Journal of Intelligent & Fuzzy Systems - Volume 37, issue 5

Authors: Kong, Fei | Wang, Yumin

Article Type: Research Article

Abstract: At present, there is less software related to sport technical behavior recognition, and there are few studies on the classification and identification of detailed actions. By introducing computer technology to analyze the efficiency and regularity of sports, not only the characteristics of athletes can be excavated, but also the visibility and dynamic tracking of sport training can be provided. The process of sports education is a fast and complex systematic process. Through the interactive system of physical education, we can use different methods to collect sports data and make a comparative analysis of athletes’ movements. Through the data mining of …the relationship between athletes’ physiological indexes and sports load, the unreasonable link in sports training can be avoided. Also, in sports training, we can use computer vision and modern biomechanics to construct a virtual sports education situation. With the classification accuracy as the fitness function, this paper collects the data through the network database, and returns the corresponding sport training parameters on this basis. The results showed that the accuracy of the model was nearly 98%, which met the actual demand. Therefore, the development of sports education assistant system can provide strong support for the process control of sports training and education. Show more

Keywords: Support vector, improved model, computer interactive system, recognition algorithm

DOI: 10.3233/JIFS-179200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6165-6175, 2019

Authors: Bo, Wang | Tianyu, Fan | Zhiyong, Li | Xiangtian, Nie

Article Type: Research Article

Abstract: There is little research on the relationship between financial innovation and economic growth, and the research on the synergy between the two is basically blank. Based on this, from a general perspective, through constructing the corresponding subsystems in combination with financial innovation and economic growth, establishing the corresponding synergy model, and discovering the synergy development relationship by studying the degree of synergy in the past period, this study builds a BP neural network simulation model to predict the degree of synergy between financial innovation and economic growth in 2018 on the basis of practice. At the same time, this study …compares it with the actual situation to verify its effectiveness. Through analysis, the research model has certain effectiveness, which is basically consistent with the actual development trend. The research proposes that the main trend of financial innovation from the perspective of generalized virtual economy is Internet finance. This is the first time to study this issue from a new perspective, theory and method, which expands the existing research results. Show more

Keywords: BP neural network, financial innovation, economic growth, synergy

DOI: 10.3233/JIFS-179201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6177-6189, 2019

Authors: Li, Tuojian | Sun, Jinhai | Zhang, Xianliang | Wang, Lei | Zhu, Penglei | Wang, Ning

Article Type: Research Article

Abstract: Competitive sports require athletes to operate in real time, and there are many uncertainties. At present, there are few applications of artificial intelligence in the prediction of competitive sports, and the relevant literature about fitness motivation is rare. Based on this, this study is based on the machine learning algorithm and uses the support vector machine to build the competitive sports model and fitness motivation evaluation. At the same time, this study combines the actual situation to construct a corresponding factor analysis model for racing sports, and this factor analysis is a combination of data mining and machine learning. Only …by adopting appropriate measures can students’ motivation of physical fitness be effectively fostered and stimulated, their active participation in physical exercise and lifelong fitness habits be fostered. On the basis of traditional SVM method, PCA-SVM model is constructed to further improve the prediction accuracy and validity of fitness motivation. In this paper, the principal components of eight kinds of operation behavior are extracted; fitness motivation is not only the direct reason for college students to participate in fitness exercise, but also the motive force of fitness behavior. Grid Search algorithm is selected to optimize the parameters of SVM. The recognition rate of Grid Search-SVM is 94.79%, and satisfactory results are obtained. Show more

Keywords: Support vector machine, racing sports, regression model, GA-SVM algorithm

DOI: 10.3233/JIFS-179202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6191-6203, 2019

Authors: Guangjing, Li | Cuiping, Zhang

Article Type: Research Article

Abstract: At present, artificial intelligence for sports static image recognition is mostly in the action judgment stage, but less analysis on the action detail stage. Based on this, based on machine learning, this study uses static images and video sequences as carriers to improve traditional algorithm research and to perform motion gesture recognition. Through performance analysis, this paper explores the traditional algorithm and uses parameter analysis to improve the feature extraction and classification of traditional algorithms. Moreover, this paper uses the multi-scale feature approximation calculation method to improve the speed of the algorithm to extract features, and the algorithm is tested …using the UCF motion data set and the self-created motion data set. In addition, this paper obtains representative motion video through data collection to test the effectiveness of the proposed algorithm. The research shows that the proposed algorithm has good performance and can provide theoretical reference for subsequent related research. Show more

Keywords: Machine learning, sports, static image, gesture recognition

DOI: 10.3233/JIFS-179203

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6205-6215, 2019

Authors: Wei, Cheng | Dan, Li

Article Type: Research Article

Abstract: Estimating the compensation risk of agricultural insurance is a hotspot of current research. The related research mainly focuses on the calculation and simulation of catastrophe risk that agricultural insurance may face. On the whole, the compensation risk of agricultural insurance mainly comes from the agricultural disasters, especially the agro meteorological disasters. Compared with property insurance, the overall compensation rate of agricultural insurance is much higher than that of property insurance, so agricultural insurance belongs to high-risk business operation. In the research, the support vector machine is used as the research technology, and the forecast model corresponding to the insurance market …is constructed. At the same time, this paper constructs SVM prediction model and VAR-based SVM prediction model. Finally, the prediction accuracy of the SVM prediction model and the VAR-based SVM prediction model are compared and analyzed. The research shows that the prediction accuracy of VAR-based SVM prediction model is higher, that is, it is easier to draw near-realistic prediction results based on parameter optimization. This paper summarizes the research, puts forward its inadequacies and merits, and provides theoretical reference for subsequent related research. Show more

Keywords: Parameter optimization, machine learning, agricultural insurance, forecast model

DOI: 10.3233/JIFS-179204

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6217-6228, 2019

Authors: Xu, Wanxiao | Ding, Mingjie

Article Type: Research Article

Abstract: The accelerated development of urbanization in China started with economic globalization and industrialization, and also in the process of economic system transition. Generally speaking, urbanization is an important indicator of a region’s economic development and social development. Urbanization equity is an important direction of sustainable economic and social development. From the economic dimension, urbanization can promote division of labor, specialization and accumulation of human capital through agglomeration effect. This paper analyzes the social equity and urbanization by using fuzzy logic and factor analysis model. Under the condition of market economy, migrant workers have no competitive advantage in the labor market …because of their low educational level. At the same time, the treatment of migrant workers in social security, cultural education and economic welfare is lower than that of urban residents. Under the unbalanced economic development reality, the economic absorption effect leads to the migrant workers rushing to the developed big cities. The results shows that by taking the dimensions of social justice of the rural migrant worker as the independent variable, psychological urbanization for the dependent variable, the regression Equation of F value is 90.424, P  = 0.000, less than 0.05 level of significance. Also, we make correlation analysis on all factors of social justice and urbanization. The experimental results show the effectiveness of proposed method. Green building is the development of sustainable development concept in architectural field. While the construction industry has brought great benefits to the development of national economy, its high investment, high pollution and inefficient development mode has also produced a huge energy load. Therefore, from the perspective of environmental and economic sustainability, the development of green buildings is particularly important. In this paper, the author makes economic benefit analysis of green building based on fuzzy logic and bilateral game model. By introducing such factors as economic benefits, cognition and government policies, this paper construct an evolutionary game model, which provides a basis for improving the economic benefits of green buildings. The results show that the first factor affecting enterprise decision-making is the incremental profit of green building developers, followed by the government’s incentive policy. After the evolution of the market, the final strategic choice will be stabilized to higher economic benefits. Generally speaking, green buildings need to effectively control incremental costs and consider scale benefits. Through management efficiency innovation and policy stimulation, the problems of huge investment cost and long payback period can be solved, so as to improve the economic benefits of green building development. Show more

