درخت حوزه‌های تخصصی

Cancer is an abnormal cell growth that occurs uncontrollably within the human body and has the potential to spread to other organs. One of the primary causes of mortality and morbidity for people is cancer, particularly lung cancer. Lung cancer is one of the non-communicable diseases (NCDs), causing 71% of all deaths globally, and is the second most common cancer diagnosed worldwide. The effectiveness of treatment and the survival rate of cancer patients can be significantly increased by early and exact cancer detection. An important factor in specifying the type of cancer is the histopathological diagnosis. In this study, we present a Simple Convolutional Neural Network (CNN) and EfficientNetB3 architecture that is both straightforward and efficient for accurately classifying lung cancer from medical images. EfficientnetB3 emerged as the best-performing classifier, acquiring a trustworthy level of precision, recall, and F1 score, with a remarkable accuracy of 100%, and superior performance demonstrates EfficientnetB3’s better capacity for an accurate lung cancer detection system. Nonetheless, the accuracy ratings of 85% obtained by Simple CNN also demonstrated useful categorization. CNN models had significantly lower accuracy scores than the EfficientnetB3 model, but these determinations indicate how acceptable the classifiers are for lung cancer detection. The novelty of our research is that less work is done on histopathological images. However, the accuracy of the previous work is not very high. In this research, our model outperformed the previous result. The results are advantageous for developing systems that effectively detect lung cancer and provide crucial information about the classifier’s efficiency.

This study aimed to evaluate the role of the banking system and, in particular, the monetary policies and provision of bank facilities, in recession and boom periods on employment in the labor market. In this research, the most important variables were employment, bank and financial development index, volume of facilities provided to the private sector, bank facilities rate, wage rate, workforce, capital stock, liquidity, degree of economic openness, inflation rate, direct foreign investment, government expenditures, and oil revenues. The research tool was the smooth transfer regression (STR) model applied to evaluate the relationship between the variables during 1989-2016. According to the results, there was a nonlinear correlation between banking variables and employment. In the section of the nonlinear model, it was observed that with a 2.87% increase in the inflation rate, the banking indexes (e.g., money market development, the volume of facilities granted, and liquidity) had a significant and different impact on employment. In this respect, it was found that the indicators of monetary policy and bank facilities had a weak effect on employment of the country, demonstrating the improper association between monetary policies and workforce supply and demand in the labor market of the country.