Browsing by Author "Nibir, Tafsirul Islam"

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Comparative Analysis of Human Face Recognition Using SURF and Neural Network Methods
(Scopus, 2021) Shamrat, F.M. Javed Mehedi; Bhowmik, Shohag Kumar; Muntasim, Mst. Fahmida; Nibir, Tafsirul Islam; Chowdhury, Tahmid Rashik; Thapa, Sittal
In computer vision, facial recognition technology is used to recognize every person. This approach is a revolution that is perfect for analyzing a graphic image or a video frame specially or differently. There are, however, systemic methods where facial appreciation schemes initiative, typically, and the effort by comparing chosen facial features from a specific image with faces in a database. It's also known as a Biometric Artificial Intelligence-based app that can see a person in extraordinary detail by dissecting structures related to their facial exteriors and figures. The professional employs a variety of techniques in order to complete the mission. The SURF and Neural Network methods are two of these methods. The writers of this paper address the methods mentioned above and how they operate. The emphasis of the debate is on the methods' accuracy rates and determining which approach produces the most reliable outcome based on facial image results.
Depression Detection of University Students' Using Machine Learning Approach
(Daffodil International University, 23-01-29) Muntasim, Mst. Fahmida; Ador, MD. Rashedul Haque; Nibir, Tafsirul Islam
In modern society, depression affects the majority of people. Many even commit suicide as a result of inadequate care. If depression is present in the patient in the early stages, it is simple to identify and cure. Since we don't know the depression level, we can't make the best choice at the appropriate time. A whole nation is built on its students. By teaching and improving the country, students represent it to the outside world. Depression is caused by a variety of factors, including challenges Bangladeshi adolescents have in their education. Determining the prevalence of depressive symptoms, their contributing variables, and strategies for reducing depression of university students are the objectives of our study. We analyzed the dataset with different samples from university students. We provide some question by a Google from, students are chosen the answer then set a range to find their depression level. About 1049 people's data were obtained from a Google form. In essence, the test was the data. In essence, the student has provided the data. We were able to determine the depression level through the analysis of that data. On this data, several algorithms have been applied. And have achieved the highest accuracy. With the help of this project, we can detect depression levels and administer the appropriate care or therapy. We're using some kind of algorithm to detect their depression level. They are five algorithms are chosen for this research. There are Decision Tree classification, Random Forest classifier, SVM, KNeighbors Classifier, and GaussianNB. Overall, the SVM algorithm prediction has the best performance. It gives 93% which is the best algorithm to prefer for this research.
Supervised Machine Learning Based Liver Disease Prediction Approach with LASSO Feature Selection
(Bulletin of Electrical Engineering and Informatics, 2021) Afrin, Saima; Shamrat, F. M. Javed Mehedi; Nibir, Tafsirul Islam; Muntasim, Mst. Fahmida; Moharram, Md. Shakil; Imran, M. M.; Abdulla, Md
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.