Journal of Intelligent & Fuzzy Systems - Volume 39, issue 4

Authors: Jin, Zhi | Ge, Dong-Yuan

Article Type: Research Article

Abstract: Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment …have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle. Show more

Keywords: Intelligent vehicle, monocular vision, driving safety information detection, vehicle characteristics

DOI: 10.3233/JIFS-179987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5017-5026, 2020

Authors: Lu, You | Fu, Qiming | Xi, Xuefeng | Chen, Zhenping

Article Type: Research Article

Abstract: Data outsourcing has gradually become a mainstream solution, but once data is outsourced, data owners will without the control of the data hardware, there is a possibility that the integrity of the data will be destroyed objectively. Many current studies have achieved low network overhead cloud data set verification by designing algorithmic structures (e.g., hashing, Merkel verification trees); however, cloud service providers may not recognize the incompleteness of cloud data to avoid liability or business factors fact. There is a need to build a secure, reliable, non-tamperable, and non-forgeable verification system for accountability. Blockchain is a chain-like data structure constructed …by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. This paper uses the Hadoop framework to implement data collection and storage of the HBase system based on big data architecture. In summary, based on the research of blockchain cloud data collection and storage technology, based on the existing big data storage middleware, a large flow, high concurrency and high availability data collection and processing system has been realized. Show more

Keywords: Blockchain, data acquisition, data processing

DOI: 10.3233/JIFS-179988

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5027-5036, 2020

Authors: Wei, Peng-Cheng | He, Fangcheng | Li, Jing

Article Type: Research Article

Abstract: Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper …designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance. Show more

Keywords: Keywords: Sequence image, fast detection of moving objects, Gamma change, SIFT algorithm

DOI: 10.3233/JIFS-179989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5037-5044, 2020

Authors: Gao, Yu | Pan, Yinsong | Huang, Hong | Mohamed, Ehab R. | Aly, Zahraa M.I.

Article Type: Research Article

Abstract: Hyperspectral remote sensing combines spectrum, ground space and images organically to provide humans with unprecedented rich information. However, a prominent problem faced in the extraction and identification of hyperspectral remote sensing information is mixed pixels, and the method to solve mixed pixels is mixed pixel decomposition. The purpose of this paper is to study the swarm intelligence algorithm of spatial-spectral feature extraction and mixed pixel decomposition of hyperspectral remote sensing images. This paper first introduces two different methods for extracting spatial spectrum features, then studies linear and non-linear spectral hybrid models, and then studies end element extraction methods based on …quantum particle swarm optimization. The degree inversion method, the experimental part is based on the accuracy of the quantum particle swarm optimization-based end-element extraction method and two spatial-spectrum feature extraction methods. The experimental results show that the algorithm proposed in this paper improves the effect of group pixel decomposition based on the swarm intelligence algorithm. The classification accuracy of the 3DLBP spatial spectrum feature proposed in this paper is 94.22%. Show more

Keywords: Hyperspectral remote sensing image, spatial spectral feature extraction, mixed pixel decomposition, swarm intelligence algorithm, abundance inversion

DOI: 10.3233/JIFS-179990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5045-5055, 2020

Authors: Bi, Shengqin

Article Type: Research Article

Abstract: In the process of globalization, machine translation has undergone a long period of evolution and development. Although the development level of machine translation has been greatly improved, the quality of machine translation is still not very high, and it is difficult to meet the needs of users. Artificial intelligence is the science that studies the laws of human intelligent activity. The application of artificial intelligence technology in the English depression and depression, combined with the Internet and intelligent knowledge base, can develop English translation systems to solve the problem of English translation to a certain extent. Based on the above …background, the research content of this article is a neural network-based artificial intelligence technology English translation system based on the intelligent knowledge base. This article is mainly based on the existing English-Chinese machine translation to find a more favorable method for English long sentence translation. By improving part-of-speech tagging and rules, the rules can match more sentence patterns to improve the quality of existing machine translations. This paper proposes an improved hybrid recommendation algorithm, and through experimental simulation, the results show that the accuracy of the algorithm is not very high. The highest is 35.64%. The possible reason may be that the k value is selected during k-means text clustering, or the N value recommended by TopN is not selected properly, but the hybrid recommendation is still better than ordinary collaborative filtering. Show more

Keywords: Intelligent knowledge base, neural network, artificial intelligence, English translation system, hybrid recommendation algorithm

DOI: 10.3233/JIFS-179991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5057-5066, 2020

Authors: Wu, Chunqiong | Yu, Rongrui | Yan, Bingwen | Huang, Zhangshu | Yu, Baoqin | Yu, Yanliang | Chen, Na | Zhou, Xiukao

Article Type: Research Article

Abstract: The IoT and Artificial intelligence, the amount of information generated on the Web site is increasing. The rise of the Hadoop distributed cloud computing platform (HDCCP) makes it possible to use multiple computing nodes for parallel computing to solve the performance problems of traditional serial algorithms. The purpose of this paper is to study data design based on cloud computing and improved k-means algorithm (KMA). This paper deeply researches Hadoop distributed cloud computing platform and clustering algorithm and other related technologies, and designs and implements a cluster analysis system (CAS) based on HP. And through an in-depth analysis of the …problems existing in the KMA, an improved scheme based on the HDP is designed. The experimental environment was conFig.d with the cluster analysis system implemented. Finally, the improved KMPA was tested experimentally from four directions: convergence speed, acceleration ratio, initialization sampling rate, and accuracy rate. We can see the experimental results that the CAS based on the HDCCP designed in this paper can provide efficient and configurable cluster analysis services. In this paper, the correct rate is 90.7%. Show more

