Skip to main content
SpringerLink
Log in

Artificial Intelligence and Machine Learning for Large-Scale Data

  • Chapter
  • First Online:
Book cover Computational Intelligence and Sustainable Systems

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

  • 658 Accesses

Abstract

The storing, the processing, and the extracting of the big data sets (BDs) have many very difficult problems for current commercial applications, researches, etc. Artificial intelligence (AI) and machine learning (ML) have also been built and studied in the strongest way in the world. Their algorithms, methods, approaches, models, etc. have been studied, developed, and applied to many different fields successfully. Unsurprisingly, they have also been surveyed for storing and handling these BDs, and in addition, they have also been used for extracting the significant values of these massive data sets (MSs) successfully. Therefore, we present all possible models of the artificial intelligence and machine learning for the large-scale data sets (LSSs) certainly in this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., van Boven, L., Weber, B., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer Needs and Solutions, 5(1–2), 28–37.

    Article  Google Scholar 

  • Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A., Sponaugle, K., Ferrante, P., Argentinis, E., Sattler, R., & Bowser, R. (2018). Artificial intelligence in neurodegenerative disease research: Use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. ActaNeuropathologica, 135(2), 227–247.

    Google Scholar 

  • Batsakis, S., Petrakis, E. G. M., & Milios, E. (2009). Improving the performance of focused web crawlers. Journal Data & Knowledge Engineering, 68(10), 1001–1013, Amsterdam, Netherlands.

    Article  Google Scholar 

  • Carrera-Trejo, V., GrigoriSidorov, Miranda-Jiménez, S., Ibarra, M. M., & CadenaMartínez, R. (2015). Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification. International Journal of Combinatorial Optimization Problems and Informatics, 6(1), 7–19.

    Google Scholar 

  • Cimatti, A., Do, M., Micheli, A., Roveri, M., & Smith, D. E. (2018). Strong temporal planning with uncontrollable durations. Artificial Intelligence, 256, 1–34. https://doi.org/10.1016/j.artint.2017.11.006.

    Article  MathSciNet  MATH  Google Scholar 

  • Cloudera. (2018). http://www.cloudera.com

  • Davatzikos, C. (2018). Quantifying anatomical and functional heterogeneity in big datasets, using machine learning methods towards a dimensional neuroimaging framework. Biological Psychiatry, 83(9), S14–S15. https://doi.org/10.1016/j.biopsych.2018.02.053.

    Article  Google Scholar 

  • Dharwadker, A. (2006). The clique algorithm. In Proceedings of the Institute of Mathematics (pp. 1–41).

    Google Scholar 

  • Hadoop. (2018). http://hadoop.apache.org

  • Hongyan Zhang, & Xiyu Liu. (2011). A CLIQUE algorithm using DNA computing techniques based on closed-circle DNA sequences. Biosystems, 105(1), 3–82.

    Google Scholar 

  • Kangwook, L., Maximilian, L., Ramtin, P., Dimitris, P., & Kannan, R. (2018). Speeding up distributed machine learning using codes. IEEE Transactions on Information Theory, 64(3), 1514–1529. https://doi.org/10.1109/tit.2017.2736066.

    Article  MathSciNet  MATH  Google Scholar 

  • McDonald, J. F. (2018). Back to the future - the integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics. Gynecologic Oncology, 149(2), 230–231. https://doi.org/10.1016/j.ygyno.2018.03.053.

    Article  Google Scholar 

  • Ognjen Rudovic, Jaeryoung Lee, Miles Dai, Bjorn Schuller, Rosalind Picard (2018) Personalized machine learning for robot perception of affect and engagement in autism therapy. arXiv:1802.01186 [cs.RO], arXiv:1802.01186v1 [cs.RO].

    Google Scholar 

  • Salton, A. W., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620, NY, USA.

    Article  Google Scholar 

  • Scozzari, A., & Tardella, F. (2008). A clique algorithm for standard quadratic programming. Discrete Applied Mathematics, 156(13), 2439–2448.

    Article  MathSciNet  Google Scholar 

  • Singh, V. K., & Singh, V. K. (2015). Vector space model: An information retrieval system. International Journal of Advanced Engineering Research and Studies E-ISSN 2249–8974,, 4(2), 141–143.

