Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.
Competitive advantage is an important issue emphasized in management and strategic planning over the past few years. This study was aimed to comprehensively evaluate competitive advantage and design and explain a hybrid model of competitive advantage based on Bourdieu capital theory and competitive intelligence using fuzzy Delphi and ISM-Gray DEMATEL in Iran Food Industry. This article applies a method to managers, with the findings indicating that the firms will not be able to achieve competitive advantage in the market unless they are formed by a high start-up capital and a high competitive intelligence and awareness on the part of the managers as to the business conditions. Such factors enable organizations to make better use of their own cultural and social capitals. Also, the optimal use of social and cultural capitals ameliorates the competitive advantages of the organizations. The authenticity and the economic values added of organizations are further enhanced by obtaining competitive advantage. Promoting organizations' credit and brand improves their economic and market value added, which, in turn augment their capitals and economic assets.
The collaboration between the universities and industries is currently in the focus of attention globally. Governments, universities, and industries are interested in good and effective collaboration, which would be beneficial for all parties. To foster University-Industry Collaboration, and to help transfer the knowledge and technology between these two parties, academics, politicians and companies are paying attention to science and technology policies more than ever. In this study, the factors affecting the improvement of University-Industry Collaboration are identified and prioritized. In the first step, 20 factors are identified and 12 factors are selected using the Fuzzy Delphi method. Then, using the BWM method, prioritizing the extracted factors is determined for industry sponsorship of the university research. Finally, based on the results, the discussion is conducted and six major strategies are presented to improve this relationship. .
Nowadays, it is impossible for retailers to establish a competitive and successful store in the marketplace through making a distinction in the outer facades of their stores, altering the pricing system, and diversifying the products offered in the store, due to the intense competition emerged in retailing markets. This leads the retailing markets to a new concept of marketplace, which is called "teaser retailing". In this research, we first explain some related variables such as emotional and social stimulants, emotional-perceptual indicators, price of product, customer satisfaction, and customer loyalty. Then we investigate the impact of emotional and social stimulants and price of products on emotional-perceptual indicators as well as the relationship between emotional-perceptual indicators and customer satisfaction, and finally the relationship between customer satisfaction and his/her loyalty. The first hypothesis was associated with the effect of the store emotional stimulants on emotional-perceptual indicators and it was not confirmed. After investigation of the second hypothesis, it was cleared that the store social stimulants affects the buyer's emotional-perceptual indicators in purchasing point. On the other hand, the third hypothesis indicated that price of products affects the buyer's emotionalperceptual indicators in purchasing point. The fourth hypothesis indicated that emotionalperceptual indicators affect the customer satisfaction, and finally the fifth one indicated that the customer satisfaction leads to his/her loyalty.
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