Keywords: Cloud computing, k-Means algorithm, cluster analysis, hadoop platform, data design

DOI: 10.3233/JIFS-179992

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5067-5074, 2020

Authors: Li, Yan | Hu, Miao | Wang, Taiyong

Article Type: Research Article

Abstract: Welding is an important method for modern material processing. In actual processing, due to the influence of processing accuracy and welding thermal deformation, various defects often appear in the appearance of the weld. At present, visual inspection is mainly used for the appearance inspection of welds. The detection of weld defects mainly depends on the work experience of the staff. Based on the above background, the purpose of this article is to visually inspect the weld surface quality. This article uses visually obtained fringe images of weld contours as information sources to explore a visual-based weld appearance detection algorithm, including …the measurement of weld formation dimensions and the detection of weld appearance defects. This algorithm overcomes manual measurements of the misjudgments and omissions caused by eye fatigue and experience differences. It improves the efficiency and accuracy of welding appearance inspection, and meets the needs of automation and intelligence of the entire welding process. In this paper, a subpixel stripe centerline extraction algorithm based on the combination of the Hessian matrix method and the center of gravity method is used; to further improve the accuracy of the extraction of the centerline of the weld seam, this article also performs the work of removing the wrong points and the compensation of the broken seam. Obtain a fringe centerline with better connectivity. Comparing the extraction algorithms of each centerline, the centerline obtained by this method has high accuracy, less time-consuming and high stability. It laid the foundation for the subsequent inspection of weld appearance. Through the training of the model, the accurate classification and recognition of surface defects of tube and plate welds have been achieved. The experimental results show that the improved vision-based welding surface defect recognition and classification proposed in this paper has better performance and accuracy. Up to 96.34%. Show more

Keywords: Weld quality inspection, machine vision, weld forming size, surface reconstruction, visual inspection

DOI: 10.3233/JIFS-179993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5075-5084, 2020

Authors: Wu, Yanhong | Dai, Xiuqing

Article Type: Research Article

Abstract: The more and more developed network has caused more and more impact on people’s life and work, providing convenient channels for people’s information exchange, and then improving people’s living and working conditions. However, when data is transmitted through the network, there are hidden security risks, especially important accounting data. Once intercepted and used by criminals, it may cause serious harm to the owner of the data. Based on the above background, the purpose of this article is to study the use of the DES algorithm to encrypt accounting data in a computing environment. This paper proposes an improved quantum genetic …algorithm and applies it to the S-box design of the DES algorithm, which improves the non-linearity of the S-box, reduces the differential uniformity, and enhances the security of the DES algorithm. This improved DES algorithm reduces the number of iterations by increasing the key length and iterative processing using a two-round function, which further increases the security of the algorithm and improves the operation speed of the encryption process. It is found that the 64 ciphertexts of the DES algorithm and the number of changed bits compared to the original ciphertext fluctuates around 32 bits, which explains the problems that should be paid attention to when using the DES algorithm to encrypt accounting data. The validity of key characters should be guaranteed to prevent key loss or leakage. Shorter data encryption regular solution. Show more

Keywords: DES algorithm, accounting data, data encryption, encryption algorithm

DOI: 10.3233/JIFS-179994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5085-5095, 2020

Authors: Zhang, Long | Liu, Leyi | Chai, Bailong | Xu, Man | Song, Yuhong

Article Type: Research Article

Abstract: Cultural landscapes are cultural property and they are an illustration of the evolution of human society and the living environment over time. As cultural landscape is being valued more and more, the use of 3D modeling is becoming more and more important. As for the 3D reconstruction technology, most of the current methods are complicated in terms of network construction, use, and storage, and then affect the reconstruction efficiency of subsequent cultural landscape heritage. To obtain the 3D reconstruction technology with high reconstruction efficiency, this paper combines the circumferential binary feature extraction algorithm and cloud computing technology, and proposes a …circumferential binary feature extraction and matching search method. The interior-point rate of the CBD algorithm in this paper is greater than 72%, which is higher than the interior point rate of other different algorithms, which indicates that the CBD algorithm in this paper is suitable for matching HD rotated images. The experimental results show that the circular binary features extracted by the article have strong adaptability and fast contrast rate. To better the 3D reconstruction of cultural landscape heritage in the later period, this paper also improves the 4PSC point cloud rough registration algorithm. The experimental results show that compared with other coarse registration algorithms, the improved point cloud coarse registration algorithm improves the registration accuracy and the registration effect is good, which proves the effectiveness of the algorithm. Show more

Keywords: Cloud computing technology, 3D reconstruction, circular binary feature extraction, high-definition image data, point cloud coarse registration, cultural landscape

DOI: 10.3233/JIFS-179995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5097-5107, 2020

Authors: Zhang, Yubao

Article Type: Research Article

Abstract: The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large …number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness. Show more

Keywords: Big data, image feature extraction, corner point, brisk algorithm, sift algorithm

DOI: 10.3233/JIFS-179996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5109-5118, 2020

Authors: Chen, Feng | Wang, Chengyue

Article Type: Research Article

Abstract: The rapid development of computers makes people’s production and life rich and colorful, and people communicate with each other in the world of the Internet. The daily downloads and uploads of network pictures are countless. The existing image recognition technology alone cannot meet the currently required functions, so technology is needed to meet the retrieval requirements. The purpose of this paper is to study the image recognition technology based on the computer platform. This paper takes vehicle image recognition as an example. By performing a deblurring operation on the vehicle image, the edge detection method is used to separate the …target vehicle image from the background, and the image is binary. Processing. Based on different eigenvalue categories, intelligent recognition of vehicle models is achieved through Bayesian classifiers. Collect experimental data through simulation experiments. Experimental data shows that after a certain number of nodes, the recognition efficiency is higher than the image recognition technology running on a stand-alone platform. The experimental data show that the image recognition technology based on a cloud computing platform is conducive to the development of image recognition technology. It can quickly solve the problems of traditional image detection systems in terms of computing efficiency and data processing ability, and has guiding significance for the development of image recognition technology. Show more

