Tahani M. Allam

Work place: Computer and Control Department, Faculty of Engineering, Tanta University, Tanta, Egypt

E-mail: tahany@f-eng.tanta.edu.eg


Research Interests:


Dr. Tahani M. Allam, a Lecturer at the Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Egypt. Tahani was born in Kuwait, and her B.Sc., M.Sc., and Ph.D. degrees have taken from the Computers and Control Engineering Department, Faculty of Engineering, Tanta University in 2002, 2010, and 2018, respectively. Tahani works as a Consultant Engineer on Management Information Systems (MIS) Project, Tanta University, Egypt, from 2010 until now. Also, I am currently working as Director of the Functional Performance Evaluation Unit at Tanta University. Her research interests include Cloud Computing, Artificial Intelligence, Machine Learning, Security, and the Internet of Things. Tahani has published more than 6 articles in various refereed international journals and conferences. (email: Tahany@f-eng.tanta.edu.eg)





Author Articles
MOTIFSM: Cloudera Motif DNA Finding Algorithm

By Tahani M. Allam

DOI: https://doi.org/10.5815/ijitcs.2023.04.02, Pub. Date: 8 Aug. 2023

Many studying systems of gene function work depend on the DNA motif. DNA motifs finding generate a lot of trails which make it complex. Regulation of gene expression is identified according to Transcription Factor Binding Sites (TFBSs). There are different algorithms explained, over the past decades, to get an accurate motif tool. The major problems for these algorithms are on the execution time and the memory size which depend on the probabilistic approaches. Our previous algorithm, called EIMF, is recently proposed to overcome these problems by rearranging data. Because cloud computing involves many resources, the challenge of mapping jobs to infinite computing resources is an NP-hard optimization problem. In this paper, we proposed an Impala framework for solving a motif finding algorithms in single and multi-user based on cloud computing. Also, the comparison between Cloud motif and previous EIMF algorithms is performed in three different motif group. The results obtained the Cloudera motif was a considerable finding algorithms in the experimental group that decreased the execution time and the Memory size, when compared with the previous EIMF algorithms. The proposed MOTIFSM algorithm based on the cloud computing decrease the execution time by 70% approximately in MOTIFSM than EIMF framework. Memory size also is decreased in MOTIFSM about 75% than EIMF.

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