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Research on Agricultural Environment Prediction Based on Deep Learning

Procedia Computer Science 2018年 139卷 33-40页

作者： Shuchang Chen Bingchan Li Jie Cao Bo Mao College of Information Engineering Collaborative Innovation Center for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance & Economics Nanjing 210023 China School of Electrical and Automation Engineering Jiangsu Maritime Institute Nanjing 211170 China Computer Science and Technology Computer Science and Engineering College Nanjing University of Science and Technology Nanjing 210094 China

The environmental security of agriculture is closely related to human beings. Analytical training of agricultural environmental data, forecasting its development trend, has positive significance for the protection of the safety of agricultural products. This paper proposes an agricultural environment prediction model based on deep learning LSTM (Long Short-Term Memory). By analyzing the agricultural environment parameters of the current period, the environmental parameters of the next moment can be predicted to achieve the purpose of early warning. The experimental results show that the model’s prediction results have little deviation from the actual values; on this basis, the LSTM model is optimized to replace LSTM with GRU(Gated Recurrent Unit), and the model is more effective.

关键词： Deep learning Agricultural environment LSTM GRU

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Author Correction:data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 476-476页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and engineering,University of Science and Tec... 详细信息

关键词： HTML knowledge mining

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A generalized concept-cognitive learning: A machine learning viewpoint

arXiv

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arXiv 2018年

作者： Mi, Yunlong Shi, Yong Li, Jinhai School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha NE68182 United States Faculty of Science Kunming University of Science and Technology Kunming650500 China

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Hierarchical routing strategy for wireless sensor network

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Journal of Information Hiding and Multimedia Signal Processing 2018年第1期9卷 256-264页

作者： Pan, Jeng-Shyang Kong, Ling Ping Sung, Tien-Wen Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Chaoyang University of Technology Taichung Taiwan School of Computer Science Engineering and Mathematics Flinders University Australia

Wireless sensor networks (WSNs) are designed for a large scale monitoring applications such as military surveillance, medical treatment, environmental monitoring and industry management. In this network, usually hundreds or thousands of low-cost sensor nodes are deployed. These sensing nodes detect the events in the environment and pass an upstream message towards a Sink node. The Sink node is responsible for processing data those are collected by sensor nodes. However, bottleneck exists for WSN: these inner nodes whose positions are close to the Sink node run out of power much earlier than those outer nodes. Because those inner nodes not only perform sensing operation, but also replay data originated from other nodes. In this paper, GAHL routing scheme based on genetic algorithm and Harel method is proposed to solve the problem. The scheme finds the small number of center heads in the network. Those center heads play a role in balancing the workload of inner nodes. With the involvement of center head, a three-tier network is built. From the simulation experiment, the scheme shows a good performance compared to other well-known algorithms. We observe a decrease in the number of center heads, as well as an improvement in the stability of the scheme. © 2018, Ubiquitous International. All rights reserved.

关键词： Genetic algorithms

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PIE: A Personalized Incentive for Location-aware Mobile Crowd Sensing

PIE: A Personalized Incentive for Location-aware Mobile Crow...

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IEEE Symposium on Computers and Communications

作者： Yao Wu Yuncheng Wu Juru Zeng Hong Chen Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

Mobile crowd sensing has the potential to acquire massive data from places and address large-scale societal problems. However, most currently existing crowd sensing systems suffer from insufficient participants. Therefore, incentive design for crowd sensing is essential and urgent. In this paper, different from the auction-based and server-dominant incentives, we design a personalized incentive, PIE, with partiality for neither the server nor the participants with budget constraint. The total payment for all the participants accords to their collective participation level, and the individual reward for each participant depends on individual contribution. We measure the individual contribution and participation level based on Voronoi diagram and Shannon entropy. Both offline and online incentives are proposed with budget constraint. Experimental study shows that our incentives are participation-aware and contribution-dependent, which encourages participants' active join, balanced distribution and flexible reward.

关键词： Sensors Servers Atmospheric measurements Particle measurements Entropy Mobile communication Computational modeling

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Fast & scalable distributed set similarity joins for big data analytics 33

Fast & scalable distributed set similarity joins for big dat...

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33rd IEEE International Conference on data engineering, ICDE 2017

作者： Rong, Chuitian Lin, Chunbin Silva, Yasin N. Wang, Jianguo Lu, Wei Du, Xiaoyong Tianjin Polytechnic University China Arizona State University United States University of California San Diego United States Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781509065431

Set similarity join is an essential operation in big data analytics, e.g., data integration and data cleaning, that finds similar pairs from two collections of sets. To cope with the increasing scale of the data, distributed algorithms are called for to support large-scale set similarity joins. Multiple techniques have been proposed to perform similarity joins using MapReduce in recent years. These techniques, however, usually produce huge amounts of duplicates in order to perform parallel processing successfully as MapReduce is a shared-nothing framework. The large number of duplicates incurs on both large shuffle cost and unnecessary computation cost, which significantly decrease the performance. Moreover, these approaches do not provide a load balancing guarantee, which results in a skewness problem and negatively affects the scalability properties of these techniques. To address these problems, in this paper, we propose a duplicate free framework, called FS-Join, to perform set similarity joins efficiently by utilizing an innovative vertical partitioning technique. FS-Join employs three powerful filtering methods to prune dissimilar string pairs without computing their similarity scores. To further improve the performance and scalability, FS-Join integrates horizontal partitioning. Experimental results on three real datasets show that FS-Join outperforms the state-of-Theart methods by one order of magnitude on average, which demonstrates the good scalability and performance qualities of the proposed technique. © 2017 IEEE.

