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

Authors: He, Han | Yan, Hongcui | Liu, Weiwei

Article Type: Research Article

Abstract: In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to …determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research. Show more

Keywords: BP neural network, fuzzy mathematical, evaluation model, college talent, scientific research

DOI: 10.3233/JIFS-179977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4913-4923, 2020

Authors: Tian, Shasha | Li, Yuanxiang | Li, Juan | Liu, Guifeng

Article Type: Research Article

Abstract: To overcome the disadvantages of low optimization accuracy and prematurity of the canonical PSO algorithm, we proposed an improved particle swarm optimization based on the interaction mechanism between leaders and individuals (PSO-IBLI), and used it to implement robot global path planning. In the PSO-IBLI algorithm, in different stages, each particle learns from the elites according to different regular. Moreover, the improved algorithm divides the execution state into two categories, where the parameters and the evaluation mechanisms are varied accordingly. In this way, the global best particles no longer walk randomly and have more learning objects. At the same time, other …particles learn from not only the global best position, their historical best positions, but also the other elites. The learning strategy makes the search mode always in the adaptive adjustment, and it improves the speed of convergence and promotes this algorithm to find a more precise solution. The experimental results suggest that the precision and convergence speed of the PSO-IBLI algorithm is higher than the other three different algorithms. Additionally, some experiments are carried out to plan the robot’s entire collision-free path using the PSO-IBLI algorithm and the other three algorithms. The results show that the PSO-IBLI algorithm can obtain the shortest collision-free way in four algorithms. Show more

Keywords: Particle swarm optimization, robot global path planning, optimization accuracy, interaction mechanism, learning object

DOI: 10.3233/JIFS-179978

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4925-4933, 2020

Authors: Cheng, Qiuyun | Ke, Yun | Abdelmouty, Ahmed

Article Type: Research Article

Abstract: Aiming at the limitation of using only word features in traditional deep learning sentiment classification, this paper combines topic features with deep learning models to build a topic-fused deep learning sentiment classification model. The model can fuse topic features to obtain high-quality high-level text features. Experiments show that in binary sentiment classification, the highest classification accuracy of the model can reach more than 90%, which is higher than that of commonly used deep learning models. This paper focuses on the combination of deep neural networks and emerging text processing technologies, and improves and perfects them from two aspects of model …architecture and training methods, and designs an efficient deep network sentiment analysis model. A CNN (Convolutional Neural Network) model based on polymorphism is proposed. The model constructs the CNN input matrix by combining the word vector information of the text, the emotion information of the words, and the position information of the words, and adjusts the importance of different feature information in the training process by means of weight control. The multi-objective sample data set is used to verify the effectiveness of the proposed model in the sentiment analysis task of related objects from the classification effect and training performance. Show more

Keywords: Deep learning, diversified features, sentiment analysis, social networks

DOI: 10.3233/JIFS-179979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4935-4945, 2020

Authors: Guo, Xiaobo | Liu, Yongping

Article Type: Research Article

Abstract: With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional …ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality. Show more

Keywords: Cloud computing, data mining, ant colony algorithm, intelligent transportation system, neural network, feature fusion

DOI: 10.3233/JIFS-179980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4947-4958, 2020

Authors: Wang, Weiqiang

Article Type: Research Article

Abstract: In smart city wireless network infrastructure, network node deployment directly affects network service quality. This problem can be attributed to deploying a suitable ordinary AP node as a wireless terminal access node on a given geometric plane, and deploying a special node as a gateway to aggregate. Traffic from ordinary nodes is to the wired network. In this paper, Pareto multi-objective optimization strategy is introduced into the wireless sensor network node security deployment, and an improved multi-objective particle swarm coverage algorithm based on secure connection is designed. Firstly, based on the mathematical model of Pareto multi-objective optimization, the multi-target node …security deployment model is established, and the security connectivity and node network coverage are taken as the objective functions, and the problems of wireless sensor network security and network coverage quality are considered. The multi-objective particle swarm optimization algorithm is improved by adaptively adjusting the inertia weight and particle velocity update. At the same time, the elite archive strategy is used to dynamically maintain the optimal solution set. The high-frequency simulation software Matlab and simulation platform data interaction are used to realize the automatic modeling, simulation analysis, parameter prediction and iterative optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization. Under the premise of meeting the performance requirements of wireless network nodes in smart cities, the experimental results show that although the proposed algorithm could not achieve the accuracy of using only particle swarm optimization algorithm to optimize the parameters of wireless network nodes in smart cities, the algorithm is completed. The antenna parameter optimization process takes less time and the optimization efficiency is higher. Show more

Keywords: Adaptive, particle swarm optimization, smart city, wireless network node deployment

