The aim of this research was to develop an artificial neural network (ANN) in order to design a nanoparticulate oral drug delivery system for insulin. The pH of polymer solution (X1), concentration ratio of polymer/insulin (X2) and polymer type (X3) in 3 level including methylated N-(4-N,N- dimethyl aminobenzyl) chitosan, methylated N-(4-pyridinyl) chitosan, and methylated N-(benzyl) chitosan are considered as the input values and the particle size, zeta potential, PdI, and entrapment efficiency (EE %) as output data. ANNs are employed to generate the best model to determining the relationships between input and response values. In this research, a multi-layer percepteron with different topologies has been tested in order to define the one with the best accuracy and performance. The optimization was used by minimizing the error between the predicted and observed values. Three training algorithms (Levenberg-Marquardt (LM), Bayesian-Regularization (BR), and Gradient Descent (GD)) were employed to train ANNs with various numbers of nodes, hidden layers and transfer functions by random selection. The accuracy of prediction data were assayed by the mean squared error (MSE).The ability of all algorithms was in the order: BR>LM>GD. Thus, BR was selected as the best algorithm.
It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced magnetic resonance images of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.
The food industry is one of the strategic industries in developing countries, such as Iran and plays a critical role in the economy, food security, and public health. The growing populations can only have food security when the food industry's supply chain is sustainable. Therefore, due to the sustainable food supply chain's great importance, having technological capabilities compared to others is considered a competitive advantage for the companies involved in the food industry, as it can distinguish them as pioneer actors. Although many technologies have been investigated and used in the sustainable supply chain recently, no study has focused on identifying and ranking key technological capabilities related to the food industry in sustainable supply chain management. Also, we have not found any study using the ISM-MICMAC method to identify, rank, and interdependence between key technology capabilities in supply chain sustainability. Accordingly, the present study sought to identify and rank key technological capabilities in the supply chain sustainability of food industry companies. In this study, after reviewing the relevant literature, eleven technological capabilities in supply chain sustainability were identified. Then using experts' opinions and Interpretive Structural Modelling (ISM), interdependence among the technological capabilities ranked. Finally, dependent and independent drivers were presented using the MICMAC analysis. The ISM analysis results indicated that communication and information technology infrastructure was the most significant driver for other technological capabilities in companies' supply chain sustainability. Moreover, logistic optimization is imperative for improving supply chain sustainability performance. Therefore, if logistic optimization is appropriately implemented, it can improve supply chain sustainability. The present study results can increase supply chain productivity and effectiveness in Iranian food industry companies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.