Keywords: Cloud computer, image recognition, edge detection method, recognition efficiency

DOI: 10.3233/JIFS-179997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5119-5129, 2020

Authors: Qi, Wanqiang

Article Type: Research Article

Abstract: The main reason that currently hinders the commercialization of electric vehicles is a bottleneck in battery, motor and electronic control technology, however, an In-depth study of electronic control technology is one of the most effective means to break through this bottleneck at present. The purpose of this paper is to solve the problem that the pure electric vehicle is difficult to meet the driver’s acceleration intention in the urban road cycle acceleration work condition and the brake energy recovery process does not consider the battery state of charge during the deceleration work condition. Proposed a control strategy that can meet …the requirements of road cycle conditions and driver’s driving intentions and take account of the vehicle operating status. Use a fuzzy control algorithm to develop a fuzzy controller that taking the motor demand speed change rate and battery state of charge as input, the motor demand torque compensation coefficient as output. The experimental results show that the modified control strategy can improve the actual output power, the actual output torque of the motor and actual driving force of the wheel under the premise of maintaining economy; it also improved the acceleration performance and climbing performance of pure electric vehicles, and can recycle braking energy efficiently. The experimental results show that the secondary development control strategy can meet the requirements of the cycle work condition CYC_ECE_EUDC for the speed and driving force and the driver’s driving intention under the premise of not sacrificing economics. Show more

Keywords: Pure electric vehicle, driving intention, fuzzy control strategy, driver PID model

DOI: 10.3233/JIFS-179998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5131-5139, 2020

Authors: Li, Yingjie | Chen, Lianjun

Article Type: Research Article

Abstract: To reduce the resource and energy waste of colleges and universities more accurately and efficiently, this paper has developed a smart classroom data analysis system based on the Internet of Things, which realizes a variety of sensor information (temperature, humidity, smoke). Environmental parameters such as carbon dioxide concentration and light intensity), remote collection of equipment information, data storage and data analysis functions, and intelligent control of smart classrooms. Data analysis uses an improved LSTM model to predict energy consumption. The model uses LSTM and bidirectional LSTM and uses the ELU activation function instead of the sigmoid and tanh activation functions …of the LSTM. Compared with the standard LSTM model and the LSTM model without the ELU activation function, the model improves the prediction accuracy, better avoid the gradient disappearance, and reduces the over-fitting. The system implementation results show that the system can effectively reduce school energy waste. Show more

Keywords: Internet of things, smart classroom, data analysis, Bi-LSTM

DOI: 10.3233/JIFS-179999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5141-5148, 2020

Authors: Liu, Kainan | Zhang, Meiyun | Hassan, Mohammed K.

Article Type: Research Article

Abstract: To monitor the scene anomaly in real-time through video and image and identify the emergencies, try to respond quickly at the beginning of the emergency and reduce the loss. This paper mainly focuses on the realization of the image recognition system for the anomalous characteristics of tourism emergencies. The problem is to study the number of people in the scenic spot based on scenic spot monitoring. The video-based population anomaly monitoring method has improved the AUC index of the W-SFM method by 0.423, and the AUC has increased by 0.0844 compared with the optical flow method; Degree-enhanced algorithm (BCOF), by …grasping the micro-blog data related to the scenic spot, comprehensively predicts the overall comfort of the current tourists in the scenic spot, and establishes a tourist state expression model. Compared with the BN algorithm and the NEG algorithm, the BCOF algorithm is the accuracy and the recall rate of tourists in the scenic spots was improved by 14% and 18% respectively. The image recognition system of tourism emergency anomaly was established, and the early warning model of tourism emergency based on group intelligence perception was used to implement early warning on scenic spots. Monitoring, can achieve an overall accuracy of 83.33%, the model has a strong predictive ability, and achieves a scenic spot Real-time monitoring of events. Show more

Keywords: Tourist scenic spot, image recognition, video recognition, emotional comfort, crowd anomaly monitoring, early warning model

DOI: 10.3233/JIFS-189000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5149-5159, 2020

Authors: He, Zhenxiang | Li, Zhenjiang | Zhang, Shengcai | Lu, Jun

Article Type: Research Article

Abstract: In cloud data centers, different virtual machine placement strategies will affect the resource utilization efficiency of the whole system. How to improve the overall resource utilization of the system by adjusting the virtual machine placement strategy under limited resources is the focus of current researchers. In this paper, a new virtual machine placement strategy is proposed, which is based on the comprehensive constraints of various system resources. In the process of solving the problem, a multi-objective ant colony optimization algorithm is used. Simulation results show that this method can effectively reduce the number of physical machines activated in the data …center, and the accuracy is high. Show more

Keywords: Cloud data center, virtual machine placement, multiple resources constraints, ant colony optimization

DOI: 10.3233/JIFS-189001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5161-5170, 2020

Authors: Xie, Chao | Xiao, Xiaoyong | Hassan, Dina K.

