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Deep learning models for multilabel ECG abnormalities classification: A comparative study using TPE optimization 多标签ECG异常分类的深度学习模型:使用TPE优化的比较研究
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0002
A. A. Rawi, Murtada K. Elbashir, Awadallah M. Ahmed
Abstract The problem addressed in this study is the limitations of previous works that considered electrocardiogram (ECG) classification as a multiclass problem, despite many abnormalities being diagnosed simultaneously in real life, making it a multilabel classification problem. The aim of the study is to test the effectiveness of deep learning (DL)-based methods (Inception, MobileNet, LeNet, AlexNet, VGG16, and ResNet50) using three large 12-lead ECG datasets to overcome this limitation. The define-by-run technique is used to build the most efficient DL model using the tree-structured Parzen estimator (TPE) algorithm. Results show that the proposed methods achieve high accuracy and precision in classifying ECG abnormalities for large datasets, with the best results being 97.89% accuracy and 90.83% precision for the Ningbo dataset, classifying 42 classes for the Inception model; 96.53% accuracy and 85.67% precision for the PTB-XL dataset, classifying 24 classes for the Alex net model; and 95.02% accuracy and 70.71% precision for the Georgia dataset, classifying 23 classes for the Alex net model. The best results achieved for the optimum model that was proposed by the define-by-run technique were 97.33% accuracy and 97.71% precision for the Ningbo dataset, classifying 42 classes; 96.60% accuracy and 83.66% precision for the PTB-XL dataset, classifying 24 classes; and 94.32% accuracy and 66.97% precision for the Georgia dataset, classifying 23 classes. The proposed DL-based methods using the TPE algorithm provide accurate results for multilabel classification of ECG abnormalities, improving the diagnostic accuracy of heart conditions.
本研究解决的问题是以往工作的局限性,即认为心电图(ECG)分类是一个多类别问题,尽管在现实生活中同时诊断出许多异常,使其成为一个多标签分类问题。本研究的目的是使用三个大型12导联ECG数据集来测试基于深度学习(DL)的方法(Inception, MobileNet, LeNet, AlexNet, VGG16和ResNet50)的有效性,以克服这一限制。使用树结构Parzen估计器(TPE)算法建立最有效的深度学习模型。结果表明,本文提出的方法对大型数据集的心电异常分类具有较高的准确度和精密度,其中宁波数据集的准确率为97.89%,精密度为90.83%,Inception模型共分类了42类;PTB-XL数据集的准确率为96.53%,精度为85.67%,Alex net模型分类了24个类别;对Georgia数据集的准确率为95.02%,精度为70.71%,对Alex net模型进行了23个类别的分类。采用逐行定义技术构建的最优模型在宁波数据集上的准确率分别为97.33%和97.71%,共分类42个类别;PTB-XL数据集准确率为96.60%,精密度为83.66%,共分类24类;格鲁吉亚数据集的准确率为94.32%,准确率为66.97%,共分类了23个类别。本文提出的基于dl的方法采用TPE算法,为ECG异常的多标签分类提供了准确的结果,提高了心脏疾病的诊断准确性。
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引用次数: 1
Robot indoor navigation point cloud map generation algorithm based on visual sensing 基于视觉感知的机器人室内导航点云图生成算法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0258
Qin Zhang, Xiushan Liu
Abstract At present, low-cost Red Green Blue Depth (RGB-D) sensors are mainly used in indoor robot environment perception, but the depth information obtained by RGB-D cameras has problems such as poor accuracy and high noise, and the generated 3D color point cloud map has low accuracy. In order to solve these problems, this article proposes a vision sensor-based point cloud map generation algorithm for robot indoor navigation. The aim is to obtain a more accurate point cloud map through visual SLAM and Kalman filtering visual-inertial navigation attitude fusion algorithm. The results show that in the positioning speed test data of the fusion algorithm in this study, the average time-consuming of camera tracking is 23.4 ms, which can meet the processing speed requirement of 42 frames per second. The yaw angle error of the fusion algorithm is the smallest, and the ATE test values of the algorithm are smaller than those of the Inertial measurement unit and Simultaneous-Localization-and-Mapping algorithms. This research algorithm can make the mapping process more stable and robust. It can use visual sensors to make more accurate route planning, and this algorithm improves the indoor positioning accuracy of the robot. In addition, the research algorithm can also obtain a dense point cloud map in real time, which provides a more comprehensive idea for the research of robot indoor navigation point cloud map generation.
