Identification of suitable websites for digital marketing – an approach using bio-inspired computing

  • Authors

    • B. Suresh Kumar
    • Deepshikha Bharghava
    • Arpan Kumar Kar
    • Chinwe Peace Igiri
    2017-12-28
    https://doi.org/10.14419/ijet.v7i1.2.9313
  • Metaheuristics, Henry Garrett Ranking, Cuckoo Search, Analytics, Machine Learning, Internet Applications.
  • Due to the immense growth of Internet usage, the point of convergence has moved from physical to the web. The size of the web is increasing at a very fast pace to cater to the fast-evolving needs of the businesses, governments, and societies. However, selecting or identifying the best website is challenging. The practical issue to solve the problem comprises two parts. The first part is to identify the assessment criteria for appraising websites. Second is to evaluate the websites in the context of these assessment criteria and screen them to address a specific need. However, this objective is extremely complex and computationally extremely expensive. This research proposes an approach to identify websites from the Internet. The proposed integrated approach uses the Henry Garrett ranking method and cuckoo search algorithm for ranking and selection of websites for planning digital marketing campaigns.

  • References

    1. [1] Crovella, M.E. and A. Bestavros, Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Transactions on networking, 1997. 5(6): p. 835-846. https://doi.org/10.1109/90.650143.

      [2] Goldman, E., Search engine bias and the demise of search engine utopianism. Yale JL & Tech., 2005. 8: p. 188.

      [3] Charlesworth, A., Internet marketing: a practical approach. 2009: Routledge.

      [4] Maharana, B., K. Nayak, and N. Sahu, Scholarly use of web resources in LIS research: a citation analysis. Library Review, 2006. 55(9): p. 598-607. https://doi.org/10.1108/00242530610706789.

      [5] James, N. and H. Busher, Ethical issues in online educational research: protecting privacy, establishing authenticity in email interviewing. International Journal of Research & Method in Education, 2007. 30(1): p. 101-113. https://doi.org/10.1080/17437270701207868.

      [6] Wind, J. and A. Rangaswamy, Customerization: The next revolution in mass customization. Journal of interactive marketing, 2001. 15(1): p. 13-32. https://doi.org/10.1002/1520-6653(200124)15:1<13::AID-DIR1001>3.0.CO;2-#.

      [7] Rashid, S.M., K. Ghose, and D.A. Cohen, BRAND IDENTITY: INTRODUCING RENEWED CONCEPT FOR COFFEE SHOPS. PEOPLE: International Journal of Social Sciences, 2015. 1(1).

      [8] BystrÖm, K., Information and information sources in tasks of varying complexity. Journal of the Association for Information Science and Technology, 2002. 53(7): p. 581-591. https://doi.org/10.1002/asi.10064.

      [9] Khatwani, G., O. Anand, and A.K. Kar. Evaluating internet information search channels using hybrid MCDM technique. in International Conference on Swarm, Evolutionary, and Memetic Computing. 2014: Springer.

      [10] Mangani, A., Online advertising: Pay-per-view versus pay-per-click. Journal of Revenue and Pricing Management, 2004. 2(4): p. 295-302. https://doi.org/10.1057/palgrave.rpm.5170078.

      [11] Van Iwaarden, J., et al., Perceptions about the quality of web sites: a survey amongst students at Northeastern University and Erasmus University. Information & Management, 2004. 41(8): p. 947-959. https://doi.org/10.1016/j.im.2003.10.002.

      [12] Higgins, J.P., et al., Measuring inconsistency in meta-analyses. BMJ: British Medical Journal, 2003. 327(7414): p. 557. https://doi.org/10.1136/bmj.327.7414.557.

      [13] Chaffey, D. and F. Ellis-Chadwick, Digital marketing. 2012: Pearson Higher Ed.

      [14] Bahl, S., The role of green banking in sustainable growth. International Journal of Marketing, Financial Services and Management Research, 2012. 1(2): p. 27-35.

      [15] Yang, X.-S. and S. Deb. Cuckoo search via Lévy flights. in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. 2009: IEEE.

      [16] Tiago, M.T.P.M.B. and J.M.C. Veríssimo, Digital marketing and social media: Why bother? Business Horizons, 2014. 57(6): p. 703-708. https://doi.org/10.1016/j.bushor.2014.07.002.

      [17] Kozinets, R.V., E-tribalized marketing? The strategic implications of virtual communities of consumption. European Management Journal, 1999. 17(3): p. 252-264. https://doi.org/10.1016/S0263-2373(99)00004-3.

      [18] Yang, X.-S., Cuckoo search and firefly algorithm: overview and analysis, in Cuckoo Search and Firefly Algorithm. 2014, Springer. p. 1-26. https://doi.org/10.1007/978-3-319-02141-6_1.

      [19] Dailami, M. and M. Klein, Government support to private infrastructure projects in emerging markets. Dealing with public risk in private infrastructure, 1997. 17322: p. 21-42.

      [20] Gandomi, A.H., X.-S. Yang, and A.H. Alavi, Mixed variable structural optimization using firefly algorithm. Computers & Structures, 2011. 89(23): p. 2325-2336. https://doi.org/10.1016/j.compstruc.2011.08.002.

      [21] Kar, A.K., Bio inspired computing–A review of algorithms and scope of applications. Expert Systems with Applications, 2016. 59: p. 20-32. https://doi.org/10.1016/j.eswa.2016.04.018.

      [22] Sayadi, M., R. Ramezanian, and N. Ghaffari-Nasab, A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 2010. 1(1): p. 1-10. https://doi.org/10.5267/j.ijiec.2010.01.001.

      [23] Yang, X.-S., A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), 2010: p. 65-74.

      [24] Fister Jr, I., D. Fister, and X.-S. Yang, A hybrid bat algorithm. arXiv preprint arXiv:1303.6310, 2013.

      [25] Labbé, C. and D. Labbé, Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science? Scientometrics, 2013. 94(1): p. 379-396. https://doi.org/10.1007/s11192-012-0781-y.

      [26] Kumar, B.S., D. Bhargava, and R.C. Poonia. Fuzzy keyword search and ranking frame work of DRS based file information management system using TF-RDF ranking strategy. in Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies. 2014: ACM.

      [27] Ruxton, G.D., The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test. Behavioral Ecology, 2006. 17(4): p. 688-690. https://doi.org/10.1093/beheco/ark016.

      [28] Yang, X.-S., Firefly algorithm, Levy flights and global optimization. Research and development in intelligent systems XXVI, 2010: p. 209-218.

      [29] Yang, X.-S., Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2010. 2(2): p. 78-84. https://doi.org/10.1504/IJBIC.2010.032124.

      [30] Lukasik, S. and S. Zak. Firefly Algorithm for Continuous Constrained Optimization Tasks. in ICCCI. 2009: Springer. https://doi.org/10.1007/978-3-642-04441-0_8.

      [31] Senthilnath, J., S. Omkar, and V. Mani, Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 2011. 1(3): p. 164-171. https://doi.org/10.1016/j.swevo.2011.06.003.

      [32] Yang, X.-S. and S. Deb, Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 2010. 1(4): p. 330-343. https://doi.org/10.1504/IJMMNO.2010.035430.

  • Downloads

  • How to Cite

    Suresh Kumar, B., Bharghava, D., Kumar Kar, A., & Peace Igiri, C. (2017). Identification of suitable websites for digital marketing – an approach using bio-inspired computing. International Journal of Engineering & Technology, 7(1.2), 239-245. https://doi.org/10.14419/ijet.v7i1.2.9313