Article Type: Research Article

Abstract: Social media has accumulated a large number of users by its community, which has greatly changed and affected people’s lifestyles. Social media not only provides convenience for users to make friends, entertainment, information acquisition and other activities, but also provides an ideal way for the development of e-commerce with the advantages of fast transmission speed and accurate audience. The content and behavior of social e-commerce platforms are mostly generated and dominated by users, who are the key subjects that determine the development of platforms and the profitability of enterprises. The main purpose of this study is to enrich the theoretical …system of data mining for social e-commerce users and provide a theoretical basis and reference for platform and business management and operation of social e-commerce. First, based on the information ecology and information dissemination perspective, this paper constructs the model of information flow in social e-commerce. Second, based on the social network analysis method, analyzes the social network of social e-commerce users; Finally, based on the integrated model of technology acceptance and use (UTAUT), the theory of perceived risk and the theory of trust, the conceptual model of influencing factors of initial information adoption by users of social e-commerce is constructed, and the key influencing factors are identified by using Delphi method and DEMATEL method. The experimental results show that the degree of centrality of the new technology application is the largest, 5.250, which is the key factor influencing the initial information adoption of social e-commerce users. User satisfaction has the largest influence on the continuous information adoption intention of social e-commerce users, with the influence factor reaching 1.223, followed by IT self-efficacy (0.948), user relationship network structure (0.771), social e-commerce platform quality (0.637), perceived usefulness (0.419) and emotional attachment intensity (0.409). Show more

Keywords: Internet of things and big data, social e-commerce, data mining, user data

DOI: 10.3233/JIFS-189002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5171-5181, 2020

Authors: Fan, Mingming | Li, Yunsong

Article Type: Research Article

Abstract: The purpose of this paper is to improve the existing computer graphics image processing technology, so that designers can produce more inspiration, improve the author’s ability to innovate. Based on the information in the field of graphics visual communication as the research object, through the elaboration of graphical information characteristics, development course, and the visual communication of computer graphical related, such as cognitive psychology, semiology theory research, analyzes the computer graphics into a kind of economic and effective way of conveying information, the significance of interface design for mobile media. Experiments demonstrate the unique advantages of graphics in the process …of information transmission. In 2022, the market size of computer graphics and vision will expand to 755.5 million RMB. It can be known that the communication mode integrating information and graphics, as the future development trend, will also be applied to more fields and play a greater role. Show more

Keywords: Computer graphics, graphics processing technology, visual communication, graphics design

DOI: 10.3233/JIFS-189003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5183-5191, 2020

Authors: Zhang, Shiyi | Zhang, Laigang | Zhao, Teng | Selim, Mahmoud Mohamed

Article Type: Research Article

Abstract: Aiming at the characteristics of time-frequency analysis of unsteady vibration signals, this paper proposes a method based on time-frequency image feature extraction, which combines non-downsampling contour wave transform and local binary mode LBP (Local Binary Pattern) to extract the features of time-frequency image faults. SVM is used for classification and recognition. Finally, the method is verified by simulation data. The results show that the classification accuracy of the method reaches 98.33%, and the extracted texture features are relatively stable. Also, the method is compared with the other 3 feature extraction methods. The results also show that the classification effect of …the method is better than that of the traditional feature extraction method. Show more

Keywords: Time-frequency image, rotating machinery, fault diagnosis

DOI: 10.3233/JIFS-189004

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5193-5200, 2020

Authors: Liu, Bingjie | Zhu, Li | Ren, Jianlan

Article Type: Research Article

Abstract: Optimization algorithms have been rapidly promoted and applied in many engineering fields, such as system control, artificial intelligence, pattern recognition, computer engineering, etc.; achieving optimization in the production process has an important role in improving production efficiency and efficiency and saving resources. At the same time, the theoretical research of optimization methods also plays an important role in improving the performance of the algorithm, widening the application field of the algorithm, and improving the algorithm system. Based on the above background, the purpose of this paper is to apply the intelligent optimization algorithm based on grid technology platform to research. …This article first briefly introduced the grid computing platform and optimization algorithms; then, through the two application examples of the TSP problem and the Hammerstein model recognition problem, the common intelligent optimization algorithms are introduced in detail. Introduction: Algorithm description, algorithm implementation, case analysis, algorithm evaluation and algorithm improvement. This paper also applies the GDE algorithm to solve the reactive power optimization problems of the IEEE14 node, IEEE30 node and IEEE57 node. The experimental results show that the minimum network loss of the three systems obtained by the GDE algorithm is 12.348161, 16.348152, and 23.645213, indicating that the GDE algorithm is an effective algorithm for solving the reactive power optimization problem of power systems. Show more

Keywords: Grid computing platform, intelligent optimization algorithm, TSP problem, hammerstein model, simulated annealing algorithm

DOI: 10.3233/JIFS-189005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5201-5211, 2020

Authors: Wang, Guangtong | Miao, Jianchun

Article Type: Research Article

Abstract: The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a “barometer” of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market …anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market. Show more

Keywords: US stock market, data mining algorithm, outlier detection method, rough entropy

DOI: 10.3233/JIFS-189006

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5213-5221, 2020

Authors: Chen, Xuanjun | Metawa, N

Article Type: Research Article

Abstract: Cloud computing technology has the characteristics of low investment costs, strong reliability, flexible expansion, and on-demand services, which can greatly reduce the application threshold of enterprise financial informatization construction, improve the return on investment of informationization, and flexibly adapt to the needs of different stages of business. To solve the problem of enterprise financial management information system based on cloud computing in big data environment. This article proposes the management concept of “business-driven value” as an expense management system. Through the investigation of the company in this article, after 9 years of construction, the number of property in each subsidiary …has dropped from an average of 23 people per company to 3.5 people per subsidiary before. Reduced by about 84.7%. Contracted human capital for the company. Compared with the 23 billion yuan in 2010, after the implementation of financial shared services, after 9 years of development, it has now reached 68.55 billion yuan, nearly three times more than before. Show more

Keywords: Cloud computing, financial management, enterprise informatization, big data

DOI: 10.3233/JIFS-189007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5223-5232, 2020

Authors: Liu, Ran | Liu, Pingfeng | Zhang, Wang | Metawee, Ahmed K.

