| Popis: |
Due to the significant increase in the usage of silver nanoparticles in different industries, the need of knowing their potential hazard on humans is an appealing area of research. Workers who work in such fields are the most threatened by the harmful effects that may occur due to the consistent exposure to Ag-NPs during work hours, where inhalation is considered the most common way by which they are exposed to airborne Ag-NPs. In order to avoid adverse effects of Ag-NPs we should have a clear insight about their possible toxicity on humans, and the factors that may trigger it. Classifying the toxicity of Ag- NPs as high toxicity, medium toxicity, and low toxicity will make it easier for us to distinguish between silver nanoparticles, and take the appropriate action toward those with high toxicity levels. This research proposes a multilayer feed forward classification artificial neural network for the purpose of classifying inhaled silver nanoparticles cytotoxicity levels on workers' alveolar epithelial cells. Artificial neural networks are well known for their ability of classification, where they can classify different data under the right category after being trained for several times. Actually, the choice of artificial neural networks to classify the cytotoxicity of Ag-NPs came from their ability to classify the cytotoxicity of new engineered Ag-NPs that may be manufactured in the future. Experiments were performed to evaluate the proposed method of classification. Results showed that the proposed method is reliable in the classification process. |