    Google Scholar 

  • Soucy, P., & Mineau, G. W. (2005) Beyond TFIDF weighting for text categorization in the vector space model. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (pp. 1130–1135). USA.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2017). English sentiment classification using only the sentiment lexicons with a JOHNSON coefficient in a parallel network environment. American Journal of Engineering and Applied Sciences, 11(1), 39–65. https://doi.org/10.3844/ajeassp.2017.

    Article  Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018a). K-Medoids algorithm used for english sentiment classification in a distributed system. Computer Modelling and New Technologies, 22(1), 20–39.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018b). English sentiment classification using a Gower-2 coefficient and a genetic algorithm with a fitness-proportionate selection in a parallel network environment. Journal of Theoretical and Applied Information Technology, 96(4), 1–50.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018c). A reformed K-nearest neighbors algorithm for big data sets. Journal of Computer Science, 14(5), 13. https://doi.org/10.3844/jcssp.2018.

    Article  Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018d). Latent semantic analysis using a Dennis coefficient for English sentiment classification in a parallel system. International Journal of Computers, Communications and Control, 13(3), 390–410.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018e). The multi-dimensional vectors and an yule-II measure used for a self-organizing map algorithm of English sentiment classification in a distributed environment. Journal of Theoretical and Applied Information Technology, 96(10), 54.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018f). English sentiment classification using a Fager & MacGowan coefficient and a genetic algorithm with a rank selection in a parallel network environment. Computer Modelling and New Technologies ISSN 1407-5814, 1407-5806,, 22(1), 57–112.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018g). English sentiment classification using a BIRCH algorithm and the sentiment lexicons-based one-dimensional vectors in a parallel network environment. International Journal of Computer Modelling and New Technologies, 22(1), 41.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018h). Sentiment classification using the sentiment scores of lexicons based on a Kuhns-II coefficient in English. International Journal of Tomography & Simulation, 31(3), 47.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018i). English sentiment classification using an YULEQ similarity measure and the one-dimensional vectors in a parallel network environment. Journal of Theoretical and Applied Information Technology ISSN 1992-8645, 1817-3195,, 96(10), 26.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018j). A fuzzy C-means algorithm and sentiment-lexicons-based multi-dimensional vectors of a SOKAL & SNEATH-IV coefficient used for English sentiment classification. Journal of Theoretical and Applied Information Technology, 96(10), 25.

    Google Scholar 

  • Vo Ngoc Phu, & Vo Thi Ngoc Tran. (2018k). A self-training - based model using a K-NN algorithm and the sentiment lexicons - based multi-dimensional vectors of a S6 coefficient for sentiment classification. Journal of Theoretical and Applied Information Technology, 96(10), 44.

    Google Scholar 

  • Vo Ngoc Phu, Nguyen DuyDat, Vo Thi Ngoc Tran, Vo Thi Ngoc Chau, & Tuan A. Nguyen. (2017). Fuzzy C-means for english sentiment classification in a distributed system. International Journal of Applied Intelligence (APIN), 46(3), 717–738. https://doi.org/10.1007/s10489-016-0858-z.

    Article  Google Scholar 

  • Vo Ngoc Phu, Vo Thi Ngoc Tran, & Jack Max. (2018). A CURE algorithm for Vietnamese sentiment classification in a parallel environment. International Journal of Computer Science, 14(4), 23. https://doi.org/10.3844/jcssp.2018.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University-DTU, Da Nang, Vietnam

    Vo Ngoc Phu

  2. School of Industrial Management (SIM), Ho Chi Minh City University of Technology – HCMUT, Vietnam National University, Ho Chi Minh City, Vietnam

    Vo Thi Ngoc Tran

Authors
  1. Vo Ngoc Phu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vo Thi Ngoc Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vo Ngoc Phu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    H. Anandakumar

  2. Bannari Amman Insititute of Technology, Sathyamangalam, Tamil Nadu, India

    R. Arulmurugan

  3. University of Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia

    Chow Chee Onn

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vo Ngoc Phu, Vo Thi Ngoc Tran (2019). Artificial Intelligence and Machine Learning for Large-Scale Data. In: Anandakumar, H., Arulmurugan, R., Onn, C. (eds) Computational Intelligence and Sustainable Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-02674-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02674-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02673-8

  • Online ISBN: 978-3-030-02674-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation