درخت حوزه‌های تخصصی

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

A strong brand differentiates the product from competitors' products, reduces search costs, minimizes perceived risks, and offers high quality from the customer's point of view. He considered that one of the most important problems of Iran's food industry is related to the brand name, especially in the field related to the internalization of awareness and association of the brand name in the customer's mind. Cognitive distortions, as psychological factors that cause perceptual errors in people, can be effective in their negative feelings and, as a result, affect the expected results of customers' behavior; Therefore, identifying the psychological components of branding in the food industry with the foundation's data approach (case study: Gaz Asari) is of great importance. The method of this research is qualitative and grounded theory method (foundation data) which has been used in this research due to some of its special features. The statistical population consists of interviews with 17 people, including all managers and expert managers in the Gaz Asari brand industry, which means analyzing the data obtained from the interviews, using the qualitative-inductive grounded theory method (including the steps of open coding, central coding, and selective coding). The results include 19 general categories in the form of a paradigm model, which include causal conditions (competitive pressure, changing customer characteristics, market uncertainty, understanding the importance of branding and lifestyle changes), central phenomenon (psychological branding), background conditions (sustainability of brand effects) flexible characteristics of the brand and branding skills, intervening conditions (customer, company capability, brand competition capacity and the challenges of Sanat Gaz administration) and strategies (differentiation strategy, modeling strategy, discovery strategy, and resistance strategy) and consequences (short consequences duration and long-term consequences).