Advertisement

Meticulous fuzzy convolution C means for optimized big data analytics: adaptation towards deep learning

  • Nagaraj BalakrishnanEmail author
  • Arunkumar Rajendran
  • Karthigaikumar Palanivel
Original Article

Abstract

In this new era, any business, industrial production, etc. are in need of information in analytics to start and continue its new move towards increasing their outcomes, efficiency, and performance. In this way, many analytics and analytics software’s are making promising results and trying to make more efficient solutions for the betterment of tomorrow. Many basic algorithms like K-means family, FCM family, etc. are used for the process. Nevertheless, processing the insignificant data, which is no way useful and may sometimes distracts the significant features that are most needed, a Deep Learning approach is used before Big-Data analytics. On the other hand, the features of the significant data should have more in-depth understanding to explore more possibilities that could help the better tomorrow. Here we propose a Meticulous Fuzzy Convolution C-Means (MFCCM) algorithm by mutating the nature of Convolutional Neural Network (CNN) to adopt the nature of significant feature understanding of deep learning method. The main novel idea behind this algorithm is to process the data through the optimized Big-Data algorithm through the process of effective feature selection. Here the process involves the enhancement of Deep Learning algorithm (CNN) with the FCM to select the significant features. This algorithm shows promising results as it gives better segmentation even in the presence of variance noisy data.

