درخت حوزه‌های تخصصی

This article deals with developing a model of digital marketing with a focus on customer experience and with the aim of optimizing the customer management process, increasing the quality and length of the customer journey (increasing loyalty), and improving the consequences of the marketing process in companies, and seeks to answer this question. What is the digital marketing model with the customer experience management approach and what components does it include? The research method is mixed (qualitative-quantitative) and since a model for this issue is not available in past research, the first part of the research explores model extraction through qualitative methods (here, foundation data analysis) and to determine Relationships within the model quantitatively explain the model using quantitative methods (here structural equations). The method of collecting information in the qualitative part, in addition to conducting library studies, in the form of in-depth interviews with experts in the fields of information technology, marketing, and commerce, information technology policy, and actually business managers and stores that provide services and products through channels and digital tools in this section, the statistical sample limit was carried out by the snowball method (saturation to the end of the problem) and the quantitative part of the research and explanation of the model was through data collection with questionnaire tools and closed interviews from the customers of several digital businesses, which was done with the method 385 people were randomly selected and available using Cochran's formula. In the end, the main conditions, background conditions, intervening conditions, phenomena, strategies, and consequences affecting the digital marketing model with the customer experience management approach were extracted and the relationships between them were determined.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.