Keywords: Urbanization rate, social security, fuzzy model, factor analysis

DOI: 10.3233/JIFS-179205

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6229-6240, 2019

Authors: Liwei, Sun

Article Type: Research Article

Abstract: At present, the application of artificial intelligence in the identification and classification of sports technology is still relatively small, and it is difficult to effectively improve the training and competition quality of athletes. Based on this, this study takes badminton as an example for analysis. Moreover, based on the complexity and multi-deformation of this motion, this study uses machine learning as the basic algorithm to design a real-time classification algorithm for badminton action. At the same time, this paper improves the traditional algorithm, designs an improved training model, and verifies the effectiveness of the design algorithm by experimental method. In …addition, this paper constructs a feature statistics and pace training system with the support of machine learning algorithms through statistical analysis and statistical badminton technical features and realizes the intelligentization of badminton batting action classification and recognition. Finally, this paper designs a comparative test for system functional testing. The system test shows that the system can effectively improve the action classification and recognition effect and can provide theoretical reference for subsequent related research. Show more

Keywords: Machine learning, badminton, motion recognition, action classification

DOI: 10.3233/JIFS-179206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6241-6252, 2019

Authors: Yuan, Xiaoyi

Article Type: Research Article

Abstract: The lack of effective evaluation of online education is a worldwide malpractice, and it is impossible to help students improve the correctness of online learning choices through existing reviews. Based on the current mainstream sentiment lexicon and text sentiment analysis, the authors use machine learning method to analyze the sentiment orientation of the legal course review text, through method that combines PMI and SVM. At the same time, this paper uses LibSVM tool to train and predict data, collect and pre-process data through network data collection, and, based on traditional algorithms, propose improved experimental scheme based on their respective advantages …and disadvantages. In addition, the model proposed in this study is used to classify and process the emotional text, and the two methods are combined to obtain the final result. Finally, this paper combines experiments to analyze the performance of the comprehensive model proposed in this study. The research shows that the classification effect of the text sentiment analysis of model is good, it can be applied to practice, and it can provide theoretical reference for subsequent related research. Show more

Keywords: Support vector machine, algorithmic optimization, online course, data network

DOI: 10.3233/JIFS-179207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6253-6263, 2019

Authors: Xiaolong, Zhang

Article Type: Research Article

Abstract: Athletes have a large amount of video information, so how to capture effective information is the key to improving athletes’ training efficiency and improving the quality of the game. From the perspective of deep learning, this study analyzes and improves traditional algorithm models based actual needs, and jointly learns multi-scale features. At the same time, in view of the problem of over-fitting in the model training process, this study uses the sparse pyramid pool strategy to adjust the pool parameterization process and reduce the complexity of feature description. In addition, the research designs experiment to analyze the performance of the …improved algorithm model and select the appropriate database to analyze the recognition effect of the algorithm model. The research shows that the algorithm of this research has a certain improvement in the recognition effect of athletes, and the recognition effect matching the artificial design features can be obtained, and it can provide theoretical reference for subsequent related research. Show more

Keywords: Deep learning, convolution algorithm, motion recognition, database management, deep learning

DOI: 10.3233/JIFS-179208

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6265-6274, 2019

Authors: Chaoming, Liang

Article Type: Research Article

Abstract: Ball sports have great variability in the game and the intelligent control of the rules of ball movement can effectively improve the training effect of athletes. However, the current research on artificial intelligence of spherical motion trajectory prediction points is basically blank. Based on this, this study is based on deep learning technology, and obtains the main experimental data through network data collection in the research and builds the table tennis spatial position image data set under various environments with accurate annotation based on the traditional deep learning. At the same time, the convolutional neural network is used as the …location recognition algorithm, and a prediction algorithm for predicting the trajectory of table tennis is proposed based on the recurrent neural network. In addition, this paper designs comparative experiments to analyze the effectiveness of the algorithm model, and evaluates the real-time recognition, location and trajectory prediction capabilities, and conducts quantitative analysis. The research shows that the algorithm has certain practical effects and can provide theoretical reference for subsequent related research. Show more

Keywords: Deep learning, neural network, trajectory, recognition algorithm, prediction model

DOI: 10.3233/JIFS-179209

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6275-6285, 2019

Authors: Yue, Wu

Article Type: Research Article

Abstract: With the rapid expansion of chain network, enterprises meet the consumption demand scattered around in a large range. In this paper, SOM neural network algorithm is introduced for empirical test. Design includes the structure of the fuzzy neural network identification and parameter identification, structural identification include input space division and the number of fuzzy rules to determine. Through summarizing and analyzing the characteristics of chain retail enterprises, this paper proposes to build a hierarchical and differentiated incentive mechanism by cultivating retail culture. The result shows that the knowledge staff is been higher the education level, the work creativity is stronger, …cooperates the demand to the team members. In the era of the knowledge economy, knowledge has replaced capital as the core source of the enterprise core competence. The performance evaluation of knowledge workers is complex and the performance of the general staff is often easier to get a more objective evaluation. In conclusion, performance characteristics of knowledge workers should include general knowledge staff quality, knowledge staff performance behavior and performance results three aspects of characteristics. Show more

Keywords: SOM neural network, fuzzy model, chain enterprises, performance analysis

DOI: 10.3233/JIFS-179210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6287-6300, 2019

Authors: Li, Bin | Wei, Xing | Li, Chao | Ding, Shuai

Article Type: Research Article

Abstract: Due to the lack of uniform standards for pathological cell detection, it is difficult to identify. In order to improve the accuracy of pathological cell identification, this study combines the actual situation of cell detection based on traditional particle algorithm to construct a C-V model based on level set algorithm and curve evolution theory, which realizes the effective separation of different substances inside the cell. At the same time, in order to effectively extract the characteristics of cell images, this paper uses the global research method to extract the features of the research object and adopts the improved gray level …co-occurrence matrix to extract the texture features, thus effectively improving the feature extraction quality. In addition, in order to study the accuracy of the algorithm model identification in this study, this paper designs a comparative experiment for performance analysis. The research shows that the proposed algorithm model has good performance, can achieve accurate recognition and feature extraction of pathological cells, has certain practical effects, and can provide theoretical reference for subsequent related research. Show more