Keywords

CNN Deep learning Fuzzy C means Big data Analytics 

Notes

References

  1. 1.
    Pentland A (2015) Social physics: how social networks can make us smarter. PenguinGoogle Scholar
  2. 2.
    Namrata D, Ghuse SA, Pandey MD, Pawar, Kanchan P, Chavhal (2018) Android-based waste management for smart cities snap-dust,” SEEE Digibook on Engineering and Technology(seeepedia.org), vol. 01, pp 12–16Google Scholar
  3. 3.
    McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Dominic B (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68Google Scholar
  4. 4.
    Gnanambikai P, Vijeyakumar KN (2018) A quadrant cluster for outlyingness detail detection to identify noise. SEEE Digibook on Engineering and Technology (seeepedia.org), vol 01, pp 178–183Google Scholar
  5. 5.
    Wamba S, Fosso et al (2015) How ‘big data can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246Google Scholar
  6. 6.
    Kaisler S et al (2013) Big data: issues and challenges moving forward. In: System sciences (HICSS), 2013 46th Hawaii international conference on. IEEE, PiscatawayGoogle Scholar
  7. 7.
    Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958MathSciNetzbMATHGoogle Scholar
  8. 8.
    Arunkumar R, Karthigaikumar P (2017) Multi-retinal disease classification by reduced deep learning features. Neural Comput Appl 28(2):329–334Google Scholar
  9. 9.
    Hastie T, Tibshirani R, Jerome F (2009) Unsupervised learning. The elements of statistical learning. Springer, New York, pp 485–585zbMATHGoogle Scholar
  10. 10.
    Nagaraj B, Vijayakumar P (2012) Controller tuning for industrial process-a soft computing approach, International Journal of Advance in Soft Computing and its Application, vol 4, No. 2, July 2012Google Scholar
  11. 11.
    Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett. 31(8):651–666Google Scholar
  12. 12.
    Nagaraj Balakrishnan RS, Arunkumar R (2018) Smart real time rescue system for fishermen. Pak J Biotechnol 15(1):73–75Google Scholar
  13. 13.
    User BA (2018) Optimal placement of TCSC by complex power flow sensitivity index approach to enhance voltage profile. SEEE Digibook Eng Technol (seeepediaorg) 1(1):(83–86)Google Scholar
  14. 14.
    Soundharya M, Arunkumar R (2015) GDI based area delay power efficient carry select adder. Green engineering and technologies (IC-GET), 2015 Online International Conference on. IEEE, PiscatawayGoogle Scholar
  15. 15.
    Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Quart  36(4):1165–1188Google Scholar
  16. 16.
    Arunkumar R, Balakrishnan N (2018) Medical image classification for disease diagnosis by dbn methods. Pak J Biotechnol 15(1):107–110Google Scholar
  17. 17.
    Mnih V et al (2015) Human-level control through deep reinforcement learning. Nature 518:529 (7540)Google Scholar
  18. 18.
    Nisi K, Nagaraj B, Agalya A (2018) Tuning of a PID controller using evolutionary multi-objective optimization methodologies and application to the pulp and paper industry. Int J Mach Learn Cybern 1(1):1–11Google Scholar
  19. 19.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436Google Scholar
  20. 20.
    Rajendran A, Thamarai M (2014) Adaptive unsupervised Fuzzy C mean based image segmentation. vol. 3, issue 6–1, December, pp 1–5Google Scholar
  21. 21.
    Tan D, Anton N(2010) Brain-computer interfaces and human-computer interaction. Brain-computer interfaces. Springer, London, pp 3–19Google Scholar
  22. 22.
    Nagaraj B, Vijayakumar P (2011) Tuning of a PID controller using soft computing methodologies applied to basis weight control in paper machine. J Korean Tech Assoc Pulp Pap Ind 43(3):1–10Google Scholar
  23. 23.
    Bauckhage C, Drachen A, Rafet S (2015) Clustering game behavior data. IEEE Trans Comput Intell AI Games 7(3): 266–278Google Scholar
  24. 24.
    Wu X et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37Google Scholar
  25. 25.
    Nagaraj B, Pelusi D, Che JI-Z (2017) Introductory Editorial. Wirel Pers Commun 94(4):1935–1936Google Scholar
  26. 26.
    Kriegel H-P, Kröger P, Zimek A (2009) Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data (TKDD) 3(1):1Google Scholar
  27. 27.
    Vishnu T et al (2015) Efficient and early detection of osteoporosis using trabecular region. In: Green engineering and technologies (IC-GET), 2015 Online International Conference on. IEEE, PiscatawayGoogle Scholar
  28. 28.
    Vantaram SR, Saber E (2012) Survey of contemporary trends in color image segmentation. J Electron Imaging 21(4):040901Google Scholar
  29. 29.
    Wang W, Zhang Y (2007) On fuzzy cluster validity indices. Fuzzy Sets Syst 158(19):2095–2117MathSciNetzbMATHGoogle Scholar
  30. 30.
    Zhang H et al (2016) Spectral–spatial sparse subspace clustering for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 54(6):3672–3684Google Scholar
  31. 31.
    Zaixin Z, Lizhi C, Guangquan C (2013) Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation. IET Image Process 8(3):150–161Google Scholar
  32. 32.
    Blake A, Isard M (2012) Active contours: the application of techniques from graphics, vision, control theory and statistics to visual tracking of shapes in motion. Springer, New YorkGoogle Scholar
  33. 33.
    Krinidis S, Chatzis V (2010) A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337MathSciNetzbMATHGoogle Scholar
