This study aims to investigate the influence of the volatility of exchange rates on manufacturing commodity exports in the ASEAN-5 (Indonesia, Singapore, Thailand, Malaysia, and the Philippines). The study used the ARCH/GARCH, ARDL, and Nonlinear ARDL to determine the symmetrical and asymmetrical influence of the volatility of the exchange rate on manufacturing exports in both the short run and long run. Five leading commodity exports for each of the ASEAN-5 countries were used and analyzed over the period January 2007-March 2019. Our strategy using the ARDL approach revealed that volatility has a significant influence on 13 commodity exports in the short term. While the Nonlinear ARDL approach revealed that volatility influenced 19 commodity exports. Additionally, in the long run, finding from ARDL and Nonlinear ARDL also indicates risk-averse behaviour by exporters. However, in the long run, the nonlinear model demonstrates that volatility asserts an asymmetric influence on nearly all commodity exports. With this, therefore, there is the need for policymakers to uphold steadiness in the exchange rate via the use of adequate foreign reserves and amplified the level of investment.
Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.
This study aims to determine the symmetric and asymmetric effects of exchange rate volatility and other explanatory variables (real exchange rate, industrial production index, and COVID-19) on sixteen (16) food products traded between Indonesia and the United States, Indonesia and China. The study used the ARCH/GARCH approach and estimate the volatility of the exchange rate. Linear and nonlinear autoregressive distributed lag (ARDL) were applied to estimate the short- and long-run effect for the period 2009:M1-2020:M12. Findings from the ARDL method indicate that, in the short-term exchange rate volatility has a significant positive/negative effect on many products exported and imported throughout the study period. Different results were found in the Nonlinear ARDL method where a significant effect occurred especially on the food products import. The result further indicates that exchange rate volatility has a more negative effect symmetrically or asymmetrically. These results imply that most Indonesian traders to the United States and China tend to behave as risk-averse in the long run when responding to the phenomenon of exchange rate volatility. As a measure of robustness, a quantile regression further confirms that exchange rate volatility consistently affects food product trade. With this, therefore, stable exchange rate policies are needed to lessen the harmful effect of volatility on trade flows and balance the risk-taking behaviour among importers and exporters.