Simulation of Shariah compliant commodity backed currency system: a Turkish case-study

In view of the continuing episodes of financial crises faced by the Turkish economy, this study analyses the potential economic benefit of implementing the Grondona system of conditional currency convertibility. The authors performed simulations of the Grondona system based on Grondona's guidelines (1975) in order to examine the impact of the system's operations on the Turkish economy. For simulations, the annual data about Turkish primary commodity imports was retrieved using the WITS (World Integrated Trade Solution) software developed by the World Bank. The monthly data about primary commodity prices was accessed from the IndexMundi website. The authors used a program developed in C++ for performing simulations, and analyzed the simulation results by using Microsoft Excel. The results of the simulations clearly show the system's role in stockpiling reserves of primary imported commodities in response to a fall in market prices, and releasing the reserves during periods of rising prices. Such a mechanism helps to stabilise the prices of primary commodities and lessen the pressure on primary commodities markets during both slump and boom periods. Graphs are also included to show how the Commodities Reserve Department's (CRD) transactions would have caused corresponding changes in the Turkish money supply. These would have a stabilizing influence on the real value of the Turkish Lira in terms of the commodities handled. In addition the paper discusses the multiple reasons why the system has been judged to be Shariah-compliant

A Two Phase Interleaved Boost Single Stage PFC Converter using Flying Capacitor

The equipment connected to an electricity distribution network customarily needs rectification. In order to decrease DC voltage ripple, a Single phase diode rectifier utilizes a large electrolytic capacitor which yields a non-sinusoidal line current. So power factor correction (PFC) techniques are required. The boost topology is utmostwidespread than others in PFC applications. Thus a two phase interleaved boost single-stage PFC converter using flying capacitor is proposed in this paper. Due to its interleaved structure, the proposed converter can operate with reduced input current ripple and peak switch currents.The proposed system is simulated in MATLAB/SIMULINK and the performance parameters such as power factor (PF) and total harmonic distortion (THD) are computed

CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis

Emerging technologies for the management of COVID19: A review

The outbreak of COVID19 has put a halt on life over the globe. For a while, everything was stopped except the spread of disease and mortality rate. This has become the greatest challenge of decade to deal with it. Globally, scientists and researchers were busy in finding a way to deal with this deadly pandemic. As this pandemic breaks out a huge demand for healthcare equipment, medicinal facilities has been rises and Industry 4.0 seems to be a hope during this pandemic which has potential to satisfy all these needs. In the battle, against this pandemic branches of computer science: Artificial Intelligence(AI), Internet of Things(IoT), Robotics, Machine Learning(ML) and Deep Learning(DL) played very important roles. Without the help of IoT and Robotics it would be impossible for frontline warriors to remain contactless with an infected person. Meanwhile, rapid testing, prediction of disease, sentiment analysis of population and many more would be only possible due to presence ML and DL algorithms. Undoubtedly, if this pandemichappened before the emergence of AI, IoT, ML, DL and Robotics; then the aftermath will surely be something else. This paper will highlight the contribution of these technologies in handling this pandemic from its treatment to management. This paper will give idea about the role of technologies, their affects, solutions provided by them, improvement needed in healthcare facilities, their role in managing sentiments of public during pandemic. The innovative part of this paper is that we are exploring each field of industry 4.0 and observing which plays the most important role

Simulation of the Grondona System of Conditional Currency Convertibility Based on Primary Commodities, Considered as a Means to Resist Currency Crises

Currency crises are a significant feature of the present-day world economy, in which financial transactions are many times larger than monetary flows in the “real economy”, so that defending a currency’s exchange-rate is a major challenge for the governments of countries which may be smaller than a single large corporation. It is made even more difficult due to the United States government and its agents openly using economic pressures to try to force other countries to obey its orders, even including regime change. Guaranteed convertibility of a currency, such as maintaining a gold standard, can in principle help to stabilise its value, but this has been absent since the end of US dollar convertibility in 1971. The Grondona system of conditional currency convertibility was not planned as a counter-measure for currency crises. However the simulation of its operation demonstrated in this paper shows clearly how its automatic counter-cyclical stock-holding in response to movements in commodity prices—and so to exchange-rate movements that alter domestic commodity prices—causes monetary flows that would resist large exchange-rate movements (among other effects), and thereby tend to ameliorate a currency crisis. Moreover, it would achieve this without the need for international negotiations, agreements or other geopolitical trade-offs

