Browsing by Author "Parvin, Masuma"

Now showing 1 - 8 of 8

Forecasting of Agricultural Loan in Bangladesh
(International Journal of Recent Technology and Engineering, 2019-09) Rahman, Md.Mosfiqur; Parvin, Masuma; Anam, Sayedul; Rubi, M.A
The agriculture sector is important to meet up the challenges of twentieth century in Bangladesh. It has huge contribution to our life. This sector secures the food security, export earnings and poverty reduction (Agricultural and MSME finance’2017, BB). In this paper, we forecast the agricultural loan disbursement, overdue and recovery in Bangladesh. Moreover, we have discussed the flaw of loan disbursement, recovery and overdue and that of the way out.
Heart Disease Prediction Using Machine Learning
(IEEE, 2023-05-24) Bilgaiyan, Saurabh; Ayon, Tajul Islam; Khan, Aliza Ahmed; Johora, Fatema Tuj; Parvin, Masuma; Alam, Mohammad Jahangir
Mainly related to the cardiovascular system, brain, kidney, and peripheral arteries, the disease is called heart disease. Heart disease can have many causes, but high blood pressure and atherosclerosis are the main ones. Additionally, structural and physiological changes in the heart with age are largely responsible for heart disease, which can occur even in healthy individuals. They are not put to use or employed in any way. If these data were investigated and examined, diseases may be predicted or perhaps prevented. By using images of cancer cells to train a dataset, diseases like cancer may be identified and their stage can be forecasted. Similarly, to that, factors like cholesterol, diabetes, heart rate, etc. can be used to predict heart disease. It is difficult and dangerous to predict cardiac disorders. We noticed that sometimes there are multiple approaches used to solve a problem. It varies depending on the circumstances. The fact that most of the data are sparse or absent since they weren't recorded with the intention of analysis presents another difficulty. With data from four hospitals in four distinct locations, we, therefore, set out to determine which strategy would be best for forecasting the diseases. This study compares the effectiveness of various data mining methods for predicting cardiac disease, including, K-nearest neighbors, Random Forest, and Multi-layer Perceptron, Logistic Regression. The effectiveness of prediction for each approach utilized is reported after an analysis of the Data Mining methodologies. The outcome demonstrated that heart problems can be predicted with greater than 97 percent accuracy.
Identifying the Writing Style of Bangla Language Using Natural Language Processing
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, IEEE, 2020-10-15) Shetu, Syeda Farjana; . Saifuzzaman, Mohd; Parvin, Masuma; Moon, Nazmun Nessa; Yousuf, Ridwanullah; Sultana, Sharmin
Bangla is one of the 8th major spoken languages around the world and like other widely spoken languages, it is a very morphologically rich language. It has two styles, one is standard literary style, known as Sadhu Bhasha and the other one is a standard colloquial style which is known as Cholito Bhasha. Mixing both the styles in a written document is considered as a grammatical error in Bangla language known as Guruchondali Dosh. This research aims to develop an algorithm to identify the style of a Bangla paragraph i.e. whether it is in Sadhu Bhasha or Cholito Bhasha from a given Bangla paragraph input. It's a contribution towards finding the Goruchondali Dosh which is a common grammatical mistake in written Bangla language as it was observed that a number of research work for identifying Bangla grammar mistakes is not so notable whereas it is a common trend in other language researchers.
Machine Learning Approach to Predict SGPA and CGPA
(2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), IEEE, 2021-07-30) Saifuzzaman, Mohd.; Parvin, Masuma; Jahan, Israt; Moon, Nazmun Nessa; Nur, Fernaz Narin; Shetu, Syeda Farjana
The prediction of SGPA and CGPA is beneficial to university students. Students will easily get an estimate of their final outcome from this project. As a result, the students will be able to brace themselves for a successful outcome. Students pass the day by participating in a variety of events. Students use social media sites such as Facebook, Instagram, and Twitter. They engage in various hobbies such as playing mobile games, listening to music, among others. As a result, they were able to move several times with these tasks. As a result, if a student spends so much time doing any of those things, she will not be able to achieve a successful grade because of the experiment; students can develop a research routine or guideline that they can apply to their other tasks. Additionally, students' behaviors will forecast their outcomes. The Authors will now see machine learning in Python being used all over the place. After that, The Authors created a smart SGPA and CGPA prediction project, as well as the results on students. The findings are predicted using the Nave Byes algorithm. The Nave Byes algorithm is a simple but effective prediction algorithm. It is a machine learning algorithm as well. As a result, students will be given an estimate of their final exam scores. They can prepare them to make a good result by following the routine of the SGPA & CGPA prediction project.
Modulation of insulin secretion and insulin sensitivity in Bangladeshi type 2 Deabetic subjects by an insulin sensitizer pioglitazone and T2DM association with PPARG gene polymorphism
(© University of Dhaka, 2025-03-10) Parvin, Masuma
Natural Language Processing Based Advanced Method of Unnecessary Video Detection
(International Journal of Electrical and Computer Engineering, 2021) Moon, Nazmun Nessa; Salehin, Imrus; Parvin, Masuma; Hasan, Md. Mehedi; Talha, Iftakhar Mohammad; Debnath, Susanta Chandra; Nur, Fernaz Narin; Saifuzzaman, Mohd.
In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.
Prediction Hepatitis C Virus and Classifier Blood Donor and Disease using an Ensemble Approach in the Machine Learning Algorithm
(Scopus, 2024-12-19) Hirok, Md Kamruzzaman; Parvin, Masuma; Sharmin, Shayla
Hepatitis C, caused by the hepatitis C virus, is a liver condition that can lead to severe complications if left untreated. The disease progresses through different stages, and while it is more easily treatable in the early stages, reaching the final stage without proper treatment makes recovery much harder, often resulting in high costs and significant pain. The current research emphasizes the importance of early detection as a simple and effective way to manage the condition. This study focuses on accurately predicting hepatitis C status, categorizing individuals as either blood donors or affected by the disease, using an ensemble machine learning approach. The research utilizes thirteen attributes and classifies the target into five categories: Blood Donor (including Blood Donor and Suspect Blood Donor) and Disease (encompassing Hepatitis, Fibrosis, and Cirrhosis). Several machine learning algorithms are employed, includeincludeing Decision Tree, K-nearest neighbor, Random Forest, and a Stacking Classifier. Among these, the Stacking Classifier outperformed the others, achieving an accuracy of 99.4%, precision of 99.7%, recall of 97.7%, and an F1-score of 98.7%.
Satisfaction Prediction of Online Education in COVID-19 Situation Using Data Mining Techniques
(Daffodil International University, 22-06-25) Poushy, Lamisha Haque; Bhuiyan, Salauddin Ahmed; Parvin, Masuma; Hossain, Refath Ara; Moon, Nazmun Nessa; Nooder, Jarin; Mahbub, Ashrarfi
This research focuses on the education-based online learning platform. Due to the coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".