\n \n \n
\n
\n\n \n \n \n \n \n Understanding the Dynamics of Dengue in Bangladesh: EDA, Climate Correlation, and Predictive Modeling.\n \n \n \n\n\n \n Meem, S. M.; Hossain, M. T.; Chowdhury, J. K.; Ullah Miah, M. S.; and Monir, M. F.\n\n\n \n\n\n\n In
TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), pages 1309-1314, 2023. \n
\n\n
\n\n
\n\n
\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@INPROCEEDINGS{10322490,\n author={Meem, Sabrina Masum and Hossain, Mohammed Tahmid and Chowdhury, Jannat Khair and Ullah Miah, Md Saef and Monir, Md Fahad},\n booktitle={TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)}, \n title={Understanding the Dynamics of Dengue in Bangladesh: EDA, Climate Correlation, and Predictive Modeling}, \n year={2023},\n volume={},\n number={},\n pages={1309-1314},\n doi={10.1109/TENCON58879.2023.10322490}}\n
\n
\n\n\n\n
\n\n\n
\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing.\n \n \n \n \n\n\n \n Murad, S. A.; Azmi, Z. R. M.; Muzahid, A. J. M.; Sarker, M. M. H.; Miah, M. S. U.; Bhuiyan, M. K. B.; Rahimi, N.; and Bairagi, A. K.\n\n\n \n\n\n\n
Sustainable Computing: Informatics and Systems,100942. 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n
@article{MURAD2023100942,\ntitle = {Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing},\njournal = {Sustainable Computing: Informatics and Systems},\npages = {100942},\nyear = {2023},\nissn = {2210-5379},\ndoi = {https://doi.org/10.1016/j.suscom.2023.100942},\nurl = {https://www.sciencedirect.com/science/article/pii/S2210537923000975},\nauthor = {Saydul Akbar Murad and Zafril Rizal M. Azmi and Abu Jafar Md. Muzahid and Md. Murad Hossain Sarker and M. Saef Ullah Miah and MD. Khairul Bashar Bhuiyan and Nick Rahimi and Anupam Kumar Bairagi},\nkeywords = {Cloud computing, Job scheduling, Backfilling, Resource management, Gap searching, SJF, LJF, FCFS},\nabstract = {A growing number of services, accessible and usable by individuals and businesses on a pay-as-you-go basis, are being made available via cloud computing platforms. The business services paradigm in cloud computing encounters several quality of service (QoS) challenges, such as flow time, makespan time, reliability, and delay. To overcome these obstacles, we first designed a resource management framework for cloud computing systems. This framework elucidates the methodology of resource management in the context of cloud job scheduling. Then, we study the impact of a Virtual Machine’s (VM’s) physical resources on the consistency with which cloud services are executed. After that, we developed a priority-based fair scheduling (PBFS) algorithm to schedule jobs so that they have access to the required resources at optimal times. The algorithm has been devised utilizing three key characteristics, namely CPU time, arrival time, and job length. For optimal scheduling of cloud jobs, we also devised a backfilling technique called Earliest Gap Shortest Job First (EG-SJF), which prioritizes filling in schedule gaps in a specific order. The simulation was carried out with the help of the CloudSim framework. Finally, we compare our proposed PBFS algorithm to LJF, FCFS, and MAX-MIN and find that it achieves better results in terms of overall delay, makespan time, and flow time.}\n}
\n
\n\n\n
\n A growing number of services, accessible and usable by individuals and businesses on a pay-as-you-go basis, are being made available via cloud computing platforms. The business services paradigm in cloud computing encounters several quality of service (QoS) challenges, such as flow time, makespan time, reliability, and delay. To overcome these obstacles, we first designed a resource management framework for cloud computing systems. This framework elucidates the methodology of resource management in the context of cloud job scheduling. Then, we study the impact of a Virtual Machine’s (VM’s) physical resources on the consistency with which cloud services are