目前,低成本的红绿蓝深度(RGB-D)传感器主要用于室内机器人环境感知,但RGB-D相机获取的深度信息存在精度差、噪声高等问题,生成的三维彩色点云图精度低。为了解决这些问题,本文提出了一种基于视觉传感器的机器人室内导航点云图生成算法。目的是通过视觉SLAM和卡尔曼滤波视觉惯性导航姿态融合算法获得更精确的点云图。结果表明,在本研究融合算法的定位速度测试数据中,摄像机跟踪的平均耗时为23.4 ms,可以满足42帧/秒的处理速度要求。融合算法的偏航角误差最小,ATE测试值小于惯性测量单元和同步定位映射算法。该研究算法可以使映射过程更加稳定和鲁棒。该算法可以利用视觉传感器进行更精确的路径规划,提高了机器人的室内定位精度。此外,研究算法还可以实时获得密集的点云图,为机器人室内导航点云图生成的研究提供了更全面的思路。
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引用次数: 0
Broadcast speech recognition and control system based on Internet of Things sensors for smart cities 基于物联网传感器的智慧城市广播语音识别与控制系统
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0067
Min Qin, Ravi Kumar, Mohammad Shabaz, Sanjay Agal, Pavitar Parkash Singh, Anooja Ammini
Abstract With the wide popularization of Internet of Things (IoT) technology, the design and implementation of intelligent speech equipment have attracted more and more researchers’ attention. Speech recognition is one of the core technologies to control intelligent mechanical equipment. An industrial IoT sensor-based broadcast speech recognition and control system is presented to address the issue of integrating a broadcast speech recognition and control system with an IoT sensor for smart cities. In this work, a design approach for creating an intelligent voice control system for the Robot operating system (ROS) is provided. The speech recognition control program for the ROS is created using the Baidu intelligent voice software development kit, and the experiment is run on a particular robot platform. ROS makes use of communication modules to implement network connections between various system modules, mostly via topic-based asynchronous data transmission. A point-to-point network structure serves as the communication channel for the many operations that make up the ROS. The hardware component is mostly made up of the main controller’s motor driving module, a power module, a WiFi module, a Bluetooth module, a laser ranging module, etc. According to the experimental findings, the control system can identify the gathered sound signals, translate them into control instructions, and then direct the robot platform to carry out the necessary actions in accordance with the control instructions. Over 95% of speech is recognized. The control system has a high recognition rate and is simple to use, which is what most industrial controls require. It has significant implications for the advancement of control technology and may significantly increase production and life efficiency.
随着物联网(IoT)技术的广泛普及,智能语音设备的设计与实现越来越受到研究者的关注。语音识别是智能机械设备控制的核心技术之一。提出了一种基于工业物联网传感器的广播语音识别和控制系统,以解决智能城市广播语音识别和控制系统与物联网传感器的集成问题。在这项工作中,提供了一种为机器人操作系统(ROS)创建智能语音控制系统的设计方法。利用百度智能语音软件开发工具包编写了ROS的语音识别控制程序,并在特定的机器人平台上进行了实验。ROS利用通信模块实现系统各模块之间的网络连接,主要是通过基于主题的异步数据传输。点对点网络结构充当构成ROS的许多操作的通信通道。硬件部分主要由主控制器的电机驱动模块、电源模块、WiFi模块、蓝牙模块、激光测距模块等组成。根据实验结果,控制系统可以识别采集到的声音信号,将其转化为控制指令,然后指挥机器人平台按照控制指令进行必要的动作。超过95%的语音被识别。该控制系统识别率高,使用简单,是大多数工业控制所要求的。它对控制技术的进步具有重要意义,可以显著提高生产和生活效率。
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引用次数: 0
Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm 基于均衡优化算法的大数据k -均值聚类增强
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0230
Sarah Ghanim Mahmood Al-kababchee, Z. Algamal, O. Qasim
Abstract Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K-means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to use techniques that properly explore the search spaces. In this research, an enhancement of K-means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support.
