Md. Arshad Ali

Work place: Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan



Research Interests: Information Security, Information Systems, Information Retrieval, Information-Theoretic Security


Md. Arshad Ali received the Bachelor of Science in Computer Science and Engineering from Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur-5200, Bangladesh in the year of 2007. In May 2009, he joined as a Part-time Teacher in the Computer Science and Engineering Faculty, HSTU, Dinajpur-5200, Bangladesh. Then, he worked as a Lecturer in the same faculty from May 2010 to May 2013. Then, he became an Assistant Professor in May 2013. Currently he is a Master’s Course Student in the Graduate School of Natural Science and Technology, Okayama University, Japan. His research interest includes Information Security, Advance Encryption Standard, Pseudo random Binary Sequence, Elliptic Curve Cryptography, and Homomorphic Encryption. Now his research field is Pseudo Random Binary Sequence. He is a member of IEEE.

Author Articles
A study and Performance Comparison of MapReduce and Apache Spark on Twitter Data on Hadoop Cluster

By Md. Nowraj Farhan Md. Ahsan Habib Md. Arshad Ali

DOI:, Pub. Date: 8 Jul. 2018

We explore Apache Spark, the newest tool to  analyze big data, which lets programmers perform in-memory computation on large data sets in a fault tolerant manner. MapReduce is a high-performance distributed BigData programming framework which is highly preferred by most big data analysts and is out there for a long time with a very good documentation. The purpose of this project was to compare the scalability of open-source distributed data management systems like Apache Hadoop for small and medium data sets and to compare it’s performance against the Apache Spark, which is a scalable distributed in-memory data processing engine. To do this comparison some experiments were executed on data sets of size ranging from 5GB to 43GB, on both single machine and on a Hadoop cluster. The results show that the cluster outperforms the computation of a single machine by a huge range. Apache Spark outperforms MapReduce by a dramatic margin, and as the data grows Spark becomes more reliable and fault tolerant. We also got an interesting result that, with the increase of the number of blocks on the Hadoop Distributed File System, also increases the run-time of both the MapReduce and Spark programs and even in this case, Spark performs far more better than MapReduce. This demonstrates Spark as a possible replacement of MapReduce in the near future.

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Pseudo Random Ternary Sequence and Its Autocorrelation Property Over Finite Field

By Md. Arshad Ali Emran Ali Md. Ahsan Habib Md. Nadim Takuya Kusaka Yasuyuki Nogami

DOI:, Pub. Date: 8 Sep. 2017

In this paper, the authors have proposed an innovative approach for generating a pseudo random ternary sequence by using a primitive polynomial, trace function, and Legendre symbol over odd characteristics field. Let p be an odd prime number, FP be an odd characteristic prime field, and m be the degree of the primitive polynomial f(x) Let w be its zero and a primitive element in Fpm* In the beginning, a primitive polynomial f(x) generates maximum length vector sequence, then the trace function Tr(.) is used to map an element of the extension field (Fpm) to an element of the prime field Fthen non-zero scalar A∈Fp is added to the trace value, and finally the Legendre symbol (a/p) is utilized to map the scalars into ternary sequence having the values, {0,1,and -1} By applying the new parameter A the period of the sequence is extended to its maximum value that is n=pm-1 Hence, our proposed sequence has some parameters such as p,m,and A This paper mathematically explains the properties of the proposed ternary sequence such as period and autocorrelation. Additionally, these properties are also justified based on some experimental results.

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