Suchergebnisse

This article analyzes a three-sector economy with a significant subsistence sector. Previous studies essentially derived first-best conditions based on an optimal subsidy system where technical efficiency is achieved. In our model, technical efficiency cannot exist from within our system. A unique feature of our model is the existence of an endogenous subsidy implying that a full-employment subsidy can only be non-uniform. Adjustment in the endogenous subsidy by the amount of wage differentials will equalize wage rates in the market economy. Full employment can be achieved if and only if the marginal products of the two modern sectors are equalized with the subsistence wage. That is, significant reduction of the subsistence sector is a necessary and sufficient condition for acquiring full employment in a three-sector economy.

Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.