Browsing by Author "Rabbani, Md Golam"

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Experience of farmers using mobile phone for farming information flow in Boro rice production: A case of Eastern Gangetic Plain
(2024-11-15) Ahmad, Babor; Sarkar, Md Abdur Rouf; Khano, Fahima; Lucky, Rozina Yeasmin; Sarker, Mou Rani; Rabbani, Md Golam; Ray, Shraboni Rani Rani; Rahman, Md Naimur; Sarker, Md Nazirul Islam
Mobile phones are widely used for personal communication, but their use for agricultural purposes is scarce in the Eastern Gangetic Plain. Given Bangladesh's heavy reliance on rice production, particularly dry season, the study addresses the gap in the literature by investigating farmers' perceptions and examining the factors that influence mobile phone usage and its impact on Boro rice production. The research entailed questionnaire surveys with 150 farmers from highly Boro-intensive regions. A perception index was used to gauge mobile phone acceptance, and Ordinary Least Square (OLS) and logit models were employed to analyze the factors affecting mobile phone usage and their influence on production levels. The perception index highlights various benefits, including easy access to farming and market information, prompt responses from extension officers, utilization of mobile apps for disease detection, and participation in online training and seminars. The logit model underscores the significance of educational status, credit access, household income, and extension services on the informative use of mobile phones among Boro rice farmers. The OLS findings showed that the informative use of mobile phones significantly increases Boro rice production by 1.6–4.5%, indicating that farmers with better access to farming information achieve higher production than non-users. Factors such as selling price, rainfall, credit availability, cultivable land, and farming experience significantly influenced Boro rice production. Overall, the study is rooted in the growing significance of mobile technology for disseminating crucial agricultural information. It seeks to thoroughly investigate its potential impact on Boro rice production, contributing to well-informed policy interventions and promoting sustainable agricultural development in the Eastern Gangetic Plain.
Multi-Functional E-Commerce Platform: Buy, Sell, Exchange
(Daffodil International University, 2025-09-17) Rabbani, Md Golam
As the digital world is fast evolving, e-commerce websites are now the most vital tools for businesses and individuals to facilitate efficient purchasing, selling, and exchanging goods. Exchange Bazar is presented in this paper as a flexible e-commerce web application to facilitate easy and handy online trade. The site offers customers one site where they can purchase new or used goods, offer items for sale, or trade products, and thus is an inexpensive accessible site for modern consumers. Through the application of the Exchange Bazar facility, customers are able to trigger and negotiate good exchange, minimize wastage, and attain sustainability as well as offer opportunity for discount shopping. Product pictures can be loaded without any inconvenience, prices can be set, sellers can manage listings, and buyers are reassured with an ensured payment system that provides multiple modes of payments and a smooth and effortless interface. The site is developed using Vue.js for dynamic front-end, Node.js using Express for backend APIs, and MySQL database for properly managed data, thereby providing good performance, scalability, and reliability. The Bootstrap visually appealing, responsive layout suitable for both desktop as well as mobile users. Exchange Bazar differs from traditional marketplaces in that it focuses on ease of use through the emphasis on core functionalities with advanced analytics. By making it possible for individuals and small businesses to sell and showcase products at zero operation costs, the system creates an affordable, inclusive virtual marketplace. Through simplicity, security, and sustainabilitybased features, Exchange Bazar provides support to developing interest in flexible, lowcost, and efficient e-commerce solutions.
Performance Measurement of Multiple Supervised Learning Algorithms for Bengali News Headline Sentiment Classification
(Scopus, 2020-06-16) Islam, Md. Majedul; Masum, Abu Kaisar Mohammad; Rabbani, Md Golam; Zannat, Raihana; Rahman, Mushfiqur
The reading newspaper is a common habit in today's life. Before reading, news articles all are focused on the news headline. Understanding the meaning of news headlines everybody can easily identify the news types. That means the containing news article provides positive or negative news. Analysis of the sentiment of the news headline is a good solution for this kind of problem. Sentiment Analysis is a chief part of Natural Language Processing. It mines any kinds of opinion and set the sentiment of any text. We proposed a method for Bengali news headline sentiment measurement with different kinds of supervised learning algorithm and their performance. Firstly, we set the sentiment of each news headline then used the classification method to predicting the news headline which was containing a positive or negative headline. After all, Bengali is one of the most used languages in this world. A lot of research work was done previously in a different language but very few in the Bengali language. So, increasing the Bengali language research resource need to develop different kinds of tools and technology
Prevalence of and Factors Associated With Tobacco Smoking in the Gambia
(Daffodil International University, 2022-05-18) Islam, Md Shariful; Al-Wajeah, Haifaa; Rabbani, Md Golam; Ferdous, Md; Mahfuza, Nusrat Sharmin; Konka, Daniel; Silenga, Eva; Ullah, Abu Naser Zafar
Objectives To examine the prevalence of and risk factors associated with tobacco smoking in the Gambia. Design A nationwide cross-sectional study. Setting The Gambia. Participants The study participants were both women and men aged between 15 and 49 years old. We included 16,066 men and women in our final analysis. Data analysis We analysed data from the Gambia Demographic and Health Survey (DHS), 2019–2020. DHS collected nationally stratified data from local government areas and rural–urban areas. The outcome variable was the prevalence of tobacco smoking. Descriptive analysis, prevalence and logistic regression methods were used to analyse data to identify the potential determinants of tobacco smoking. Results The response rate was 93%. The prevalence of current tobacco smoking was 9.92% in the Gambia in 2019–2020, of which, 81% of the consumers smoked tobacco daily. Men (19.3%) smoked tobacco much higher than women (0.65%) (p<0.001). People aged 40–49 years, with lower education, and manual workers were the most prevalent group of smoking in the Gambia (p<0.001). Men were 33 times more likely to smoke tobacco than women. The chance of consuming smoked tobacco increased with the increase of age (adjusted OR (AOR) 9.08, 95% CI 5.08 to 16.22 among adults aged 40–49 years, p<0.001). The strength of association was the highest among primary educated individuals (AOR 5.35, 95% CI 3.35 to 8.54). Manual workers (AOR 2.73) and people from the poorest households (AOR 1.86) were the risk groups for smoking. However, place of residency and region were insignificantly associated with smoking in the Gambia. Conclusions Men, older people, manual workers, individuals with lower education and lower wealth status were the vulnerable groups to tobacco smoking in the Gambia. Government should intensify awareness programmes on the harmful effects of smoking, and introduce proper cessation support services among tobacco smoking users prioritising these risk groups.
