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![Figure 2: General structure of ANN [5]](profile/Harun-Yazgan/publication/281743910/figure/fig2/AS:485904503578627@1492860069691/General-structure-of-ANN-5_Q320.jpg)
![Figure 4: Layers of ANFIS [7]](profile/Harun-Yazgan/publication/281743910/figure/fig3/AS:669282551734279@1536580803460/Layers-of-ANFIS-7_Q320.jpg)
![Figure 5: Learning System of Anfis [8]](profile/Harun-Yazgan/publication/281743910/figure/fig4/AS:669282551734280@1536580803474/Learning-System-of-Anfis-8_Q320.jpg)
Contexts in source publication
Context 1
... in the pharmaceutical market is a complex combination of push and pull driven demand and regulatory and pricing pressures (Figure 1). Third parties decide on formularies that influence prescription behavior, while pharmaceutical companies use lawyers, managed care, detailing, and direct-to-consumer campaigns to influence the Food and Drug Administration (FDA), pharmacy benefit providers, doctors and patients to prolong the patent, promote on formulary, prescribe and request their products [2]. ...
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Citations
The healthcare industry has been focusing efforts on optimizing inventory management procedures through the incorporation of Information and Communication Technology, in the form of tracking devices and data mining, to establish ideal inventory models. In this paper, a roadmap is developed towards a technological assessment of the Internet of Things (IoT) in the healthcare industry, 2010–2020. According to the roadmap, an IoT-based healthcare asset management system (IoT-HAMS) is proposed and developed based on Artificial Neural Network (ANN) and Fuzzy Logic (FL), incorporating IoT technologies for management to optimize the supply of resources.
