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Umme Sara
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Faculty Profile

 

Umme Sara

Assistant Professor &

Head Dept. of CSE                                                             

 

Contact Information

Name of the Dept.

Computer Science and Engineering

Phone

(+880)-1882567003

Email

ummesara@niter.edu.bd

Academic Details

M.Sc

Computer Science and Engineering,

Jahangirnagar University

B.Sc

Computer Science and Engineering,

Jahangirnagar University

Employments History

1.NITER(National Institute of Textile Engineering and research), Nayarhat , Savar, Dhaka

Working as a Lecturer

13th October ,2015 to till now

2.Gono University, Savar, Dhaka

Worked as a lecturer

14th March, 2014 to 12th October.

2015

   
Programming Competency:
Site : Online Judge (UVA )

 

Training

1.  Pedagogy Teaching Technique

Received training on for 15 days, 4th January to 19th January, 2018 run by SEIP project.

Research Interest

1.  Digital Image processing, Signal Processing, Computer Vision System, Biological Vision System

2.  Artificial intelligence, Machine learning, Deep learning ,Pattern recognition, Big Data Analysis

Publications

1.    U.Sara,  A. Rajbongshi, R. Shakilb, B. Akter, M.S.Uddin, ”VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system”, Data in Brief, Volume 43, 2022, 108422, ISSN 2352-3409. DOI: https://doi.org/10.1016/j.dib.2022.108422 

Available at: https://www.sciencedirect.com/science/article/pii/S235234092200619

2.    A.Rajbongshi ,S. Sazzad ,R. Shakilb,B. Akter,U.Sara,”A comprehensive guava leaves and fruits dataset for guava disease recognition”,Data in Brief,Volume 42, June 2022, 108174,Elsevier. DOI: https://doi.org/10.1016/j.dib.2022.108174 

(https://www.sciencedirect.com/science/article/pii/S235234092200378X?via%3Dihub)

3.    Umme Sara, Aditya Rajbongshi, Rashiduzzaman Shakil, Bonna Akter, Sadia Sazzad, Mohammad Shorif Uddin, An extensive sunflower dataset representation for successful identification and classification of sunflower diseasesData in Brief,Volume 42, 2022,108043, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108043. 

(https://www.sciencedirect.com/science/article/pii/S2352340922002542)

4.    Islam S, Sara U, Kawsar A, Rahman A, Kundu D, Dipta DD. Sgbba: an efficient method for prediction system in machine learning using imbalance dataset. Int. J. Adv. Comput. Sci. Appl. 2021;12.

5.   Anichur Rahman,Umme Sara et.al,”DistB-SDoIndustry: Enhancing Security in Industry 4.0 Services based on Distributed Blockchain through Software Defined Networking-IoT Enabled Architecture,” International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020. DOI10.14569/IJACSA.2020.0110980

6.   Umme SaraMorium AkterMohammad Shorif Uddin,” Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Computer and Communications,Vol.07 No.03(2019), Article ID:90911,11 pages.  Doi :   10.4236/jcc.2019.73002

7.   Mohammad Reduanul Haque, Shifat Jaman, Md Golam Saklayen, Md Mohsin Khondoker, Abu Bakkar Siddik, Umme Sara, Mohammad Shorif Uddin,” Towards the development of an energy-efficient smart home through IoT”, International Journal of Advanced Technology and Engineering Exploration, ISSN (Print):2394-5443    ISSN (Online):2394-7454,Vol-6,no.-58 September-2019 DOI:10.19101/IJATEE.2019.650052

8.   Umme Sara,” Study on Different Image Quality Assessment Techniques for Gray Scale Images,” FEB 2019 | IRE Journals | Volume 2 Issue 9 | ISSN: 2456-8880.

 
Conference

1.    B. Akter, A. Rajbongshi, S. Sazzad, R. Shakil, J. Biswas and U. Sara, "A Machine Learning Approach to Detect the Brain Stroke Disease," 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2022, pp. 897-901, doi: 10.1109/ICSSIT53264.2022.9716345.

 
 

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