Browsing by Author "Kobra, Khadijatul"

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Analysis of Bangla Transformation of Sentences Using Machine Learning
(Springer, 2023-04-17) Das, Rajesh Kumar; Sammi, Samrina Sarkar; Kobra, Khadijatul; Ajmain, Moshfiqur Rahman; khushbu, Sharun Akter; Noori, Sheak Rashed Haider
In many languages, various language processing tools have been developed. The work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. The most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%.
Multi Head Text Mining Applying Deep Learning and Bangla NLP Using COVID-19 Feedback From Students
(Daffodil International University, 23-02-12) Kobra, Khadijatul; Sammi, Samrina Sarkar
The COVID-19 epidemic bound administration all around the globe to lock down their frontier, their companies, their schools, and citizens from leaving their houses unless absolutely required. The mental health of both individuals and society as a whole can be seriously harmed by being so imprisoned. This negatively affects students' social and emotional well-being. Having schools, colleges, and universities closed had a significant negative influence on students' academic lives. We physically examined Bangla text that was physically obtained from students using a google form. It was all written in Bangla. With two label classes—positive and negative—through text categorization, we looked at how covid affected social relationships, mental health, and academic success. We tested several ML algorithms like logistic regression, decision trees, random forests, multi-naive Bayes, KNN, SGD, linear SVM, and RBF SVM, and discovered that SGD had the highest accuracy for academic impact. We get the greatest accuracy for the influence on social life column using KNN, and the best score for the impact on mental health with both multi-naive Bayes and SGD. Additionally, we used the CNN, LSTM, CNN-LSTM, BiLSTM, and CNN-BiLSTM as deep learning models in the aforementioned three columns, and we achieved an academic impact accuracy of 80%, mental health impact accuracy of 98.75% and Social life impact accuracy 83.75% using LSTM. The accuracy is 92.50%, 85%, and 92.50% using BiLSTM for academic, mental, and social impact columns. while its accuracy in terms of using CNN, CNN-LSTM, and CNN-BiLSTM is 82.50%, 70%, 92.50%; 85%, 90%, 92.50%; and 85%, 85%, 90% for academic, mental, and social impact columns, respectively.
Multihead Text Mining from COVID-19 Feedback Using Machine Learning, Deep Learning, and Hybrid Deep Learning Approaches
(2024-08-24) Kobra, Khadijatul; Sammi, Samrina Sarkar; Rahman, Naimur; Khushbu, Sharun Akter; Islam, Mirajul
This study examines the impact of the COVID-19 epidemic on students in Bangladesh through text classification using various machine learning (ML) algorithms and deep learning (DL) models. The pandemic led to emergency crisis protocols in the country, including self-quarantine and the closure of educational and governmental institutions, resulting in significant negative impacts on individuals’ physical and mental health, including anxiety, sadness, and terror. To better understand the psychological effects of the epidemic, the authors collected survey data from 400 students in various divisions of Bangladesh using self-administered questionnaires through Google Forms. Preprocessing techniques such as tokenization, filtering, and n-gram modeling were used in the analysis. The study deployed eight different ML algorithms and DL models, including LSTM, BiLSTM, and CNN, to classify the effects on students’ academic, mental, and social lives. The results show that the ML classifier algorithms were highly effective, achieving accuracies of 95.00%, 93.75%, and 95.00% for academic, mental, and social life impact, respectively. Furthermore, hybrid DL models, such as CNN-LSTM and CNN-BiLSTM, produced good scores in predicting the impacts on students’ lives. Overall, this study provides valuable insights into the impacts of the COVID-19 epidemic on students’ academic, mental, and social well-being in Bangladesh.
Smart Noorani Quran Shikkha: A Mobile Application to Help Learning Quran
(Daffodil International University, 2019-12) Najnin, Mst. Rabeya; Kobra, Khadijatul
This project is learning Quran properly with grammar and pronunciation application. It is an android based project where any ages people learning Quran easily with proper Arabic grammar with pronunciation. The Holy book of Muslim is The Quran. There are many people are not able to learning Quran properly. They haven’t enough time for going to teacher for learning Quran. For this reason we have built an android application that helps those type of people. We try to make more easily this application for learning Quran. Users can learn Quran easily by staying at home using our application. User can access easily, can read and heard sound properly, can learn Quran anywhere any time.