Skip to main content
SpringerLink
Log in

Selection of browsers for smartphones: a fuzzy hybrid approach and machine learning technique

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The telecommunication segment has grown tremendously over the past few decades. Particularly smartphones have now turned out to be essential and have outperformed many gadgets like computers, cameras, etc. In this current scenario, smartphones become an essential product for all kinds of consumers such as students, teachers, businessmen, etc. And the consumers also like an extensive number of enhanced and better-quality features being embedded into them. Along with this growth, there is a fast growth of mobile application software providers also. Apart from calling, many consumers use smartphones for browsing the internet. Many android developers provide browser application software with several advancements. This puts the consumers into confusion to select a better browser for their smartphone to accomplish their requirements. Hence the consumers need a proven methodology to select a better browser for their smartphones. To select a better browser, in this paper a hybrid multi-criteria decision making model is proposed by integrating grey relational analysis (GRA) and fuzzy analytical hierarchy process (FAHP). The findings are compared and validated through a machine learning approach also.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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. Aggarwal A, Choudhary C, Mehrotra D (2018) Evaluation of smartphones in Indian market using EDAS. Procedia Comput Sci 132:236–243. https://doi.org/10.1016/j.procs.2018.05.193

    Article  Google Scholar 

  2. Farshidi S, Jansen S, Jong RD, Brinkkemper S (2018) A decision support system for software technology selection. J Decis Syst 27:98–110. https://doi.org/10.1080/12460125.2018.1464821

    Article  Google Scholar 

  3. Gordon W (2013) Browser speed tests: Chrome 24, Firefox 18, Internet Explorer 10, and Opera 12.12. https://lifehacker.com/browser-speed-tests-chrome-24-firefox-18-internet-ex-5976082 Accessed 04 September 2020.

  4. Cervantes E (2018) Need for speed – What’s the fastest Android browser. https://www.androidauthority.com/best-fastest-android-browsers-337802/ Accessed 04 September 2020.

  5. Tsai WH, Lee PL, Chen SP, Hsu W, Lin TW (2009) A study of the selection criteria for enterprise resource planning systems. Int J Bus Syst Res 3:456–480. https://doi.org/10.1504/IJBSR.2009.027200

    Article  Google Scholar 

  6. Şen CG, Baraçli H, Şen S (2009) A literature review and classification of enterprise software selection approaches. Int J Inf Technol Decis Mak 8:217–238. https://doi.org/10.1142/S0219622009003351

    Article  Google Scholar 

  7. Ivancevich SH, Ivancevich DM, Elikai F (2007) Accounting software selection and satisfaction: a comparative analysis of vendor and user perceptions. Rev Bus Inf Syst 11(3):43–52. https://doi.org/10.19030/rbis.v11i3.4415

    Article  Google Scholar 

  8. Şahin M (2021) Location selection by multi-criteria decision-making methods based on objective and subjective weightings. Knowl Inf Syst 63:1991–2021. https://doi.org/10.1007/s10115-021-01588-y

    Article  Google Scholar 

  9. Naveed QN, Aseere AM, Muhammad AH, Islam S, Qureshi MRN et al (2021) Evaluating and ranking mobile learning factors using a multi-criterion decision-making (MCDM) approach. Intell Autom Soft Comput 29(1):111–129. https://doi.org/10.32604/iasc.2021.015009

    Article  Google Scholar 

  10. Kao JC, Wang CN, Nguyen VT, Husain ST (2022) A fuzzy MCDM model of supplier selection in supply chain management. Intell Autom Soft Comput 31(3):1451–1466. https://doi.org/10.32604/iasc.2022.021778

    Article  Google Scholar 

  11. Khan AW, Hussain I, Zamir M (2021) Analytic hierarchy process-based prioritization framework for vendor’s reliability challenges in global software development. J Softw Evol Process 33(3):e2310. https://doi.org/10.1002/smr.2310

    Article  Google Scholar 

  12. Puzovic S, Vasovic JV, Radojicic M, Paunovic V (2019) An integrated MCDM approach to PLM software selection. Acta Polytech Hung 16(4):45–65. https://doi.org/10.12700/APH.16.4.2019.4.3

    Article  Google Scholar 

  13. Deng JL (1989) Introduction to grey system. J Grey Sys 1(1):1–24

    MathSciNet  MATH  Google Scholar 

  14. Xue L, Cho YJ, He W, Yao L, Zou W (2021) Construction and application of the student satisfaction evaluation system for the extracurricular education services in colleges. Comput Appl Eng Educ 29(2):370–384. https://doi.org/10.1002/cae.22206

    Article  Google Scholar 

  15. Pitchipoo P, Venkumar P, Rajakarunakaran S (2013) Fuzzy hybrid decision model for supplier evaluation and selection. Int J Prod Res 51(13):3903–3919. https://doi.org/10.1080/00207543.2012.756592

    Article  Google Scholar 

  16. Ullah W, Ibrar M, Khan A, Khan M (2021) Multiple attribute decision making problem using GRA method with incomplete weight information based on picture hesitant fuzzy setting. Int J Intell Sys 36(2):866–889. https://doi.org/10.1002/int.22324

    Article  Google Scholar 

  17. Hong Z, Feng Y, Li Z, Wang Y, Zheng H, Li Z, Tan J (2019) An integrated approach for multi-objective optimisation and MCDM of energy internet under uncertainty. Future Gener Comput Sys 97:90–104. https://doi.org/10.1016/j.future.2019.02.046

