Work place: American International University- Bangladesh, Dhaka, 1213, Bangladesh
Research Interests: Data Mining, Data Structures and Algorithms, Analysis of Algorithms
Sharfuddin Mahmood has completed his B.Sc and M.Sc degree in Computer Science from American International University- Bangladesh. His major was Information and Database Technologies. Currently his is focusing on Data Mining technologies and algorithms. His area of research is Data mining and knowledge discovery, Software Engineering and intelligent systems.
DOI: https://doi.org/10.5815/ijmsc.2023.03.04, Pub. Date: 8 Aug. 2023
Since the inception of Blockchain, the computer database has been evolving into innovative technologies. Recent technologies emerge, the use of Blockchain is also flourishing. All the technologies from Blockchain use a mutual algorithm to operate. The consensus algorithm is the process that assures mutual agreements and stores information in the decentralized database of the network. Blockchain’s biggest drawback is the exposure to scalability. However, using the correct consensus for the relevant work can ensure efficiency in data storage, transaction finality, and data integrity. In this paper, a comparison study has been made among the following consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Vote (PoV). This study aims to provide readers with elementary knowledge about blockchain, more specifically its consensus protocols. It covers their origins, how they operate, and their strengths and weaknesses. We have made a significant study of these consensus protocols and uncovered some of their advantages and disadvantages in relation to characteristics details such as security, energy efficiency, scalability, and IoT (Internet of Things) compatibility. This information will assist future researchers to understand the characteristics of our selected consensus algorithms.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2016.04.05, Pub. Date: 8 Apr. 2016
Software testers should prioritize test cases so that important ones are run earlier in the regression testing process to reduce the cost of regression testing. Test case prioritization techniques schedule test cases for execution in an order that improves the performance of regression testing. One of the performance goals i.e. the fault detection rate, measures how quickly faults are detected during the testing process. Improved rate of fault dependency detection can provide faster feedback on software and let developers debug the leading faults at first that cause other faults to appear later. Another performance goal i.e. severity detection rate among faults, measures how quickly more severe faults are detected earlier during testing process. Previous studies addressed the second goal, but did not consider dependency among faults. In this paper an algorithm is proposed to prioritize test cases based on rate of severity detection associated with dependent faults. The aim is to detect more severe leading faults earlier with least amount of execution time and to identify the effectiveness of prioritized test case.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2015.08.04, Pub. Date: 8 Aug. 2015
Advances in computer mediated communication technologies have sparked and continue to facilitate the proliferation of online courses, degree programs, and educational institutions. Leading the way with these advances has been the use of asynchronous discussion forums. However merely setting up a discussion forum does not always ensure quality participation and interaction. The way the course is managed has an impact on the participation as well. This paper compares the difference in course management over four study periods and discusses the resulting consequences on the participation and achievement of the students. This paper also investigates the quality of interaction as perceived by fully online students. The main benefits of this research are that it provides a guideline regarding what course management factors can make the difference in online participation in fully online courses, and how the quality of interaction can be designed.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2015.06.06, Pub. Date: 8 Jun. 2015
K-means algorithm is one of the most popular algorithms for data clustering. With this algorithm, data of similar types are tried to be clustered together from a large data set with brute force strategy which is done by repeated calculations. As a result, the computational complexity of this algorithm is very high. Several researches have been carried out to minimize this complexity. This paper presents the result of our research, which proposes a modified version of k-means algorithm with an improved technique to divide the data set into specific numbers of clusters with the help of several check point values. It requires less computation and has enhanced accuracy than the traditional k-means algorithm as well as some modified variant of the traditional k-Means.[...] Read more.
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