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学者姓名:陈福集
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The prediction of the development trend of internet public opinion is a very significant reference for monitoring and control of the network public opinion by the relevant government departments. On account of small sample characteristics of online public opinion and the needs for both accuracy and stability in the prediction model, in this paper, an improved grey wolf optimization algorithm (IGWO) based on the initialization of the good-point set method, nonlinear parameter control and the weighting of the leading wolf is proposed. Using IGWO to optimize the super parameters of SVM regression model, a network public opinion prediction model based on improved grey wolf optimized support vector machine regression (IGWO-SVR) is established. Empirical research is carried out with Baidu indexes such as COVID-19 as public opinion data samples. The experimental results of 12 test functions show that the improved grey wolf optimization algorithm has relatively strong global search ability, faster convergence speed and better stability. The IGWO-SVR model has relatively outstanding accuracy and stability in the prediction of the development trend of public opinion, which can provide better decision-making support for public opinion supervision department of government. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
Keyword :
Decision making Decision making Forecasting Forecasting Geometry Geometry Optimization Optimization Regression analysis Regression analysis Social aspects Social aspects Support vector machines Support vector machines
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GB/T 7714 | Lin, Ling , Chen, Fuji , Xie, Jialiang et al. Prediction of network public opinion based on improved grey wolf optimized support vector machine regression [J]. | System Engineering Theory and Practice , 2022 , 42 (2) : 487-498 . |
MLA | Lin, Ling et al. "Prediction of network public opinion based on improved grey wolf optimized support vector machine regression" . | System Engineering Theory and Practice 42 . 2 (2022) : 487-498 . |
APA | Lin, Ling , Chen, Fuji , Xie, Jialiang , Li, Feng . Prediction of network public opinion based on improved grey wolf optimized support vector machine regression . | System Engineering Theory and Practice , 2022 , 42 (2) , 487-498 . |
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No.2, Identifying the accurate boundaries of a species is critical to have a better perspective of any biological studies. Therefore, species delimitation is a subject of extensive part of studies in the framework of biology. Erodium species possess significant pharmacological and biological activities. The whole plant was used as astringent and haemostatic in uterine and other bleeding Therefore, due to the importance of these plant species, we performed a molecular data analysis for this species. For this study, we used 60 randomly collected plants from 5 species in five provinces. Amplification of genomic DNA using 10 primers produced 52 bands, of which 50 were polymorphic (98.48%). The obtained high average PIC and MI values revealed high capacity of ISSR primers to detect polymorphic loci among Erodium species. The genetic similarities of five collections were estimated from 0.77 to 0.91. According to Inter-Simple sequence repeats (ISSR) markers analysis, E. cicutarium and E. malacoides had the lowest similarity and the species of E. malacoides and E. oxyrrhynchum had the highest similarity. The aims of present study are: 1) can ISSR markers identify Erodium species, and 2) to investigate the species inter-relationship? The present study revealed that ISSR markers can identify the species.
Keyword :
Erodium Erodium ISSR markers ISSR markers Species Identification Species Identification Structure Structure
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GB/T 7714 | Mowang, Shu-Chen , Chen, Fu-Ji , Zeenat . GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA [J]. | GENETIKA-BELGRADE , 2021 , 53 (2) : 837-849 . |
MLA | Mowang, Shu-Chen et al. "GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA" . | GENETIKA-BELGRADE 53 . 2 (2021) : 837-849 . |
APA | Mowang, Shu-Chen , Chen, Fu-Ji , Zeenat . GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA . | GENETIKA-BELGRADE , 2021 , 53 (2) , 837-849 . |
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[目的/意义]微博舆情监管是政府推进网络社会治理所面临的难题.对微博舆情进行研究有助于深入了解微博舆情传播规律,为政府监管微博舆情提供建议.[方法/过程]首先通过分析微博舆情的社交网络结构特点,对BA无标度网络进行改进.随后将模糊观点与Deffuant-Weisbuch模型融合,提出一种基于改进模糊相似度的舆情演化规则.最后通过仿真实验分析微博舆情演化特征.[结果/结论]研究发现,模糊观点的类型对舆情演化的周期与规模有影响.用户对于热门发现微博的关注度对舆情传播有影响.
