检索结果-内蒙古大学图书馆

Rapid phase stability calculations in fluid flow simulation using simple discriminating functions

COMPUTERS & CHEMICAL ENGINEERING 2018年 108卷 112-127页

作者： Gaganis, Vassilis Tech Univ Crete Mineral Resources Engn Dept Khania 73100 Greece

This paper presents a new phase stability method that is applicable when repeated phase behavior calculations are needed as it is the case with multiphase fluid flow compositional simulation in upstream petroleum engineering. Two discriminating functions act as classifiers in such a way that a positive value of one of the two functions determines the stability state of the mixture. The two functions are generated off line, prior to the simulation, and their expressions are very simple so that they can be evaluated rapidly in a non-iterative way for every discretization block and at each timestep during the simulation. The CPU time required for phase stability calculations is dramatically reduced while still obtaining correct classification results corresponding to the global minimum of the system Gibbs energy function. The method can be applied to any chemical engineering problem where the class of several objects needs to be determined repeatedly and quickly. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multiphase fluid flow simulation Phase stability analysis Upstream petroleum engineering classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Automated identification of sugar beet diseases using smartphones

引用

PLANT PATHOLOGY 2018年第2期67卷 399-410页

作者： Hallau, L. Neumann, M. Klatt, B. Kleinhenz, B. Klein, T. Kuhn, C. Roehrig, M. Bauckhage, C. Kersting, K. Mahlein, A-K. Steiner, U. Oerke, E-C. Univ Bonn Inst Crop Sci & Resource Conservat INRES Plant Dis & Plant Protect Bonn Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol Bonn Germany Washington Univ Dept Comp Sci & Engn St Louis MO USA Cent Inst Decis Support Syst Crop Protect ZEPP Bad Kreuznach Germany Informat Syst Integrated Plant Prod ISIP Bad Kreuznach Germany Tech Univ Dortmund Comp Sci Dept Dortmund Germany

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB-image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary-class and multi-class classification approaches, i.e. the separation between diseased and non-diseased, and the differentiation among leaf diseases and non-infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision-making in integrated disease control.

关键词： classification algorithm disease identification erosion band signature RGB images sugar beet

来源：评论

学校读者我要写书评

暂无评论

Validating effectiveness of subgroup identification for longitudinal data

引用

STATISTICS IN MEDICINE 2018年第1期37卷 98-106页

作者： Andrews, Nichole Cho, Hyunkeun Western Michigan Univ Dept Stat Kalamazoo MI 49008 USA Univ Iowa Dept Biostat Iowa City IA 52242 USA

In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness;however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.

关键词： classification algorithm effectiveness of subgrouping personalized treatment random effects linear model

来源：评论

学校读者我要写书评

暂无评论

Cost-sensitive Classifiers in Credit Rating A Comparative Study on P2P Lending 7

Cost-sensitive Classifiers in Credit Rating A Comparative St...

引用

7th International Conference on Computers Communications and Control (ICCCC)

作者： Wang, Haomin Kou, Gang Peng, Yi Univ Elect Sci & Technol China Sch Management & Econ Chengdu Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538619346

In credit rating, borrowers are classified into some grades, based on their past credit experience, to recognize potential borrowers who are of different probability of default. classification algorithms are commonly used, which is important to reduce loss for banks or investors. Since multiple classes are imbalanced, as well as losses of misclassification across classes are not uniform, multi-class cost-sensitive classifiers should be paid more attention in credit rating. Based on existing literatures, in this paper, we review several cost-sensitive classifiers and compare them with three assumed cost matrices. The empirical study utilizes data collected from Lending Club which is the largest U.S. P2P loan platform. The results are aimed at giving insights on cost-sensitive classifiers with various performances in credit rating, which are concerned by researchers and practitioners of credit lending.

关键词： data mining cost-sensitive learning classification algorithm credit rating P2P lending

来源：评论

学校读者我要写书评

暂无评论

Applied Analysis of Social Network Data in Personal Credit Evaluation 1

引用

7th International Conference on Artificial Intelligence and Mobile Services (AIMS) Held as Part of the Services Conference Federation (SCF)

作者： Wang, Yanyong Yang, Jian Saifuding, Daniyaer Fan, Jiejie Li, Ranran Zhao, Chongchong Xu, Jie Xing, Chunxiao Univ Sci & Technol Sch Comp & Commun Engn Beijing 10083 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (数字)9783319943619

ISBN: (纸本)9783319943619;9783319943602

With the development of the times, the traditional personal credit is facing a severe test. This paper makes an exploratory study on the practical application and development of personal credit evaluation by using the MicroBlog data. According to the previous study of personal credit evaluation literature to dig out the credit-related indicators. We summed up the three major attributes of "Attributes of Demographic", "Tweets Content", and "User Relationship Structure". We use support vector machine (SVM), naive Bayesian (NB), logical regression (LR) and AdaBoost classification algorithm, according to the actual problem modeling, to analysis of social network data on personal credit. Compared with other algorithms, the AUC value of AdaBoost algorithm achieves the best effect with 0.564 under the equalization setting.

关键词： Social data classification algorithm AdaBoost Personal credit assessment

来源：评论

学校读者我要写书评

暂无评论

PERFORMANCE OF PATTERN RECOGNITION algorithmS IN IDENTIFYING BANKING NETWORKS 7

PERFORMANCE OF PATTERN RECOGNITION ALGORITHMS IN IDENTIFYING...