    Article  Google Scholar 

  18. Sun L, Ma J, Zhang Y, Dong H, Hussain FK (2016) Cloud-FuSeR: Fuzzy ontology and MCDM based cloud service selection. Future Gener Comput Sys 57:42–55. https://doi.org/10.1016/j.future.2015.11.025

    Article  Google Scholar 

  19. Yang M, Nazir S, Qingshan Xu, Ali S (2020) Deep learning algorithms and multicriteria decision-making used in big data: a systematic literature review. Complexity 2836064:1–18. https://doi.org/10.1155/2020/2836064

    Article  Google Scholar 

  20. Brnabic A, Hess LM (2021) Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Med Inform Decis Mak 21:1–19. https://doi.org/10.1186/s12911-021-01403-2

    Article  Google Scholar 

  21. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:1–21. https://doi.org/10.1007/s42979-021-00592-x

    Article  MathSciNet  Google Scholar 

  22. Cavalcante IM, Frazzon EM, Forcellini FA, Ivanovc D (2019) A supervised machine learning approach to data-driven simulation of resilient Asupplier selection in digital manufacturing. Int J Inform Manage 49:86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004

    Article  Google Scholar 

  23. Barbareschi M, Barone S, Mazzocca N (2021) Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study. Knowl Inf Syst 63:1577–1596. https://doi.org/10.1007/s10115-021-01565-5

    Article  Google Scholar 

  24. Nieto Y, Gacía-Díaz V, Montenegro C, González CC, Crespo RG (2019) Usage of machine learning for strategic decision making at higher educational institutions. IEEE Access 7:75007–75017. https://doi.org/10.1109/ACCESS.2019.2919343

    Article  Google Scholar 

  25. Patel HH, Prajapati P (2018) Study and analysis of decision tree based classification algorithms. Int J Comput Sci Eng 6:74–78. https://doi.org/10.26438/ijcse/v6i10.7478

    Article  Google Scholar 

  26. Alonso JA, Teresa Lamata M (2006) Consistency in the analytic hierarchy process: a new approach. Int J Uncertain Fuzz 14(4):445–459. https://doi.org/10.1142/S0218488506004114

    Article  MATH  Google Scholar 

  27. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26. https://doi.org/10.1016/0377-2217(90)90057-I

    Article  MATH  Google Scholar 

  28. Alias MA, Hashim SZM, Samsudin S (2009) Using fuzzy analytic hierarchy process for southern Johor river ranking. Int J Adv Soft Comput Appl 1(1):62–76

    Google Scholar 

  29. Zadeh LA (1965) Fuzzy sets Inf Cont 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  Google Scholar 

  30. Zhang S, Meng F (2021) A group decision making method with intuitionistic triangular fuzzy preference relations and its application. Appl Intell 51:2556–2573. https://doi.org/10.1007/s10489-020-01879-x

    Article  Google Scholar 

  31. Arman H, Vencheh AH, Arman A, Moslehi A (2021) Revisiting the approximated weight extraction methods in fuzzy analytic hierarchy process. Int J Intell Sys 36(4):1644–1667. https://doi.org/10.1002/int.22355

    Article  Google Scholar 

  32. How to Build Decision Tree for Classification – (Step by Step Using Entropy and Gain) https://kindsonthegenius.com/blog/how-to-build-a-decision-tree-for-classification-step-by-step-procedure-using-entropy-and-gain/ Accessed 04 September 2020.

  33. Podgorelec V, Kokol P, Stiglic B, Rozman I (2002) Decision trees: an overview and their use in medicine. J Med Sys 26(5):445–463. https://doi.org/10.1023/A:1016409317640

    Article  Google Scholar 

  34. Triantaphyllou E (1997) A sensitivity analysis approach for some deterministic multi-criteria decision-making methods”. Decis Sci 28(1):151–194. https://doi.org/10.1111/j.1540-5915.1997.tb01306.x

    Article  Google Scholar 

  35. Pamučar DS, Božanić D, Ranđelović A (2017) Multi-criteria decision making: an example of sensitivity analysis. Serb J Manag 12(1):1–27. https://doi.org/10.5937/sjm12-9464

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. P.S.R Engineering College, Sivakasi, 626140, Tamil Nadu, India

    Ramathilagam Arunagiri, Pitchipoo Pandian, Valarmathi Krishnasamy & Ramani Ramasamy

  2. Ramco Institute of Technology, Rajapalayam, 626117, Tamil Nadu, India

    Rajakarunakaran Sivaprakasam

Authors
  1. Ramathilagam Arunagiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pitchipoo Pandian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valarmathi Krishnasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ramani Ramasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rajakarunakaran Sivaprakasam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

*Ramathilagam Arunagiri and Pitchipoo Pandin developed the decision model. * Ramathilagam Arunagiri, Valarmathi Krishnasamy and Ramani Ramasamy collected the data. * Ramathilagam Arunagiri and Ramani Ramasamy prepared the charts. *Ramathilagam Arunagiri, Pitchipoo Pandian and Rajakarunakaran Sivaprakasam prepared the manuscript. * All authors reviewed the manuscript.

Corresponding author

Correspondence to Ramathilagam Arunagiri.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arunagiri, R., Pandian, P., Krishnasamy, V. et al. Selection of browsers for smartphones: a fuzzy hybrid approach and machine learning technique. Knowl Inf Syst 65, 1963–1988 (2023). https://doi.org/10.1007/s10115-022-01778-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01778-2

Keywords

Search

Navigation