http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/15215 Wed, 24 Dec 2025 00:42:29 GMT 2025-12-24T00:42:29Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16179 A Machine Learning Based Approach to Classify Tense from English Text Ayman, Umme; Islam, Md. Shafiqul; Rahat, Md. Azmain Mahtab; Raza, Dewan Mamun; Chakraborty, Narayan Ranjan; Bijoy, Md. Hasan Imam This paper investigates the classification of tense in English text using machine learning algorithms. Support Vector Machine (SVM), Random Forest (RF), Multinomial Naive Bayes (MNB), Decision Tree (DT), XGBoost, and K-Nearest Neighbors (KNN) are the six classifiers used in the study. The dataset was collected from diverse sources including novels, books, blogs, articles, social media platforms, newspapers, websites and some of them self-made. The data underwent preprocessing steps such as cleaning, normalization, and feature extraction using TfidfVectorizer. Among the other algorithms, SVM achieved the highest accuracy at 97.17%. Classifier performance was assessed with metrics such as F1-score, recall, accuracy, and precision. To evaluate performance, ROC curves, and confusion matrices were also examined. The study underlines the necessity for focused approaches and draws attention to the significant gaps in the field of natural language processing (NLP) regarding tense classification studies. By leveraging machine learning, this research aims to enhance the accuracy and contextual appropriateness of tense classification, thereby improving cross-cultural communication and understanding in machine translation systems. This research contributes to NLP by offering a robust approach to tense classification and demonstrates the potential of SVM in achieving high accuracy for this task. Future work will focus on addressing limitations such as short training data, overfitting and tense conversion. Conference paper Thu, 19 Dec 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16179 2024-12-19T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16178 Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI Islam, Taminul; Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hena, Most. Hasna; Akash, Shopnil; Jardan, Yousef A. Bin; Wondmie, Gezahign Fentahun; Nafidi, Hiba-Allah; Bourhia, Mohammed Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%. Conference paper Mon, 01 Jan 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16178 2024-01-01T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16177 ALPR: ResNet50 powered Bangla License Plate Detectionand OCR by Root Mean Square Propagation Optimizer and Linear SVM Classifier Chowdhury, Abdulla Nasir; Summit, Samya Pal; Laskar, Md. Fuad Ahmed; Chowdhury, Gulam Mahfuz; Chowdhury, Ishmam Ahmed; Hasan, Mehedi This paper implements the MATLAB Image Processing Toolbox in detecting the license plate region using several user-defined functions in order to pre-process and process the image up until the point of extraction of characters. The extracted characters were then classified by utilizingResNet50 from the Deep Learning Toolbox of MATLAB, custom training it on above a thousand images of Bangla and English characters and numbers alongside possible categories of noise extracted from the ROI after processing the image which resulted in a datastore of 103 total categories. The output is converted to a string and saved in an excel sheet to be accessed later on. In this ALPR model, the model will scan through the images of vehiclesfrom a folder in a destination specified by the code to identify the license plates and characters and perform necessary actions on them. The aim of this paper is to properly implement the Image Processing Toolbox by MATLAB in order to identify the Region of Interest and study the performance of the Linear SVM(Support Vector Machine) classifier with ResNet50 when it comes to Bangla OCR. The training and validation accuracy achieved by using the Root Mean Square Optimizer was 97.57%. The final accuracies and precision achieved while testing the model on 50% of the image dataset was 99.2%. Moreover, theER (Error Rate) and FPR (False Positive Rate)were limited within 0.02%. The model scored 100% on F1 scores and Matthews Correlation Coefficient for every category of image classified Conference paper Mon, 10 Jun 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16177 2024-06-10T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16156 NewsNet: A Comprehensive Neural Network Hybrid Model for Efficient Bangla News Categorization Rana, Shakil; Haque, Md. Injamul; Sultana, Naznin; Amid, Abdul Fattah; Hosen, Md Jabed; Islam, Saiful Through the internet, Bangla news has grown enormously within the modern era of digital information. Every news outlet came up with its own categorizing system in order to handle such a huge quantity of content. The organization and categorization of online Bangla news articles, however, might not always correspond with the particular requirements of different users because of the heterogeneous nature of these platforms. Also, multiclass Bangla text classification has become increasingly important for Bangla newspaper platforms to enhance their recommendation system and reduce the manual labor required to classify their various article categories. To address the above limitation, we introduced NewsNet a text classification approach by combining the embedding layer, convolutional neural network(cnn), and recurrent neural network. In recurrent neural networks(rnn), we have employed two models including gated recurrent unit and bidirectional-LSTM (biLSTM) respectively. We have also used several preprocessing techniques such as Label encoder and tokenization correspondingly. We have experimented with our model on a Kaggle dataset called “Bangla Newspaper Dataset”.NewsNet achieved a good accuracy of 94.57%, 94.51% precision, 94.32% recall, and 94.43% f1 score respectively. NewsNet has demonstrated superior performance compared to other approaches on this Kaggle dataset. Conference paper Mon, 04 Nov 2024 00:00:00 GMT http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16156 2024-11-04T00:00:00Z