executed. After that, we developed a priority-based fair scheduling (PBFS) algorithm to schedule jobs so that they have access to the required resources at optimal times. The algorithm has been devised utilizing three key characteristics, namely CPU time, arrival time, and job length. For optimal scheduling of cloud jobs, we also devised a backfilling technique called Earliest Gap Shortest Job First (EG-SJF), which prioritizes filling in schedule gaps in a specific order. The simulation was carried out with the help of the CloudSim framework. Finally, we compare our proposed PBFS algorithm to LJF, FCFS, and MAX-MIN and find that it achieves better results in terms of overall delay, makespan time, and flow time.\n
\n\n\n
\n\n\n
\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Medical Named Entity Recognition (MedNER): A Deep Learning Model for Recognizing Medical Entities (Drug, Disease) from Scientific Texts.\n \n \n \n \n\n\n \n Ullah Miah, M. S.; Sulaiman, J.; Sarwar, T. B.; Islam, S. S.; Rahman, M.; and Haque, M. S.\n\n\n \n\n\n\n In
IEEE EUROCON 2023 - 20th International Conference on Smart Technologies, July 2023. IEEE\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@inproceedings{Ullah_Miah_2023, title={Medical Named Entity Recognition (MedNER): A Deep Learning Model for Recognizing Medical Entities (Drug, Disease) from Scientific Texts}, url={http://dx.doi.org/10.1109/EUROCON56442.2023.10199075}, DOI={10.1109/eurocon56442.2023.10199075}, booktitle={IEEE EUROCON 2023 - 20th International Conference on Smart Technologies}, publisher={IEEE}, author={Ullah Miah, M. Saef and Sulaiman, Junaida and Sarwar, Talha Bin and Islam, Saima Sharleen and Rahman, Mizanur and Haque, Md. Samiul}, year={2023}, month=jul }\n
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Yus - A Deep Learning Algorithm for Collision Avoidance through Object and Vehicle Detection.\n \n \n \n \n\n\n \n Beg, M. S.; Ismail, M. Y.; Miah, M. S. U.; and Peeie, M. H.\n\n\n \n\n\n\n
Journal of Advanced Research in Applied Sciences and Engineering Technology, 31(1): 226–236. jun 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{Mohammad_Sojon_Beg_2023,\tdoi = {10.37934/araset.31.1.226236},\turl = {https://doi.org/10.37934%2Faraset.31.1.226236},\tyear = 2023,\tmonth = {jun},\tpublisher = {Akademia Baru Publishing},\tvolume = {31},\tnumber = {1},\tpages = {226--236},\tauthor = {Mohammad Sojon Beg and Muhammad Yusri Ismail and M. Saef Ullah Miah and Mohamad Heerwan Peeie},\ttitle = {Yus - A Deep Learning Algorithm for Collision Avoidance through Object and Vehicle Detection},\tjournal = {Journal of Advanced Research in Applied Sciences and Engineering Technology}}
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Predicting Carboxymethyl Cellulase assay (CMCase) production using Artificial Neural Network and explicit feature selection approach.\n \n \n \n \n\n\n \n Miah, M. S. U.; Sulaiman, J.; Zamli, K. Z.; Rashid, S. S.; and Chowdhury, A. J. K.\n\n\n \n\n\n\n In
2023 IEEE 8th International Conference for Convergence in Technology (I2CT), apr 2023. IEEE\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@inproceedings{Ullah_Miah_2023,\tdoi = {10.1109/i2ct57861.2023.10126364},\turl = {https://doi.org/10.1109%2Fi2ct57861.2023.10126364},\tyear = 2023,\tmonth = {apr},\tpublisher = {{IEEE}},\tauthor = {M. Saef Ullah Miah and Junaida Sulaiman and Kamal Z. Zamli and Shah Samiur Rashid and Ahmed Jalal Khan Chowdhury},\ttitle = {Predicting Carboxymethyl Cellulase assay ({CMCase}) production using Artificial Neural Network and explicit feature selection approach},\tbooktitle = {2023 {IEEE} 8th International Conference for Convergence in Technology (I2CT)}}
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n 4D: A Real-Time Driver Drowsiness Detector Using Deep Learning.\n \n \n \n \n\n\n \n Jahan, I.; Uddin, K. M. A.; Murad, S. A.; Miah, M. S. U.; Khan, T. Z.; Masud, M.; Aljahdali, S.; and Bairagi, A. K.\n\n\n \n\n\n\n
