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检索条件"任意字段=18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017"

共 91 条记录，以下是41-50 订阅

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Evidence-Based Systematic Literature Reviews in the Cloud 19th

Evidence-Based Systematic Literature Reviews in the Cloud

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Ruiz-Rube, Ivan Person, Tatiana Miguel Mota, Jose Manuel Dodero, Juan Rafael Gonzalez-Toro, Angel Univ Cadiz ESI Dept Comp Engn Puerto Real Cadiz Spain

ISBN: (纸本)9783030034962;9783030034955

Systematic literature reviews and mapping studies are useful research methods used to lay the foundations of further research. these methods are widely used in the Health Sciences and, more recently, also in Computer Science. Despite existing tool support for systematic reviews, more automation is required to conduct the complete process. this paper describes CloudSERA, a web-based app to support the evidence-based systematic review of scientific literature. the tool supports researchers to carry out studies by additional facilities as collaboration, usability, parallel searches and search integration with other systems, the flexible data scheme of the tool enables the integration of bibliographic databases of common use in Computer Science and can be easily extended to support additional sources. It can be used as a service in a cloud environment or as on-premises software.

关键词： Systematic literature review Mapping study Information retrieval Bibliographic data

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Predicting the Household Power Consumption Using CNN-LSTM Hybrid Networks 19th

Predicting the Household Power Consumption Using CNN-LSTM Hy...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Tae-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

Prediction of power consumption is an integral part of the operation and planning of the electricity supply company. In terms of power supply and demand, For the stable supply of electricity, the reserve power must be prepared. However, it is necessary to predict electricity demand because electricity is difficult to store. In this paper, we propose a CNN-LSTM hybrid network that can extract spatio-temporal information to effectively predict the house power consumption. Experiments have shown that CNN-LSTM hybrid networks, which linearly combine convolutional neural network (CNN), long short-term memory (LSTM) and deep neural network (DNN), can extract irregular features of electric power consumption. the CNN layer is used to reduce the spectrum of spatial information, the LSTM layer is suitable for modeling temporal information, the DNN layer generates a predicted time series. the CNN-LSTM hybrid approach almost completely predicts power consumption. Finally, the CNN-LSTM hybrid method achieves higher root mean square error (RMSE) than traditional predictive methods for the individual household power consumption data sets provided by the UCI repository.

关键词： Power consumption prediction Deep learning Convolutional neural network Long short-term memory

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General Structure Preserving Network Embedding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhu, Sinan Jia, Caiyan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Network embedding has attracted increasing attention in recent years since it represents large scale networks in low-dimensional space and provides an easier way to analysis networks. Existing embedding methods either focus on preserving the microscopic topology structure, or incorporate the mesoscopic community structure of a network. However, in the real world, a network may not only contain community structure, but have bipartite-structure, star-structure or other general structures, where nodes in each cluster have similar patterns of connections to other nodes. Empirically, general structure is important for describing the features of networks. In this paper, based on nonnegative matrix factorization framework, we propose GS-NMF which is capable of integrating topology structure and general structure into embedding process. the experimental results show that GS-NMF overcomes the limitation of previous methods and achieves obvious improvement on node clustering, node classification, and visualization.

关键词： Network embedding General structure Nonnegative matrix factorization

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Measuring the Impact of the international Relationships of the Andalusian Universities Using Dimensions database 19th

Measuring the Impact of the International Relationships of t...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Garcia-Sanchez, P. Cobo, M. J. Univ Cadiz Dept Comp Sci & Engn Cadiz Spain

ISBN: (纸本)9783030034962;9783030034955

Researchers usually have been inclined to publish papers with close collaborators: same University, region or even country. However, thanks to the advancements in communication technologies, members of international research networks can cooperate almost seamlessly. these networks usually tend to publish works with more impact than the local counterparts. In this paper, we try to demonstrate if this assumption is also valid in the region of Andalusia (Spain). the *** database is used to obtain the articles where at least one author is from an Andalusian University. the publication list is divided into 4 geographical areas: local (only one affiliation), regional (only Andalusian affiliations), national (only Spanish affiliations) and international (any affiliation). Results show that the average number of citations per paper increases as the author collaboration networks increases geographically.

关键词： Bibliometric analysis international collaboration Andalusian universities Dimensions.ai

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Merging ELMs with Satellite data and Clear-Sky Models for Effective Solar Radiation Estimation 19th

Merging ELMs with Satellite Data and Clear-Sky Models for Ef...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Cornejo-Bueno, L. Casanova-Mateo, C. Sanz-Justo, J. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Valladolid LATUV Lab Teledetecc Valladolid Spain

ISBN: (纸本)9783030034962;9783030034955

this paper proposes a new approach to estimate Global Solar Radiation based on the use of the Extreme learning Machine (ELM) technique combined with satellite data and a clear-sky model. Our study area is the radiometric station of Toledo, Spain. In order to train the Neural Network proposed, one complete year of hourly global solar radiation data (from the 1st of May 2013 to the 30th of April 2014) is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the ELM we have selected as a reference a physical-based method which considers the relation between a clear-sky index and a cloud cover index. then a measure of the Root Mean Square Error (RMSE) and the Pearson's Correlation Coefficient (r(2)) is obtained to evaluate the performance of the suggested methodology against the reference model. We show the improvement of the results obtained by the ELM with respect to those obtained by the physical-based method considered.