Analytics of machine learning-based algorithms for text classification

Text classification is the most vital area in natural language processing in which text data is automatically sorted into a predefined set of classes. The application of text classification is wide in commercial works like spam filtering, decision making, extracting information from raw data, and many other applications. Text classification is more significant for many enterprises since it eliminates the need for manual data classification, a more expensive and time-consuming mechanism. In this paper, a comparative analysis of text classification is done in which the efficiency of different machine learning algorithms on different datasets is analyzed and compared. Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Random Forest (RF) are Machine Learning based algorithms used in this work. Two different datasets are used to make a comparative analysis of these algorithms. This paper further analyzes the machine learning techniques employed for text classification on the basis of performance metrics viz accuracy, precision, recall and f1- score. The resullltsss reveals that Logistic Regression and Support Vector Machine outperforms the other models in the IMDB dataset, and kNN outperforms the other models for the SPAM dataset as per the results obtained from the proposed system

An Analysis of Yusuf (AS)'s Counter-Cyclical Principle and its Implementation in the Modern World

Objective - This study examines the present-day implementation of the counter-cyclical principle suggested by Yusuf (AS) around four thousand years ago, in response to the King of Egypt's dream, to overcome the famine of seven years through saving grain during seven years of abundance. In general, the counter-cyclical principle encourages saving during times of plenty and spending during times of scarcity, activities which today help to stabilise the business-cycle.Method - Library research is applied since this paper relies on secondary data by thoroughly reviewing the most relevant literature. This paper reviews the commodity-based currency systems proposed before, during and after the Second World War by several prominent economists (particularly Keynes, 1938; Graham, 1940; Hayek, 1943; Grondona, 1950 and Lietaer, 2001) all of which basically incorporated the counter-cyclical principle of Prophet Yusuf (AS). The primary purpose of these commodity-based currency systems is to stabilise the real value of money in order to improve macroeconomic stability. Additionally, this paper provides an in-depth analysis of Grondona system of conditional currency convertibility.Results - The Grondona system would partially stabilise the real value of each country's national currency in terms of a range of durable, essential, basic imported commodities, thereby also partially stabilising the prices of the selected commodities in terms of the national currency of each country implementing the system.Conclusion - The Grondona system of conditional currency convertibility as compared to other commodity-based currency systems is more practical. Its primary advantage in comparison to other proposals of commodity reserve currency is that it could be implemented in parallel with the existing monetary system. Accordingly, it could be taken as a preliminary step towards a monetary system based on real money such as gold dinar.Keywords : Counter-cyclical principle; Grondona system; Commodity-based currency system (s).</p

Credit Risk and Profitability of Banking Sector in Sri Lanka

This paper aims to investigate the impact of credit risk on the profitability of the banking sector in Sri Lanka. The profitability is measured with and Return on Assets. At the same time, credit risk is quantified with four indicators: Non-performing loan Ratio (NPLR), Loan to Deposit Ratio (LDR), Net Charge off Ratio (NCOR), and Capital Adequacy Ratio (CAR). Data from thirteen banks over eight years from 2010 to 2017 was analyzed using panel data regression analysis. The finding shows that the Profitability of the Banking Sector in Sri Lanka has been determined by important determinants such as credit risk. The study further finds that non-performing loans have negative and significant return on assets. However, the net charge-off ratio and the loan to deposit ratio are not important variables for expanding the bank's profitability. On the other hand, the CAR positively impacts returns on assets. The study suggested the need to strengthen the management of credit risk in order to preserve Sri Lankan banks' current profitability