Journal of Intelligent & Fuzzy Systems - Volume 43, issue 6

Authors: Lin, Liangcheng | Xu, Yonggang | Zhang, Yue | Kang, Chaoqun | Sun, Jian

Article Type: Research Article

Abstract: In order to ensure the safe transmission of the information of the secondary distribution system across the regional network, this paper studies a security monitoring method of the secondary distribution system across the regional network based on the Internet of things technology and the improved fuzzy clustering algorithm. The Internet of things technology is used to collect the information transmission in cross region network of the secondary power distribution system and store it in the database; Combined with the shadow set to improve the basic fuzzy C-means clustering algorithm, the improved fuzzy C-means clustering algorithm is obtained. The cross region …information transmission in the clustering database is divided into two categories: security and risk, and the risk information obtained by clustering is divided into four risk types, so as to realize the security monitoring of information transmission in cross region network of secondary power distribution system. The results show that the average monitoring rate of this method can reach 93.93%, the information collection is efficient and accurate, the number of packet losses is low, and the clustering results are stable and reliable, which can ensure the safe information transmission of cross region network of the secondary power distribution system. Show more

Keywords: Internet of things technology, fuzzy clustering algorithm, secondary power distribution system, network cross region transmission, security monitoring, cluster analysis

DOI: 10.3233/JIFS-221154

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7807-7819, 2022

Authors: Guo, Zhendong | Li, Xiaohong | Zhang, Kai | Guo, Xiaoyong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-213002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7821-7831, 2022

Authors: Jia, Qilong | Fan, Song

Article Type: Research Article

Abstract: This paper studies the robot-written character identification problem under an end-to-end semi-supervised deep learning framework consisting of semi-supervised learning and deep learning modules. The learning framework allows a deep neural network to be trained on labeled and pseudo-labeled samples where pseudo-labeled samples refer to the samples with labels predicted by the semi-supervised learning module. Moreover, to guarantee the feasibility of the learning framework, a two-stage strategy is proposed for training the deep neural network. Specifically, the two-stage training strategy adopts pseudo-labeled samples firstly to train a deep neural network, then the deep neural network is refined using labeled samples one …more time. As a result, more samples can be used for training a deep neural network, which is significant to the performance improvement of a deep neural network in the case of inadequate labeled samples. More importantly, the deep neural networks trained under the proposed learning framework perform better than the famous deep neural networks in a robot-written character identification experiment. Show more

Keywords: Deep learning, semi-supervised learning, robot-written character, neural networks

DOI: 10.3233/JIFS-221389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7833-7846, 2022

Authors: Jiang, Rui | Liu, Shulin

Article Type: Research Article

Abstract: In recent years, with the steady development of the national economy and the continuous improvement of people’s living standards, the desire for material pursuits has gradually transformed into the pursuit of spiritual food, and the attention to health and body is highly valued. It gave birth to and promoted the development of the sports industry. High-standard college stadiums provide many conveniences for students and faculty, and the construction and management of college stadiums are also an important part of the development of my country’s sports industry. However, there are still some drawbacks in the management mode and utilization efficiency of …college stadiums. The utilization efficiency evaluation of college stadiums is frequently looked as the multiple attribute group decision-making (MAGDM) problem. Depending on the VIKOR process and fuzzy number intuitionistic fuzzy sets (FNIFSs), this paper designs a novel FNIF-VIKOR process to assess the resource utilization efficiency of college stadiums. First of all, some basic theories related to FNIFSs are briefly introduced. In addition, the weights of attributes are obtained objectively by utilizing CRITIC weight method. Afterwards, the conventional VIKOR process is extended to FNIFSs to obtain the final order of the alternative. Eventually, an application case for utilization efficiency evaluation of college stadiums and some comparative analysis are fully given. The results show that the built algorithms method is useful for assessing the resource utilization efficiency of college stadiums. Show more

Keywords: Multiple attribute group decision making (MAGDM), fuzzy number intuitionistic fuzzy sets (FNIFSs), VIKOR method, CRITIC method, utilization efficiency evaluation

DOI: 10.3233/JIFS-221452

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7847-7861, 2022

Authors: Fan, Xin | Tian, Shengwei | Yu, Long | Han, Min | Liu, Lu | Cheng, Junlong | Wu, Weidong | Kang, Xiaojing | Zhang, Dezhi

Article Type: Research Article

Abstract: Automatic segmentation of aortic true lumen based on deep learning can save the time for diagnosis of aortic dissection. However, fuzzy boundary, small true lumen region, and high similarity usually leads to inaccurate prediction. To make better use of the details supplemented by the encoder to restore boundaries, we decompose the recovery of detail features in the decoder into two sub-processes: calibration and distraction mining. And we propose a novel calibration and distraction mining (CDM) module. It utilizes deep features to calibrate shallow features so that features are concentrated in the main region. Then, it leverages the distraction mining procedure …to extract false-negative features as a supplement to calibrated features and recover details of the segmentation object. We construct CDM-Net and verify its performance on the Aorta-CT dataset (private dataset), it achieves the Dice similarity coefficient of 96.94% and the Jaccard index coefficient of 94.08%, which is the best compared with 10 latest methods. Similarly, we explore its robustness on three more public datasets, including ISIC 2018 dataset (skin lesion segmentation), the 2018 data science bowl dataset (nucleus segmentation), LUNA dataset (lung segmentation). Experimental results prove that our method produces competitive results on all three data sets. Through quantitative and qualitative research, the proposed CDM-Net has good performance and can process aortic slices with complex semantic features, additional experiments show that it has good robustness, and it has the potential to be applied and expanded conveniently. Show more

