Browse Journals

[1] T. A. Akugre, K. Nantomah and M. M. Iddrisu, On certain properties of a degenerate sigmoid function, Eur. J. Math. Anal. 3 (2023), 17.

DOI: https://doi.org/10.28924/ada/ma.3.17

[2] T. A. Akugre, K. Nantomah and M. M. Iddrisu, On some properties of the degenerate hyperbolic functions, J. Math. Anal. Model. 5(1) (2024), 26-40.

DOI: https://doi.org/10.48185/jmam.v5i1.961

[3] Y. J. Bagul and C. Chesneau, Sigmoid functions for the smooth approximation to the solute value function, Moroccan J. of Pure and Appl. Anal (NJPAA) 7(1) (2021), 12-19.

DOI: https://doi.org/10.2478/mjpaa-2021-0002

[4] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2016.

[5] J. Dombi and T. Jonas, The generalized sigmoid function and its connection with logical operators, Int. J. Approx. Reasoning 143 (2022), 121-138.

DOI: https://doi.org/10.1016/j.ijar.2022.01.006

[6] J. Han and C. Moraga, The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning. In: J. Mira,  F. Sandoval (eds) From Natural to Artificial Neural Computation. IWANN 1995, Lecture Notes in Computer Science, Vol 930, Springer, Berlin, Heidelberg, 1995.

DOI: https://doi.org/10.1007/3-540-59497-3_175

[7] D. W. Hosmer and S. Lemeshow, Applied Logistic Regression, Wiley, New York, Second Edition, 2000.

[8] N. Kyurkchiev and S. Markov, Sigmoid functions: Some approximation and modelling aspects: Some moduli in programming environment MATHEMATICA, Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2015.

[9] K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.