U. A. Md. Ehsan Ali

Work place: Dept. of Computer Science & Engineering, Stamford University Bangladesh, Dhaka, Bangladesh

E-mail: ehsan_cse@hstu.ac.bd


Research Interests: Artificial Intelligence, Computational Learning Theory, Image Processing, Data Mining


U. A. Md. Ehsan Ali received his B. Sc. degree in Computer Science and Engineering from Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh in 2013.  Now, he is pursuing M. Sc. degree in Computer Science and Engineering from Rajshahi University of Engineering & Technology (RUET), Rajshahi, Bangladesh. His main working interest is based on Image Processing, Expanding the Applications of Artificial Intelligence, Machine Learning, Data Mining, Data Security etc. Currently, he is working as an Assistant Professor in Dept. of Computer Science and Engineering in Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh. He has several scientific research publications in various aspects of Computer Science and Engineering.

Author Articles
EUR/USD Exchange Rate Prediction Using Machine Learning

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

DOI: https://doi.org/10.5815/ijmsc.2022.01.05, 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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Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

By Pankaj Bhowmik Pulak Chandra Bhowmik U. A. Md. Ehsan Ali Md. Sohrawordi

DOI: https://doi.org/10.5815/ijitcs.2021.05.03, Pub. Date: 8 Oct. 2021

A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

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A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload

By U. A. Md. Ehsan Ali Emran Ali Md. Sohrawordi Md. Nahid Sultan

DOI: https://doi.org/10.5815/ijmsc.2021.03.03, Pub. Date: 8 Aug. 2021

Security in digital communication is becoming more important as the number of systems is connected to the internet day by day. It is necessary to protect secret message during transmission over insecure channels of the internet. Thus, data security becomes an important research issue. Steganography is a technique that embeds secret information into a carrier such as images, audio files, text files, and video files so that it cannot be observed.  In this paper, based on spatial domain, a new image steganography method is proposed to ensure the privacy of the digital data during transmission over the internet. In this method, least significant bit substitution is proposed where the information embedded in the random bit position of a random pixel location of the cover image using Pseudo Random Number Generator (PRNG). The proposed method used a 3-3-2 approach to hide a byte in a pixel of a 24 bit color image. The method uses Pseudo Random Number Generator (PRNG) in two different stages of embedding process. The first one is used to select random pixels and the second PRNG is used select random bit position into the R, G and B values of a pixel to embed one byte of information. Due to this randomization, the security of the system is expected to increase and the method achieves a very high maximum hiding capacity which signifies the importance of the proposed method.

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