Keyword :
微博舆情 微博舆情 模糊理论 模糊理论 舆情演化 舆情演化
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GB/T 7714 | 郭耿 , 陈福集 , 陈蕾雯 . 基于观点模糊相似度的微博舆情演化研究 [J]. | 情报科学 , 2020 , 38 (1) : 82-86 . |
MLA | 郭耿 et al. "基于观点模糊相似度的微博舆情演化研究" . | 情报科学 38 . 1 (2020) : 82-86 . |
APA | 郭耿 , 陈福集 , 陈蕾雯 . 基于观点模糊相似度的微博舆情演化研究 . | 情报科学 , 2020 , 38 (1) , 82-86 . |
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Daily PM2.5 level has significant influence on human health, which is attracting increasing attention. The prediction of PM2.5 grades has thus become an important factor closely related to social development. In the past decades, many prediction methodologies for PM2.5 have been developed, including regression analysis, neural network model, and support vector machine model. Despite these progresses, it still remains a great challenge to predict the PM2.5 grades more accurately and efficiently. In this work, we applied meteorological pattern analysis to assist the support vector machine (SVM) model for PM2.5 class prediction. Cosine similarity was first used to extract three most relevant ones from six common meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) to give the needed meteorological pattern for SVM model. Higher prediction accuracy was then obtained with the selected pattern composed by relative humidity, wind speed and wind direction. Moreover, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm were investigated for optimizing the parameters in the process of SVM classification, with PSO-SVM presenting the highest accuracy and efficiency (forecasting time significantly reduced by 25%). We further introduced the criteria of precision, recall and F1-score to evaluate the prediction results of PSO-SVM in each PM2.5 grade. Meanwhile, comparative studies confirmed that PSO-SVM displayed better performance than Adaboost and ANN models for the applied meteorological pattern analysis assisted PM2.5 grades prediction. These obtained results indicate the validity of meteorological pattern analysis for efficient air quality forecasting.
Keyword :
Air pollution Air pollution Meteorological pattern analysis Meteorological pattern analysis Particle swarm optimization algorithm Particle swarm optimization algorithm PM2.5 grades PM2.5 grades SVM SVM
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GB/T 7714 | Liu, Wei , Guo, Geng , Chen, Fuji et al. Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm [J]. | ATMOSPHERIC POLLUTION RESEARCH , 2019 , 10 (5) : 1482-1491 . |
MLA | Liu, Wei et al. "Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm" . | ATMOSPHERIC POLLUTION RESEARCH 10 . 5 (2019) : 1482-1491 . |
APA | Liu, Wei , Guo, Geng , Chen, Fuji , Chen, Yihui . Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm . | ATMOSPHERIC POLLUTION RESEARCH , 2019 , 10 (5) , 1482-1491 . |
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Collaborative filtering (CF) is a widely used technique in recommender systems. With rapid development in deep learning, neural network-based CF models have gained great attention in the recent years, especially autoencoder-based CF model. Although autoencoder-based CF model is faster compared with some existing neural network-based models (eg, Deep Restricted Boltzmann Machine-based CF), it is still impractical to handle extremely large-scale data. In this paper, we practically verify that most non-zero entries of the input matrix are concentrated in a few rows. Considering this sparse characteristic, we propose a new method for training autoencoder-based CF. We run experiments on two popular datasets MovieLens 1 M and MovieLens 10 M. Experimental results show that our algorithm leads to orders of magnitude speed-up for training (stacked) autoencoder-based CF model while achieving comparable performance compared with existing state-of-the-art models.
Keyword :
autoencoder autoencoder collaborative filtering collaborative filtering deep learning deep learning recommender system recommender system
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GB/T 7714 | Wang, Yi-Lei , Tang, Wen-Zhe , Yang, Xian-Jun et al. An efficient method for autoencoder-based collaborative filtering [J]. | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2019 , 31 (23) . |
MLA | Wang, Yi-Lei et al. "An efficient method for autoencoder-based collaborative filtering" . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 31 . 23 (2019) . |
APA | Wang, Yi-Lei , Tang, Wen-Zhe , Yang, Xian-Jun , Wu, Ying-Jie , Chen, Fu-Ji . An efficient method for autoencoder-based collaborative filtering . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2019 , 31 (23) . |
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[目的/意义]旨在为微博舆情危机预警提供参考.[方法/过程]根据微博舆情的传播特性提取6个重要属性要素,通过相似度的直觉模糊熵确定各属性的权重,基于直觉模糊推理进行微博舆情预警等级研判.以6个微博舆情案例进行实验分析,并与其他危机预警方法进行比对分析.[结果/结论]提出的方法能真实估计微博舆情危机等级,并符合经验判断.
Keyword :
微博舆情 微博舆情 直觉模糊推理 直觉模糊推理 预警 预警
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GB/T 7714 | 肖鸿雁 , 陈海汉 , 陈福集 . 基于直觉模糊推理的微博舆情危机预警研究 [J]. | 情报探索 , 2019 , (7) : 29-38 . |
MLA | 肖鸿雁 et al. "基于直觉模糊推理的微博舆情危机预警研究" . | 情报探索 7 (2019) : 29-38 . |
APA | 肖鸿雁 , 陈海汉 , 陈福集 . 基于直觉模糊推理的微博舆情危机预警研究 . | 情报探索 , 2019 , (7) , 29-38 . |
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[目的/意义]微博舆情监管是政府推进网络社会治理所面临的难题.对微博舆情进行研究有助于深入了解微博舆情传播规律,为政府监管微博舆情提供建议.[方法/过程]首先对微博舆情传播机制进行分析;随后在此基础上提出了有限随机动态链接的网络拓扑结构与多主体有界信任模型对元胞自动机模型进行了改进;最后通过仿真实验分析微博舆情演化特征.[结果/结论]研究发现,该模型存在“信息孤岛”现象,该现象的产生与初始沉默元胞比例和热门微博关注度有关,并且初始沉默元胞比例对舆情的演化规模与周期有影响,热门微博关注度对舆情观点演化有影响.