数据挖掘的主要聚类方法有多种用途,包括基因分析。在聚类研究中,利用数据特征将一组未标记的数据分成簇,这是一个无监督学习问题。一个集群中的数据彼此之间的可比性比其他组中的数据更强。然而,聚类的数量对K-means算法的性能有直接影响。为了找到这些现实世界优化问题的最佳解决方案,有必要使用适当探索搜索空间的技术。本文提出了一种基于均衡优化的K-means聚类算法。建议的方法在选择最佳属性的同时调整簇的数量以找到最优答案。研究结果表明,在基于五个数据集的簇内距离和Rand指数方面,与现有算法相比,所建议的方法是有用的。通过所示的结果以及与其他传统方法的比较,发现该方法在同一聚类内元素的内部维度以及Rand指数方面都更好。总之,建议的技术可以成功地用于数据聚类,并可以提供重要的支持。
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引用次数: 1
Wireless sensor node localization algorithm combined with PSO-DFP 结合PSO-DFP的无线传感器节点定位算法
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0323
Jingjing Sun, Peng Zhang, Xiaohong Kong
Abstract In wireless communication technology, wireless sensor networks usually need to collect and process information in very harsh environment. Therefore, accurate positioning of sensors becomes the key to wireless communication technology. In this study, Davidon–Fletcher–Powell (DFP) algorithm was combined with particle swarm optimization (PSO) to reduce the influence of distance estimation error on positioning accuracy by using the characteristics of PSO iterative optimization. From the experimental results, among the average precision (AP) values of DFP, PSO, and PSO-DFP algorithms, the AP value of PSO-DFP was 0.9972. In the analysis of node positioning error, the maximum node positioning error of PSO-DFP was only about 21 mm. The results showed that the PSO-DFP algorithm had better performance, and the average positioning error of the algorithm was inversely proportional to the proportion of anchor nodes, node communication radius, and node density. In conclusion, the wireless sensor node location algorithm combined with PSO-DFP has a better location effect and higher stability than the traditional location algorithm.
在无线通信技术中,无线传感器网络通常需要在非常恶劣的环境中采集和处理信息。因此,传感器的准确定位成为无线通信技术的关键。本文将Davidon-Fletcher-Powell (DFP)算法与粒子群算法(PSO)相结合,利用粒子群算法迭代优化的特点,降低距离估计误差对定位精度的影响。从实验结果来看,在DFP、PSO和PSO-DFP算法的平均精度(AP)值中,PSO-DFP算法的AP值为0.9972。在节点定位误差分析中,PSO-DFP的最大节点定位误差仅为21 mm左右。结果表明,PSO-DFP算法具有更好的定位性能,算法的平均定位误差与锚节点比例、节点通信半径和节点密度成反比。综上所述,结合PSO-DFP的无线传感器节点定位算法比传统的定位算法具有更好的定位效果和更高的稳定性。
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引用次数: 0
Intelligent medical IoT health monitoring system based on VR and wearable devices 基于VR和可穿戴设备的智能医疗物联网健康监测系统
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0291
Yufei Wang, Xiaofeng An, Weiwei Xu
Abstract In order to improve the shortcomings of the traditional monitoring equipment that is difficult to measure the daily physical parameters of the elderly and improve the accuracy of parameter measurement, this article designs wearable devices through the Internet of Things technology and virtual reality technology. With this device, four daily physical parameters of the elderly, such as exercise heart rate, blood pressure, plantar health, and sleep function, are measured. The feasibility of the measurement method and equipment is verified by experiments. The experimental results showed that the accuracy of the measurement method based on the reflective photoplethysmography signal was high, with the mean and difference values of the subjects’ heart rate basically lying around 0 BPM and in good agreement between the estimated heart rate and the reference value. In the blood pressure measurements, the correlation coefficient between the P r s {P}_{rs} estimate and the reference value was 0.81. The estimation accuracy of the device used in the article was high, with the highest correlation coefficient of 0.96 ± 0.02 for subjects’ heart rate at rest, and its estimation error rate was 0.02 ± 0.01. The P n t h {P}_{{n}th} value for subject B8 exceeded the threshold of 0.5 before subject B21, and subject B8 had more severe symptoms, which was consistent with the actual situation. The wearable device was able to identify the subject’s eye features and provide appropriate videos to help subjects with poor sleep quality to fall asleep. The article provides a method and device that facilitates healthcare professionals to make real-time enquiries and receive user health advice.