Keywords: Aortic true lumen, semantic segmentation, calibration, distraction mining

DOI: 10.3233/JIFS-220242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7863-7875, 2022

Authors: Geng, Kaifeng | Wu, Shaoxing | Liu, Li

Article Type: Research Article

Abstract: Although re-entrant hybrid flow shop scheduling is widely used in industry, its processing and delivery times are typically determined using precise values that frequently ignore the influence of machine failure, human factors, the surrounding environment, and other uncertain factors, resulting in a significant gap between theoretical research and practical application. For fuzzy re-entrant hybrid flow shop scheduling problem (FRHFSP), an integrated scheduling model is established to minimize the maximum completion time and maximize the average agreement index. According to the characteristics of the problem, a hybrid NSGA-II (HNSGA-II) algorithm is designed. Firstly, a two-layer encoding strategy based on operation and …machine is designed; Then, a hybrid population initialization method is designed to improve the quality of the initial population; At the same time, crossover and mutation operators and five neighborhood search operators are designed to enhance the global and local search ability of the algorithm; Finally, a large number of simulation experiments verify the effectiveness and superiority of the algorithm. Show more

Keywords: Re-entrant hybrid flow shop, multi objective optimization, fuzzy scheduling, average agreement index

DOI: 10.3233/JIFS-221089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7877-7890, 2022

Authors: Zhang, Xianyong | Fan, Yunrui | Yao, Yuesong | Yang, Jilin

Article Type: Research Article

Abstract: Attribute reduction based on rough sets is an effective approach of data learning in intelligent systems, and it has two basic types. Traditional classification-based attribute reducts mainly complete the classification task, while recent class-specific reducts directly realize the class-pattern recognition. Neighborhood rough sets have the covering-structure extension and data-diversity applicability, but their attribute reducts concern only the neighborhood classification-based reducts. This paper proposes class-specific attribute reducts based on neighborhood rough sets, so as to promote the optimal identification and robust processing of specific classes. At first, neighborhood class-specific reducts are defined, and their basic properties and heuristic algorithms are acquired …by granulation monotonicity. Then, hierarchical relationships between the neighborhood classification-based and class-specific reducts are analyzed, and mutual derivation algorithms are designed. Finally, the theoretical constructions and mutual relationships are effectively verified by both decision table examples and data set experiments. The neighborhood class-specific reducts robustly extend the existing class-specific reducts, and they also provide a hierarchical mechanism for the neighborhood classification-based reducts, thus facilitating wide applications of class-pattern processing. Show more

Keywords: Rough sets, neighborhood rough sets, attribute reduction, class-specific attribute reducts, classification-based attribute reducts

DOI: 10.3233/JIFS-213418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7891-7910, 2022

Authors: Chen, Fu | Huang, Bogang

Article Type: Research Article

Abstract: Health literacy is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health literacy evaluation of College students is frequently viewed as the multiple attribute group decision making (MAGDM) issue. In such paper, Taxonmy method is designed for solving the MAGDM under probabilistic double hierarchy linguistic term sets (PDHLTSs). First, the expected …function of PDHLTSs and Criteria Importance Though Intercrieria Correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest probabilistic double hierarchy linguistic development attribute values from the probabilistic double hierarchy linguistic positive ideal solution (PDHLPIS). Finally, a numerical example for physical health literacy evaluation of College students is given to illustrate the built method. Show more

Keywords: Multiple attribute group decision making (MAGDM), PDHLTSs, Taxonmy method, CRITIC method, physical health literacy evaluation

DOI: 10.3233/JIFS-221164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7911-7922, 2022

Authors: Sukheja, Deepak | Shah, Javaid Ahmad | Madhu, G. | Nagini, S. | Kiranmayee, B.V. | Kautish, Sandeep

Article Type: Research Article

Abstract: Making the correct decision in a real-time situation is extremely difficult. In today’s technological age, computational methods are available, and they may assist the company’s top leaders in making sound decisions and strengthening the organization. There are several techniques for dealing with decision-making problems, one of which is the use of Hendecagonal fuzzy numbers. These fuzzy numbers are used to represent the ambiguity or ambiguity of eleven linguistic variables. To address these shortcomings, we use the ranking method, relativity function, and comparison matrix to aid in decision-making because we have eleven constraints (linguistic variables) that can be expressed in a …hendecagonal fuzzy number matrix (HdcgFNM). The raking technique, relativity function, and comparison matrix were evaluated using a case study. Show more

Keywords: Fuzzy logic, decision making, Hendecagonal fuzzy numbers (HdcgFNM), relativity function

DOI: 10.3233/JIFS-212416

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7923-7936, 2022

Authors: Lakshmana Kumar, R. | Subramanian, R. | Karthik, S.

Article Type: Research Article

Abstract: Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the …network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods. Show more

Keywords: Mobile adhoc network, flow pollination, neighbourhood distance, ant colony optimization

DOI: 10.3233/JIFS-212450

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7937-7948, 2022