1. 1. C. Bishop, Pattern Recognition and Machine Learning, http: //www. springer. com /in/ book/ 9780387310732; ISBN: 978-0-387-31073-2
   2. S. Sharma, R. Tiwari, A. Shukla, V. Singh, Identification of People Using Gait Biometrics, International Journal of Machine Learning and Computing 1 (2011) 409–415
   3. G. Vinodhini, R.M. Chandrasekaran, A comparative performance evaluation of neural network based approach for sentiment classification of online reviews, J. King Saud Univ. - Comput. Inf. Sci. 28 (2016) 2–12. doi:10.1016/j.jksuci.2014.03.024.
   4. M. Paliwal, U. a. Kumar, Neural networks and statistical techniques: A review of applications, Expert Syst. Appl. 36 (2009) 2–17. doi:10.1016/j.eswa.2007.10.005.
   5. L.M. Mina, N.O.R. Ashidi, M. a T. Isa, Breast Abnormality Detection in Mammograms Using Artificial Neural Network, (2015) 258–263. doi:10.1109/I4CT.2015.7219577.
   6. P. Li, Y. Wang, J. He, L. Wang, Y. Tian, T.-S. Zhou, et al., High performance personality heartbeat classification model for long-term ECG signal., IEEE Trans. Biomed. Eng. 9294 (2016). doi:10.1109/TBME.2016.2539421
   7. E.P. Ijjina, C. Krishna Mohan, Hybrid deep neural network model for human action recognition, Appl. Soft Comput. 46 (2015) 936–952. doi:10.1016/j.asoc.2015.08.025.
   8. H.K. Lam, U. Ekong, H. Liu, B. Xiao, H. Araujo, S.H. Ling, et al., A study of neural-network-based classifiers for material classification, Neurocomputing. 144 (2014) 367–377. doi:10.1016/j.neucom.2014.05.019.
   9. A. Nazemi, M. Dehghan, A neural network method for solving support vector classification problems, Neurocomputing. 152 (2015) 369–376. doi:10.1016/j.neucom.2014.10.054.
   10. Q. Nie, L. Jin, S. Fei, J. Ma, Neural network for multi-class classification by boosting composite stumps, Neurocomputing. 149 (2015) 949–956. doi:10.1016/j.neucom.2014.07.039.
   11. P. Szymczyk, M. Szymczyk, Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks, Neurocomputing. 148 (2015) 354–362. doi:10.1016/j.neucom.2014.06.025.
   12. S.T. Sarkar, A.P. Bhondekar, M. Macaš, R. Kumar, R. Kaur, A. Sharma, et al., Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification, Neural Networks. 71 (2015) 142–149. doi:10.1016/j.neunet.2015.07.014.
   13. A. Bahrammirzaee, A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems, Neural Comput. Appl. 19 (2010) 1165–1195. doi:10.1007/s00521-010-0362-z.
   14. D.O. Cardoso, D.S. Carvalho, D.S.F. Alves, D.F.P. Souza, H.C.C. Carneiro, C.E. Pedreira, et al., Financial credit analysis via a clustering weightless neural classifier, Neurocomputing. 183 (2016) 70–78. doi:10.1016/j.neucom.2015.06.105. 
   15. A. Shukla, R. Tiwari, R. Kala, Real Life Applications of Soft Computing, 2010, CRC Press, Taylor and Francis Group, LLC; ISBN: 978-1-4398-2287-6