Skip to main content

Advertisement

Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSN) are part of our daily life as they play a vital role in applications of various domains. Energy optimization is a major challenge of WSN as they are operated through the battery. Data exchange is a significant and often performed operation in any WSN which has to be operated with less energy consumption. Bio-inspired clustering protocols are proving a success in minimizing energy consumption and are of current research interest by many researchers. This paper briefs on work carried on the classification of mushroom into edible or non-edible. Since most mushrooms are dangerous to health and may lead to death, henceforth is it is essential to identify the edibility of mushroom. Mushroom features identified by sensor such as ring, odur, spore_print_color, stalk_color_above, stalk_surface_above, and gill size are forwarded to base station (BS). Ant Lion optimization clustering algorithm (ALOC) is adopted in the routing of information to BS. ALOC avoids improper clustering reducing multiple messages at BS. The work is divided into two phases in the initial phase sensing of mushroom features and forwarding to BS is performed through WSN. In the second phase, the decision is taken whether mushroom is edible or not applying class-based association rules (CBA). The simulation results show ALOC is better than LEACH, ABC PSO and MLEACH through evaluation results in terms of network lifetime, energy consumption and retained alive nodes. correlation-based feature selection (CFS) with three filter search techniques: genetic algorithm (GA), evolutionary algorithm (EA), and particle swarm optimization (PSO) at BS as an evaluation method for selection of significant feature selection. CBA rules were generated using these subsets of significant features; hence resulted in a limited number of strong rules which are reliable and sufficient enough to classify the mushroom as edible or not. The patterns and rules generated using the proposed approach avoid the generation of duplicate and irrelevant rules and henceforth simplifies the analysis process using a reliable and interesting set of rules.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ahmed ESA, Ali BES, Osman EO, Ahmed TAM. Performance evaluation and comparison of IEEE 802.11 and IEEE 802.15.4 ZigBee MAC protocols based on different mobility models. Int J Future Gener Commun Netw. 2016;9(2):9–18.

    Article  Google Scholar 

  2. News of bridge collapse at Minnesota, USA.2007 http://www.nytimes.com/2007/08/02/us/02bridge.html.

  3. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. A survey on sensor networks. IEEE Commun Mag. 2002;40(8):102–14.

    Article  Google Scholar 

  4. Yamuna Devi CR, Shivaraj B, Manjula SH, Venugopal KR, Patnaik LM. EESOR: Energy efficient selective opportunistic routing in wireless sensor networks. Springer: Recent trends in computer networks and distributed systems security communications in computer and information science, vol. 420. New York: Springer; 2014. p. 16–31.

    Google Scholar 

  5. Xie R, Jia X. Transmission-efficient clustering method for wireless sensor networks using compressive sensing. IEEE Trans Parallel Distrib Syst. 2014;25(3):806–15.

    Article  Google Scholar 

  6. Boughanmi N, Esseghir M, Merghem-Boulahia L, Khoukhi L. Energy efficient aggregation in wireless sensor networks, ICD/ERA, UMR 6279, Troyes University of Technology, 12 rue Marie Curie, 10000 Troyes. Berlin: Springer; 2013.

    Google Scholar 

  7. Devika G, Karegowda AG. Survey of WSN routing protocols. IJAEC. 2019;11(1):34–51 (article-3).

    Google Scholar 

  8. Chong C-Y, Kumar SP. Sensor networks: evolution, opportunities, and challenges. Proc IEEE. 2003;91(8):1247–56.

    Article  Google Scholar 

  9. Youssef M, Youssef A, Younis M. Overlapping multi-hop clustering for wireless sensor networks. IEEE Trans Parallel Distrib Syst. 2009;20(12):1844–56.

    Article  Google Scholar 

  10. Devika G, Karegowda AG. A pragmatic study of LEACH and its descendant routing protocols in WSN. Int J Comput Intell Inform. 2015;4(4):300–7.

    Google Scholar 

  11. Devika G, Premasudha BG, Karegowda AG. A comparative study of energy efficient hierarchical wireless sensor network protocols. Int J Appl Res Inf Technol Comput. 2015;6(3):189.

    Article  Google Scholar 

  12. Valverde ME, Hernández-pérez T, Paredeslópez O. Review article edible mushrooms: improving human health and promoting edible mushrooms: improving human health and promoting. Int J Microbiol. 2015;2015:1–14.

    Article  Google Scholar 

  13. Chowdhury DR, Ojha S. An empirical study on mushroom disease diagnosis: a data mining approach. Int Res J Eng Technol. 2017;4(1):530–4.

    Google Scholar 

  14. Mirjalili S. The ant lion optimizer. Elsevier Adv Eng Softw. 2015;83:80–98.

    Article  Google Scholar 

  15. Yogarajan G, Revathi T. A discrete ant lion optimization (DALO) algorithm for solving data gathering tour problem in wireless sensor networks. Middle-East J Sci Res. 2016;24(10):3113–20.

