Browsing by Author "Lima, Khadija Akter"

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A Novel Data and Model Centric Artificial Intelligence Based Approach in Developing High-Performance Named Entity Recognition for Bengali Language
(PLOS ONE, 2023-09-22) Lima, Khadija Akter; Hasib, Khan Md; Azam, Sami; Karim, Asif; Montaha, Sidratul; Noori, Sheak Rashed Haider; Jonkman, Mirjam
Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.
Bengali Named Entity Recognition Using Deep Learning
(Daffodil International University, 2021-05-05) Lima, Khadija Akter; Asadujjaman, Md.
Named Entity Recognition (NER) is considered fundamental for extracting information in Natural Language Processing (NLP), and this task aims to classify each word of a text document into a list of predefined named entity classes. Numerous architectures for highresource languages with high exactness, such as English and Chinese, have been built over time. In recent years, the NER challenge for low-resource languages like Bangla has piqued researchers' interest. To perform the NER task in low resource language Bangla, this work proposes a novel neural network that reduces the need for most feature engineering and aspires to utilize minimal information to get optimal performance. In this research, we have used a new dataset to observe various deep learning models' performance in respect of non-contextual word embedding such as word2vec, glove, and fastText. Consequently, a hybrid architecture made out of bidirectional Gated Recurrent Unit (BGRU), Convolutional Neural Network (CNN), and Conditional Random Field (CRF) emerged triumphant with the F1 Macro Score of 91.90%, and F1 Micro Score of 98.21%. Since precision, recall, and F1 were measured differently in different studies, this value may change. All of the experimental models have also been subjected to a previously introduced method for measuring precision, recall, and F1, with the proposed model scoring 86.83% on F1. The proposed BGRU-CNN-CRF architecture provides peak performance for all the non-contextual word embedding specified and has the highest accuracy for the word2vec word embedding. In addition, this study demonstrates the impact of a well-annotated dataset on accuracy by creating a unique dataset.
SkNet
(Scopus, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul
Skin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.