ORCID Profile
0000-0002-9054-7557
Current Organisation
BRAC University
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Publisher: IEEE
Date: 08-2015
Publisher: Trans Tech Publications, Ltd.
Date: 09-2013
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.411-414.2200
Abstract: Interoperability is a key issue in implementing an e-Government system. Grid Computing based service for interoperability (e-Gov Grid Computing) could be a solution for resource sharing and interoperability of e-Gov systems. The main objective of this paper is to develop an e-Gov Grid Computing Model through e-Gov policy in Bangladesh in order to ensure good governance. The necessary reformation which are required for implementation of ICT Grid Computing system for e-Government are administrative reform for governmental and non-governmental organizations for ensuring transparency and accountability, economic and financial systems reform, politics and political parties reform, planning and policy reform, judicial reform, reform in organizations engaged in ensuring law and order.
Publisher: IEEE
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 05-2015
Publisher: Hindawi Limited
Date: 19-09-2022
DOI: 10.1155/2022/5305353
Abstract: The food security of more than half of the world’s population depends on rice production which is one of the key objectives of precision agriculture. The traditional rice almanac used astronomical and climate factors to estimate yield response. However, this research integrated meteorological, agro-chemical, and soil physiographic factors for yield response prediction. Besides, the impact of those factors on the production of three major rice ecotypes has also been studied in this research. Moreover, this study found a different set of those factors with respect to the yield response of different rice ecotypes. Machine learning algorithms named Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) have been used for predicting the yield response. The SVR shows better results than XGBoost for predicting the yield of the Aus rice ecotype, whereas XGBoost performs better for forecasting the yield of the Aman and Boro rice ecotypes. The result shows that the root mean squared error (RMSE) of three different ecotypes are in between 9.38% and 24.37% and that of R-squared values are between 89.74% and 99.13% on two different machine learning algorithms. Moreover, the explainability of the models is also shown in this study with the help of the explainable artificial intelligence (XAI) model called Local Interpretable Model-Agnostic Explanations (LIME).
Publisher: IEEE
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 12-2019
No related grants have been discovered for MD GOLAM RABIUL ALAM.