Article Type: Research Article

Abstract: The objective of this study is to promote the structural optimization of the banking industry and improve the national economic level. The analysis method based on the co-integration test is adopted to study the relationship between market structure optimization and economic growth in the banking industry. Firstly, the current economic growth condition, development trend, and the development of the banking industry are analyzed. Secondly, the model between the bank market institutions and the economy is constructed, and the data source of the model is analyzed. Thirdly, the stationarity test, co-integration test, and regression analysis of the studied data are carried …out based on the co-integration test. The results show that there is a significant negative correlation between the concentration of banks and the overall economy, and there is a significant negative correlation between the market structure of banks and the downgrading growth of various industries. Also, the variables of social material input level and human capital input have a significant positive correlation with the economy. It is hoped that the results of this study can provide a good guiding significance for China’s economic development. Show more

Keywords: Banking market, economic growth, stationary analysis, co-integration test, regression analysis

DOI: 10.3233/JIFS-189008

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5233-5242, 2020

Authors: Lei, Zhen | Zhu, Liang | Fang, Youliang | Li, Xiaolei | Liu, Beizhan

Article Type: Research Article

Abstract: Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the …abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data. Show more

Keywords: Artificial intelligence, bridge health monitoring, data anomaly detection, KNN algorithm, multivariate time series

DOI: 10.3233/JIFS-189009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5243-5252, 2020

Authors: Zhang, Xiaoxian | Zhang, Jianpei | Yang, Jing

Article Type: Research Article

Abstract: The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network …nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal. Show more

Keywords: Feature learning, social network, representation learning, neural network

DOI: 10.3233/JIFS-189010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5253-5262, 2020

Authors: Shang, Kun

Article Type: Research Article

Abstract: In the process of informatization, there are also some new problems, mainly information can’t be shared and integrated, distributed resources can’t be used effectively, these problems make the industry face new challenges. The goal of this paper is to combine the grid technology and ontology organically, to build a unified information system integration and interoperation platform based on semantics, to realize information sharing and accelerate the pace of informatization. The method is to construct the whole structure of the system according to the actual needs of the system. This paper firstly analyzes the current research status and existing problems of …semantic grid service matching, and proposes a semantic layered matching algorithm based on Massimo Paolucci elastic matching algorithm. To verify the feasibility and effectiveness of the hierarchical matching algorithm based on semantics, a prototype system named SGSM was designed and its functional model, matching process and performance were studied. Experimental results show that for the semantic-based hierarchical matching algorithm proposed in this paper, the threshold value of service semantic correlation degree is 0.84, the threshold value of service basic concept matching degree is 0.89, the threshold value of service comprehensive similarity degree is 0.66, and the threshold value of service quality matching degree is 0.78. Statistics through the experiment, the above three methods of recall, respectively, 33%, 62%, 85%, the precision is respectively: 29%, 57%, 88%, and illustrate the hierarchical matching algorithm based on semantic is feasible in practical application, compared with the traditional service based on keyword matching algorithm and Massimo Paolucci elastic matching algorithm on the recall and precision are improved significantly. Show more

Keywords: Grid environment, semantic research, web services, service discovery

DOI: 10.3233/JIFS-189011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5263-5272, 2020

Authors: Li, Zhancang

Article Type: Research Article

Abstract: The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter …bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method. Show more

Keywords: Image analysis, text recognition, image separation

DOI: 10.3233/JIFS-189012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5273-5281, 2020

Authors: Nie, Zhongchun | Tao, Weijun | Huan, Shi

Article Type: Research Article

Abstract: Nowadays, urbanization has become a trend, and the realization of urbanization cannot be separated from the implementation of various projects. In the process of project implementation, the most critical issue is safety, so it is extremely necessary to monitor the project safety. Traditional manual monitoring cannot meet the development of today’s project, and the design of an automatic monitoring system for project safety has become a hot spot. In this paper, based on image processing and monitoring technology, and engineering safety monitoring and control system based on image quality analysis is studied, which can detect the engineering safety in real-time. …Firstly, the image acquisition equipment is used to collect engineering images, and image processing is carried out to improve the image quality. Secondly, the convolutional neural network is used to realize image security analysis and detect the unsafe risk in engineering. Finally, combined with network technology, the automatic monitoring and control system of engineering safety based on image quality analysis is realized. Through simulation analysis, it is found that image processing can effectively remove noise and other interference and improve image quality. And the convolutional neural network can effectively detect the safety problems in the project, which shows that the design and implementation of the project safety monitoring and control system, it can achieve real-time safety monitoring in the implementation of the project, and has a good application effect in the project safety monitoring. Show more

Keywords: Engineering safety, image quality analysis, convolutional neural network, monitoring and control system

DOI: 10.3233/JIFS-189013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5283-5290, 2020

Authors: Duan, Zhimei | Yuan, Xiaojin | Zhu, Rongfei

Article Type: Research Article

Abstract: Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above …problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand. Show more

Keywords: Energy big data, fuzzy rough set, demand prediction, structure optimization

DOI: 10.3233/JIFS-189014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5291-5300, 2020