Keywords: Particle algorithm, neural network, cell detection, model

DOI: 10.3233/JIFS-179211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6301-6313, 2019

Authors: Gao, Shuyan | Xu, Jiaqi | Lu, Weiheng

Article Type: Research Article

Abstract: Traditional nuclear magnetic resonance technology has grayscale inhomogeneity in brain tumor detection, which directly affects the formulation of follow-up treatment plans. In order to improve the detection effect of nuclear magnetic resonance on brain tumors, this study uses a convolutional neural network as the basis algorithm to construct an algorithm model suitable for multimodal MRI image recognition. At the same time, combined with the actual case, this paper uses the model to segment and identify brain tumors, and this paper combines the principle of machine learning and collects data for data training to construct a multi-channel deep deconvolution network model. …In addition, in order to explore the effectiveness of the algorithm in this study, the performance analysis was carried out by comparative experiment method, and the multi-faceted performance of the model was studied, and the corresponding test result images were obtained. Through experimental comparison, it can be seen that the algorithm model constructed in this study has certain validity, can be applied to practice, and can provide theoretical reference for subsequent related research. Show more

Keywords: Nuclear magnetic resonance, brain tumor, diagnosis, segmentation, convolutional neural network

DOI: 10.3233/JIFS-179212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6315-6324, 2019

Authors: Lijun, Cheng | Yubo, Zhang

Article Type: Research Article

Abstract: The Internet of Things (IOT) is the main technical support of smart agriculture. The sensor equipment of the Internet of Things (IOT) in agriculture is developing in the direction of low cost, self-adaptation, high reliability and low power consumption. In the future, the sensor network will gradually have the characteristics of distributed, multi-protocol compatibility, self-organization and high throughput. In this paper, the authors analyze the intelligent agricultural system and control mode based on fuzzy control and sensor network. Intelligent agriculture is based on the most efficient use of various agricultural resources to minimize agricultural energy consumption and costs. It is …supported by Internet of Things technologies such as comprehensive perception, reliable transmission and intelligent processing. Using ROF technology, the WiFi signal is pulled far, and the wireless coverage is expanded greatly. At the same time, through the combination of wireless sensor technology such as ZigBee, the transmission and centralized control of sensing signals are realized, and the monitoring system of intelligent agricultural greenhouse is established. The simulation results show that the system can effectively improve the level of intelligence and information of agricultural greenhouse management, and greatly improve crop production efficiency. Show more

Keywords: Intelligent agriculture, wireless technology, sensor networks, fuzzy control

DOI: 10.3233/JIFS-179213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6325-6336, 2019

Authors: Li, Wenxian

Article Type: Research Article

Abstract: Reasonable fire risk assessment system can demonstrate the occurrence of fire and ensure the safe evacuation of fire. The selected indicators of the evaluation system play a fundamental role in the establishment of the system. In the evaluation model, the general problem is transformed into a specific mathematical model by using the method of fuzzy information processing, which makes the evaluation result more direct and measurable. This paper uses a measure of feature attributes to measure the contribution of clusters, that is, the method of calculating the weight of features. When the value of the equilibrium discriminant function reaches the …minimum value, the clustering result under the optimal condition can be obtained. Then, the author analyzes the fire risk assessment and factor analysis of buildings based on multi-target decision and fuzzy mathematical model. The simulation results show that the improved fuzzy model proposed in this paper makes the calculation results more accurate. The fire risk analysis and control system based on the theory of fuzzy information processing can be widely used in various high-rise buildings to ensure safety. Show more

Keywords: Multi-objective decision, high-rise building, fire risk, fuzzy mathematics

DOI: 10.3233/JIFS-179214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6337-6348, 2019

Authors: Wenwen, Liang

Article Type: Research Article

Abstract:  In order to realize the intelligent evaluation of effective teaching quality and make up for the lack of research in this aspect, in the research, BP neural network is used as the basis for model construction analysis. Political education in colleges and universities is an important course, and its teaching quality evaluation is particularly important. Through comparative analysis, LMBP is selected as the learning algorithm, and the neural network evaluation model mechanism of college classroom teaching quality evaluation system is determined through theory and practical methods, and the simulation model is simulated by MATLAB as a simulation tool. At the …same time, this paper uses the experimental method to carry out simulation training experiments in the MATLAB neural network toolbox, select the training algorithm for comparative analysis, and display the results in the form of statistical graphs. In addition, this paper sets the convergence speed and error curve as evaluation indicators, determines the appropriate training algorithm, and verifies the validity of the model. The research indicates that the BP teaching quality evaluation model based on BP neural network is a reasonable and feasible evaluation model and can provide theoretical reference for subsequent related research. Show more

Keywords: BP neural network, teaching quality, model, training function, simulation analysis

DOI: 10.3233/JIFS-179215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6349-6361, 2019

Authors: Peng, Qin

Article Type: Research Article

Abstract:  With the full spread of various IT application systems, a large number of business data are stored in the business systems of enterprises. In this paper, the author analyzes the aviation industry management mode based on big data analysis. In this paper, the author analyses aviation industry management model and exchange rate index analysis based on error correction model and fuzzy mathematics. The BP algorithm uses the error of the output layer to estimate the error of the direct predecessor layer of the output layer, and then gradually estimates the error forward, and thus, the error of all layers is …obtained. The weights and thresholds of the layers are adjusted according to the error so that the modified network output can approach the expected value. The aviation industry data include both the financial and internal data of airlines, and the external data such as flight information and user data. From the ETL process, building an enterprise data warehouse is an important strategy for the development of the aviation industry. It has a positive effect on the application of automatic data mining and business intelligence in the aviation industry. On this basis, we put forward relevant suggestions for aviation industry management. Show more

Keywords: Big data, aviation industry, operation management, error correction model, fuzzy mathematics

DOI: 10.3233/JIFS-179216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6363-6375, 2019

Authors: Zhen, Zhen | Yanqing, Yao

Article Type: Research Article

Abstract: Technological innovation in manufacturing industry is a kind of R&D activity that produces new technologies, including input and output of technological innovation. In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, …and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises. Show more

Keywords: SVM Algorithms, data mining, manufacturing enterprises, science and technology level

DOI: 10.3233/JIFS-179217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6377-6388, 2019

Authors: Yangjun, Ren | Chuanxu, Wang | Lang, Xu | Chao, Yu | Suyong, Zhang

Article Type: Research Article

Abstract: Using the panel data for China’s 30 provinces from 2008 to 2016, this paper analyzes the impact of producer services agglomeration on green economic efficiency at its spillover effects, through spatial autocorrelation test and the establishment of spatial econometric models. It comes to the results as follows: First, China’s regional green economic efficiency is significant positive spatial dependence. Second, the producer services specialized agglomeration not only inhibits the green economic efficiency of one region but also has significantly negative spatial spillover effects on adjacent areas, while the producer services diversified agglomeration only enhance the green economic efficiency in the region. …Third, the impact of the agglomeration mode selection of producer services industry on green economic efficiency in the eastern region is basically consistent with the empirical analysis at the national level, while the green economic efficiency improvement in the central region only benefits from producer services specialized agglomeration, and the green economic efficiency in the western region is not significantly affected by the producer services agglomeration mode selection. Show more