Thalassemia Prediction Using Machine Learning Model
(©Daffodil International University, 2022-01-13) Rabbani, Md Golam; Zaman, Sharmila; Hemel, Reaz Uddin
Thalassemia is a genetic blood disease inherited from parents. It is the most common and concerning genetic disorder globally. Minor to major anemia and transfusion dependence is the main symptom of this disease. In South Asian countries like Bangladesh, every year there are many children born with thalassemia traits. Among various types of thalassemia, beta-thalassemia is the most severe one that causes weakness, serious anemia, shortness of breath, even failing organs like the kidney, heart. This study aims to classify thalassemia depending on the values of various hemoglobin (Hb) indices like Hb A, Hb B, Hb E, and Hb F collected from the data of a thalassemia center of Bangladesh. This work is to depict the epidemiological aspects of thalassemia from the data of the common people of all stages of Bangladesh. We applied various machine learning classifiers such as Logistic Regression (LR), Decision Tree, Support Vector Machine (SVM), Random Forest, and KNearest Neighbors (KNN), etc. to classify thalassemia. For evaluating the performance of the classifiers, we calculated accuracy, precision, recall and f1-score. We also plotted the ROC curve. From the ROC curve, it is observed that AUC (Area Under the Curve) has a big area. After conducting the study, we got the final result that concludes that among all the algorithms, the Random Forest and K-Nearest Neighbors (KNN) have shown the best accuracy which is 99.14%. The both precision and recall for the Random Forest is 99.00% and for KNN is 99.00% and 100% respectively.
Thalassemia Prediction Using Machine Learning Model
(©Daffodil International University, 2022-01-13) Rabbani, Md Golam; Zaman, Sharmila; Hemel, Reaz Uddin
Thalassemia is a genetic blood disease inherited from parents. It is the most common and concerning genetic disorder globally. Minor to major anemia and transfusion dependence is the main symptom of this disease. In South Asian countries like Bangladesh, every year there are many children born with thalassemia traits. Among various types of thalassemia, beta-thalassemia is the most severe one that causes weakness, serious anemia, shortness of breath, even failing organs like the kidney, heart. This study aims to classify thalassemia depending on the values of various hemoglobin (Hb) indices like Hb A, Hb B, Hb E, and Hb F collected from the data of a thalassemia center of Bangladesh. This work is to depict the epidemiological aspects of thalassemia from the data of the common people of all stages of Bangladesh. We applied various machine learning classifiers such as Logistic Regression (LR), Decision Tree, Support Vector Machine (SVM), Random Forest, and KNearest Neighbors (KNN), etc. to classify thalassemia. For evaluating the performance of the classifiers, we calculated accuracy, precision, recall and f1-score. We also plotted the ROC curve. From the ROC curve, it is observed that AUC (Area Under the Curve) has a big area. After conducting the study, we got the final result that concludes that among all the algorithms, the Random Forest and K-Nearest Neighbors (KNN) have shown the best accuracy which is 99.14%. The both precision and recall for the Random Forest is 99.00% and for KNN is 99.00% and 100% respectively.
Thalassemia Prediction Using Machine Learning Model
(©Daffodil International University, 2022-01-13) Rabbani, Md Golam; Zaman, Sharmila; Hemel, Reaz Uddin
Thalassemia is a genetic blood disease inherited from parents. It is the most common and concerning genetic disorder globally. Minor to major anemia and transfusion dependence is the main symptom of this disease. In South Asian countries like Bangladesh, every year there are many children born with thalassemia traits. Among various types of thalassemia, beta-thalassemia is the most severe one that causes weakness, serious anemia, shortness of breath, even failing organs like the kidney, heart. This study aims to classify thalassemia depending on the values of various hemoglobin (Hb) indices like Hb A, Hb B, Hb E, and Hb F collected from the data of a thalassemia center of Bangladesh. This work is to depict the epidemiological aspects of thalassemia from the data of the common people of all stages of Bangladesh. We applied various machine learning classifiers such as Logistic Regression (LR), Decision Tree, Support Vector Machine (SVM), Random Forest, and KNearest Neighbors (KNN), etc. to classify thalassemia. For evaluating the performance of the classifiers, we calculated accuracy, precision, recall and f1-score. We also plotted the ROC curve. From the ROC curve, it is observed that AUC (Area Under the Curve) has a big area. After conducting the study, we got the final result that concludes that among all the algorithms, the Random Forest and K-Nearest Neighbors (KNN) have shown the best accuracy which is 99.14%. The both precision and recall for the Random Forest is 99.00% and for KNN is 99.00% and 100% respectively.