Authors: Li, Feng

Article Type: Research Article

Abstract: The diameter and distance parameters of a network play very significant roles in analyzing the efficiency of a communication network, these parameters provide some efficient ways to measure information time delay in communication networks. We use the lexicographic product method to construct a larger network model, which is called the lexicographic product network by some specified small graphs. Network models based on the lexicographic product method contain these small graphs as sub-networks, and many desirable properties of these sub-networks are preserved. By using algebra graph theory, we investigated the diameter parameters of the lexicographic product network, and established an enumeration …formula which only depends on the parameters of sub-networks. By analyzing the diameter formula and comparing it with other network models, it is proved that the lexicographic product network has a smaller time delay. Show more

Keywords: Communication network, transmission delay, lexicographic product, maximum distance, graph

DOI: 10.3233/JIFS-189015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5301-5309, 2020

Authors: Hu, Zhengquan | Liu, Yu | Niu, Xiaowei | Lei, Guoping

Article Type: Research Article

Abstract: As aerospace technology, computer technology, network communication technology and information technology become more and more perfect, a variety of sensors for measurement and remote sensing are constantly emerging, and the ability to acquire remote sensing data is also continuously enhanced. Synthetic Aperture Radar Interferometry (InSAR) technology greatly expands the function and application field of imaging radar. Differential InSAR (DInSAR) developed based on InSAR technology has the advantages of high precision and all-weather compared with traditional measurement methods. However, DInSAR-based deformation monitoring is susceptible to spatiotemporal coherence, orbital errors, atmospheric delays, and elevation errors. Since phase noise is the main error …of InSAR, to determine the appropriate filtering parameters, an iterative adaptive filtering method for interferogram is proposed. For the limitation of conventional DInSAR, to improve the accuracy of deformation monitoring as much as possible, this paper proposes a deformation modeling based on ridge estimation and regularization as a constraint condition, and introduces a variance component estimation to optimize the deformation results. The simulation experiment of the iterative adaptive filtering method and the deformation modeling proposed in this paper shows that the deformation information extraction method based on differential synthetic aperture radar has high precision and feasibility. Show more

Keywords: InSAR, DInSAR, deformation monitoring, variance component estimation

DOI: 10.3233/JIFS-189016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5311-5318, 2020

Authors: Lei, Feng | Yu, You | Zhang, Daijun | Feng, Li | Guo, Jinsong | Zhang, Yong | Fang, Fang

Article Type: Research Article

Abstract: In recent years, with the rapid development of satellite technology, remote sensing inversion has been used as an important part of environmental monitoring. Remote sensing inversion has been prepared for large-scale water environment monitoring in the watershed that is difficult for the traditional water environment monitoring methods. This paper will discuss some shortcomings of traditional remote sensing inversion methods, and proposes a remote sensing inversion method based on convolutional neural network, which realizes large-scale remote sensing smart and automatic inversion monitoring of the water environment. The results show that the method is practical and effective, and can achieve high recognition …accuracy for water blooms. Show more

Keywords: Water quality monitoring, water environment, data mining, deep learning, remote sensing; artificial intelligence

DOI: 10.3233/JIFS-189017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5319-5327, 2020

Authors: Zheng, Yan | Luo, Qiang | Wang, Haibao | Wang, Changhong | Chen, Xin

Article Type: Research Article

Abstract: The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.

Keywords: Mobile robot, path planning, ant colony algorithm, heuristic information, global optimization

DOI: 10.3233/JIFS-189018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5329-5338, 2020

Authors: He, Han | Hong, Yuanyuan | Liu, Weiwei | Kim, Sung-A

Article Type: Research Article

Abstract: At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of …data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field. Show more

Keywords: Information entropy, data mining, financial time series, clustering, multimedia

DOI: 10.3233/JIFS-189019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5339-5345, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5347-5347, 2020

Authors: Kolivand, Hoshang | Balas, Valentina E. | Paul, Anand | Ramachandran, Varatharajan

Article Type: Editorial

Abstract: This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of computational human performance modelling for human-in-the-loop machine systems.

Keywords: Computational Intelligence, Human automation, cyber physical system, Artificial Intelligence, System design

DOI: 10.3233/JIFS-189020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5349-5357, 2020

Authors: Ratna Raju, B | Swamy, G.N | Padma Raju, K.

Article Type: Research Article

Abstract: The Colorectal cancer leads to more number of death in recent years. The diagnosis of Colorectal cancer as early is safe to treat the patient. To identify and treat this type of cancer, Colonoscopy is applied commonly. The feature selection based methods are proposed which helps to choose the subset variables and to attain better prediction. An Imperialist Competitive Algorithm (ICA) is proposed which helps to select features in identification of colon cancer and its treatment. Also K-Nearest Neighbor (KNN) classifier is used to retain a minimal Euclidean distance between the feature of query vector and all the data in …the nature of prototype training. Experimental results have proved that the proposed method is superior when compared to other methods in its metrics of performance. Better accuracy is achieved by the proposed method. Show more

Keywords: Colorectal cancer (CRC), Colonoscopy, feature selection, imperialist competitive algorithm (ICA), k-Nearest Neighbor (k-NN) classifier

DOI: 10.3233/JIFS-189021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5359-5368, 2020