Keywords: Production services agglomeration, green economic efficiency, spatial Durbin model, spillover effects

DOI: 10.3233/JIFS-179218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6389-6402, 2019

Authors: Qiang, Qunli

Article Type: Research Article

Abstract: Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In …addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research. Show more

Keywords: Real estate, generalized additive model, machine learning, K-means Algorithm

DOI: 10.3233/JIFS-179219

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6403-6414, 2019

Authors: Li, Qinyang

Article Type: Research Article

Abstract: With the advent of green economy, it is of great significance to objectively calculate the green innovation efficiency of provincial industrial enterprises in China for achieving sustainable economic development. In this paper, the author analyzes the regional technological innovation and green economic efficiency based on DEA model and fuzzy evaluation. Based on the latest development of traditional efficiency and productivity analysis theory, this study calculates the green innovation efficiency of 30 provincial industrial enterprises by using SBM model, while considering the relaxation of economic input-output problem. The results show that the SBM model improves the accuracy and authenticity of the …economic efficiency evaluation of green innovation. The efficiency of green economy in most provinces is on the rise. At the same time, the intensity of R&D and industrial structure play a positive role in improving the efficiency of green economy. Through cluster analysis, the differences and causes of green economic efficiency of regional industrial enterprises are analyzed. In addition, the provinces should also consider the factors affecting the green economic efficiency of industrial enterprises, implement the innovation-driven development strategy in an all-round way, and promote the development of green economy. Show more

Keywords: DEA model, Fuzzy evaluation, Technological innovation, Particle swarm optimization

DOI: 10.3233/JIFS-179220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6415-6425, 2019

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6427-6427, 2019

Authors: Liu, Q. | Shi, Fu-Gui

Article Type: Research Article

Abstract: In the present paper we present a new approach to the fuzzification of groups, which is defined by the hazy associative law (a new fuzzy associative law) on hazy binary operations. It is also called an M -hazy group. The basic idea is to generalize the binary operation of classical algebras to fuzzy binary operation. Some properties of M -hazy groups and M -hazy subgroups are discussed. Besides, we also study the homomorphisms between two M -hazy groups.

Keywords: Hazy associative law, M-hazy group, M-hazy subgroup, M-hazy homomorphism

DOI: 10.3233/JIFS-180001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6429-6442, 2019

Authors: Tarahomi, Mehran | Izadi, Mohammad

Article Type: Research Article

Abstract: Cloud computing is a new framework, which is facing a numerous type of challenges including resource management and energy consumption of data centers. One of the most important duties of cloud service providers is to manage resources and schedule tasks for reducing energy consumption in data centers. In this paper, fuzzy logic is used for finding most adequate DC, improved DVFS algorithm is used for selecting ideal host and developed version of EDF-VD algorithm is utilized for Task scheduling and load balance in cloud computing. Our approach improvement to the current methods including EEVS, DVFS, Homogeneous, MBFD and EEVS-N.

Keywords: Load balancing, task scheduling, reducing energy consumption, cloud computing

DOI: 10.3233/JIFS-181016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6443-6455, 2019

Authors: Dehghan, Ehsan | Amiri, Maghsoud | Shafiei Nikabadi, Mohsen | Jabbarzadeh, Armin

Article Type: Research Article

Abstract: In this paper, a mixed-integer nonlinear programming model is developed for a general edible oil closed loop supply chain network design problem under hybrid uncertainty which is then transformed to its linear counterpart. In order to cope with the hybrid uncertainty in input parameters, scenario-based and fuzzy- based parameters, a new approach is proposed including a novel robust fuzzy programming and an efficient method based on the Me measure. Furthermore, the performance of the proposed model is compared with that of other models. Finally, numerical studies and simulation are performed to verify our mathematical formulation and demonstrate the benefits of …the proposed model. Show more

Keywords: Mixed-integer programming, edible oil supply chain, closed loop supply chain, Network design, robust possibilistic programming, stochastic programming

DOI: 10.3233/JIFS-18117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6457-6470, 2019

Authors: Daneshmandpour, Navid | Danyali, Habibollah | Helfroush, Mohammad Sadegh

Article Type: Research Article

Abstract: This paper proposes a self-recovery method of fragile watermarking. Generally, self-recovery methods embed two types of data into the original image: check-bits for tamper detection and reference data for image recovery. Generating reference data is the primary challenge of every self-recovery method for more tamper resiliency and higher reconstruction quality. The proposed Multi-Rate Reference Embedding (MRRE) method makes unique reference data with several redundancy rates, instead of generating multiple reference data. According to the proposed methodology, the image is compressed by a source coding algorithm and the compressed data is separated into ten parts. Each part is protected by a …channel coding algorithm based on pre-assigned redundancy rates. A fuzzy-based rate allocation system is used to assign the redundancy rates based on the importance of data. The generated data is packetized and randomly embedded into an image block. For tamper detection purpose, check-bits are generated by an MD5 hash function for every block. Both reference data and check-bits are embedded into three least significant bits (LSB) of the image pixels. To increase restoration efficiency, the proposed MRRE method provides ten scales of image recovery named highly-scalable self-recovery. The simulation results show an improvement in both tamper tolerability and reconstruction quality in comparison with the most recent methods. Show more

Keywords: Multi-rate reference data, highly-scalable self-recovery, fuzzy-based rate allocation, source-channel coding scheme, tamper tolerability

DOI: 10.3233/JIFS-181874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6471-6481, 2019

Authors: Hasheminejad, Seyed Ali | Bagherpour, Morteza | Nouri, Siamak | Pishvaee, Mir Saman

Article Type: Research Article

Abstract: Obviously, financial aspect is the most important pillar of technology development. Furthermore, the role of venture capital in developing small and medium size knowledge-based institutions is vital. However, startup portfolio selection and venture capital firms’ syndication have always been critical challenges in VC industry and the need for integrated methods based on sophisticated quantitative techniques are always being felt. In this research, startup portfolio optimization is simulated which is more similar to real world problems rather than other research. In order to attain this goal, preferences of startups as decision-makers and the interaction between investees and investors are considered. Concerning …the complexity of the problem, the best-known model to simulate this problem is an agent-based modeling and also by using harmony search algorithm, the optimization procedure is successfully implemented. Through implementing this procedure, not only the portfolio’s return on investment is optimized but also venture capital firms’ syndication and their share is then determined. Finally, several numerical illustrations are solved using the proposed combinatorial model. Show more

Keywords: Venture Capital, Agent Based Modeling, Harmony Search, Startup Portfolio Optimization

DOI: 10.3233/JIFS-181914

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6483-6497, 2019

Authors: Ammar, El Saeed | Eljerbi, Tarek

Article Type: Research Article

Abstract: In solving real life fractional programming problem, we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To overcome these limitations, the fuzzy rough approach is applied to this problem. In this paper, an efficient method is proposed for solving fuzzy rough multiobjective integer linear fractional programming problem where all the variables and parameters are fuzzy rough numbers. Here, the fuzzy rough multiobjective problem transformed into an equivalent multiobjective integer linear fractional programming problem. Furthermore, from the obtained problem, five crisp multiobjective integer linear fractional programming problems are constructed and the resultant problems are …solved as a crisp integer linear programming problem by using Dinkelbach concept. Finally, the effectiveness of the proposed procedure is illustrated through numerical examples. Show more

Keywords: Fractional programming, integer programming problem, triangular fuzzy rough numbers, fuzzy rough linear programming

DOI: 10.3233/JIFS-182552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6499-6511, 2019

Authors: Ha, Shumin | Liu, Heng | Li, Shenggang

Article Type: Research Article

Abstract:  An adaptive fuzzy backstepping control method is proposed for uncertain fractional-order chaotic systems subject to system constraints, including unknown external disturbance and input saturation in this article. The system constraints are resolved by modeling the saturation limiter as portions of unknown functions and a backstepping recursive algorithm is proposed. In each step, the system unknown nonlinearity is approximated by a fuzzy logic system, and fuzzy parameters are updated by the corresponding fractional-order adaptation laws online. In the end, an adaptive fuzzy controller that assures the convergence of tracking error is designed. Formal proof and two simulations are applied for confirming …the effectiveness of the designed controller. Show more

Keywords: Adaptive fuzzy control, fractional-order chaotic system, backstepping control, input saturation

DOI: 10.3233/JIFS-182623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6513-6525, 2019

Authors: Chen, Yang

Article Type: Research Article

Abstract: General type-2 fuzzy logic systems (GT2 FLSs) have drawn great attentions since the alpha-planes representation of general type-2 fuzzy sets (GT2 FSs) was proposed. The iterative of type-reduction (TR) algorithms are difficult to apply in practical applications. In the enhanced types of algorithms, the Nagar-Bardini (NB) algorithms decrease the computation complexity greatly. In terms of the Newton-Cotes quadrature formulas of numerical integration techniques, the paper extends the NB algorithms to three different forms of weighted NB (WNB) algorithms according to the comparisons between the sum operation in NB algorithms and the integral operation in continuous version of NB (CNB) algorithms. …The NB algorithms just become a special case of the WNB algorithms. Four simulation examples are used to illustrate and analyze the performances of the WNB algorithms while performing the centroid TR of GT2 FLSs. It also shows that, in general, the WNB algorithms have smaller absolute error and faster convergence speed compared with the NB algorithms, which provides the potential value for T2 FLSs designers and users. Show more

Keywords: alpha-planes representation, general type-2 fuzzy logic systems, type-reduction, weighted Nagar-Bardini algorithms, simulation

DOI: 10.3233/JIFS-182644

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6527-6544, 2019

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Hypothesis tests are a statistical decision-making tool for testing if a hypothesized parameter value is supported by the sample data or not. Vagueness and impreciseness in the sample data require fuzzy techniques to be employed in the analysis. These techniques can be based on intuitionistic fuzzy sets, hesitant fuzzy sets, type-2 fuzzy sets, neutrosophic sets, or spherical fuzzy sets. In this paper, Z-fuzzy numbers are used to capture the vagueness in the sample data and develop Z-fuzzy hypothesis testing. A Z-fuzzy number is represented by a restriction function that is usually a triangular or trapezoidal fuzzy number and a reliability …function representing the confidence level to the restriction function. Illustrative examples for left and right sided hypothesis testing and sensitivity analyses are presented. Show more

Keywords: Z-fuzzy number, hypothesis testing, statistical decision making, restriction function, reliability function

DOI: 10.3233/JIFS-182700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6545-6555, 2019

Authors: Liu, Yuan | Xie, Min | Zhu, Jinjin | Hao, Jingjing

Article Type: Research Article

Abstract: Natural monotonic linguistic language is widely used to express experts’ uncertain subjective appraisal opinion, such as “More than”, “At least”, “Less than” and “At most”, which reveals explicit information about performance range and implicit information about his hiding preference on a linguistic scale. A novel computational method for monotonic hesitant fuzzy linguistic terms is developed to transfer experts’ uncertain appraisal information to decision-making data, which can systematically consider and mine expert’s obvious explicit and hidden implicit appraisal information. Specially, the comprehensive meanings of monotone decreasing and increasing hesitant fuzzy linguistic terms are investigated, in which both explicit and implicit appraisal …information are explored to reveal its actual meaning. Additionally, Weibull distribution functions with three parameters are fitted considering the comprehensive meaning of monotone increasing appraisals, which is determined by a multi-objective programming model following ABC classification method. Symmetry principle is employed to confirm the expression of monotone decreasing appraisals, which are transferring from monotone increasing appraisals with same length of domain field. Moreover, feasibility analysis is explored to show the influence of parameters on decision-making precision. Finally, a numerical study is conducted to show the feasibility and advantage of the new method, which can effectively improve the precision of computational transfer by comparing to previous method. Show more

Keywords: Monotonic hesitant fuzzy linguistic term set, implicit appraisal information, Weibull distribution, ABC analysis

DOI: 10.3233/JIFS-182754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6557-6571, 2019

Authors: Rashid, Junaid | Adnan Shah, Syed Muhammad | Irtaza, Aun

Article Type: Research Article

Abstract: Medical and health text documents pose a challenge for data handling and retrieving the relevant and meaningful documents. Automatically retrieval of significant knowledge with a better understanding of medical and health documents is a challenging task. One popular approach for thematically understand the medical and health text documents and finding the topics from these documents is topic modeling. In this research, we propose a novel topic modeling approach Fuzzy k-means latent semantic analysis (FKLSA) by using the fuzzy clustering. Our method generates local and global term frequencies through the bag of words (BOW) model. Principal component analysis is used for …removing high dimensionality negative impact on global term weighting. Previous work shows that in medical and health documents redundancy issue has a negative impact on the quality of text mining. Therefore, the main achievement of FKLSA is the handling of the redundancy issue in medical and text documents and discover semantically more precise topics. FKLSA is socially utilized for finding the themes from medical and health text corpus. These topics are further used for text classification and clustering tasks in text mining. Experimental results show that FKLSA performs better than LDA and RedLDA for redundant corpora. FKLSA’s time performance is also stable with an increase in number of topics and thus better than LDA and LSA on a big twitter heath dataset. Quantitative evaluations of the real-world dataset for health and medical documents show that FKLSA gives a higher performance as compared to state-of-the-art topic models like Latent Dirichlet allocation and Latent semantic analysis. Show more

Keywords: Topic modeling, bag-of-words, term weighting, fuzzy k-means, principal component analysis

DOI: 10.3233/JIFS-182776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6573-6588, 2019

