References
- Awad MA, Khan LR. Web navigation prediction using multiple evidence combination and domain knowledge. IEEE Trans Syst Man Cybern A Syst Hum. 2007;37(6):1054–1062. doi: 10.1109/TSMCA.2007.904781
- Awad MA, Khan L, Thuraisingham B. Predicting WWW surfing using multiple evidence combination. VLDB J. 2008;17(3):401–417. doi: 10.1007/s00778-006-0014-1
- Choudhary A, Rishi R, Dhaka VS, et al. Influence of introducing an additional hidden layer on the character recognition capability of a BP neural network having one hidden layer. Int J Eng Technol. 2010;2(1):24–28.
- Awad MA, Khalil I. Prediction of user’s web browsing behaviour: application of Markov model. IEEE Trans Syst Man Cybern B Syst Hum. 2012;42(4):1131–1142. doi: 10.1109/TSMCB.2012.2187441
- Jindal H, Sardana N. Web navigation prediction using Markov based models: an experimental study. Int J Web Eng Technol. 2016;4(11):1–25.
- Multiclass classification. Available from: https://en.wikipedia.org/wiki/Multiclass_classification (accessed on March, 2019).
- Chen J. Neural Network. Available from: https://www.investopedia.com/terms/n/neuralnetwork.asp (accessed on August 25, 2019).
- Narvekar M, Banu SS. Predicting user’s web navigation behavior using hybrid approach. Int Conf Adv Comput Technol Appl. 2015;45:3–12.
- Lucio F, Pessoa C. Multilayer perceptron versus hidden Markov models: comparisons and applications to image analysis and visual pattern recognisition. a Qualifying Examination Report, 1995.
- Le Hegarat-Mascle S, Bloch I, Vidal-Madjar D. Application of Dempster–Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens. 1997;35(4):1018–1031. doi: 10.1109/36.602544
- Yan XP, Xie YB, Xiao HL. Application of Dempster-Shafer theory to oil monitoring. Available from: http://www.plant-maintenance.com/articles/Dempster-Shafer.shtml (accessed on January, 2019).
- Talaveran A, Aguasca R, Galvan B, et al. Application of Dempster–Shafer theory for the quantification and propagation of the uncertainty caused by the use of AIS data. Reliab Eng Syst Saf. 2013;111:95–105. doi: 10.1016/j.ress.2012.10.007
- Dishashree Gupta. Fundamentals of deep learning – activation functions and when to use them? Available from: https://www.analyticsvidhya.com/blog/2017/10/fundamentals-deep-learning-activation-functions-when-to-use-them/ (accessed on 26-02-2019).
- Geron A. Hands-on machine learning with Scikit-learn & Tensorflow. concepts, tools, and techniques to build intelligent systems, O’Reilly, SPD, 2018.
- Jindal H, Sardana N. Analyzing performance of deep learning techniques for web navigation prediction. Procedia Computer Science (Accepted on August, 2019).
- Shaoanlu. SGD > Adam?? Which one is the best optimizer: dogs-vs-cats toy experiment. Available from: https://shaoanlu.wordpress.com/2017/05/29/sgd-all-which-one-is-the-best-optimizer-dogs-vs-cats-toy-experiment/.
- Bikal Basnet. Data mining, deep learning, NN: LSTM. LSTM Optimzer Choice. Available from: https://deepdatascience.wordpress.com/2016/11/18/which-lstm-optimizer-to-use/ (accessed on 26-02-2019).
- Mark D. How to pick the best learning rate for your machine learning project. Available from: https://medium.com/octavian-ai/which-optimizer-and-learning-rate-should-i-use-for-deep-learning-5acb418f9b2.