Skip to main content
SpringerLink
Log in

Computational Intelligence Techniques for Big Data Analytics: A Contemplative Perspective

  • Conference paper
  • First Online:
Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 832))

  • 617 Accesses

Abstract

Big data analytics has become an area of great research potential with the emergence of enabling technologies of Web 3.0 and Web 4.0. The proliferation of mobile devices, sensor devices, cloud computing and cyber-physical systems has generated such huge amount of data that if analysed properly are an asset to any organisation or institution. Traditional methods for data analysis are no longer sufficient to handle this data. So, newer and more efficient tools and technologies need to be developed to tap this potential. Computational intelligence techniques like artificial neural networks, evolutionary intelligence, genetic algorithms, federated machine learning, etc., are some of the methods used for big data analytics. In this regard, this paper aims to address the details of different computational intelligence techniques used for big data analytics and provide an in-depth detail of each technique. This study will provide insights and knowledge to the research community to have a one-stop solution to identify the pros and cons of various computational techniques. Further researchers will be able to identify which technique will suit their particular analytics problem at hand.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
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

Similar content being viewed by others

References

  1. C.-W. Tsai, C.-F. Lai, H.-C. Chao, A.V. Vasilakos, Big data analytics: a survey. J. Big Data 2, 21 (2015). https://doi.org/10.1186/s40537-015-0030-3

    Article  Google Scholar 

  2. M. Chen, S. Mao, Y. Liu, Big data: a survey. Mob. Netw. Appl. 19, 171–209 (2014)

    Article  Google Scholar 

  3. C. Ma, H.H. Zhang, X. Wang, Machine learning for big data analytics in plants. Trends Plant Sci. 19, 798–808 (2014). https://doi.org/10.1016/j.tplants.2014.08.004

    Article  Google Scholar 

  4. R. Sahal, M.H. Khafagy, F.A. Omara, Exploiting coarse-grained reused-based opportunities in big data multi-query optimization. J. Comput. Sci. 26, 432–452 (2018). https://doi.org/10.1016/j.jocs.2017.05.023

  5. M.M. Rathore, A. Ahmad, A. Paul, S. Rho, Urban planning and building smart cities based on the internet of things using big data analytics. Comput. Netw. 101 (2016). https://doi.org/10.1016/j.comnet.2015.12.023

  6. F. Burstein, C.W. Holsapple, Handbook on Decision Support Systems, vol. 1 (Springer, Berlin, Heidelberg, 2008). https://doi.org/10.1007/978-3-540-48713-5

  7. A.B. Porto-Pazos, N. Veiguela, P. Mesejo, M. Navarrete, A. Alvarellos, O. Ibáñez, A. Pazos, A. Araque, Artificial astrocytes improve neural network performance. PLoS ONE 6, 1–8 (2011). https://doi.org/10.1371/journal.pone.0019109

    Article  Google Scholar 

  8. S. Jadhav, H. Channe, Comparative study of K-NN, Naive Bayes and decision tree classification techniques. Presented at the (2016)

    Google Scholar 

  9. N. Straton, R.R. Mukkamala, R. Vatrapu, Big social data analytics for public health: predicting facebook post performance using artificial neural networks and deep learning, in 2017 IEEE International Congress on Big Data (BigData Congress) (2017), pp. 89–96. https://doi.org/10.1109/BigDataCongress.2017.21

  10. J. Lohokare, R. Dani, S. Sontakke, Automated data collection for credit score calculation based on financial transactions and social media, in 2017 International Conference on Emerging Trends Innovation in ICT (ICEI), pp. 134–138 (2017). https://doi.org/10.1109/ETIICT.2017.7977024

  11. A. Kelemen, Y. Liang, S. Franklin, Learning high quality decisions with neural networks in “conscious” software agents. WSEAS Trans. Syst. 9, 1109–2777 (2005)

    Google Scholar 

  12. C. Castillo, M. El-Haddad, J. Pfeffer, M. Stempeck, Characterizing the life cycle of online news stories using social media reactions, in Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW (Association for Computing Machinery, 2014), pp. 211–223. https://doi.org/10.1145/2531602.2531623

  13. M. Mohammed, B. Al-Khateeb, A.N. Rashid, D.A. Ibrahim, M.K.A. Ghani, S.A. Mostafa, Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput. Electr. Eng. 70, 871–882 (2018)

    Article  Google Scholar 

  14. I. Ali, O.M.L. Alharbi, Z.A. Alothman, A.Y. Badjah, A. Alwarthan, A.A. Basheer, Artificial neural network modelling of amido black dye sorption on iron composite nano material: kinetics and thermodynamics studies. J. Mol. Liq. 250, 1–8 (2018). https://doi.org/10.1016/j.molliq.2017.11.163

  15. M. Elbisy, Sea wave parameters prediction by support vector machine using a genetic algorithm. J. Coast. Res. 31 (2015). https://doi.org/10.2112/JCOASTRES-D-13-00087.1

  16. W. Chen, H.R. Pourghasemi, A. Kornejady, N. Zhang, Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305, 314–327 (2017). https://doi.org/10.1016/j.geoderma.2017.06.020

  17. M. Ghiassi, D. Zimbra, S. Lee, Targeted twitter sentiment analysis for brands using supervised feature engineering and the dynamic architecture for artificial neural networks. J. Manag. Inf. Syst. 33, 1034–1058 (2016). https://doi.org/10.1080/07421222.2016.1267526