Authors: Meng, Xiao-Li | Gong, Liu-Tang | Yao, Jen-Chih

Article Type: Research Article

Abstract:   Evaluating the performances of a set of entities called decision making units (DMUs) which convert multiple inputs into multiple outputs has long been considered as a difficult task because one is dealing with complex economics. This work proposes an inequality approach to evaluate the performances of DMUs. Inequalities consist of expressions of the production possibility set and the line segments joining the evaluated DMU to the positive output-axes. However, in real-world application involving performance measurement, inputs and outputs are often imprecise and fluctuated. In this case, a fuzzy inequality approach is proposed to evaluate the performances. What is more, …fuzzy relative efficiency is dependent upon the number of solutions. Furthermore, the minimal element is used to distinguish the fuzzy relative efficient DMUs. Finally, two numerical examples are used to illustrate the fuzzy approach and compare the results with those obtained with alternative fuzzy approaches. Show more

Keywords: Fuzzy data envelopment analysis, Fuzzy inequality, The production possibility set, The positive output-axes, Minimal element

DOI: 10.3233/JIFS-182823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6589-6600, 2019

Authors: Sun, Zhe | Zheng, Jinchuan | Man, Zhihong | Wang, Hai | Shao, Ke | He, Defeng

Article Type: Research Article

Abstract:  This paper presents a novel adaptive fuzzy sliding mode (AFSM) control scheme for a vehicle steer-by-wire (SbW) system. Initially, the dynamics of the SbW system are described by a second-order differential equation where the Coulomb friction and the self-aligning torque are treated as external disturbances. Furthermore, an AFSM controller is designed for the SbW system, which utilizes an adaptive law to estimate both the Coulomb friction and the self-aligning torque, a sliding mode control component to deal with the parametric uncertainties and unmodeled dynamics, and a fuzzy strategy to strike a good balance between the chattering-alleviation and the tracking precision. …The stability of the control system is verified in the sense of Lyapunov, and the selection of control parameters is provided in detail. Lastly, experiments are carried out under various road conditions. The experimental results demonstrate that the developed AFSM controller possesses superiority in terms of higher tracking accuracy, stronger robustness and a better balance between the control precision and smoothness in comparison with a conventional sliding mode (CSM) controller and a boundary layer-based adaptive sliding mode (BLASM) controller. Show more

Keywords: Adaptive fuzzy sliding mode (AFSM), steer-by-wire (SbW), vehicle, self-aligning torque

DOI: 10.3233/JIFS-182824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6601-6612, 2019

Authors: Farzaneh, Ghorbani | Mohsen, Afsharchi | Vali, Derhami

Article Type: Research Article

Abstract: This paper proposes a novel multi-agent unit commitment model under Smart Grid (SG) environment to minimize the demand satisfaction error and production cost. This is a distributed solution applicable in non-deterministic environments with stochastic parameters intending to solve Distributed Stochastic Unit Commitment (DSUC) problem. We use multi-agent reinforcement learning (RL) in which agents learn as independent learners to cooperatively satisfy the demand profile. The learning mechanism proceeds using a reward signal, which is given based on the performance of the entire system as well as the impact of the joint action of the agents. The learning agent utilizes a novel …multi-agent version of Fuzzy Least Square Policy Iteration (FLSPI) as a model-free RL algorithm to approximate Q-function. Based on this approximation, the agent makes the best decision to achieve the goals while considering the constraints governing the system. Uncertainty sources in our definition of the problem are fluctuations in the predicted demand function, random productions of clean energy generators and the possibility of accidental failure in power generators. Training for one time interval (i.e. one season or one year) consisting of several time intervals (i.e. days) can be simultaneously conducted by one trial in our method. We have conducted our experiment in two different frameworks. These frameworks are defined based on the problem complexity in terms of the number of generators, the uncertainties in the environment and the system constraints. The results show that the learning agent learns to satisfy the demand profile as well as other constrains. Show more

Keywords: Multi-agent reinforcement learning, Stochastic Unit Commitment, fuzzy approximation

DOI: 10.3233/JIFS-182879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6613-6628, 2019

Authors: Ghasemi Nejad, S.M. | Borzooei, R.A.

Article Type: Research Article

Abstract: In this paper, the notions of (semi) topological basic algebra and (semi) topological implication basic algebra are introduced, along with evaluating their properties. Then, different operations are defined based on basic algebras and the relationship between semicontinuity and continuity of operations is considered. In addition, the separation axioms on (semi) topological basic algebras are investigated by considering some conditions implying that a (semi) topological basic algebra becomes a T i - space, for i  ∈ {0, 1, 2}. In the sequel, some relations between (weak) ideals and (weak) filters of basic algebras are obtained and (left) topological (implication) basic algebra …is constructed by using the concepts of (weak) filters, which is a zero dimensional, normal, disconnected, locally compact and completely regular (left) topological space. Further, the notion of quotient basic algebras are presented along with evaluating the interaction of topological basic algebras and topological quotient basic algebras. Finally, it is proved that there is an implication basic algebra IB * with cardinality n  + 1 and filter F *  = F  ∪ {z * }, which z * ∉ IB for any implication basic algebra IB of cardinality n and filter F . Accordingly, it is proved that there is at least one nontrivial regular and normal topological implication basic algebra of cardinality n . Show more

Keywords: Basic algebra, topological basic algebra, continuous, separation axioms, topological quotient basic algebras, 54A05, 54A10, 03G12, 03G25

DOI: 10.3233/JIFS-182947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6629-6644, 2019

Authors: Jian, Jie | Zhan, Nian | Su, Jiafu

Article Type: Research Article

Abstract: In the field of engineering economy, engineering investment selection is a common problem, where the preference information is usually intuitionistic and fuzzy. To deal with the consistency and integrity of the information in the selection process, the aim of this article is to extend the superiority and inferiority ranking method and use the interval-valued intuitionistic fuzzy theory, where the individual evaluation values and the weights information of criteria and decision-makers are all described by interval-valued intuitionistic fuzzy numbers. First, some concepts of interval-valued intuitionistic fuzzy set are introduced. Then, the interval-valued intuitionistic fuzzy superiority and inferiority ranking (IVIF-SIR) method is …developed. Moreover, an engineering investment selection model based on IVIF-SIR method is investigated. Finally, an illustration of choosing investment alternatives is used to prove the developed approach and a comparative study is also use to demonstrate the effectiveness. Show more

Keywords: SIR method, multiple criteria group decision making, interval-valued intuitionistic fuzzy Set

DOI: 10.3233/JIFS-190001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6645-6653, 2019

Authors: Frizzo Stefenon, Stéfano | Silva, Marcelo Campos | Bertol, Douglas Wildgrube | Meyer, Luiz Henrique | Nied, Ademir