摘要:为了改善传统监测设备难以测量老年人日常身体参数的缺点,提高参数测量的准确性,本文通过物联网技术和虚拟现实技术设计可穿戴设备。通过该装置,可以测量老年人的运动心率、血压、足底健康、睡眠功能等4项日常身体参数。实验验证了测量方法和设备的可行性。实验结果表明,基于反射光脉搏波信号的测量方法精度较高,被试心率均值和差值基本在0 BPM左右,估计值与参考值吻合较好。在血压测量中,P rs {P}_{rs}估计值与参考值的相关系数为0.81。本文所用装置的估计精度较高,与被试静息心率的相关系数最高为0.96±0.02,估计错误率为0.02±0.01。受试者B8的P nt h {P}_{{n}th}值在受试者B21之前超过了0.5的阈值,且受试者B8的症状更为严重,这与实际情况相符。该可穿戴设备能够识别受试者的眼部特征,并提供合适的视频,帮助睡眠质量较差的受试者入睡。本文提供了一种方法和设备,方便医疗保健专业人员进行实时查询和接收用户健康建议。
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引用次数: 1
Development of a digital employee rating evaluation system (DERES) based on machine learning algorithms and 360-degree method 基于机器学习算法和360度方法的数字化员工评价系统(DERES)的开发
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0008
Gulnar Balakayeva, Mukhit Zhanuzakov, Gaukhar Kalmenova
Abstract Increasing the efficiency of an enterprise largely depends on the productivity of its employees, which must be properly assessed and the correct assessment of the contribution of each employee is important. In this regard, this article is devoted to a study conducted by the authors on the development of a digital employee rating system (DERES). The study was conducted on the basis of machine learning technologies and modern assessment methods that will allow companies to evaluate the performance of their departments, analyze the competencies of the employees and predict the rating of employees in the future. The authors developed a 360-degree employee rating model and a rating prediction model using regression machine learning algorithms. The article also analyzed the results obtained using the employee evaluation model, which showed that the performance of the tested employees is reduced due to remote work. Using DERES, a rating analysis of a real business company was carried out with recommendations for improving the efficiency of employees. An analysis of the forecasting results obtained using the rating prediction model developed by the authors showed that personal development and relationship are key parameters in predicting the future rating of employees. In addition, the authors provide a detailed description of the developed DERES information system, main components, and architecture.
企业效率的提高在很大程度上取决于企业员工的生产力,必须对员工的生产力进行正确的评估,正确评估每个员工的贡献是很重要的。在这方面,本文致力于作者对数字员工评级系统(DERES)的开发进行的研究。该研究是在机器学习技术和现代评估方法的基础上进行的,这些方法将使公司能够评估其部门的绩效,分析员工的能力并预测未来员工的评级。作者使用回归机器学习算法开发了360度员工评级模型和评级预测模型。本文还对使用员工评价模型得到的结果进行了分析,结果表明被测试员工的绩效由于远程工作而降低。使用DERES,对一家真实商业公司进行评级分析,并提出提高员工效率的建议。运用所建立的评价预测模型对预测结果进行分析,发现个人发展和人际关系是预测员工未来评价的关键参数。此外,作者还对开发的DERES信息系统、主要组件和体系结构进行了详细的描述。
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引用次数: 0
Smart robots’ virus defense using data mining technology 基于数据挖掘技术的智能机器人病毒防御
Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0065
Jiao Ye, Hemant N. Patel, Sankaranamasivayam Meena, Renato R. Maaliw, Samuel-Soma M. Ajibade, Ismail Keshta
Abstract In order to realize online detection and control of network viruses in robots, the authors propose a data mining-based anti-virus solution for smart robots. First, using internet of things (IoT) intrusion prevention system design method based on network intrusion signal detection and feedforward modulation filtering design, the overall design description and function analysis are carried out, and then the intrusion signal detection algorithm is designed, and finally, the hardware design and software development for a breach protection solution for the IoT are completed, and the integrated design of the system is realized. The findings demonstrated that based on the mean value of 10,000 tests, the IoT’s average packet loss rate is 0. Conclusion: This system has high accuracy, good performance, and strong compatibility and friendliness.
摘要为了实现机器人网络病毒的在线检测与控制,提出了一种基于数据挖掘的智能机器人反病毒解决方案。首先,采用基于网络入侵信号检测和前馈调制滤波设计的物联网(IoT)入侵防御系统设计方法,进行总体设计描述和功能分析,然后设计入侵信号检测算法,最后完成针对物联网的入侵防御解决方案的硬件设计和软件开发,实现系统的集成化设计。结果表明,以10000次测试的平均值计算,物联网的平均丢包率为0。结论:该系统准确度高,性能好,兼容性和友好性强。
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引用次数: 0
Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications 结合人工蜂群算法和以人为本的web应用AI的运动训练视频运动矢量隐写算法
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0093
Jinmao Tong, Zhongwang Cao, Wenjiang J. Fu
Abstract In multimedia correspondence, steganography schemes are commonly applied. To reduce storage capacity, multimedia files, including images, are always compressed. Most steganographic video schemes are, therefore, not compression tolerant. In the frame sequences, the video includes extra hidden space. Artificial intelligence (AI) creates a digital world of real-time information for athletes, sponsors, and broadcasters. AI is reshaping business, and although it has already produced a significant impact on other sectors, the sports industry is the newest and most receptive one. Human-centered AI for web applications has substantially influenced audience participation, strategic plan execution, and other aspects of the sports industry that have traditionally relied heavily on statistics. Thus, this study presents the motion vector steganography of sports training video integrating with the artificial bee colony algorithm (MVS-ABC). The motion vector stenography detects the hidden information from the motion vectors in the sports training video bitstreams. Artificial bee colony (ABC) algorithm optimizes the block assignment to inject a hidden message into a host video, in which the block assignment is considered a combinatorial optimization problem. The experimental analysis evaluates the data embedding performance using steganographic technology compared with existing embedding technologies, using the ABC algorithm compared with other genetic algorithms. The findings show that the proposed model can give the highest performance in terms of embedding capacity and the least error rate of video steganography compared with the existing models.
