Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes
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DOI: 10.1016/j.chaos.2015.11.029
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References listed on IDEAS
- Yilmaz, Ergin & Uzuntarla, Muhammet & Ozer, Mahmut & Perc, Matjaž, 2013. "Stochastic resonance in hybrid scale-free neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5735-5741.
- Xiaohu Li & Feng Xu & Jinhua Zhang & Sunan Wang, 2013. "A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, June.
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Cited by:
- Kaya, Ceren & Erkaymaz, Okan & Ayar, Orhan & Özer, Mahmut, 2018. "Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 164-174.
- Scabini, Leonardo F.S. & Bruno, Odemir M., 2023. "Structure and performance of fully connected neural networks: Emerging complex network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
- He, Haoming & Xiao, Min & Lu, Yunxiang & Wang, Zhen & Tao, Binbin, 2023. "Control of tipping in a small-world network model via a novel dynamic delayed feedback scheme," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
- Soriano-Sánchez, A.G. & Posadas-Castillo, C., 2018. "Smart pattern to generate small–world networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 415-422.
- Matjaž Perc & Mahmut Ozer & Janja Hojnik, 2019. "Social and juristic challenges of artificial intelligence," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.
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Keywords
Diabetic; Small-world network; Feed forward artificial neural network;All these keywords.
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