Article Type: Research Article

Abstract: Reliability in the electric power system is fundamental to the development of society, for which rapid and accurate methods of fault identification are required. Faults in distribution insulators are hardly visible and the fault behavior is often intermittent, which makes its diagnosis a difficult task. Fault diagnosis with the ultrasound equipment has been used efficiently since this equipment is directional and not influenced by sunlight. However, the interpretation of the signal generated by this equipment requires an experienced operator and they are also susceptible to provide false diagnostics. The use of advanced algorithms to classify electrical system conditions has been …proven as a great alternative to automate operator decisions. This article proposes the use of artificial intelligence algorithms such as single-layer and multilayer Perceptron for classification of distribution insulators conditions. The use of artificial neural networks for insulator classification is an innovative subject. Some researchers have already worked on partial discharges however not specifically for fault classification in insulators of distribution networks. The application of this technique can make the inspection of the electrical system automated and, in this way, more accurate and efficient. The results of the analysis showed that the application of signal linearization technique joint with artificial intelligence is a good alternative to locate faults in insulators. Show more

Keywords: Fault identification, artificial neural network, grid inspection, classification, insulators

DOI: 10.3233/JIFS-190013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6655-6664, 2019

Authors: Akl, Ahmed | El-Henawy, Ibrahim | Salah, Ahmad | Li, Kenli

Article Type: Research Article

Abstract: Hyperparameter optimization is a crucial step in the implementation of any machine learning model. This optimization process includes regularly modifying the hyperparameter values of the model in order to minimize the testing error. A deep neural learning model hyperparameter optimization process includes optimizing both the model parameters and architecture. Optimizing a model’s parameters involves deciding the values of parameters, such as learning rate and batch size. Optimizing architectural hyperparameters includes deciding the shape of the deep neural learning model, i.e. , the number of layers of individual types and the number of neurons in a certain layer. The state-of-the-art hyperparameter …optimization methods don’t optimize the position of the hyperparameter within the model architecture. In this work, we study the effect of changing a hyperparameter within the deep learning model architecture. Thus, we propose an arch itectural pos ition opt imization (ArchPosOpt ) method for model architectural hyperparameter optimization. ArchPosOpt extends three different hyperparameter optimization techniques, namely grid search, random search, and Tree-structured Parzen Estimator (TPE), to include a new dimension of hyperparameter optimization problem – the hyperparameter position. We show through a set of experiments that the position of the hyperparameters does matter for model performance as well as the hyperparameter values. Show more

Keywords: Deep neural networks, hyperparameter optimization, CNN, architectural optimization, hyperparameter position

DOI: 10.3233/JIFS-190033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6665-6681, 2019

Authors: Fayaz, Muhammad | Ullah, Israr | Shah, Abdul Salam | Kim, DoHyuen

Article Type: Research Article

Abstract: Intelligent optimized energy management and prediction model in electric vehicles received attraction of the researchers in the last couple of years. Several techniques and models have been proposed in the literature for optimized energy management and control, but the trade-off between occupant comfort index and the energy consumption is still a significant challenge to the research community. In this paper, we have proposed a model based on learning to optimization and learning to control for user comfort maximization and efficient energy consumption. The proposed model is comprised of three layers; prediction module, learning to optimization module and learning to control …module. In the prediction module, we have used the Kalman filter for noise removal and prediction of environmental parameters. In learning to optimization module, the bat algorithm has been used for user comfort maximization and energy consumption minimization. Furthermore, we have used the learning module with optimization module in order to tune the user preferences parameters in the comfort index formula used in the bat optimization algorithm. Likewise, the learning module has been used with the conventional fuzzy logic controller in order to improve its performance. In the conventional fuzzy logic controller, the membership functions boundaries are usually determined through hit and trial method, and once the membership functions are determined, they remain fixed for the entire process. In the learning to control module, the membership functions tuning is carried out. The membership functions are continuously tuned to get effective results. Experimental results indicate that the proposed method performs better as compared to the conventional methods and achieves improved user comfort with reduced energy consumption. Show more

Keywords: Energy optimization, energy consumption, user comfort, bat algorithm, electric vehicles, learning to control

DOI: 10.3233/JIFS-190095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6683-6706, 2019

Authors: Saritha, S. | Santhosh Kumar, G.

Article Type: Research Article

Abstract: The spatial colocation problem is totally different from the traditional association rule problem, as it operates on spatial data and not on conventional transaction data. In this work, a spatial colocation mining framework is proposed that mines spatial colocation of image-objects present in images using a tensor factorization approach. The framework takes in image data directly, tensorize it and perform the mining task, thus eliminating the need of converting into a transaction based approach. An interestingness measure called, spatial dominance is also proposed in this work. This measure is an indicator of the prevalence of the mined colocation pattern. Algorithms …are designed in this framework, first to map the classified pixels as members of image-objects, which is a pre-stage before mining and second to find spatial colocation patterns. Experiment results are provided to show the strength of the spatial colocation mining algorithm. Show more

Keywords: Data mining, spatial colocation, tensors, image-objects

DOI: 10.3233/JIFS-190122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6707-6716, 2019

Authors: Ziari, Shokrollah | Bica, Alexandru Mihai

Article Type: Research Article

Abstract: In this paper, an iterative numerical method has been developed to solve nonlinear fuzzy Volterra integral equations based on three-point quadrature formula. The error estimation of the method is obtained based on Lipschitz condition and in order to confirm the yielded theoretical results, we perform the iterative method on some numerical examples.

Keywords: Nonlinear fuzzy Volterra-Hammerstein integral equations, Iterative numerical method, L-Lipschitz fuzzy functions

DOI: 10.3233/JIFS-190149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6717-6729, 2019

Authors: de Jesús Rubio, José | Garcia, Enrique | Ochoa, Genaro | Elias, Israel | Cruz, David Ricardo | Balcazar, Ricardo | Lopez, Jesus | Novoa, Juan Francisco

Article Type: Research Article

Abstract:  An unscented Kalman filter can be applied for the experimental learning of the solar dryer for oranges drying and the greenhouse for crop growth to know better the processes and to improve their performances. The contributions of this document are: a) an unscented Kalman filter is designed for the learning of nonlinear functions, b) the unscented Kalman filter is applied for the experimental learning of the two mentioned processes.

Keywords: Unscented Kalman filter, greenhouse, solar dryer, experimental learning

DOI: 10.3233/JIFS-190216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6731-6741, 2019

Authors: Poornappriya, T.S. | Durairaj, M.