摘要在多媒体通信中,隐写技术是一种常用的通信技术。为了减少存储容量,多媒体文件(包括图像)总是被压缩。因此,大多数隐写视频方案都不能容忍压缩。在帧序列中,视频包含了额外的隐藏空间。人工智能(AI)为运动员、赞助商和广播公司创造了一个实时信息的数字世界。人工智能正在重塑商业,尽管它已经对其他行业产生了重大影响,但体育产业是最新的,也是最容易接受的。以人为中心的web应用程序人工智能对观众参与、战略计划执行以及传统上严重依赖统计数据的体育产业的其他方面产生了重大影响。因此,本研究结合人工蜂群算法(MVS-ABC)提出运动训练视频的运动矢量隐写。运动矢量速记法从运动矢量中检测运动训练视频比特流中的隐藏信息。人工蜂群(ABC)算法将块分配优化为在主机视频中注入隐藏信息,其中块分配被认为是一个组合优化问题。通过实验分析,比较隐写技术与现有嵌入技术的数据嵌入性能,比较ABC算法与其他遗传算法的数据嵌入性能。研究结果表明,与现有模型相比,该模型在嵌入容量和错误率方面具有最高的性能。
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引用次数: 0
A lattice-transformer-graph deep learning model for Chinese named entity recognition 中文命名实体识别的格-变换-图深度学习模型
IF 3 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-2014
Min Lin, Yanyan Xu, Chenghao Cai, Dengfeng Ke, Kaile Su
Abstract Named entity recognition (NER) is the localization and classification of entities with specific meanings in text data, usually used for applications such as relation extraction, question answering, etc. Chinese is a language with Chinese characters as the basic unit, but a Chinese named entity is normally a word containing several characters, so both the relationships between words and those between characters play an important role in Chinese NER. At present, a large number of studies have demonstrated that reasonable word information can effectively improve deep learning models for Chinese NER. Besides, graph convolution can help deep learning models perform better for sequence labeling. Therefore, in this article, we combine word information and graph convolution and propose our Lattice-Transformer-Graph (LTG) deep learning model for Chinese NER. The proposed model pays more attention to additional word information through position-attention, and therefore can learn relationships between characters by using lattice-transformer. Moreover, the adapted graph convolutional layer enables the model to learn both richer character relationships and word relationships and hence helps to recognize Chinese named entities better. Our experiments show that compared with 12 other state-of-the-art models, LTG achieves the best results on the public datasets of Microsoft Research Asia, Resume, and WeiboNER, with the F1 score of 95.89%, 96.81%, and 72.32%, respectively.
命名实体识别(NER)是对文本数据中具有特定含义的实体进行定位和分类,通常用于关系提取、问题回答等应用。汉语是一种以汉字为基本单位的语言,但汉语命名实体通常是一个包含多个汉字的词,因此词与字之间的关系在汉语的NER中都起着重要的作用。目前已有大量研究表明,合理的词信息可以有效地改进中文NER的深度学习模型。此外,图卷积可以帮助深度学习模型更好地进行序列标记。因此,在本文中,我们将词信息和图卷积结合起来,提出了我们的网格-变换-图(LTG)深度学习模型。该模型通过位置注意来关注额外的单词信息,因此可以使用格变换来学习字符之间的关系。此外,自适应的图卷积层使模型能够学习更丰富的字符关系和单词关系,从而有助于更好地识别中文命名实体。我们的实验表明,与其他12个最先进的模型相比,LTG模型在微软亚洲研究院、Resume和WeiboNER的公共数据集上取得了最好的结果,F1得分分别为95.89%、96.81%和72.32%。
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引用次数: 1
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Journal of Intelligent Systems
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