关键词： Solar radiation estimation Extreme learning machines Meteosat data

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Feature Selection and Interpretable Feature Transformation: A Preliminary Study on Feature engineering for Classification Algorithms 19th

Feature Selection and Interpretable Feature Transformation: ...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Tallon-Ballesteros, Antonio J. Tuba, Milan Xue, Bing Hashimoto, Takako Univ Seville Seville Spain Singidunum Univ Belgrade Serbia Victoria Univ Wellington Wellington New Zealand Chiba Univ Commerce Konodai Ichikawa City Chiba Japan

ISBN: (纸本)9783030034962;9783030034955

this paper explores the limitation of consistency-based measures in the context of feature selection. these kinds of filters are not very widespread in large-dimensionality problems. Typically, the number of selected of attributes is very small and the ability to do right predictions is a drawback. the principal contribution of this work is the introduction of a new approach within feature engineering to create new attributes after the feature selection stage. the experimentation on multi-class problems with a feature space in the order of tens of thousands shed light on that some improvements took place with the new proposal. As a final insight, some new relationships were discovered due to the combined application of feature selection and feature transformation. Additionally, a new measure for classification problems which relates the number of features and the number of classes or labels is also proposed.

关键词： Classification Feature engineering Feature selection data mining Feature discovery Feature transformation

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Deep-learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Fuentes-Hurtado, Felix Morales, Sandra Mossi, Jose M. Naranjo, Valery Fedulov, Vadim Woldbye, David Klemp, Kristian Torm, Marie Larsen, Michael Univ Politecn Valencia Inst Invest & Innovac Bioingn Camino Vera S-N Valencia 46022 Spain Univ Copenhagen Dept Neurosci & Pharmacol Copenhagen Denmark Univ Copenhagen Lab Neural Plast Dept Neurosci Copenhagen Denmark Rigshosp Glostrup Dept Ophthalmol Copenhagen Denmark

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (AUC = 0.99 +/- 0.01 in a P = 15 leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. the presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. the result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

关键词： Optical coherence tomography Deep-learning Glaucoma

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Bibliometric Network Analysis to Identify the Intellectual Structure and Evolution of the Big data Research Field 19th

Bibliometric Network Analysis to Identify the Intellectual S...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Lopez-Robles, J. R. Otegi-Olaso, J. R. Porto Gomez, I. Gamboa-Rosales, N. K. Gamboa-Rosales, H. Robles-Berumen, H. Univ Basque Country UPV EHU Dept Graph Design & Engn Projects Alameda Urquijo S-N Bilbao 48013 Spain Deusto Univ Deusto Business Sch Bilbao Spain Autonomous Univ Zacatecas Acad Unit Elect Engn Zacatecas Zacatecas Mexico

ISBN: (纸本)9783030034962;9783030034955

Big data has evolved from being an emerging topic to a growing research area in business, science and education fields. the Big data concept has a multidimensional approach, and it can be defined as a term describing the storage and analysis of large and complex data sets using a series of advanced techniques. In this respect, the researches and professionals involved in this area of knowledge are seeking to develop a culture based on data science, analytics and intelligence. To this end, it is clear that there is a need to identify and examine the intellectual structure, current research lines and main trends. In this way, this paper reviews the literature on Big data evaluating 23,378 articles from 2012 to 2017 and offers a holistic approach of the research area by using SciMAT as a bibliometric and network analysis software. Furthermore, it evaluates the top contributing authors, countries and research themes that are directly related to Big data. Finally, a science map is developed to understand the evolution of the intellectual structure and the main research themes related to Big data.

关键词： Big data Bibliometric network analysis Information management Strategic intelligence SciMAT

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Cross-Media Retrieval of Tourism Big data Based on Deep Features and Topic Semantics 1

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Li, Yang Du, Junping Lin, Zijian Ye, Lingfei Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

As an Internet application, smart tourism has greatly enriched the tourism information. In this paper, we propose a unified modeling and expression method of attraction texts and images based on the text deep representation model and convolution neural network. According to the cross-media characteristics of tourism big data, we propose a semantic learning and analysis method for cross-media data, and correlate tourism texts with images based on deep features and topic semantics. Experimental results show that the proposed method can achieve better results for semantic analysis and cross-media retrieval of tourism big data.

关键词： Text deep representation model Convolution neural network Semantic learning Topic semantics Cross-media retrieval

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Cost Sensitive Matrix Factorization for Face Recognition 1

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Wan, Jianwu Yang, Ming Wang, Hongyuan Changzhou Univ Sch Informat Sci & Engn Changzhou Peoples R China Nanjing Normal Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

In this paper, we propose a cost sensitive matrix factorization (CSMF) for face recognition. To make the face representation cost sensitive, CSMF adopts a more flexible feature embedding strategy. It contains two main steps: (1) matrix factorization for the learning of latent semantic representation and (2) cost sensitive latent semantic regression. In this way, the face images are embedded into their label space with the misclassification loss minimized. the experimental results on Extended Yale B and ORL demonstrate its effectiveness.

关键词： Cost sensitive learning Matrix factorization Face recognition Latent semantic regression

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