Zuhairan Yunmi Yunan and W. Alejandro Pacheco-Jaramillo
This paper aims to examine various indicators related to corruption and determine their impact on financial globalization in emerging countries. It will consider other factors…
Abstract
Purpose
This paper aims to examine various indicators related to corruption and determine their impact on financial globalization in emerging countries. It will consider other factors that may impact financial globalization and focus on how corruption within political, executive and public sector institutions can affect this process.
Design/methodology/approach
This paper uses a generalized method of moments (GMM) for a data sample of emerging countries covering 2000–2020. Corruption measurements are derived from the varieties of democracy data sets and Transparency International. It also includes data on foreign direct investment, portfolio flows, foreign exchange and international debt as separate indicators of financial globalization. These measures provide more detailed information on the types of financial transactions occurring across countries.
Findings
The results reveal that foreign investors may be less likely to enter certain sectors of the economy due to concerns about unethical practices and difficulties navigating the regulatory landscape in countries with high levels of corruption. This can lead to underdevelopment in sectors that are attractive to foreign investment and a reliance on a narrow range of sectors.
Originality/value
This paper offers valuable insights by integrating corruption and financial globalization indicators, using the GMM for robust analysis. It highlights how corruption influences foreign investment decisions, potentially leading to sectoral underdevelopment and overreliance in emerging countries.
Details
Keywords
Shokoofa Mostofi, Sohrab Kordrostami, Amir Hossein Refahi Sheikhani, Marzieh Faridi Masouleh and Soheil Shokri
This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining…
Abstract
Purpose
This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.
Design/methodology/approach
This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.
Findings
The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.
Originality/value
The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.