    Google Scholar 

  16. Hall MA. Correlation-based feature selection for machine learning.PHD Thesis, Compuer science department, Waikota, 1999.

  17. Alkronz ES, Moghayer KA, Meimeh M, Gazzaz M, Abu-Nasser BS, Abu-Naser SS. Prediction of whether mushroom is edible or poisonous using back-propagation neural network. Int J Acad Appl Res. 2019;3(2):1–8.

    Google Scholar 

  18. Verma SK, Dutta M. Mushroom classification using ANN and ANFIS algorithm. OSR J Eng. 2018;08(01):94–100.

    Google Scholar 

  19. Beniwal S, Das B. Mushroom classification using data mining techniques. Int J Pharma Bio Sci. 2015;6(1):1170–6.

    Google Scholar 

  20. Wibowo A, Rahayu Y, Riyanto A, Hidayatulloh T (2018) Classification algorithm for edible mushroom identification. In: 2018 international conference on information and communications technology (ICOIACT), Yogyakarta; 2018, pp. 250–253. https://doi.org/10.1109/ICOIACT.2018.8350746.

  21. Pavankumar P, Agarwal R. CBIR: classification based association rules and approaches in datamining. Int J Pure Appl Math. 2018;119(18):689–702.

    Google Scholar 

  22. Pinky NJ, Mohidul Islam SM, Alice RS. Edibility detection of mushroom using ensemble methods. Int J Image Graphics Signal Process. 2019;4:55–62.

    Article  Google Scholar 

  23. Han J, Kamber M. Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. San Francisco: Morgan Kaufmann; 2001.

    Google Scholar 

  24. Agresti A. An introduction to categorical data analysis. Wiley series in probability and mathematical statistics. 2nd ed. New Jersey: Wiley-Interscience; 2007.

    Book  Google Scholar 

  25. Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley; 1989.

    MATH  Google Scholar 

  26. Nizal I, Shaharanee M, Jamil J. Evaluation and optimization of frequent association rule based classification. Asia-Pac J Inf Technol Multimed. 2014;3(1):1–13.

    Google Scholar 

  27. Zhou XJ, Dillon TS. A statistical-heuristic feature selection criterion for decision tree induction. IEEE Trans Pattern Anal Mach Intell. 1991;13(8):834–41.

    Article  Google Scholar 

  28. Ismail S, Zainal AR, Mustapha A (2018) Behavioural features for mushroom classification. In: 2018 IEEE symposium on computer applications & industrial electronics (ISCAIE). https://doi.org/10.1109/iscaie.2018.8405508.

  29. Karegowda AG, Jayaram MA, Manjunath AS. Feature subset selection problem using wrapper approach in supervised learning. Int J Comput Appl (IJCA) Impact factor 08. 2010;1:13–7.

    Google Scholar 

  30. Karegowda AG, Manjunath AS, Jayaram MA. Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag IJTKM. 2010;3(2):271–7.

    Google Scholar 

  31. Karegowda AG, Jayaram MA, Manjunath AS. Feature subset selection using cascaded GA & CFS: a filter approach in supervised learning. Int J Comput Appl (IJCA). 2011;1:1–5.

    Google Scholar 

  32. Karegowda AG, Jayaram MA, Manjunath AS. Cascading k-means with ensemble learning: enhanced categorization of diabetic data. J Intell Syst. 2012;23(3):237–54.

    Google Scholar 

  33. Karegowda AG. Enhanced categorization of wheat seeds by integrating ensemble methods with decision tree identified significant features. Int J Data Min Emerg Technol. 2014;4(1):10–5.

    Article  Google Scholar 

  34. Karegowda AG, Jayaram MA. Significant feature set driven, optimized FFN for enhanced classification. Int J Comput Intell Inform. 2013;2(4):248–55 (ISSN: 2231-0258, ISSN: 2231-0258).

    Google Scholar 

  35. Kandris D, Nakas C, Vomvas D. Applications of wireless sensor networks: an up-to-date survey. Appl Syst Innov. 2020;3:14. https://doi.org/10.3390/asi3010014.

    Article  Google Scholar 

  36. Wang Y, Du J, Zhang H, Yang X. Mushroom toxicity recognition based on multigrained cascade forest. Sci Program. 2020. https://doi.org/10.1155/2020/8849011 (Article ID 8849011).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Devika.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Soft Computing and its Engineering Applications” guest edited by Kanubhai K. Patel, Deepak Garg, Atul Patel and Pawan Lingras.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devika, G., Ramesh, D. & Karegowda, A.G. Mushroom Edibility Identification Applying CBR and Ant Lion Techniques in Multi-sensor Environment. SN COMPUT. SCI. 2, 225 (2021). https://doi.org/10.1007/s42979-021-00582-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00582-z

Keywords