Keyword :
元胞自动机 元胞自动机 微博舆情 微博舆情 演化模型 演化模型 舆情演化 舆情演化
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GB/T 7714 | 郭耿 , 陈海汉 , 陈福集 et al. 基于改进元胞自动机的微博舆情演化研究 [J]. | 情报理论与实践 , 2019 , 42 (5) : 165-170 . |
MLA | 郭耿 et al. "基于改进元胞自动机的微博舆情演化研究" . | 情报理论与实践 42 . 5 (2019) : 165-170 . |
APA | 郭耿 , 陈海汉 , 陈福集 , 陈蕾雯 . 基于改进元胞自动机的微博舆情演化研究 . | 情报理论与实践 , 2019 , 42 (5) , 165-170 . |
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在网络舆情不断突发的背景下,能够对舆情进行热度预测,及时进行舆论的积极引导,让民众接受和形成正面的认知印象,有利于网络舆情得到良好的发展.对人工蜂群(Artificial bee colony)算法的轮盘赌选择方式进行改进,引入阶段因子β提高人工蜂群算法全局搜索能力,并基于改进的ABC-BP模型对网络舆情热度走势进行预测研究.通过与其他模型对比分析得出该模型预测精确度更高.上述理论研究可为政府和企业及时把握网络舆情热度趋势提供一有效模型,为制定可行的引导措施和营销策略提供参考.
Keyword :
人工蜂群 人工蜂群 神经网络 神经网络 网络舆情预测 网络舆情预测
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GB/T 7714 | 陈福集 , 肖鸿雁 . 基于改进ABC-BP模型的网络舆情热度预测研究 [J]. | 图书馆学研究 , 2018 , (9) : 84-89,封3 . |
MLA | 陈福集 et al. "基于改进ABC-BP模型的网络舆情热度预测研究" . | 图书馆学研究 9 (2018) : 84-89,封3 . |
APA | 陈福集 , 肖鸿雁 . 基于改进ABC-BP模型的网络舆情热度预测研究 . | 图书馆学研究 , 2018 , (9) , 84-89,封3 . |
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采用SWOT模型系统地分析福建省发展大数据产业所面临的机遇和挑战,然后依据福建省的现实情况给出福建省发展大数据产业的对策与建议:从政策法规的宣传推广、信息基础设施的建设和大数据人才的培养等方面加快完善大数据发展的条件和环境;从数据开放与大数据技术、应用创新和福建特色等方面促进大数据的收集和利用:从引导管控及安全技术等方面加强大数据安全工作。
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GB/T 7714 | 于娟 , 施文洁 , 黄恒琪 et al. 基于SWOT分析的福建省大数据产业发展研究 [J]. | 福州大学学报:哲学社会科学版 , 2018 , 32 (1) . |
MLA | 于娟 et al. "基于SWOT分析的福建省大数据产业发展研究" . | 福州大学学报:哲学社会科学版 32 . 1 (2018) . |
APA | 于娟 , 施文洁 , 黄恒琪 , 陈福集 . 基于SWOT分析的福建省大数据产业发展研究 . | 福州大学学报:哲学社会科学版 , 2018 , 32 (1) . |
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[目的/意义]网民对社会现象及问题表达意见、态度使得网络舆情对社会的影响力越来越大,构建模型对网络舆情的发展进行预测具有现实意义.[方法/过程]通过信息熵理论控制种群初始化,利用遗传算法较强的全局搜索能力和粒子群算法的局部搜索能力实现对BP神经网络权值的优化,构建混合算法优化的BP神经网络的网络舆情预测模型并进行实证分析及对比实验.[结果/结论]结果表明,该模型在预测性能上具有更好的优越性及稳定性.
Keyword :
BP神经网络 BP神经网络 信息熵 信息熵 粒子群算法 粒子群算法 网络舆情 网络舆情 遗传算法 遗传算法
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GB/T 7714 | 黄亚驹 , 陈福集 , 游丹丹 . 基于混合算法和BP神经网络的网络舆情预测研究 [J]. | 情报科学 , 2018 , 36 (2) : 24-29 . |
MLA | 黄亚驹 et al. "基于混合算法和BP神经网络的网络舆情预测研究" . | 情报科学 36 . 2 (2018) : 24-29 . |
APA | 黄亚驹 , 陈福集 , 游丹丹 . 基于混合算法和BP神经网络的网络舆情预测研究 . | 情报科学 , 2018 , 36 (2) , 24-29 . |
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