Electronics, 12(1): 235. January 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{Jahan_2023, title={4D: A Real-Time Driver Drowsiness Detector Using Deep Learning}, volume={12}, ISSN={2079-9292}, url={http://dx.doi.org/10.3390/electronics12010235}, DOI={10.3390/electronics12010235}, number={1}, journal={Electronics}, publisher={MDPI AG}, author={Jahan, Israt and Uddin, K. M. Aslam and Murad, Saydul Akbar and Miah, M. Saef Ullah and Khan, Tanvir Zaman and Masud, Mehedi and Aljahdali, Sultan and Bairagi, Anupam Kumar}, year={2023}, month=jan, pages={235} }\n
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n Distributed Ledger Technology Based Integrated Healthcare Solution for Bangladesh.\n \n \n \n \n\n\n \n Islam, M. A.; Islam, M. A.; Jacky, M. A. H.; Al-Amin, M.; Miah, M. S. U.; Khan, M. M. I.; and Hossain, M. I.\n\n\n \n\n\n\n
IEEE Access, 11: 51527–51556. 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{Islam_2023, title={Distributed Ledger Technology Based Integrated Healthcare Solution for Bangladesh}, volume={11}, ISSN={2169-3536}, url={http://dx.doi.org/10.1109/ACCESS.2023.3279724}, DOI={10.1109/access.2023.3279724}, journal={IEEE Access}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Islam, Md. Ariful and Islam, Md. Antonin and Jacky, Md. Amzad Hossain and Al-Amin, Md. and Miah, Md. Saef Ullah and Khan, Md. Muhidul Islam and Hossain, Md. Iqbal}, year={2023}, pages={51527–51556} }\n
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n .\n \n \n \n \n\n\n \n Saef Ullah Miah, M.; and Sulaiman, J.\n\n\n \n\n\n\n Material Named Entity Recognition (MNER) for Knowledge-Driven Materials Using Deep Learning Approach, pages 199–208. Springer Nature Singapore, 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@inbook{Saef_Ullah_Miah_2023, title={Material Named Entity Recognition (MNER) for Knowledge-Driven Materials Using Deep Learning Approach}, ISBN={9789811994838}, ISSN={2367-3389}, url={http://dx.doi.org/10.1007/978-981-19-9483-8_17}, DOI={10.1007/978-981-19-9483-8_17}, booktitle={Lecture Notes in Networks and Systems}, publisher={Springer Nature Singapore}, author={Saef Ullah Miah, M. and Sulaiman, Junaida}, year={2023}, pages={199–208} }\n
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n .\n \n \n \n \n\n\n \n Miah, M. S. U.; Sulaiman, J.; Sarwar, T. B.; Ferdous, I. U.; Islam, S. S.; and Haque, M. S.\n\n\n \n\n\n\n Target and Precursor Named Entities Recognition from Scientific Texts of High-Temperature Steel Using Deep Neural Network, pages 203–208. Springer Nature Switzerland, 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@inbook{Miah_2023, title={Target and Precursor Named Entities Recognition from Scientific Texts of High-Temperature Steel Using Deep Neural Network}, ISBN={9783031398216}, ISSN={1611-3349}, url={http://dx.doi.org/10.1007/978-3-031-39821-6_16}, DOI={10.1007/978-3-031-39821-6_16}, booktitle={Database and Expert Systems Applications}, publisher={Springer Nature Switzerland}, author={Miah, M. Saef Ullah and Sulaiman, Junaida and Sarwar, Talha Bin and Ferdous, Imam Ul and Islam, Saima Sharleen and Haque, Md. Samiul}, year={2023}, pages={203–208} }\n
\n
\n\n\n\n
\n\n\n
\n
\n\n \n \n \n \n \n \n ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy.\n \n \n \n \n\n\n \n Sumit, S. S.; Rambli, D. R. A.; Mirjalili, S.; Miah, M. S. U.; and Ejaz, M. M.\n\n\n \n\n\n\n
MethodsX, 10: 101936. 2023.\n
\n\n
\n\n
\n\n
\n\n \n \n
Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{Sumit_2023,\tdoi = {10.1016/j.mex.2022.101936},\turl = {https://doi.org/10.1016%2Fj.mex.2022.101936},\tyear = 2023,\tpublisher = {Elsevier {BV}},\tvolume = {10},\tpages = {101936},\tauthor = {Shahriar Shakir Sumit and Dayang Rohaya Awang Rambli and Seyedali Mirjalili and M. Saef Ullah Miah and Muhammad Mudassir Ejaz},\ttitle = {{ReSTiNet}: An Efficient Deep Learning Approach to Improve Human Detection Accuracy},\tjournal = {{MethodsX}}}
\n
\n\n\n\n
\n\n\n\n\n\n