درخت حوزه‌های تخصصی

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

Nowadays, customer satisfaction is regarded as a strategic and influential factor for the success of various businesses. On the other hand, tourism, the world's largest and most diverse industry, is also a significant source of income and job creation for many countries. Therefore, the present study examines the effect of nostalgia, experiential marketing, and experiential values as relatively novel concepts on satisfaction in the tourism industry. In light of the COVID-19 outbreak, the research population includes all tourists who have previously visited the Kashan Rosewater Festival. Furthermore, since the population size is uncertain, it is assumed unlimited. A convenience sampling method was used to select sample members from the population. Methodologically, it is a descriptive survey study based on the subject matter under investigation. It is also a correlational study. A partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the questionnaire results. Based on our findings, nostalgia affects pull motivation, patronage behavior, and future travel intention. However, pull motivation does not influence travel intentions.

Organizational DNA expresses a method of investigation, thinking, reflection and thought about organizations, which examines the patterns of organizations, management practices, leadership style and other concepts related to the organization. Organizational DNA uses very diverse approaches to understand and recognize the organization, instead of organizational models and forms, by examining issues such as teamwork, decision-making, and development and improvement of human resources as separate or at least independent variables. In this research, by using the theoretical framework and using quantitative and qualitative methods, it has been done to identify the dominant DNA in Tocafulad Holding companies and determine the relationship model of hidden components using covariance analysis. The statistical population includes 435 employees of Isfahan Tokafulad companies and 6 company experts who used a mixed or mixed design to conduct the research. The technique of structural equations has been used to improve the model and evaluate the components and indicators to check and identify the model. In order to answer the research question, the steps are: 1) presentation of the conceptual model of the relationship between the corporate DNA and the investigated components, 2) quantitative evaluation of the components and indicators of the model, 3) determining the mutual influence of the hidden variables and the obvious variables of the model and 4) the relationship Between the apparent variables of the components with each other and the verification of the initial conceptual model has been carried out and then the model has been improved and finally the dominant DNA has been determined. The research showed that semantic DNA with a factor load of 0.93 is the dominant DNA in 4 out of 6 companies under investigation. It was also found that planning and payment system are the least important and change processes are the most important in line with the improvement model. In the improved model based on covariance analysis, the mutual effects of payment system and planning with mission, leadership style with teamwork, performance management with decision-making, interpersonal relations with change processes and change processes with work group should be considered.