Browsing by Author "Rahman, Md. Obaidur"

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Abundance and distribution of anthropogenic marine litter in Hatiya and Nijhum Dwip Island, Bangladesh
(Journal of Marine Studies, 2024-11-27) Mahmud, Md. Nasim; Rahman, Md. Obaidur; Jahan, Roksana
Marine litter is commonly found throughout the oceans, and creates a significant threat to the marine ecosystem. The purpose of the study was to investigate the abundance and distribution of marine litter in Hatiya and Nijhum Dwip Islands, Bangladesh during the post-monsoon and to determine beach cleanliness using the clean-coast index (CCI). A 100-meter line transect was established at each beach, divided into five sections of 20 meters each and positioned perpendicular to the shoreline at the water's edge. A total of 11 types of marine litter were observed. Namar Bazar, Nijhum Dwip Sea beach showed a higher density of litter (0.30 items/m compared to Kamalar Dighi, Hatiya (0.13 items/m ). Over 70% of marine litter originated from land-based sources. Plastics were abundant litter at the Kamalar Dighi (46.66%) and Namar Bazar (61.29%). Different size ranges of marine litter were exhibited at the Kamalar Dighi (1.27-25.4 cm) and Namar Bazar (2-74 cm). Based on the mean CCI value, Hatiya and Nijhum Dwip beaches were classified as clean (2.4) and moderate (4.96), respectively. This study, therefore, suggested the conceptual policy framework including short-term (i.e., cleanness of beaches, create awareness, establishment of storages, etc.) and long-term management approaches that would be implemented for sustainable management of marine litter to ensure the conservation of marine biodiversity in the Hatiya and Nijhum Dwip Island.
Internet of Things (IoT) based ECG System for Rural Health Care
(Scopus, 2021) Rahman, Md. Obaidur; Kashem, Mohammod Abul; Nayan, Al-Akhir; Akter, Most. Fahmida; Rabbi, Fazly; Ahmed, Marzia; Asaduzzaman, Mohammad
Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.
Internet of Things Based Electrocardiogram Monitoring System Using Machine Learning Algorithm
(Daffodil International University, 2022-08-08) Rahman, Md. Obaidur; Shamrat, F. M. Javed Mehedi; Kashem, Mohammod Abul; Akte, Most. Fahmida; Chakraborty, Sovon; Ahmed, Marzia; Mustary, Shobnom
In Bangladesh’s rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring systemfor heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients.
PROPAGATION OF ION ACOUSTIC SOLITON AROUND THE CRITICAL VALUES OF ANY SPECIFIC PARAMETER IN UNMAGNETIZED COLLISIONLESS RELATIVISTIC PLASMAS
(CUET, 1-Sep-2024) Rahman, Md. Obaidur
The main purpose of the present work is to investigate how electrostatic plasma parameters
Sentiment Analysis on Twitter Tweets about Covid-19 Vaccines Using NlP and Supervised KNN Classification Algorithm
(Indonesian Journal of Electrical Engineering and Computer Science, 2021) Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Imran, M. M.; Muna, Jannatun Naeem; Billah, Md. Masum; Das, Protiva; Rahman, Md. Obaidur
The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Among them, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.