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20240623213015.0 |
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|a 1245345715
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|q (electronic bk.)
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|q (electronic bk.)
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|a 10.1007/978-3-030-73280-6
|2 doi
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|a (OCoLC)1245674237
|z (OCoLC)1245345715
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|a HCDD
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|a Asian Conference on Intelligent Information and Database Systems
|n (13th :
|d 2021 :
|c Online)
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1 |
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|a Intelligent information and database systems :
|b 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7-10, 2021, Proceedings /
|c Ngoc Thanh Nguyen, Suphamit Chittayasothorn, Dusit Niyato, Bogdan Trawiński (eds.).
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3 |
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|a ACIIDS 2021
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| 260 |
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|a Cham :
|b Springer,
|c 2021.
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| 300 |
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|a 1 online resource (881 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Lecture Notes in Computer Science ;
|v 12672
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| 490 |
1 |
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|a LNCS sublibrary, SL 7, Artificial Intelligence
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| 588 |
0 |
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|a Print version record.
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| 500 |
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|a "The 12th and 13th events were initially planned to be held in Phuket (Thailand). However, the global pandemic relating to COVID-19 resulted in both editions of the conference being held online in virtual space."--Preface
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| 505 |
0 |
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|a Intro -- Preface -- Conference Organization -- Contents -- Data Mining Methods and Applications -- Mining Partially-Ordered Episode Rules in an Event Sequence -- 1 Introduction -- 2 Problem Definition -- 3 The POERM Algorithm -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Investigating Crossover Operators in Genetic Algorithms for High-Utility Itemset Mining -- 1 Introduction -- 2 Preliminaries -- 3 The Headless Chicken Test for HUIM Using GAs -- 4 Experiments and Results -- 4.1 Runtime -- 4.2 Discovered HUIs -- 4.3 Convergence -- 5 Conclusion -- References
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| 505 |
8 |
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|a Complexes of Low Dimensional Linear Classifiers with L1 Margins -- 1 Introduction -- 2 Linear Separability of Feature Vectors -- 3 Dual Hyperplanes and Vertices in the Parameter Space -- 4 Perceptron Criterion Function -- 5 Basis Exchange Algorithms -- 6 Reduced Criterion Functions -- 7 Complexes of Linear Classifiers -- 8 Concluding Remarks -- References -- Automatic Identification of Bird Species from Audio -- 1 Introduction -- 2 State of Art -- 2.1 Preprocessing and Feature Extraction -- 2.2 Deep Learning for Sound Classification -- 2.3 Other Approaches -- 3 Our Approach -- 3.1 Pre-processing
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| 505 |
8 |
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|a 3.2 Feature Extraction -- 3.3 Deep Learning Model Architectures -- 4 Experiments and Evaluation -- 4.1 Evaluation Metrics -- 4.2 Experiments -- 4.3 Best Results -- 5 Conclusions and Future Work -- References -- Session Based Recommendations Using Recurrent Neural Networks -- Long Short-Term Memory -- 1 Introduction -- 2 Methods of Content Recommendation -- 2.1 Multi-Layer Perceptron -- 2.2 Autoencoders -- 2.3 Boltzmann Machines -- 3 Evaluation of Recommendation Systems -- 3.1 MAE -- 3.2 MSE -- 3.3 Precision -- 3.4 Recall -- 4 Using the LSTM as a Recommender System -- 4.1 Recurrent Neural Networks
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| 505 |
8 |
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|a 4.2 Long Short-Term Memory -- 4.3 Data Preparation -- 4.4 Model Definition -- 5 Results and Discussions -- 5.1 Experiment -- 5.2 Recall and Precision Results -- 6 Conclusions and Future Work -- References -- UVDS: A New Dataset for Traffic Forecasting with Spatial-Temporal Correlation -- 1 Introduction -- 2 Literature Review -- 2.1 Traffic Forecasting Using Graph Neural Network -- 2.2 Public Traffic Flow Datasets -- 3 UVDS: Urban Vehicle Detection System Dataset -- 3.1 Data Description and Research Challenges -- 3.2 Graph Construction -- 4 Baseline Results and Discussion -- 4.1 Baseline Models
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| 505 |
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|a 4.2 Experimental Results -- 4.3 Open Research Issues Using UVDS Dataset -- 5 Conclusion -- References -- A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database -- 1 Introduction -- 2 A Frequent Temporal Inter-object Pattern Mining Task -- 3 The Parallelized Frequent Temporal Inter-object Pattern Mining Algorithm on a Time Series Database -- 3.1 BranchTree -- 3.2 From BranchTree to PTP -- 4 Empirical Evaluation -- 5 Conclusion -- References -- An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels -- 1 Introduction -- 2 Related Work
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|a 3 Preliminaries.
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|a This book constitutes the refereed proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021, held in Phuket, Thailand, in April 2021.* The 67 full papers accepted for publication in these proceedings were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: data mining methods and applications; machine learning methods; decision support and control systems; natural language processing; cybersecurity intelligent methods; computer vision techniques; computational imaging and vision; advanced data mining techniques and applications; intelligent and contextual systems; commonsense knowledge, reasoning and programming in artificial intelligence; data modelling and processing for industry 4.0; innovations in intelligent systems. *The conference was held virtually.
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|a Includes author index.
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| 650 |
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|a Artificial intelligence
|v Congresses.
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| 650 |
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0 |
|a Database management
|v Congresses.
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| 650 |
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7 |
|a Artificial intelligence
|2 fast
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| 650 |
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7 |
|a Database management
|2 fast
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| 655 |
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|a proceedings (reports)
|2 aat
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| 655 |
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|a Conference papers and proceedings
|2 fast
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| 655 |
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|a Conference papers and proceedings.
|2 lcgft
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| 655 |
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|a Actes de congrès.
|2 rvmgf
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| 700 |
1 |
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|a Nguyen, Ngoc Thanh
|c (Computer scientist)
|1 https://id.oclc.org/worldcat/entity/E39PBJyrXJQyVj9XYCk9Q4X68C
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| 700 |
1 |
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|a Chittayasothorn, Suphamit.
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| 700 |
1 |
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|a Niyato, Dusit.
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| 700 |
1 |
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|a Trawiński, Bogdan.
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| 776 |
0 |
8 |
|i Print version:
|a Nguyen, Ngoc Thanh.
|t Intelligent Information and Database Systems.
|d Cham : Springer International Publishing AG, ©2021
|z 9783030732790
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| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 12672.
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| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 7,
|p Artificial intelligence.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-73280-6
|y Click for online access
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| 903 |
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|a SPRING-COMP2021
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| 994 |
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|a 92
|b HCD
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