Abstract
The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kazienko, P., Chawla, N.: Applications of Social Media and Social Network Analysis. Springer, Cham (2015)
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, ACL 2012, Stroudsburg, PA, USA, pp. 115–120. Association for Computational Linguistics (2012)
Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., Donaldson, L.: Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ Qual. Saf. 22(3), 251–255 (2013)
Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data 2(1), 5 (2015)
Kohli, C., Suri, R., Kapoor, A.: Will social media kill branding? Bus. Horiz. 58(1), 35–44 (2015)
Stavrianea, A., Kavoura, A., Giannakopoulos, G., Sakas, D.P., Kyriaki-Manessi, D.: Social media’s and online user-generated content’s role in services advertising. In: AIP Conference Proceedings, vol. 1644, pp. 318–324. AIP (2015)
Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)
Bastani, S., Jafarabad, A.K., Zarandi, M.H.F.: Fuzzy models for link prediction in social networks. Int. J. Intell. Syst. 28(8), 768–786 (2013)
Rini, D.P., Shamsuddin, S.M., Yuhaniz, S.S.: Particle swarm optimization for ANFIS interpretability and accuracy. Soft Comput. 20(1), 251–262 (2016)
Morteza Zanaganeh, S., Mousavi, J., Shahidi, A.F.E.: A hybrid genetic algorithmadaptive network-based fuzzy inference system in prediction of wave parameters. Eng. Appl. Artif. Intell. 22(8), 1194–1202 (2009)
Ahmed, K., Ewees, A.A., El Aziz, M.A., Hassanien, A.E., Gaber, T., Tsai, P.-W., Pan, J.-S.: A Hybrid Krill-ANFIS Model for Wind Speed Forecasting, pp. 365–372. Springer, Cham (2017)
Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Torabi, M.Y., Alimardani, F.: Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45(6), 1406–1413 (2012)
Boyacioglu, M.A., Avci, D.: An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst. Appl. 37(12), 7908–7912 (2010)
Rezaeianzadeh, M., Tabari, H., Yazdi, A.A., Isik, S., Kalin, L., Kalin, L.: Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 25(1), 25–37 (2014)
Ziasabounchi, N., Askerzade, I.: ANFIS based classification model for heart disease prediction. Int. J. Eng. Comput. Sci. 14, 7–12 (2014)
Hafez, A.I., Zawbaa, H.M., Emary, E., Mahmoud, H.A., Hassanien, A.E.: An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 19–24. IEEE (2015)
Chen, S., Yan, R.: Parameter estimation for chaotic systems based on improved boundary chicken swarm optimization. In: International Symposium on Optoelectronic Technology and Application 2016, p. 101571K. International Society for Optics and Photonics (2016)
Meng, X., Liu, Y., Gao, X., Zhang, H.: A new bio-inspired algorithm: chicken swarm optimization. In: International Conference in Swarm Intelligence, pp. 86–94. Springer (2014)
Roslina, Zarlis, M., Yanto, I.T.R., Hartama, D.: A framework of training ANFIS using chicken swarm optimization for solving classification problems. In: 2016 International Conference on Informatics and Computing (ICIC), pp. 437–441, October 2016
Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Ahmed, K., Hassanien, A.E., Ezzat, E., Bhattacharyya, S. (2018). Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-74690-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74689-0
Online ISBN: 978-3-319-74690-6
eBook Packages: EngineeringEngineering (R0)