern clothing. Analyze and divide the specific areas of clothing image, extract the main color of clothing image, share and recommend clothing image and color extraction results. This research not only has certain research significance, but also has certain practical application value. Show more

Keywords: Main color, feature extraction, clothing, image retrieval

DOI: 10.3233/JIFS-189911

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

Authors: Li, Mengmeng | Zhang, Chiping | Chen, Minghao | Xu, Weihua

Article Type: Research Article

Abstract: Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough …sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model. Show more

Keywords: Covering rough sets, local rough sets, local covering rough sets, multigranulation rough sets

DOI: 10.3233/JIFS-202274

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

Authors: Liu, Xiaowei

Article Type: Research Article

Abstract: How to measure the level of intellectual property protection has been the focus of academic and practical circles, however, rigorous empirical evidence remains scant. The influencing factors of intellectual property protection are summarized and analyzed, and the evaluation index system of intellectual property protection at the provincial level is constructed with judicial, administrative, pluralistic, and environmental protections as the first-class indicators. The feasibility and reliability of the index system are tested empirically by using the 2019 data of intellectual property protection in certain provinces and cities in China. The index system can objectively reflect the level of intellectual property protection …in provincial regions. The research has certain enlightenment to strengthen the performance appraisal of intellectual property protection and improve the level of intellectual property protection in the region and even in the country. Show more

Keywords: Intellectual property protection, fuzzy comprehensive evaluation, index system

DOI: 10.3233/JIFS-189910

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

Authors: Zhaohua, Leng

Article Type: Research Article

Abstract: With the prevalence of international trade protectionism and transformation and upgrading of domestic market structure, the contradiction between demand and competitive development port market of the Yangtze River Delta in China has become increasingly prominent. Nineteen major ports of Yangtze River Delta in China were selected, using the methods of factor analysis, fuzzy clustering, gravitational model, the spatial effects in the hinterland were calculated from three dimensions: central potential, spatial gravity and distribution convenience, and the regional coordination of port services was analyzed. The results show that the potential of Shanghai Port and Ningbo-Zhoushan Port in China is stronger, and …the distribution difference is quite obvious. The spatial gravity of each port city superimposes to form an obvious dense semi-circular zone, and the participation ability of the marginal ports is weak. The distribution convenience in hinterland changes from location-dependent to traffic-dependent. The service gap in the hinterland of the port system is more significant, but the expansions of spatial effects make regional coordination gradually improve. Show more

Keywords: Yangtze River Delta, port service, spatial effect, regional coordination

DOI: 10.3233/JIFS-189919

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

Authors: Nawar, Ashraf S. | Atef, Mohammed | Khalil, Ahmed Mostafa

Article Type: Research Article

Abstract: The aim of this paper is to introduce and study different kinds of fuzzy soft β -neighborhoods called fuzzy soft β -adhesion neighborhoods and to analyze some of their properties. Further, the concepts of soft β -adhesion neighborhoods are investigated and the related properties are studied. Then, we present new kinds of lower and upper approximations by means of different fuzzy soft β -neighborhoods. The relationships among our models (i.e., Definitions 3.9, 3.12, 3.15 and 3.18) and Zhang models [48 ] are also discussed. Finally, we construct an algorithm based on Definition 3.12, when k  = 1 to solve the decision-making …problems and illustrate its applicability through a numerical example. Show more

Keywords: Fuzzy soft β-covering, Fuzzy soft β-neighborhoods, Fuzzy soft β-adhesion neighborhoods, Decision-making

DOI: 10.3233/JIFS-201822

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

Authors: Li, Peng | Wei, Cuiping

Article Type: Research Article

Abstract: With the sharp increase in the elderly population and the gradual invalidation of traditional long-term care style, the supply-demand contradiction for nursing homes services appears. A suitable evaluation mechanism is very useful to resolve the contradiction. The evaluation process can be seen as a multiple criteria decision making (MCDM) problem. Because some criteria are subjective and the evaluation process usually needs more than one decision maker (DM), probabilistic linguistic information is suitable to express DMs’ opinions. Therefore, we propose a novel EDAS method with probabilistic linguistic information based on D-S evidence theory for evaluating nursing homes. First, a new score …function for probabilistic linguistic term set (PLTS) is put forward in order to compare PLTSs and use EDAS method conveniently. Then, a novel uncertainty measure based on D-S evidence theory is proposed to obtain the criteria weights. Furthermore, a novel EDAS method for PLTSs based on cobweb area model is put forward to reduce the effect of some extreme values influencing the decision result. Finally, our method is applied to a real case of evaluating nursing homes in Nanjing city, and the effectiveness of our method is illustrated by comparing the traditional decision methods. Show more