关键词： MapReduce

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Relationship between Capital Operation and Market Value Management of Listed Companies Based on Random Forest Algorithm

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Procedia Computer Science 2017年 108卷 1271-1280页

作者： Wen Long Linqiu Song Lingxiao Cui Research Center on Fictitious Economy & Data Science CAS Beijing China School of Economics and Management UCAS Beijing China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

This paper analyzes the influence of capital operations on the performance of listed companies under different market conditions by combining various capital operations with market value management. Random Forest algorithm is adopted and other machine learning methods are used to compare. We find that capital operation is significantly related to market value management and different capital operations have different effects on companies in various conditions. The result shows Random Forest algorithm has the highest classification accuracy and is more stable under different thresholds. Our findings will help to establish a market or industry benchmark which provides a scientific basis and decision support to the target companies when they operate their capitals.

关键词： Random Forest capital operation market value management financial strategy

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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Research and Realization of Ten-print data Quality Control Techniques for Imperial Scale Automated Fingerprint Identification System

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Journal of Forensic Science and Medicine 2017年第2期3卷 90-96页

作者： Qian Wang Wei Wang Wei Zhang Tong Zhao Guangnv Jin Division of Forensic Science Liaoning Provincial PoliceShenyang Beiji ng Oriental Golden Fing er Technology Company Limited School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijingPeople's Republic of China

As the first individualization-information processing equipment put into practical service worldwide,Automated Fingerprint Identification System(AFIS)has always been regarded as the first choice in individualization of criminal suspects or those who died in mass *** integrating data within the existing regional large-scale AFIS database,many countries are constructing an ultra large state-of-the-art AFIS(or Imperial Scale AFIS)***,it is very important to develop a series of ten-print data quality controlling process for this system of this type,which would insure a substantial matching efficiency,as the pouring data come into this imperial scale *** the image quality of ten-print data is closely relevant to AFIS matching proficiency,a lot of police departments have allocated huge amount of human and financial resources over this issue by carrying out manual verification works for ***,quality control method above is always proved to be inadequate because it is an astronomical task involved,in which it has always been problematic and less affiant for potential ***,we will implement quality control in the above procedure with supplementary-acquisition effect caused by the delay of feedback instructions sent from the human verification *** this article,a series of fingerprint image quality supervising techniques has been put forward,which makes it possible for computer programs to supervise the ten-print image quality in real-time and more accurate manner as substitute for traditional manual *** its prominent advantages in the human and financial expenditures,it has also been proved to obviously improve the image quality of the AFIS ten-print database,which leads up to a dramatic improvement in the AFIS-matching accuracy as well.

关键词： Big data fingerprint data quality control Imperial Scale Automated Fingerprint Identification System rank sum ratio

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Many-objective instance matching in linked open data

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Journal of Information Hiding and Multimedia Signal Processing 2018年第2期9卷 383-391页

作者： Xue, Xingsi Tang, Zhengyi Chen, Junfeng Pan, Jeng-Shyang College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China College of IOT Engineering Hohai University 200 North Jinling Road ChangzhouJiangsu213022 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Information Science and Engineering Fujian University of Technology Fujian China Department of Information Management Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung41349 Taiwan

Instance matching is a key solution to the instance heterogeneity problem in Linked Open data (LOD), which is able to identify the semantic correspondences between instances. Since different matching algorithms, called matchers, do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of instances in order to increase evidence towards a potential match or mismatch. How to select, combine and tune different instance matchers to obtain the high quality instance alignment is one of the main challenges in LOD domain. Recently, Evolutionary Algorithms (EAs) are appearing as an effective methodology to address this challenge. However, traditional EA based approaches ignore the effects brought about by different instance mapping’s preference on different matchers, which could significantly reduce the alignment’s quality, and the weights determined in this way could be problem-specific, which might not be reused in other instance matching scenarios. To overcome this drawback, in this paper, we propose a Non-dominated Sorting Genetic Algorithm (NSGA)-III based many-objective instance matching approach, which can tradeoff each mapping’s preference on various matchers and determine the optimal instance alignment without tuning different matchers’ aggregating weights. The experiment is conducted on the ISLab Instance Matching Benchmark (IIMB) provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of our proposal. © 2018, Ubiquitous International. All rights reserved.

关键词： Mapping

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