DOI: 10.3233/JIFS-179981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4959-4969, 2020

Authors: Wen, Xiaoxian | Ma, Yunhui | Fu, Jiaxin | Li, Jing

Article Type: Research Article

Abstract: In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the …user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better. Show more

Keywords: Data clustering, social network, user context, behavior analysis, cold start

DOI: 10.3233/JIFS-179982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4971-4979, 2020

Authors: Chang, Min

Article Type: Research Article

Abstract: Traditional network security job service model and single security technology cannot keep up with the changes of complex network structure and different intrusion measures. Network security job service model relying on rough dataset analysis algorithm has many advantages, such as low management cost, high flexibility and wide applicability. Rough dataset analysis algorithm can not only collect data, but also process data, but overcome the shortcomings of traditional network security job service model. It will improve response speed and reduce network burden. This paper introduces the construction of network security job service model, which based on rough dataset analysis algorithm into …a new network security framework. Show more

Keywords: Key words: Policy management, dynamic network, rough dataset analysis algorithms, network security job service model

DOI: 10.3233/JIFS-179983

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4981-4987, 2020

Authors: Li, Gang | Jia, Jian

Article Type: Research Article

Abstract: The incidence of POCD may be further increased in elderly patients due to degenerative changes in the central nervous system. Once POCD occurs in these patients, it will not only prolong the length of hospital stay, increase medical expenses, but also seriously affect the quality of life of patients, delay the postoperative rehabilitation process, and bring a heavy burden to the family and society. Based on this, this study combines with image recognition technology to study the effect of trypsin inhibitor on postoperative POCD in elderly patients with hip fracture. The hip CT image segmentation algorithm based on concatenated convolutional …neural network is used to realize the automatic phased segmentation of hip CT images. In addition, this study combines with image analysis to study the effect of trypsin inhibitor on postoperative POCD in elderly patients with hip fracture, and the image analysis method was based on the previous research methods. The research results show that the proposed method has certain effects. Show more

Keywords: Trypsin, elderly, hip fracture, postoperative, cognitive function

DOI: 10.3233/JIFS-179984

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4989-4997, 2020

Authors: Lin, Hongbo | Zhao, Jinghua | Liang, Shuang | Kang, Huilin

Article Type: Research Article

Abstract: Aiming at the image features of stock data, considering the picture features of stock data and the characteristics of CNN’s good at extracting picture features, the paper proposed a stock price trend prediction model CNN-M based on a Convolutional Neural Network. At the same time, based on the excellent image feature extraction ability of the residual network, this paper proposed a residual network-based stock price trend prediction model ResNet-M based on the Conventional Neural Network. The experimental results showed that the prediction ability of the improved residual network-based prediction model Resnet-M is superior to the CNN model.

Keywords: Convolutional neural network, stock price trend prediction, deep residual neural network

DOI: 10.3233/JIFS-179985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 4999-5008, 2020

Authors: Gao, Yuanheng | Wang, Leilei | Zhang, Heqing

Article Type: Research Article

Abstract: Today, with the rapid development of urbanization, the ecological and environmental problems of the city have become increasingly serious and have become the focus of the world. The most important issue facing the majority of ecological workers is how to apply the theory of ecology to solve today’s problems. The various environmental problems faced in urban life and the sustainable development of the city’s ecological civilization. How ecological planning is used to coordinate the relationship between people and the natural environment and natural resources is increasingly gaining attention and expanding the range and scope of its applications. However, as an …ecological suitability analysis based on ecological planning, many analytical methods and systems are still being explored and developed due to the geographical complexity and factor diversity involved. In recent years, with the rapid development of computer hardware and software technology, pattern recognition has received more and more attention, pattern recognition and image processing technology has become more and more perfect, and has been successfully applied in more and more fields. This thesis begins to focus on the urban ecological suitability content based on pattern recognition technology and image processing. The main contents of this thesis include: introducing the background of urban ecological suitability and the status quo of ecological suitability analysis and existing research methods. According to the structure of the urban ecosystem and the national standards for the construction of ecological systems and ecological cities, an indicator system for ecological suitability evaluation is established. A pattern recognition system and common pattern recognition and image processing methods are introduced. Based on some common evaluation methods and models, the pattern recognition technology theory and image processing technology are introduced into the urban ecological suitability analysis. Based on the image system theory and vector projection principle, the ecological suitability analysis is established. Associated projection model. The model considers the evaluation sample and the quality standards at each level as vectors, and respectively projects the same vector ideal. Based on establishing the ecological suitability evaluation index system and standards, the ecological suitability was evaluated by using the model. Show more

Keywords: Ecological suitability, image processing, grey pattern recognition, urban ecological environment

DOI: 10.3233/JIFS-179986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5009-5016, 2020