درخت حوزه‌های تخصصی

Every company should think about producing new goods, the production of alternative goods is necessary in order to stabilize the level of sales or new sales; Customers also want new products, and if the company neglects to do so, competitors will certainly do so. Therefore, the present article seeks to provide a model appropriate to the conditions of gas refining companies for product development. In this study, the qualitative research method has been used as a grand theory; Using theoretical sampling and theoretical saturation, 20 experts of gas refining companies (Parsian, South Pars, Fajr Jam) were interviewed. Based on the basic theory, the data were analyzed in three stages of coding (open, axial and selective) using MAXQDA software. In the open coding phase, the data were labeled conceptually and then those that were common to each other were named into a category; the obtained categories were 43 categories. In the central coding stage, logical relationships were found between the categories and the obtained categories were included in the open coding under more general categories; finally, in the selective coding stage, all categories were summarized in one main category. The main category of the research was named "Interfering factors in new product development". The final result of this research, according to the researcher's attention and reflection on the details of the process of each of the three refining companies, is to provide an existing common model for all three companies and also to provide a common desired model for all three companies. In the open coding phase, the data was labeled conceptually and then those that were common to each other were named into a category; The obtained categories were 43 categories. In the central coding stage, logical relationships were found between the categories and the obtained categories were included in the open coding under more general categories; Finally, in the selective coding stage, all categories were summarized in one main category. The main category of the research was named "Interfering factors in new product development". The final result of this research, according to the researcher's attention and reflection on the details of the process of each of the three refining companies studied, providing a common model for all three companies and also providing a common model for all three companies.

This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. The recently proposed Convolutional Neural Network (CNN) model is MobileNet, which has both offline and real-time accuracy and speed to provide fast and predictable real-time results. This also solved a problem related to the face that occurs in the identification and recognition of the face. This paper presents the different methods and models used by numerous researchers in literature to solve the issue of faces. They get a better result in using the highest number of layers. It is also noted that the combination of a machine learning approach with multiple image-based dataset increases the efficiency of the classifier to predict knowledge related to face detection and recognition