Design and Development of Logistic Artificial Neural Network for Electronic Commerce
An-Gyoon Jeon1, Sang-Hyun Lee2

1An-Gyoon Jeon, Assistant Professor, Department of LINC+, Chonbuk National University, Chonbuk, Korea.
2Sang-Hyun Lee, Assistant Professor, Department of Computer Engineering, Honam University, Gwangju, Korea.
Manuscript received on 08 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1226-1230 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F12110476S519/2019©BEIESP
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Abstract: This study shows that logistic neural network can be a better alternative to diffusion modeling of technological innovation including electronic commerce in the neural network based integrated approach predicts and models the diffusion of technology innovation. Especially, diffusion model by logistic neural network shows better performance through discussion of different diffusion related dynamics such as internal and external influence of growth process and comparison of traditional diffusion theory. The results of this study can be applied to other similar technologies as well as e – commerce growth modeling.
Keywords: Logistic Neural Network, Mixed Model, Real-Time Scheduling, CAN Communication.
Scope of the Article: Artificial Intelligence