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Conference Papers
Abstract
In recent years, natural language modeling has achieved a major breakthrough with its sophisticated theoretical and technical advancements. Leveraging the power of deep learning, transformer models have created a disrupting impact in the domain of natural language processing. However, the benefits of such advancements are still inscribed between few highly resourced languages such as English, German, and French. Low-resourced language such as Khmer is still deprived of utilizing these advancements due to lack of technical support for this language. In this study, our objective is to apply the state-of-the-art language models within two empirical use cases such as Sentiment Analysis and News Classification in the Khmer language. To perform the classification tasks, we have employed FastText and BERT for extracting word embeddings and carried out three different type of experiments such as FastText, BERT feature-based, and BERT fine-tuning-based. A large text corpus including over 100,000 news articles has been used for pre-training the transformer model, BERT. The outcome of our experiment shows that in both of the use cases, a pre-trained and fine-tuned BERT model produces the outperforming results.
Abstract
Online shopping is becoming one of the most de-manding everyday needs, nowadays. These days people are feeling comfortable shopping online. The number of its customers is increasing day by day as well as raising some problems. The major problem is that the customers can not choose the quality-full product by reading every review of an online product. Besides, the product reviews are helpful to improve the services of an e-commerce site but required huge manpower and time. We have focused on Bangla text and aimed to solve these problems by the application of Deep Neural Network (DNN) and Natural Language Processing (NLP). In this study, we have proposed two deep learning NLP models: one is for sentiment analysis and the other one is for Product Review Classification intended to improve both the quality and services. Significantly, our proposed models result in high accuracy: 0.84 and 0.69 for both Sentiment Analysis and Product Review Classification, respectively. Undoubtedly, these models can help the customers to choose the right product and the service provider to improve their services.
Abstract
Lower Back Pain (LBP) is one of the leading causes of disability around the world that affects several important parts of the human body such as the muscles, nerves, and bones of the back. The early diagnosis and proper treatment can only prevent acute LBP from infecting into chronic LBP. The aim of this study is to enhance the classification performance of LBP by identifying the most relevant feature subset from a broader feature space of an LBP dataset. To serve the aim, we have proposed a Genetic Algorithm (GA)-based feature selection approach that has been proved to significantly improve the classification performance of LBP. For the purpose of classification, we have used seven different classification algorithms, namely Logistic Regression, Ridge Regression, Gaussian Naive Bayes, Random Forest, Decision Tree, k-Nearest Neighbors (KNN), and Support Vector Machine (SVM). After applying our proposed GA-based feature selection approach along with the base classifiers, we have obtained a significant average increment in accuracy, precision, recall, f1-score, and AUC score by 3.1%, 0.64%, 4.37%, 2.64%, and 3.83% respectively. The k-Nearest Neighbors outperforms the other models with the highest accuracy (=85.2%), precision (=89.9%), and f1 score (=88.9%).
Abstract
The garment industry is one of the most dominating industries in this era of industrial globalization. It is a highly labor-intensive industry that requires a large number of human resources to produce its goods and fill up the global demand for garment products. Because of the dependency on human labor, the production of a garment company comprehensively relies on the productivity of the employees who are working in different departments of the company. A common problem in this industry is that the actual productivity of the garment employees sometimes does not meet the targeted productivity that was set for them by the authorities to meet the production goals in due time. When the productivity gap occurs, the company faces a huge loss in production. This study aims to solve this problem by predicting the actual productivity of the employees. To achieve this aim, a Deep Neural Network (DNN) model has been proposed to predict the actual productivity of the employees. The experimental results of this study have shown that the proposed model yields a promising prediction performance with a minimal Mean Absolute Error (=0.086) which is less than the baseline performance error (=0.15). Such prediction performance can indisputably help the manufacturers to set an accurate target, minimize the production loss and maximize the profit.
Abstract
Educational Data Mining (EDM) is an emerging research field concerned with the application of data mining, machine learning, and statistics in the discipline of education. Many researchers have already focused on EDM and exploring the educational data using several traditional data mining techniques to improve the educational performance of the students by extracting the concealed patterns and predicting the final outcome. In this study, we aim to propose a Deep Neural Network (DNN) based model to predict the final CGPA of the undergraduate business students with a minimal error than the traditional approaches. We have considered the performance of a decision tree model as the baseline performance. Experiments in this study have shown that our proposed DNN model can predict the CGPA with a significantly minimal error rate. To measure the performance of our model we have considered the three evaluation metrics namely Mean Squared Error (=0.008), Mean Absolute Error (=0.067), and Mean Absolute Percentage Error (=2.074). Our proposed model has successfully shown a promising prediction performance by reducing the MSE, MAE, and MAPE by 0.0146, 0.0431, and 6.043 respectively, compared to the baseline model.
Journal Papers
Abstract