Md. Soumon Aziz Sarkar

Work place: Hajee Mohammad Danesh Science & Technology University, Dinajpur, 5200, Bangladesh



Research Interests: Data Structures and Algorithms, Data Mining, Distributed Computing, Systems Architecture, Artificial Intelligence, Computer systems and computational processes


Md. Soumon Aziz Sarkar obtained his B.Sc. degree in Computer Science and Engineering from Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh in 2017. He had gained extensive knowledge of coding, design, and software development cycles as well as proficiency in several programming languages. As a software developer, he has developed some systems on a freelance basis. He is currently interested in working on Artificial Intelligence, Machine Learning, Cryptocurrency, Data Mining, Database Systems, and Distributed Systems. In his spare time, he enjoys playing table tennis, reading books, and swimming.

Author Articles
EUR/USD Exchange Rate Prediction Using Machine Learning

By Md. Soumon Aziz Sarkar U. A. Md. Ehsan Ali

DOI:, Pub. Date: 8 Feb. 2022

Nowadays artificial intelligence is used in almost every sector of our day-to-day life. AI is used in preventative maintenance, quality control, demand forecasting, rapid prototyping, and inventory management among other places. Also, its use in the economic market has gained widespread. The use of artificial intelligence has made a huge contribution to price forecasting in the currency market or the stock market. This research work explores and analyzes the use of machine learning techniques as a linear regression in the EUR/USD exchange rate in the global forex market to predict future movements and compare daily and hourly data forecasts. As a reason for comparison, linear regression was applied in both hourlies and daily's almost equivalent data sets of the EUR/USD exchange rate and showed differences in results. Which has opened a new door of research on this market. It has been found that the percentage of accuracy of the daily data forecast is higher than the hourly data forecast at the test stage. 

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