Article Type: Research Article

Abstract: The prompt enhancement of Telecom turned to be a vibrant and economical industry, which comprises an intrinsically great perspective for customer churn, requiring exact churn prediction models. In recent times, there has been phenomenal responsiveness in the development of feature selection methods for a large number of datasets. Through this research work, a High Relevancy and Low Redundancy (HRLR) approach by consuming Vague Set (VS) has proposed for selecting the subset of features from the features set. This proposed method is based on the Minimum Redundancy and Maximum Relevancy (MRMR) approach by using Vague Set. The proposed HRLR-VS method is …based on the filtered approach feature selection, where the features are selected only when the measure of feature-class relevancy is maximized and a measure of feature-feature redundancy is minimized. The collaboration of similarity measures and ranking algorithms are prepared by utilizing the vital notions of Vague Sets information energies by Information Gain, Gain Ratio, and Chi-Square methods. The projected approach has been employed with the Particle Swarm Optimization for probing the best feature subset. Further, it measures the efficacy of the projected approach HRHL-VS for telecommunication dataset. The performance metrics like Accuracy, Kappa Statistics, True Positive Rate, Precision, F-Measure, Recall, MAE, RRSE, RMSE and RAE are considered in this paper for evaluating the proposed HRLR-VS method. The proposed HRRL-VS method has compared with existing literature approaches like mRMR and FCBF. From the result obtained in this paper, the proposed HRLR-VS method better results in all aspects for selecting the feature subset in telecommunication dataset. Show more

Keywords: Feature Selection, Vague Set, Information Gain, Gain Ratio, Chi-Square, Particle Swarm Optimization, Euclidean Distance, Cosine Similarity, Pearson’s Correlation Coefficient

DOI: 10.3233/JIFS-190242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6743-6760, 2019

Authors: Zerhari, Btissam | Lahcen, Ayoub Ait | Mouline, Salma

Article Type: Research Article

Abstract: Attribute and class noises are the two important sources of Corruptions (noise) contained in real-world datasets which may deteriorate data interpretation and accuracy. Class noise has potentially serious negative impacts compared to attribute noise, however, the existing major class noise detection methods are not able to address this problem efficiently. To overcome issues related to detection and the elimination of class noise, we suggest a new noise filtering approach able to identify and remove class noise, called Multi-Iterative Partitioning Class Noise Filter (MIPCNF). Since there is no single filter that consistently outperforms its counterparts in all database types and in …different levels of noise, our approach relies on an algorithm in which several rounds of class noise detection are performed on different partitions of the data using several classifiers. Therefore, we use different filtering strategies: iterative noise filter, partitioning filter and ensemble-based filter. The experimental results, on 14 real-world datasets, and statistical analysis, show that our method is not only overcoming the higher noise but also over-performing latest class noise detection and elimination strategies in different levels of noise. Show more

Keywords: Class noise, Noise Detection, Noise Elimination, Partitioning Filter, Large Data

DOI: 10.3233/JIFS-190261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6761-6772, 2019

Authors: Maheshwari, Karan | Joseph Raj, Alex Noel | Mahesh, Vijayalakshmi G.V. | Zhuang, Zhemin | Rufus, Elizabeth | Shivakumara, Palaiahnakote | Naik, Ganesh R.

Article Type: Research Article

Abstract: In today’s world, there have been lots of unique optical character recognition systems. One drawback of these systems is that they cannot work effectively on natural scene images where the text is not only subject to different orientations, lightning, and background but can be of multiple scripts as well. The paper, proposes a state of the art algorithm to detect texts of different dialects and orientations in an image. The whole text detection pipeline is divided into two parts. First, extraction of probable text regions in an image is performed based on a combination of statistical filters, which results in …a high recall. These regions are then fed to an Artificial Neural Networks (ANN) based classifier which classifies whether the proposed regions are text or non-text, which increases the overall precision. The validity of the algorithm is verified on the most challenging bilingual text detection dataset MSRA-TD500 and a promising F1 score of 0.67 is reported. Show more

Keywords: Text detection, entropy and variance filters, invariant moments, artificial neural networks, bilingual text detector

DOI: 10.3233/JIFS-190339

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6773-6784, 2019

Authors: Vanitha, V. | Krishnan, P.

Article Type: Research Article

Abstract: An e-learning system offering a personalised learning path will be vastly appealing to the learners. Adaptive techniques when employed in e-learning can sustain the interest and motivation of the learners and help them to complete the enrolled courses successfully. In addition, it would improve their performance and thus, enhance the overall learning experience. Personalisation takes into consideration the characteristics of the individual learner and the diversity in his/her needs. The main challenge is finding a match between these individual characteristics and the sequence of the learning content. It is a complex task to implement as it involves selection of the …appropriate material from a vast amount of the available learning materials. It is a challenge to perform this process manually as it requires both technical savvy and pedagogical skills. In this paper, a stigmergy model is proposed, which was applied to build a customised learning path. The aim was to provide personalisation that satisfied the needs of an individual in a widely heterogeneous e-learning environment. Compared with the traditional teaching method, this tailored learning path, generated using the proposed approach, shows promise and was found to enhance the performance of the learners. Show more

Keywords: Learning path, learning content sequence, personalised E-learning, ant colony optimisation, curriculum sequencing

DOI: 10.3233/JIFS-190349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6785-6800, 2019

Authors: Ajeena Beegom, A.S. | Rajasree, M.S.

Article Type: Research Article

Abstract: Scientific workflow applications include a set of tasks, which have complex inter dependencies with each other, along with a large number of parallel tasks. The problem of scheduling such application tasks involves careful decisions on determining the sequence in which it can be processed, causing high impact on the cost of execution and makespan (execution time), when executed on a cloud computing system. Achieving optimal schedule, which can optimize both of these objectives while keeping the dependencies between tasks intact is a real challenge. In this work, a non-dominated sorting based particle swarm optimization approach to find an optimal schedule …for workflow applications in cloud computing systems is proposed. A graph is used to represent tasks in the workflow and the dependencies among tasks. The optimization problem is modelled using integer programming formulation, subject to capacity and dependency constraints among tasks and Virtual Machines (VM). Simulation studies and result comparison with other representative algorithms in the literature shows that the proposed algorithm is promising. Show more

Keywords: Cloud computing, workflow scheduling, non-dominated sorting, particle swarm optimization, pareto-optimality

DOI: 10.3233/JIFS-190355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6801-6813, 2019

Authors: Naz, Farah | Kamran, Muhammad | Mehmood, Waqar | Khan, Wilayat | Alkatheiri, Mohammed Saeed | Alghamdi, Ahmed S. | Alshdadi, Abdulrahman A.

Article Type: Research Article

Abstract: The figurative language involving sarcasm on social networks is evolving the way how the humans use computers to communicate. Consequently, artificial intelligence techniques are applied in various scenarios to make the social networking more intelligent - for instance, identification of figurative language. Identifying both literal and non-literal meaning is not easy for a machine and it is hard even for people. Therefore, novel and exact frameworks ready to identify figurative languages are important. In sarcasm detection, this is even more challenging because sarcasm changes the polarity of an evidently positive or negative expression into its inverse. To maintain a …distance for a sarcastic message being comprehended in its unintended actual meaning, in micro-blogging sites, for example messages on Twitter, sarcasm is frequently set apart with a hashtag for example, ’#sarcastic’, '#sarcasm', ’#not’ etc. Moreover, the customer reviews may also contain some element of sarcasm. To contribute to this area, we gathered the data of tweets and reviews from Twitter, thesarcasmdetector.com, and Kaggle and proposed a mechanism for detecting sarcasm automatically using a classifier. A detailed experimental study was also conducted to evaluate the proposed mechanism. The results of this study were quite promising and proved the effectiveness of our approach. Show more

Keywords: Computational semantics, sarcasm detection, intelligent social networking, understanding uncertainty

DOI: 10.3233/JIFS-190596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6815-6828, 2019