Keywords: Evaluation, nursing home, probabilistic linguistic term set, D-S evidence theory

DOI: 10.3233/JIFS-201866

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

Authors: Tao, Tian | Tiantian, Zhang | Xiaoning, Li | Dajian, Tong

Article Type: Research Article

Abstract: A TOPSIS evaluation index system of entropy weight of enterprise internal control quality based on fuzzy matter-element model was established to master the enterprise internal control quality comprehensively and analyze its influences on enterprise innovation performance accurately. Firstly, a composite fuzzy matter-element model was established based on the fuzzy matter-element theory. Secondly, weights of evaluation indexes were determined by the entropy weight method. Thirdly, the concept of relative closeness was developed by comparison with positive and negative ideal indexes. Finally, the internal control quality levels of 781 listed companies in Chin were measured by TOPSIS method. Results show that: weights …of five level-1 indexes for enterprise internal control quality evaluation are different. Specifically, weights of Law & Regulation, Operation and Strategy are higher than those of Financial Statements and Assets Safety. In the level-2 index system, weights of “major litigation and arbitration cases”, “turnover of total assets” and “Tobin’s Q” occupy 66% of total weights. Listed companies which occupy the top10 position in term of internal control quality mainly belong to industries requiring strict monitoring and control over safety production. Enterprise internal control quality differs significantly among different industries. Research conclusions can provide methods and practical references to evaluate internal control quality of Chinese enterprises. Show more

Keywords: Fuzzy matter-element model, entropy weight TOPSIS evaluation, enterprise internal control quality, evaluation indexes

DOI: 10.3233/JIFS-189901

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

Authors: Jing, Xu | Yixuan, Lu | Panqian, Dai | Kaixin, Yu | Kun, Chen

Article Type: Research Article

Abstract: This manuscript aims to explore the impact of production organization mode on the inventory and delivery rate of pump enterprises and optimize the production management of pump enterprises. The system dynamic model and simulation test method were used to compare the changes in the inventory, productivity and delivery rate of pump enterprise under the traditional make-to-order (MTO) and make-to-stock (MTS) organizational modes. Following the above analysis, the optimized pump order production management mode combined with MTO & MTS was put forward, and the fuzzy comprehensive evaluation method was used to evaluate the three above modes. When customer demand scarcely fluctuates …and is easily predictable, the MTO production organization mode can be adopted; when customer demand fluctuates sharply and is difficultly predicted, the MTO & MTS combined mode should be selected. According to the results of the fuzzy comprehensive evaluation, the MTO & MTS mode is the best choice for pump enterprises. Considering that the pump demand is sensitive to the season and natural environment, the optimized mode is suitable for the production and inventory management of pump enterprise. Show more

Keywords: Make to order, make to stock, pump, production management

DOI: 10.3233/JIFS-189906

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

Authors: Wang, Degang | Li, Dongling | Sun, Li | Zhang, Zhenke | Cheng, Jie | Yu, Miao

