Profile of Md. Sohrawordi

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Md. Sohrawordi

Assistant Professor

Department of Computer Science and Engineering (CSE)

Faculty of Computer Science and Engineering

Hajee Mohammad Danesh Science & Technology University, Dinajpur.

E-mail: mdsohrawordi@hstu.ac.bd

Mobile: +8801722980888


CAREER OBJECTIVE

    To obtain a position in a research environment where I can maximize my educational and training experience and enhance my capabilities to research, teach, and develop new technologies for mankind.

RESEARCH INTEREST

    Bioinformatics, Biomedical, Image Processing, Artificial Intelligence, Deep Learning, Data Mining, cryptography

EDUCATION

  1. M.Sc. Engineering in CSE, 2022

    Rajshahi University of Engineering & Technology (RUET), Rajshahi-6204

  2. B.Sc. in Computer Science and Engineering, 2013

    Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200

  3. Higher Secondary Certificate, 2008

    Dinajpur Govt. College, Dinajpur, Bangladesh

  4. Secondary School Certificate, 2006

    Nashratpur Pally Unnayan Adarsha High School, Chirirbandar, Dinajpur, Bangladesh


PROFESSIONAL EXPERIENCES

  1. Assistant Professor
    Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

    May 03, 2019 to Present

  2. Lecturer
    Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

    May 02, 2016 to May 02, 2019


PUBLICATIONS

Journal Papers

  1. Md. Sohrawordi, Md. Ali. Hossain, and Md. Al Mehedi Hasan, “PLP_FS: Prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple f_score feature selection,” Briefings in Bioinformatics (IF= 13.994, SCIE, Q1) , 2022. 
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  2. Md Sohrawordi and Md Ali Hossain, “Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques”, Biochimie ( IF= 4.372, SCIE, Q1) , Vol. 192, PP. 125-135, 2022, DOI: https://doi.org/10.1016/j.biochi.2021.10.001.

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  3. Pankaj Bhowmik, Pulak Chandra Bhowmik, U A Md Ehsan Ali, and Md. Sohrawordi, “Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning”, International Journal of Information Technology and Computer Science, vol.13, no.5 pp.30-40,2021, DOI: 10.5815/ijitcs.2021.05.03.

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  4. U A Md Ehsan Ali, Emran Ali, Md Sohrawordi, and Nahid Sultan, “A LSB Based Image Steganography Using Random Pixel and Bit Selection for High Payload”, International Journal of Mathematical Sciences and Computing, vol. 7, no.3, pp.24-31,2021, DOI: 10.5815/ijmsc.2021.03.03.

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  5. Md. Sohrawordi, U A Md Ehsan Ali, Md. Palash Uddin, and Md. Mahabub Hossain, “A Modified Round Robin CPU Scheduling Algorithm with Dynamic Time Quantum”, International Journal of Advanced Research (IJAR), vol. 7, no. 2, pp. 422-429, 2019.

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  6. U A Md Ehsan Ali, Md. Sohrawordi, Md. Palash Uddin, “A Robust and Secured Image Steganography using LSB and Random Bit Substitution”, American Journal of Engineering Research (AJER). Vol. 8, no. 2, pp.39-44, 2019.

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  7. Abir Chandra Roy, Md. Abdullah Al Mamun, Khairat Hossain, Md. Ariful Islam, Md. Palash Uddin, Masud Ibn Afjal and Md. Sohrawordi, Developing Operating System Simulation Software for Windows-Based System by C# .NET Framework and an Android Application by JAVA and XML, Journal of Operating Systems Development & Trends (JoOSDT), Volume-04, Issue-1, pp-9-18, 2017.

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  8. Md. Sohrawordi, Md. Murad Hossain, Md. Palash Uddin, Adiba Mahjabin Nitu, and Md. Rashedul Islam, Android-based Walking Assistant for Blind and Low-vision People Suggesting the Shortest Path using Floyd-Warshall Algorithm, Journal of Innovation & Development Strategy, Vol. 9, no. 2, pp. 62-69,2015.

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Conference Papers

  1. M. R. Islam, M. T. Islam and M. Sohrawordi, "Selective HybridNET: Spectral-Spatial Dimensionality Reduction for HSI Classification," 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, 2023, pp. 1-5, doi: 10.1109/ECCE57851.2023.10101534.

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  2. M. T. Islam, M. Kumar, M. R. Islam and M. Sohrawordi, "Spectral-Spatial Feature Reduction for Hyperspectral Image Classification with Optimized Technique Series," 2022 12th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 2022, pp. 256-259, doi: 10.1109/ICECE57408.2022.10088705.

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  3. M. T. Islam, M. Kumar, M. R. Islam and M. Sohrawordi, "Subgrouping-Based NMF with Imbalanced Class Handling for Hyperspectral Image Classification," 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2022, pp. 739-744, doi: 10.1109/ICCIT57492.2022.10055177

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  4. Md. Sohrawordi and Md. Al Mehedi Hasan, “LyFor: Prediction of lysine formylation sites from sequence-based features using support vector machine,” 2020 IEEE Region 10 Symposium (TENSYMP), 2020.

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  5. P. Bhowmik, M. Sohrawordi, U A M. Ehsan Ali, M. N. Hasan, and P. K. Roy, “Analysis of Social Media Data to Classify and Detect Frequent Issues Using Machine Learning Approach,” 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), 2020.

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Books

  1. Pankaj Bhowmik, Md. Sohrawordi, U A Md Ehsan Ali, and Pulak Chandra Bhowmik, “An Empirical Feature Selection Approach for Phishing Websites Prediction with Machine Learning”, Bangabandhu and Digital Bangladesh. ICBBDB 2021. Communications in Computer and Information Science, vol 1550. Springer, Cham. https://doi.org/10.1007/978-3-031-17181-9_14 .

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  2. Md Sohrawordi and Md Ali Hossain, “Incorporation of Kernel Support Vector Machine for Effective Prediction of Lysine Formylation from Class Imbalance Samples”, International Conference on Big Data, IoT and Machine Learning (BIM), 2021

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PROJECTS

  1. Incorporation of Deep Learning Classifier for Identification of Lysine Phosphoglycerylation in Protein Using Multiple Sequence-Based Features.

    Funded by: Institute of Research and Training (IRT) of HSTU

    Position: Principle Investigator

    Description: FY 2021-2022; Completed

  2. Prediction of Formylated Lysine Site from Protein Sequence Using Support Vector Machine Classifier.

    Funded by: Institute of Research and Training (IRT) of HSTU

    Position: Principle Investigator

    Description: FY 2020-2021; Completed

  3. Lysine Acetylation Site Identification Using Multi-Source Features of Amino Acid Sequences

    Funded by: Institute of Research and Training (IRT) of HSTU

    Position: Principle Investigator

    Description: FY 2019-2020; Completed


SOCIAL NETWORK

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