    Article  Google Scholar 

  18. A. Khajanchi, Artificial neural networks: the next intelligence (2003)

    Google Scholar 

  19. M.A. Mohammed, M.K. Abd Ghani, R.I. Hamed, S.A. Mostafa, M.S. Ahmad, D.A. Ibrahim, Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. 21, 255–262 (2017). https://doi.org/10.1016/j.jocs.2017.04.003

  20. M.A. Mohammed, M.K. Abd Ghani, N. Arunkumar, O.I. Obaid, S.A. Mostafa, M.M. Jaber, M.A. Burhanuddin, B.M. Matar, S. Khalid Abdullatif, D.A. Ibrahim, Genetic case-based reasoning for improved mobile phone faults diagnosis. Comput. Electr. Eng. 71, 212–222 (2018). https://doi.org/10.1016/j.compeleceng.2018.07.053

  21. M.J.P. Castanho, F. Hernandes, A.M. De Ré, S. Rautenberg, A. Billis, Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Syst. Appl. 40, 466–470 (2013). https://doi.org/10.1016/j.eswa.2012.07.046

  22. E. Cantu-Paz, Efficient and accurate parallel genetic algorithms (Kluwer Academic Publishers, Boston, 2000)

    MATH  Google Scholar 

  23. M. Hajeer, D. Dasgupta, A. Semenov, J. Veijalainen, Distributed evolutionary approach to data clustering and modelling, in 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 142–148 (2014). https://doi.org/10.1109/CIDM.2014.7008660

  24. H. Takagi, Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89, 1275–1296 (2001). https://doi.org/10.1109/5.949485

    Article  Google Scholar 

  25. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002). https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  26. L.A. Zadeh, Fuzzy sets. Inf. Control. 8, 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X

  27. S. Salleh, S. Bahrom, H. Jamaluddin, Fuzzy logic model for dynamic multiprocessor scheduling. Matematika (1999)

    Google Scholar 

  28. S. Salleh, Task allocation in a multiprocessor system using fuzzy logic. J. Teknol. 69–79 (1996). https://doi.org/10.11113/jt.v25.1009

  29. C. Shanthi, N. Pappa, An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images. ISA Trans. 68, 425–432 (2017). https://doi.org/10.1016/j.isatra.2016.10.021

  30. Z. Lv, F. Shen, J. Zhao, T. Zhu, Neural Information Processing—23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III. 344–351 (2016). https://doi.org/10.1007/978-3-319-46675-0

  31. J. Kennedy, Swarm intelligence, in Handbook of Nature-Inspired Innovative Computing: Integrating Classical Models with Emerging Technologies (2006), pp. 187–220 (Chapter 6)

    Google Scholar 

  32. W. Hu, H. Wang, Z. Qiu, C. Nie, L. Yan, B. Du, An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent. World Wide Web 20, 775–795 (2017). https://doi.org/10.1007/s11280-016-0416-y

    Article  Google Scholar 

  33. S. Banerjee, N. Agarwal, Analyzing collective behavior from blogs using swarm intelligence. Knowl. Inf. Syst. 33, 523–547 (2012). https://doi.org/10.1007/s10115-012-0512-y

    Article  Google Scholar 

  34. C. Barba-González, J. García-Nieto, A. Nebro, J. Cordero, J. Durillo, I. Navas Delgado, J. Aldana Montes, JMetalSP: a framework for dynamic multi-objective big data optimization. Appl. Soft Comput. 69 (2017). https://doi.org/10.1016/j.asoc.2017.05.004

  35. S. Kovalchuk, E. Krotov, P. Smirnov, D. Nasonov, A. Yakovlev, Distributed data-driven platform for urgent decision making in cardiological ambulance control. Futur. Gener. Comput. Syst. (2016). https://doi.org/10.1016/j.future.2016.09.017

    Article  Google Scholar 

  36. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). https://doi.org/10.1016/j.media.2016.05.004

  37. Z. Fan, D. Bi, L. He, M. Shiping, S. Gao, C. Li, Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder. Neurocomputing 243, 12–20 (2017). https://doi.org/10.1016/j.neucom.2017.02.066

  38. J. Konečný, H.B. McMahan, D. Ramage, P. Richtárik, Federated Optimization: Distributed Machine Learning for On-Device Intelligence (2016)

    Google Scholar 

  39. J. Konečný, H.B. McMahan, F.X. Yu, P. Richtárik, A.T. Suresh, D. Bacon, Federated Learning: Strategies for Improving Communication Efficiency (2016)

    Google Scholar 

  40. H.B. Mcmahan, E. Moore, D. Ramage, B. Agüera y Arcas, Federated learning of deep networks using model averaging (2012)

    Google Scholar 

  41. D. Jatain, V. Singh, N. Dahiya, A contemplative perspective on federated machine learning: taxonomy, threats and vulnerability assessment and challenges. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.05.016

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chaudhary Devi Lal University, Sirsa, India

    Divya & Vikram Singh

  2. Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Divya & Naveen Dahiya

Authors
  1. Divya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikram Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naveen Dahiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Divya .

Editor information

Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Department of CSE, Central University of Jammu, Jammu and Kashmir, India

    Yashwant Singh

  3. Department of Electrical Engineering, IIT Patna, Patna, India

    Maheshkumar H. Kolekar

  4. Department of Management Studies and School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Arpan Kumar Kar

  5. School of Technology, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Paulo J. S. Gonçalves

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Divya, Singh, V., Dahiya, N. (2022). Computational Intelligence Techniques for Big Data Analytics: A Contemplative Perspective. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-16-8248-3_32

Download citation

Publish with us

Policies and ethics

Search

Navigation