Authors: Lee, Hoyoung

Article Type: Research Article

Abstract: Korean banking industry has achieved significant growth in financial market, however, these banks are lacking with entrepreneurship activities due to low information system risk management. Objective of this study is to examine the effect of artificial intelligence, information system risk management and corporate entrepreneurship on business performance of Korean banks. The current study introduced artificial intelligence as one of the elements to boost risk management activities, corporate entrepreneurship and business performance. This objective was achieved through a research survey among Korean banks. Questionnaires were distributed among the employees of banks by using simple random sampling. Partial Least Square (PLS)-Structural Equation …Modeling (SEM) was used for data analysis. Results of the study revealed that artificial intelligence has key role to influence information system risk management. It has positive role to enhance information system risk management practices. Information system risk management practices has vital importance to promote corporate entrepreneurship which increases the business performance of banks. This study is important for Korean banks to make various strategies for risk management, corporate entrepreneurship and business performance. Show more

Keywords: Artificial intelligence, information system, risk management, corporate entrepreneurship, business performance

DOI: 10.3233/JIFS-189022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5369-5386, 2020

Authors: Hussain, Hafezali Iqbal | Anwar, Nazratul Aina Mohamad | Razimi, Mohd Shahril Ahmad

Article Type: Research Article

Abstract: The current study looks at the impact of compliance to Shari’ah principles on the capital structure for Malaysian firms. Examination of impact of compliance is based on the classification by the Securities Commission of Malaysia. Given that the literature on adjustment tends to ignore non-linear models, the current study utilises Generalised Regression Neural Network (GRNNs). Results are compared to conventional panel data regression models via performing a hold-out sample. Initial results confirm stability of the data allowing predictive ability. The results indicate that compliant firms tend to finance a greater portion of their financing imbalance via equities relative to …non-compliant firms. This provides a strong indication towards compliant firms reducing overall risk taking where the financing pattern incorporates a greater aspect of risk sharing which is in-line with Shari’ah principles. In addition, two more factors are ranked as important in deciding compliant firms issue choice to resolve financial imbalance: profitability and size. The rest of the determinants have low impact on explaining net debt issues. Diagnostics for results provide evidence of lower RMSE and MSE for GRNNs for the training, testing and overall datasets. The potential benefit of this research allows managers and investors of Islamic capital markets to understand potential risk exposure and financing costs of compliant firms. Findings also provide a roadmap for development of a sustainable capital market model which has wider implications on a global scale. Show more

Keywords: Capital structure, generalised regression neural networks, Islamic finance, Islamic capital markets, sustainable capital markets

DOI: 10.3233/JIFS-189023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5387-5395, 2020

Authors: Mihardjo, Leonardus W. Wasono | Djoemadi, Faizal R. | Sasmoko, | Rukmana, Riza AN

Article Type: Research Article

Abstract: In the field of Industry 4.0, various studies considered the engineering industries, however, the limitations of Internet of things (IoT) implementation is not discussed by these studies and literature missed the crucial challenges of IoT implementation, particularly in the Indonesian engineering companies. Therefore, the objective of the current study is to investigate the factors which limits the implementation of IoT. Additionally, the moderating role of employee innovative capabilities is also considered. A survey was carried out in the Indonesian engineering companies for data collection. Data collection was carried out form the employees of these companies. After data collection, structural equation …modeling (SEM) was used to analyse the data with the help of Partial Least Square (PLS). Findings of the study demonstrated that there are five key factors which effect the IoT implementation. These factors include; willingness to use, hardware characteristics, replacement of existing technology, failure threat and implementation cost. All these factors have the ability to influence IoT implementation. Additionally, employee innovative capability also has the ability to encourage or discourage the IoT implementation. Therefore, this study is most significant for the engineering industry of Indonesia as it provides the influencing factors towards IoT implementation. Show more

Keywords: Internet of things (IoT), implementation, Industry 4.0, innovative capabilities, willingness to use

DOI: 10.3233/JIFS-189024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5397-5406, 2020

Authors: Sivaram, Murugan | Batri, K. | Mohammed, Amin Salih | Porkodi, V. | Kousik, N.V.

Article Type: Research Article

Abstract: This article explores the odd and even point crossover based Tabu Genetic Algorithm. The search optimization tools equipped with exploration and exploitation operators. Those operators assist the optimization tools for finding the optimal solution. Few problems demand vigorous exploration and minimal exploitation. The vigorous exploration needs some specialized operators, which is capable of carrying out the task. In this article, we explore one such possible operator using odd and even point (OEP) crossover. The resultant hybrid GA namely OEP crossover based Tabu GA has two tuning factors namely tenure period and OEP crossover probability (Podd). The tenure period may be …a single entity or a group of entities. However, Podd is single, as the tenure period is involved with group of entities, it demands some fine tuning. The fine tuning may alter the proportion of exploration and exploitation. Hence, we proposed a method for selecting the tenure period. The proposed exploration operator and the method for fixing the tenure period has been tested over the data fusion problem in information retrieval. The results look promising. Show more

Keywords: Tabu search, genetic algorithm, OEP crossover, Tabu GA, information retrieval, data fusion, tenure period

DOI: 10.3233/JIFS-189025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5407-5416, 2020

Authors: Khalid, Nadeem

Article Type: Research Article

Abstract: Artificial intelligence learning at higher educational institutions is one of the emerging concepts having vital importance to promote entrepreneurship activities among the university students. However, Malaysian Universities are lacking with the artificial intelligence learning activities. The objective of the study is to examine the role of artificial intelligence learning to promote entrepreneurship performance with the help of entrepreneurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. 500 questionnaires were distributed among the universities and …data were collected from the teaching staff. After collection of data, it was analysed with the help of Partial Least Square (PLS)-Structural Equation Modeling (SEM). It is concluded that artificial intelligence learning is most significant to promote entrepreneurial performance among university students. Entrepreneurial orientation and strategic entrepreneurship play a key role to transfer the positive effect of artificial intelligence learning on entrepreneurial performance. Additionally, government funding and attitude towards entrepreneurship also has significant role. Show more

Keywords: Artificial intelligence learning, higher educational institutions, strategic entrepreneurship, government funding, entrepreneurial orientation, performance, entrepreneurial attitude

DOI: 10.3233/JIFS-189026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5417-5435, 2020

Authors: Alhaidar, Abdul Rahman | Sikkandar, Mohamed Yacin | Alkathiry, Abdulaziz A.

Article Type: Research Article

Abstract: Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different …tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P  < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P  < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method. Show more

Keywords: Vertical ground reaction force, signals, semblance, continuous wavelet transform, k-means clustering

DOI: 10.3233/JIFS-189027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5437-5448, 2020

Authors: Arokiaraj Jovith, A. | Kasmir Raja, S.V. | Razia Sulthana, A.

Article Type: Research Article

Abstract: Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division …Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission. Show more

Keywords: Interference, link state protocol, point-to-point, time division multiple access

DOI: 10.3233/JIFS-189028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5449-5458, 2020

Authors: Somjai, Sudawan | Jermsittiparsert, Kittisak | Chankoson, Thitinan

Article Type: Research Article

Abstract: The adoption of AI is an ongoing phenomenon in today’s economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation …modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization. Show more

Keywords: AI adoption, initial-subsequent economic impact, efficiency, economic output, ASEAN

DOI: 10.3233/JIFS-189029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5459-5474, 2020

Authors: Sleaman, Walead Kaled | Yavuz, Sırma

Article Type: Research Article

Abstract: Robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be …increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation. Show more

Keywords: Deep reinforcement learning, autonomous agent, adaptive agent, autonomous exploration, control mobile robot, deep convolutional neural network

DOI: 10.3233/JIFS-189030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5475-5486, 2020

Authors: Daming, Li | Lianbing, Deng | Zhiming, Cai

Article Type: Research Article

Abstract: The sponge index is the core of the sponge city flood forecast. Whether the model is reasonable or not directly affects the final forecast result. The study of classification problems using neural network models is an important branch of the artificial neural network application field. The classification and pattern recognition functions can be used to achieve flood classification and sponge index monitoring. In this paper, the author analyze the evaluation method of sponge city potential based on neural network and fuzzy mathematical evaluation. After training, the BP neural network model can effectively evaluate the potential of the sponge city, and …based on the input of special information on rain conditions, it can analyze and calculate the flood risk level. It can be seen that this network model has a high mapping capability and can be correctly classified. Therefore, it is feasible to use BP neural network to solve the real-time classification of flood risk. The sponge city potential method and underground drainage system proposed in this paper can provide a reference for promoting sponge city construction. Show more

Keywords: Sponge indicator, monitoring and tracking, neural network algorithm, internet of things

DOI: 10.3233/JIFS-189031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5487-5498, 2020

Authors: Zhan, Wenjing | Chen, Yue

Article Type: Research Article

Abstract: Artificial intelligence speech recognition mostly judges the accuracy of grammar or sentence in the detection of pronunciation error, but has little research on pronunciation judgment, so it cannot effectively correct the pronunciation. This study analyzes the application of image target recognition in English learning task. Task-based approach emphasizes the process of English learning, not the result, the purposeful communication and meaning expression, encourages learners to open their mouths, and emphasizes that English language learning activities and their tasks are realistic in life. In addition, this paper introduces the DNN adaptive technique based on KL divergence regularization to adapt the acoustic …model. Finally, this paper uses the experimental contrast method to compare and analyze the algorithm of this research with the traditional algorithm. The research shows that the recognition ability of the algorithm for confusing phonemes is improved than that of traditional algorithms, and this conclusion provides a powerful result for the introduction of error correction algorithms into education networks. By using the platform of autonomous learning center, students can improve their English level by completing the tasks chosen by teachers or by themselves and through training. Show more

Keywords: Deep learning, image target recognition, DNN algorithm, English learning

DOI: 10.3233/JIFS-189032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5499-5510, 2020

Authors: Yuan, Qinying

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218 .

DOI: 10.3233/JIFS-189033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5511-5520, 2020

Authors: Liu, Ying | Fan, Zhongqi | Qi, Hongliang

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218 .

DOI: 10.3233/JIFS-189034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5521-5534, 2020

Authors: Cui, Jinying

Article Type: Research Article

Abstract: The corpus software has many functions, such as keyword retrieval, context co-occurrence, word list generation and word frequency statistics. It can quickly and accurately provide various corpus and information, such as word-formation collocation, context, word frequency and so on. In this paper, the author analyzes the application of deep learning and target visual detection in English vocabulary online teaching. Deep learning is a kind of machine learning algorithm which includes multi-layer non-linear mapping and tries to obtain high-level abstract representation of data. By extracting features from information, the identifiable components in the image can be extracted. The results show that …the application of corpus in College English vocabulary teaching can promote students’autonomous use of corpus in English vocabulary learning. The simulation experiment improves the performance of the system by choosing parameters, and the classification accuracy is more than 90%. Corpus can enable students to learn real and natural language and master natural collocation. At the same time, corpus can help students understand the semantic and pragmatic norms of words in communication and recognize the characteristics of register variants. Future research can use Map-reduce technology to accelerate the training process, save training time and test more hyperparameters. Show more

Keywords: Corpus, deep learning, target recognition, natural language algorithms, data simulation

DOI: 10.3233/JIFS-189035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5535-5545, 2020