Article Type: Research Article

Abstract: There are 11 provinces along the coastal regions in the Chinese Mainland and the scientific division of marine management boundaries among different provinces within the extents of the territorial sea is of important significance in promoting the sustainable development of the marine economy in China. A fuzzy evaluation index system that includes 14 indices of three subsets was established based on a case study of maritime boundary delimitation between Jiangsu and Shandong Provinces to evaluate the advantages and disadvantages of maritime boundary delimitation and determine the optimal scheme and offset shortages in the current evaluation index system and evaluation methods. …Combined weights of indices were calculated through the analytic hierarchy process (AHP) and entropy weight method. The comprehensive evaluation indices were used to evaluate three maritime boundary delimitation schemes, namely, historical boundary delimitation scheme, angle bisector delimitation scheme, and equidistance delimitation scheme. Results show that the equidistance delimitation scheme is relatively superior to the two other schemes. The evaluation index is 0.504761 and the evaluation grade is “good”. The angle bisector delimitation scheme is the second optimal. The evaluation index is 0.361641 and the grade is “moderate”. The historical boundary delimitation scheme is the poorest. The evaluation index is 0.135345 and the grade is “poor”. In the late optimization of maritime boundary delimitation schemes, more consideration should be given to people’s livelihood and protection of national maritime rights and interests. The fuzzy comprehensive evaluation method based on AHP-entropy weight can not only provide a quantitative top-down sequence of schemes in terms of quality and solve estimation problems in maritime boundary delimitation scheme, but also assist decision-makers to select the best scheme. Therefore, it possesses great application values. Show more

Keywords: Maritime boundary delimitation, delimitation scheme, AHP-entropy weight method, fuzzy evaluation method

DOI: 10.3233/JIFS-189904

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

Authors: Jing, Hongjun | Yao, Ping | Song, Lichen | Zhang, Jiahao | Zhao, Yanlong | Zhang, Zhong

Article Type: Research Article

Abstract: With modern economic and social development, the technical and economic requirements of highway maintenance and construction projects have become increasingly complicated. Meticulous and in-depth investigation and demonstration guided by scientific theories and methods are of considerable importance to highway maintenance scheme decision. As basis for the selection of highway asphalt pavement recycling maintenance scheme, the factors influencing the decision are analytically demonstrated and an evaluation system is proposed, including three major decision indexes: applicability of recycling mode, recycled pavement quality recovery index, and economic benefit. According to the principles of data statistics and analysis, this study proposes a calculation method …for the recycled pavement quality recovery index, analyzes the economic benefits of decision schemes using economic models such as recycling ratio and cost, and puts forward an optimal evaluation method of engineering cost and its fuzzy score intervals. Index weights are calculated through the analytic hierarchy process, and the comprehensive decision evaluation system and comprehensive evaluation method are established. Subsequently, the decision-making method is analyzed on the basis of the decision system by combining the related data. Results show maximum weight of the pavement quality recovery index and minor differences among four recycling schemes in the quality recovery index and applicability. The decision-making results are simplified with clear hierarchical feature because of the fuzzy score intervals of each index. Findings can provide a reference for the asphalt pavement recycling scheme decision. Show more

Keywords: Road engineering, recycling maintenance, intelligent decision-making, fuzzy evaluation

DOI: 10.3233/JIFS-189900

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

Authors: Preethi, D. | Vimala, J.

Article Type: Research Article

Abstract: This paper introduces the concept of homomorphism on fuzzy hyperlattice ordered group ( FHLOG ) . It studies how the binary and the fuzzy hyperoperations of a FHLOG can be transformed into the binary and the fuzzy hyperoperations of another FHLOG . The notion of fuzzy hypercongruence relation on FHLOG is also defined. The paper also establishes the redox reaction of copper, gold and americium forms three FHLOG s. Besides, homomorphism and composition function of FHLOG …s using the redox reactions are developed. Therefore, the paper develops a relation among three different metal’s redox reactions in which the binary and the fuzzy hyperoperations, are preserved. Show more

Keywords: Lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice, fuzzy hyperlattice ordered group, homomorphism, redox reactions

DOI: 10.3233/JIFS-189888

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

Authors: Vasudevan, Nisha | Venkatraman, Vasudevan | Ramkumar, A | Sheela, A

Article Type: Research Article

Abstract: Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall …electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error. Show more

Keywords: Energy management, smart home, energy forecast, power management, efficiency

DOI: 10.3233/JIFS-189886

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

Authors: Krishnamoorthy, Amrutha | Sindhura, Vijayasimha Reddy | Gowtham, Devarakonda | Jyotsna, C. | Amudha, J.