引用

7th International Conference on Computing, Communications and Informatics (ICACCI)

作者： Sasidharan, Arun Palakkeel, Prashobhan Amrita Vishwa Vidyapeetham Dept Management Bangalore Karnataka India

ISBN: (纸本)9781538653142

A review of literature shows that bank branching can be influenced by economic, geographic, legal, cultural and demographic characteristics apart from institutional decision making. Identifying the branching pattern can help in answering many questions like how a particular banking group is different from other? It can reveal secrets regarding strategic decision making about diversification and growth of banking networks. Present study apply machine learning algorithms to recognize the branching pattern of banking networks in India and compare the differences across groups and types. An evaluation of the performance of the algorithms shows that it is very useful employ a narrative approach for getting important insights from the data compared to traditional approaches. The study also provides a visual map evaluation to enrich the outcomes from the pattern recognition exercise.

关键词： Pattern recognition algorithm Machine learning classification algorithm Banking networks

来源：评论

学校读者我要写书评

暂无评论

NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BOOSTING 9

NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BO...

引用

9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Verma, Parag Anwar, Shayan Khan, Shadab Mane, Sunil B. Coll Engn Dept Comp Engn & IT Pune Maharashtra India

ISBN: (纸本)9781538644300

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

关键词： Intrusion detection XGBoost AdaBoost Machine learning decision trees classification algorithm clustering algorithm gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Mango Grove Relevant Information Extraction Using GF-2 Satellite Data 7

Mango Grove Relevant Information Extraction Using GF-2 Satel...

引用

7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Ye, Huichun Huang, Wenjiang Cui, Bei Dong, Yingying Huang, Shanyu Ren, Chuanshuai Chinese Acad Sci Key Lab Earth Observat Hainan Prov Inst Remote Sensing & Digital Earth Sanya Peoples R China Chinese Acad Sci Key Lab Digital Earth Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Univ Cologne Inst Geog Cologne Germany

ISBN: (纸本)9781538650394

The foundation of information extraction based on remote sensing imaging involves spectral band information. Such a method often suffers from the distinctive problem of surface features. In general, artificial orchard planting is relatively regular;thus, it shows textural features that differ from other vegetation types in images with a specific spatial scale. This study used mango groves as research object. By introducing spectral index, texture feature parameters, and by using support vector machine classification method, based on GF-2 satellite images, mango grove information extraction was studied under different combinations of spectra band, vegetation index, and texture feature parameters. The findings show that the information extraction via single spectra band information has lower accuracy. Introduction of a combination of spectra index and spectra band information can improve extraction accuracy of mango groves;however, the overall classification accuracy still remains low. In addition, the introduction of texture information and spectra band information combination can dramatically improve extraction accuracy. Producer's accuracy and user's accuracy increased to 85.7% and 93.5%, respectively. Under different combination modes, the extracted mango grove accuracy of the combination of integrated spectra band information, textural feature, and vegetation index is optimal. Producer's accuracy and user's accuracy increased to 89.3% and 97.4%, respectively. Relative to the spectra band information, the extraction accuracy improved by 20.6% and 11.0%, respectively. As a result, the support vector machine of integrated spectra and texture can effectively extract the spatial distribution information of mango groves. This method can provide a technical reference for remote sensing extraction of artificial orchards.

关键词： GF-2 satellite data Mango grove Texture information Information extraction classification algorithm Support vector machine

来源：评论

学校读者我要写书评

暂无评论

A Chinese Text Classier Based on Strong Class Feature Selection and Bayesian algorithm

A Chinese Text Classier Based on Strong Class Feature Select...

引用

作者： Yanqiu Chen Peili Sun Department of Computer Science Dalian Neusoft Institute of Information Dalian P.R.China Economic Management Institute Dalian Ocean University

For improve the efficiency and accuracy of Chinese text categorization,this paper presents a new Chinese text classier,in which word segmentation system is based on forward scan corpus,and word frequency statistics method is different in the training stage and the test stage,after that,a novel feature selection is proposed according to word frequency,mutual information and classificatory information,Finally a fast Bayes theory classier is *** prove this classier is simple and effective.

关键词： Chinese text categorization feature selection feature weighting classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Revealing Political Sentiment with Twitter: The Case Study of the 2016 Italian Constitutional Referendum 10

Revealing Political Sentiment with Twitter: The Case Study o...

引用

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Campanale, Marco Caldarola, Enrico G. Enel Italia SRL Rome Italy CNR Inst Intelligent Syst & Ind Technol Adv Mfg Bari Italy

ISBN: (纸本)9781538660515

In this work we argue about the use of Twitter as a Palantir for predicting and revealing the political orientation of users in election campaigns. Specifically, our study aims at revealing the political orientation of a Twitter user in the context of the 2016 Italian Constitutional Referendum. After having collected and processed over 1,200,000 tweets, we classified them as YES-oriented, NO-oriented or UNCERTAIN, by exploiting the Naive Bayes Multinomial Text classification algorithm. We found that Twitter is used massively for political deliberation and just by counting the messages related to a political party we can reveal the election result. Moreover, the words used by the YES party and the NO party twits are in line with the language used by politicians in the real word and this strongly supports our analysis. Furthermore, our approach can be applied to different scenarios, in which it is necessary to distinguish between two main classes and another UNCERTAIN class. Finally, the results obtained by our classification methodology are very promising and encourage us to continue investigating this topic, deriving suggestions for further research.

关键词： Opinion minding Sentiment Analysis Politics classification algorithm WEKA Naive Bayes Multinomial Text

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：