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Mohammad Farhad Bulbul

Work place: Department of Mathematics, Jashore University of Science and Technology, Jashore-7408, Bangladesh

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Research Interests: Image Processing, Pattern Recognition, Computer Vision, Computational Learning Theory

Biography

Mohammad Farhad Bulbul is currently working as an Assistant Professor in the Department of Mathematics at Jashore University of Science and Technology, Jashore. He received his PhD degree from the Department of Information Science, Peking University, Beijing, China, in 2016. His PhD research direction was “Statistical Learning and Intelligent Information Processing”. He achieved the Peking University President’s award for his outstanding performance in doctoral study (Including course work and research). From 2012 to 2016, he held the research assistantship position with the school of Mathematical Sciences, Peking University. He has published some papers in SCIE indexed journals (Clarivate Analytics journals), IEEE International Conference Proceedings (EI Indexed) and Lecture Notes in Computer Science (Springer-Verlag, EI Indexed). His research focuses on Computer Vision, Deep Learning, Pattern Recognition, and Image Processing.

Author Articles
Comparison on Trapezoidal and Simpson’s Rule for Unequal Data Space

By Md. Nayan Dhali Mohammad Farhad Bulbul Umme Sadiya

DOI: https://doi.org/10.5815/ijmsc.2019.04.04, Pub. Date: 8 Nov. 2019

Numerical integration compromises a broad family of algorithm for calculating the numerical value of a definite integral. Since some of the integration cannot be solved analytically, numerical integration is the most popular way to obtain the solution. Many different methods are applied and used in an attempt to solve numerical integration for unequal data space. Trapezoidal and Simpson’s rule are widely used to solve numerical integration problems. Our paper mainly concentrates on identifying the method which provides more accurate result. In order to accomplish the exactness we use some numerical examples and find their solutions. Then we compare them with the analytical result and calculate their corresponding error. The minimum error represents the best method. The numerical solutions are in good agreement with the exact result and get a higher accuracy in the solutions.

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