Article Type: Research Article

Abstract: Extraction of eye gaze events is highly dependent on automated powerful software that charges exorbitant prices. The proposed open-source intelligent tool StimulEye helps to detect and classify eye gaze events and analyse various metrics related to these events. The algorithms for eye event detection in use today heavily depend on hand-crafted signal features and thresholding, which are computed from the stream of raw gaze data. These algorithms leave most of their parametric decisions on the end user which might result in ambiguity and inaccuracy. StimulEye uses deep learning techniques to automate eye gaze event detection which neither requires manual decision …making nor parametric definitions. StimulEye provides an end to end solution which takes raw streams of data from an eye tracker in text form, analyses these to classify the inputs into the events, namely saccades, fixations, and blinks. It provides the user with insights such as scanpath, fixation duration, radii, etc. Show more

Keywords: Eye tracking, fixations, saccades, scanpath, deep learning

DOI: 10.3233/JIFS-189893

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

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P.

Article Type: Research Article

Abstract: The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference …system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model. Show more

Keywords: Cloud computing, IaaS selection, Sugeno Fuzzy inference system, CSP selection, compute service, MCDM

DOI: 10.3233/JIFS-189883

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

Authors: Jeyanthi, R. | Sahithi, Madugula | Sireesha, N.V.L. | Srinivasan, Mangala Sneha | Devanathan, Sriram

Article Type: Research Article

Abstract: In process industries, measurements usually contain errors due to the improper instrumental variation, physical leakages in process streams and nodes, and inaccurate recording/reporting. Thus, these measurements violate the laws of conservation, and do not conform to process constraints. Data reconciliation (DR) is used to resolve the difference between measurements and constraints. DR is also used in reducing the effect of random errors and more accurately estimating the true values. A multivariate technique that is used to obtain estimates of true values while preserving the most significant inherent variation is Principal Component Analysis (PCA). PCA is used to reduce the dimensionality …of the data with minimum information loss. In this paper, two new DR techniques are proposed moving-average PCA (MA-PCA) and exponentially weighted moving average PCA (EWMA-PCA) to improve the performance of DR and obtain more accurate and consistent data. These DR techniques are compared based on RMSE. Further, these techniques are analyzed for different values of sample size, weighting factor, and variances. Show more

Keywords: Data reconciliation, MA-PCA, EWMA-PCA

DOI: 10.3233/JIFS-189892

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2021

Authors: Shabir, Muhammad | Mubarak, Asad | Naz, Munazza

Article Type: Research Article

Abstract: The rough set theory is an effective method for analyzing data vagueness, while bipolar soft sets can handle data ambiguity and bipolarity in many cases. In this article, we apply Pawlak’s concept of rough sets to the bipolar soft sets and introduce the rough bipolar soft sets by defining a rough approximation of a bipolar soft set in a generalized soft approximation space. We study their structural properties and discuss how the soft binary relation affects the rough approximations of a bipolar soft set. Two sorts of bipolar soft topologies induced by soft binary relation are examined. We additionally discuss …some similarity relations between the bipolar soft sets, depending on their roughness. Such bipolar soft sets are very useful in the problems related to decision-making such as supplier selection problem, purchase problem, portfolio selection, site selection problem etc. A methodology has been introduced for this purpose and two algorithms are presented based upon the ongoing notions of foresets and aftersets respectively. These algorithms determine the best/worst choices by considering rough approximations over two universes i.e. the universe of objects and universe of parameters under a single framework of rough bipolar soft sets. Show more

Keywords: Rough sets, bipolar soft sets, rough bipolar soft sets, bipolar soft topology, decision making

