I have completed my B.S in ECE from Chittagong University of Engineeering and Technology, Bangladesh in 2022. My research focus is in apllied Machine Learning and Deep Learning in a wide range of fields including Power System, Biomedical imaging, and Computer Vision. It also includes embedded and IoT systems.
I am actively lokking for Ph.D position in relevant research fields.
The increase of consumer end load demand is leading to a path to the smart handling of power sector utility. In recent era, the civilization has reached to such a pinnacle of technology that there is no scope of energy wastage. Consequently, questions arise on power generation sector. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. Artificial Intelligent, Conventional and Probabilistic methods are employed in load forecasting. However the conventional and probabilistic methods are less adaptive to the acute, micro and unusual change of the demand trend. With the recent development of Artificial intelligence, machine learning has become the most popular choice due to its higher accuracy based on time, demand and trend based feature extractions. Even though machine learning based models have got the potential, most of the contemporary research works lack in precise and factual feature extractions which results in lower accuracy and higher convergence time. Thus the proposed model takes into account the extensive features derived from both long and short time lag based auto-correlation. Also, for an accurate prediction from these extracted features two Extreme Gradient Boosting (XGBoost) Regression based models: (i) PredXGBR-1 and (ii) PredXGBR-2 have been proposed with definite short time lag feature to predict hourly load demand. The proposed model is validated with five different historical data record of various zonal area over a twenty years of-2 time span. The average accuracy (𝑅2 ) of PredXGBR-1 and PredXGBR-2 are 61.721% and 99.0982% with an average MAPE (error) of 8.095% and 0.9101% respectively.
@inproceedings{10.1007/978-981-19-7528-8_42,author={Zabin, Rifat and Barua, Labanya and Ahmed, Tofael},title={PredXGBR: A Machine Learning Based Short-Term Electrical Load Forecasting Architecture},booktitle={Proceedings of International Conference on Information and Communication Technology for Development},year={2023},publisher={Springer Nature Singapore},address={Singapore},pages={535--546},doi={https://doi.org/10.1007/978-981-19-7528-8_42},}
Conference
Energy Consumption Optimization of Zigbee Communication: An Experimental Approach with XBee S2C Module
Rifat Zabin, and Khandaker Foysal Haque
In Proceedings of International Conference on Information and Communication Technology for Development: ICICTD, 2023
Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor Internet of Things (IoT) applications. One of the basic constraints of Zigbee and similar wireless sensor networks (WSN) standards is limited power source as in most of the cases they are battery powered. Thus, it is very important to optimize the energy consumption to have a good network lifetime. Even though tuning the power transmission level to a lower value might make the network more energy efficient, it also hampers the network performances very badly. This work aims to optimize the energy consumption by finding the right balance and trade-off between the transmission power level and network performance through extensive experimental analysis. Packet delivery ratio (PDR) is taken into account for evaluating the network performance. This work also presents a performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contribution of this work includes (i) to optimize the energy consumption by evaluating the most optimized transmission power level of Zigbee where the network performance is also good in terms of PDR (ii) identifying and quantizing the trade-offs of PDR, transmission power levels, current and energy consumption (iii) creating an indoor and outdoor Zigbee testbed based on commercially available Zigbee module XBee S2C to perform any sort of extensive performance analysis.
@inproceedings{zabin2023energy,doi={https://doi.org/10.1007/978-981-19-7528-8_41},title={Energy Consumption Optimization of Zigbee Communication: An Experimental Approach with XBee S2C Module},author={Zabin, Rifat and Haque, Khandaker Foysal},booktitle={Proceedings of International Conference on Information and Communication Technology for Development: ICICTD},pages={521--534},year={2023},organization={Springer}}
2020
Conference
An IoT based efficient waste collection system with smart bins
Khandaker Foysal Haque, Rifat Zabin, Kumar Yelamarthi, and 2 more authors
In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), 2020
Waste collection and management is an integrated part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self-sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation
@inproceedings{haque2020iot,title={An IoT based efficient waste collection system with smart bins},author={Haque, Khandaker Foysal and Zabin, Rifat and Yelamarthi, Kumar and Yanambaka, Prasanth and Abdelgawad, Ahmed},booktitle={2020 IEEE 6th World Forum on Internet of Things (WF-IoT)},pages={1--5},year={2020},publisher={IEEE},doi={10.1109/WF-IoT48130.2020.9221251},}
