درخت حوزه‌های تخصصی

This research aims to design an integrated model for digital marketing based on marketing intelligence and validate the model in the ISACO Company. The research uses a mixed-methods descriptive survey design. In the qualitative phase, the model and nine theoretical hypotheses were proposed using grounded theory and a semi-structured questionnaire. Fourteen academic experts and managers in the area of marketing and sales were interviewed until theoretical saturation was achieved. Then, the model and proposed hypotheses were validated using the quantitative method of structural equation modeling. A questionnaire rated by the Likert scale was distributed among 340 active employees in the relevant areas to the research topic with at least a Bachelor’s degree chosen by the convenience sampling method in the ISACO Company and affiliated companies. The structural model and hypotheses related to causal factors, core category, contextual factors, strategies, and consequences (except the moderating role of environmental factors) were validated by the research findings.

The purpose of this article is to measurement and testing a high performance organizational culture model in Iran's petrochemical industries.This research is of quantitative type and in terms of purpose, exploratory and data collection method is survey and from the point of view of cross-sectional time. Data collection tool is a researcher-made questionnaire whose validity has been confirmed by industrial and academic elites. The reliability of the questionnaire was confirmed by Cronbach's alpha test (coefficient of 0.815). The statistical population of the study included 8050 employees of Petropars, Jam, Lavan and Fanavaran petrochemical companies working in Iranian Petrochemical Industries. The sample size was calculated by the Cochran's formula of 385 people. 420 questionnaires were distributed and 400 correct questionnaires were collected. The sampling method is stratified random. Data analysis was performed using structural equation modeling using Amos Software. The results of statistical analysis were confirmed by confirmatory factor analysis and in terms of significance by the method of structural equations up to the level of the main categories and after confirmation of formal and content from 400 experts and managers of petrochemical industries. In the next step with a quantitative approach, the current state of high performance organizational culture according to the pattern the extract was assayed. For this purpose, mean comparison, regression and repetition tests used. The results obtained from field survey indicated that the high-performance organizational culture model was approved and dimensions of Behavioral factors, Internal bedding factors, External bedding factors and Process factors were placed in the first to fourth priorities, respectively.

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.