DOI: 10.3233/JIFS-202958

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

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

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-202638

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2021

Authors: Recal, Füsun | Demirel, Tufan

Article Type: Research Article

Abstract: Although Machine Learning (ML) is widely used to examine hidden patterns in complex databases and learn from them to predict future events in many fields, utilization of it for predicting the outcome of occupational accidents is relatively sparse. This study utilized diversified ML algorithms; Multinomial Logistic Regression (MLR), Support Vector Machines (SVM), Single C5.0 Tree (C5), Stochastic Gradient Boosting (SGB), and Neural Network (NN) in classifying the severity of occupational accidents in binary (Fatal/NonFatal) and multi-class (Fatal/Major/Minor) outcomes. Comparison of the performance of models showed Balanced Accuracy to be the best for SVM and SGB methods in 2-Class and SGB …in 3-Class. Algorithms performed better at predicting fatal accidents compared to major and minor accidents. Results obtained revealed that, ML unveils factors contributing to severity to better address the corrective actions. Furthermore, taking action related to even some of the most significant factors in complex accidents database with many attributes can prevent majority of severe accidents. Interpretation of most significant factors identified for accident prediction suggest the following corrective measures: taking fall prevention actions, prioritizing workplace inspections based on the number of employees, and supplementing safety actions according to worker’s age and experience. Show more

Keywords: Accidents severity, classification, data mining, feature selection; machine learning

DOI: 10.3233/JIFS-202099

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

Authors: Wang, Shuang | Ding, Lei | Sui, He | Gu, Zhaojun

Article Type: Research Article

Abstract: Cybersecurity risk assessment is an important means of effective response to network attacks on industrial control systems. However, cybersecurity risk assessment process is susceptible to subjective and objective effects. To solve this problem, this paper introduced cybersecurity risk assessment method based on fuzzy theory of Attack-Defense Tree model and probability cybersecurity risk assessment technology, and applied it to airport automatic fuel supply control system. Firstly, an Attack-Defense Tree model was established based on the potential cybersecurity threat of the system and deployed security equipment. Secondly, the interval probability of the attack path was calculated using the triangular fuzzy quantification of …the interval probabilities of the attack leaf nodes and defensive leaf nodes. Next, the interval probability of the final path was defuzzified. Finally, the occurrence probability of each final attack path was obtained and a reference for the deployment of security equipment was provided. The main contributions of this paper are as follows: (1) considering the distribution of equipment in industrial control system, a new cybersecurity risk evaluation model of industrial control system is proposed. (2) The experimental results of this article are compared with other assessment technologies, and the trend is similar to that of other evaluation methods, which proves that the method was introduced in this paper is scientific. However, this method reduces the subjective impact of experts on cybersecurity risk assessment, and the assessment results are more objective and reasonable. (3) Applying this model to the airport oil supply automatic control system can comprehensively evaluate risk, solve the practical problems faced by the airport, and also provide an important basis for the cybersecurity protection scheme of the energy industry. Show more

Keywords: Cybersecurity risk assessment, fuzzy set theory, attack-defense tree

DOI: 10.3233/JIFS-201126

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

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

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

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Ramasubramanian Gobichettipalayam, Kanagachidambaresan

Article Type: Research Article

Abstract: A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. …Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. Show more

Keywords: Service level agreement, broker-based negotiation framework, game theory decision system, E-commerce application

DOI: 10.3233/JIFS-189882

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

Authors: Pradhan, Rosy | Khan, Mohammad Rafique | Sethy, Prabir Kumar | Majhi, Santosh Kumar

Article Type: Research Article

Abstract: The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO …or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction. Show more

Keywords: Antlion optimization, quantum information theory, model order reduction, metahueristic optimization, CEC benchmark

DOI: 10.3233/JIFS-189894

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

Authors: Lili, Xu | Feng, Liu | Xuejian, Chu

Article Type: Research Article

Abstract: This study examines the application of the business model of supply chain finance depending on the core enterprise, to the credit financing of transportation capacity enterprises. It studies the credit transmission characteristics regarding core enterprise credit radiation, presents the core enterprise credit segmentation and credit pricing, and transforms them into the calculation of credit guarantee and the default probability of core enterprises. Credit guarantee is regarded as a constraint of financial institutions’ credit decisions. Using probability density and logistic tools, we construct a profit maximization model for financial institutions and solve their optimal credit decision for a specific interest rate. …Through numerical experiments, we verify the validity of the model and conclude that increasing the business volume between financing enterprises and core enterprises or reducing the probability of default can effectively improve financial institutions’ credit line. Show more

Keywords: Credit segmentation, credit pricing, default probability, transportation capacity financing, credit decision

DOI: 10.3233/JIFS-201818

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