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Titlebook: Intelligent Data Engineering and Automated Learning -- IDEAL 2014; 15th International C Emilio Corchado,José A. Lozano,Hujun Yin Conference

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發(fā)表于 2025-3-21 18:26:11 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Intelligent Data Engineering and Automated Learning -- IDEAL 2014
副標(biāo)題15th International C
編輯Emilio Corchado,José A. Lozano,Hujun Yin
視頻videohttp://file.papertrans.cn/470/469584/469584.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Intelligent Data Engineering and Automated Learning -- IDEAL 2014; 15th International C Emilio Corchado,José A. Lozano,Hujun Yin Conference
描述This book constitutes the refereed proceedings of the 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014, held in Salamanca, Spain, in September 2014..The 60 revised full papers presented were carefully reviewed and selected from about 120 submissions. These papers provided a valuable collection of recent research outcomes in data engineering and automated learning, from methodologies, frameworks, and techniques to applications. In addition the conference provided a good sample of current topics from methodologies, frameworks, and techniques to applications and case studies. The techniques include computational intelligence, big data analytics, social media techniques, multi-objective optimization, regression, classification, clustering, biological data processing, text processing, and image/video analysis.
出版日期Conference proceedings 2014
關(guān)鍵詞Web application; agent-based simulation; ant colony optimization; cloud computing; combinatorial optimiz
版次1
doihttps://doi.org/10.1007/978-3-319-10840-7
isbn_softcover978-3-319-10839-1
isbn_ebook978-3-319-10840-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
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Automatic Content Related Feedback for MOOCs Based on Course Domain Ontology,matic feedback. Moreover, we provide facilitators with feedback of students posts, such as frequent topics students ask about. Experimental results from one of the courses offered by . show the potential of our approach in creating a responsive learning environment.
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Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensembling Filter (IPF), which can overcome these problems. The properties of this proposal are discussed in a controlled experimental study against SMOTE and its most well-known generalizations. The results show that the new proposal performs better than exiting SMOTE generalizations for all these different scenarios.
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Automatic Validation of Flowmeter Data in Transport Water Networks: Application to the ATLLc Water ng the different sensors. The methodology is applied to real-data acquired from the ATLLc Water Network. The results demonstrate the performance of the proposed methodology in detecting errors in measurements and in reconstructing them.
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On Interlinking Linked Data Sources by Using Ontology Matching Techniques and the Map-Reduce Framewtation. In order to assess the performance of the proposed scheme an exemplifying prototype is implemented between DBpedia and LinkedMDB datasets. The obtained results are promising and pave the way towards benchmarking the proposed interlinking procedure with other ontology matching systems.
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Use of Empirical Mode Decomposition for Classification of MRCP Based Task Parameters,ality reduction using Principal Component Analysis (PCA). Classification was performed using simple logistic regression. A best overall classification accuracy of 77.2% was achieved using this approach. Results provide evidence that BCI can be potentially used in tandem with bionics for neuro-rehabilitation.
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