  34. 34.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838zbMATHGoogle Scholar
  35. 35.
    Nagaraj B, Vijayakumar P (2012) Tuning of a PID controller using soft computing methodologies applied to moisture control in paper machine. Int J Intell Autom Soft Comput J USAVol 18(4):399–411Google Scholar
  36. 36.
    Zhu H et al (2012) Parallel multi-temporal remote sensing image change detection on GPU. In: Parallel and Distributed Processing Symposium Workshops & Ph.D. Forum (IPDPSW), IEEE 26th International. IEEE, PiscatawayGoogle Scholar
  37. 37.
    Ezhilarasan T, Kumaresan S (2018) CBIR system based on optimized integration of color and texture features. In: SEEE Digibook on Engineering and Technology (seeepedia.org), vol 01, pp 282–287Google Scholar
  38. 38.
    Rajendran A, Balakrishnan N, Mithya V (2016) Malleable fuzzy local median C means algorithm for effective biomedical image segmentation. Sens Imaging 17(1):24Google Scholar
  39. 39.
    Nagaraj B, Dev VV (2012) Design of differential evolution optimized PI controller for a temperature process. J Control Instrum 3(3):1–10Google Scholar
  40. 40.
    Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22(5):717–727Google Scholar
  41. 41.
    Kumanan D, Nagaraj B (2013) Tuning of proportional integral derivative controller based on firefly Algorithm Systems. Sci Control Eng J 1:52–58Google Scholar
  42. 42.
    Hinton GE et al. “Improving neural networks by preventing co-adaptation of feature detectors.” arXiv preprint arXiv:1207.0580 (2012)Google Scholar
  43. 43.
    Jeyakkannan N, Nagaraj B (2014) Online monitoring of geological methane storage and leakage based on wireless sensor networks. Asian J Chem 26:s23–s26Google Scholar
  44. 44.
    Pibre L et al (2016) “Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch”. Electron Imaging 8:1–11Google Scholar
  45. 45.
    Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88Google Scholar
  46. 46.
    Nagaraj B, Murugananth R (2010) Optimum PID controller tuning using soft computing methodologies for industrial process. J Comput Sci India 4(5):1761–1768Google Scholar
  47. 47.
    Nagaraj B, Murugananth R (2010) Optimum tuning algorithms for PID controller—a soft computing approach. Int J Indian Pulp Pap Tech Assoc 22(2):127–129Google Scholar
  48. 48.
    Nagaraj B, Subha S, Rampriya B (2008) Tuning algorithms for PID controller using soft computing techniques. Int J Comput Sci Netw Secur 8(4):278–281Google Scholar
  49. 49.
    Balakrishnan N, Nisi K (2018) A deep analysis on optimization techniques for appropriate PID tuning to incline efficient artificial pancreas. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3687-7 Google Scholar
  50. 50.
    Paul, Anand T, Aruldoss Albert, Victoire, Jeyakumar AE (2003) Particle swarm approach for retiming in VLSI. In: Circuits and systems, 2003 IEEE 46th Midwest Symposium on. vol. 3. IEEE, PiscatawayGoogle Scholar
  51. 51.
    Paul A, Rho S, Bharnitharan K (2014) Interactive scheduling for mobile multimedia service in M2M environment. Multimed Tools applications 71(1):235–246Google Scholar
  52. 52.
    Paul A et al (2013) Video search and indexing with reinforcement agent for interactive multimedia services. ACM Trans Embed Comput Syst (TECS) 12(2):25Google Scholar
  53. 53.
    Paul A, Daniel A, Ahmad A, Rho S (2017) Cooperative cognitive intelligence for internet of vehicles. IEEE Syst J 11(3):1249–1258Google Scholar
  54. 54.
    Bhattacharjee D, Paul A, Kim JH, Karthigaikumar P (2018) An immersive learning model using evolutionary learning. Comput Electr Eng 65:236–249Google Scholar
  55. 55.
    Venkatesan C, Karthigaikumar P, Paul A, Satheeskumaran S, Kumar R (2018) ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access 6:9767–9773Google Scholar
  56. 56.
    Pugalendhi G, Velusamy D, Paul A, Kim KJ (2017) Fuzzy-based trusted routing to mitigate packet dropping attack between data aggregation points in smart grid communication network. Computing 99(1):81–106MathSciNetzbMATHGoogle Scholar
  57. 57.
    Ahmad A, Rathore MM, Paul A, Rho S (2016) Defining human behaviors using big data analytics in social internet of things. In: Advanced information networking and applications (AINA), 2016 IEEE 30th international conference on. IEEE, Piscataway, pp 1101–1107Google Scholar
  58. 58.
    Paul A (2014) Real-time power management for embedded M2M using intelligent learning methods. ACM Trans Embed Comput Syst (TECS) 13(5 s):148Google Scholar
  59. 59.
    Shi J, Lei Y, Wu J, Paul A, Kim M, Jeon G (2017) Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation. J Real-Time Image Proc 13(3):645–663Google Scholar
  60. 60.
    Bharill N, Tiwari A, Malviya A (2016) Fuzzy based scalable clustering algorithms for handling big data using apache spark. IEEE Trans Big Data 2(4):339–352Google Scholar
  61. 61.
    Bradley PS, Fayyad UM, Reina C (1998) Scaling clustering algorithms to large databases. In: KDD, vol 98, pp 9–15Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Nagaraj Balakrishnan
    • 1
    Email author
  • Arunkumar Rajendran
    • 1
  • Karthigaikumar Palanivel
    • 1
  1. 1.Karpagam College of EngineeringCoimbatoreIndia

Personalised recommendations