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000958857 020__ $$a9783030631192$$q(electronic book)
000958857 020__ $$a3030631192$$q(electronic book)
000958857 020__ $$z9783030631185$$q(print)
000958857 0247_ $$a10.1007/978-3-030-63119-2$$2doi
000958857 035__ $$aSP(OCoLC)on1224365588
000958857 035__ $$aSP(OCoLC)1224365588
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000958857 08204 $$a006.3/3$$223
000958857 1112_ $$aICCCI (Conference)$$n(12th :$$d2020 :$$cOnline)
000958857 24510 $$aAdvances in Computational Collective Intelligence :$$b12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings /$$cMarcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (eds.).
000958857 2463_ $$aICCCI 2020
000958857 260__ $$aCham :$$bSpringer,$$c2020.
000958857 300__ $$a1 online resource (828 pages).
000958857 336__ $$atext$$btxt$$2rdacontent
000958857 337__ $$acomputer$$bc$$2rdamedia
000958857 338__ $$aonline resource$$bcr$$2rdacarrier
000958857 4901_ $$aCommunications in Computer and Information Science ;$$v1287
000958857 500__ $$a"Due to the the COVID-19 pandemic the conference was held online."
000958857 500__ $$aIncludes author index.
000958857 5050_ $$aIntro -- Preface -- Organization -- Contents -- Data Mining and Machine Learning -- Rule Induction of Automotive Historic Styles Using Decision Tree Classifier -- 1 Introduction -- 2 Artificial Intelligence and Style -- 2.1 Research Relevant to the Application of AI in Design -- 2.2 The Definition and Classification of Design -- 2.3 Potential of Using Data Mining and Decision Tree to Classify Styles -- 3 Method -- 3.1 Choice of Features and Style -- 3.2 Choice of Case Study -- 3.3 Classification Methods and Tools -- 4 Results -- 4.1 Decision Tree Classification Model Diagram
000958857 5058_ $$a4.2 The Average Accuracy of Ten Decision Trees -- 4.3 Correlation Between Design Features and Accuracy -- 4.4 The Accumulated Number of Statistical Design Features -- 4.5 Entropy, Information Gain and Gain Ratio -- 5 Discussion -- 5.1 Case Study of Automotive-Style Classification -- 5.2 Summary -- 6 Conclusion -- References -- Deep Learning for Multilingual POS Tagging -- 1 Introduction -- 2 Related Work -- 2.1 Deep Neural Network -- 2.2 Max-Margin Tensor Neural Networks -- 2.3 Convolutional Neural Network -- 2.4 Recurrent Neural Network -- 3 Part-of-Speech Taggers -- 4 Experiments
000958857 5058_ $$a4.1 Data Set -- 4.2 Model Setup -- 4.3 Results -- 5 Conclusion -- References -- Study of Machine Learning Techniques on Accident Data -- 1 Introduction -- 2 Dataset -- 3 The Methodology -- 3.1 Clustering to Subgroup Similar Types of Accidents -- 3.2 Classification/Predictive Models for Each Cluster -- 4 Experiments, Result Analysis and Discussion -- 4.1 Results of Cluster Analysis -- 4.2 Selecting Influential Attributes by Random Forest Analysis -- 4.3 Classification and Rule Generation -- 4.4 Rule Generation Using PART -- 5 Conclusion and Possible Future Work -- References
000958857 5058_ $$aSoil Analysis and Unconfined Compression Test Study Using Data Mining Techniques -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Mymensingh -- 3.2 Rangamati -- 4 Methodology and Results -- 4.1 Models -- 4.2 Accuracy Metric -- 4.3 Results -- 5 Conclusion -- References -- Self-sorting of Solid Waste Using Machine Learning -- 1 Introduction -- 1.1 Waste Recycling -- 1.2 Literature Review of Self-sorting Bins -- 2 Self-sorting Bin Design -- 2.1 Mechanical Design -- 2.2 Electrical Design -- 2.3 Sensors -- 3 Software Architecture -- 3.1 Classification Models -- 3.2 Combined Classifier
000958857 5058_ $$a4 Classifiers Performance -- 5 Conclusion -- References -- Clustering Algorithms in Mining Fans Operating Mode Identification Problem -- 1 Introduction -- 2 Problem Description -- 3 Description of the Industrial Fan Station -- 4 Methodology -- 4.1 Source Data Characteristics and Preprocessing -- 4.2 Algorithms Description -- 5 Applications to Real-Life Data and Algorithms Comparison -- 6 Conclusions -- References -- K-Means Clustering for Features Arrangement in Metagenomic Data Visualization -- 1 Introduction -- 2 Related Work -- 3 Features Clustering in Synthetic Metagenomic Images
000958857 506__ $$aAccess limited to authorized users.
000958857 520__ $$aThis book constitutes refereed proceedings of the 12th International Conference on International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November - December 2020. Due to the the COVID-19 pandemic the conference was held online. The 68 papers were thoroughly reviewed and selected from 314 submissions. The papers are organized according to the following topical sections: data mining and machine learning; deep learning and applications for industry 4.0; recommender systems; computer vision techniques; decision support and control systems; intelligent management information systems; innovations in intelligent systems; intelligent modeling and simulation approaches for games and real world systems; experience enhanced intelligence to IoT; data driven IoT for smart society; applications of collective intelligence; natural language processing; low resource languages processing; computational collective intelligence and natural language processing.
000958857 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 1, 2021).
000958857 650_0 $$aExpert systems (Computer science)$$vCongresses.
000958857 650_0 $$aIntelligent agents (Computer software)$$vCongresses.
000958857 650_0 $$aArtificial intelligence$$vCongresses.
000958857 650_0 $$aSemantic Web$$vCongresses.
000958857 650_0 $$aHuman-computer interaction$$vCongresses.
000958857 7001_ $$aHernes, Marcin.
000958857 7001_ $$aWojtkiewicz, Krystian.
000958857 7001_ $$aSzczerbicki, Edward.
000958857 77608 $$iPrint version:$$aHernes, Marcin$$tAdvances in Computational Collective Intelligence : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings$$dCham : Springer International Publishing AG,c2021$$z9783030631185
000958857 830_0 $$aCommunications in computer and information science ;$$v1287.
000958857 852__ $$bebk
000958857 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-63119-2$$zOnline Access$$91397441.1
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