Browsing by Author "Imran, Abdullah Al"

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Incorporating Supervised Learning Algorithms with NLP Techniques to Classify Bengali Language Forms
(Scopus, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Incorporating Supervised Learning Algorithms with NlP Techniques to Classify Bengali Language Forms
(ACM International Conference Proceeding Series, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Incorporating Supervised Learning Algorithms with NLP Techniques to Classify Bengali Language Forms
(ACM International Conference Proceeding Series, 2020-01-10) Parves, Abdul Bari; Imran, Abdullah Al; Rahman, Md. Riazur
Every language has its own root, form, and grammar, and so does Bengali. Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere. However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature. This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing (NLP). In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with 99.5% accuracy, 99.0% precision, 100% recall, 0.995 AUC score and, 0.995 F1 score. Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.
Predicting Absenteeism at Work Using Tree-based Learners
(ICMLSC 2019 the 3rd International Conference on Machine Learning and Soft Computing, ACM Digital Library, 2019-01-25) Wahid, Zaman; Satter, A. K. M. Zaidi; Imran, Abdullah Al; Bhuiyan, Touhid
Absenteeism at workplace acts as a crucial role in demonstrating the productive and profitable capacity of a company. Thus the knowledge of absenteeism of employees' becomes the foundation for an organization in its multiple dimensions. Because the proper determination of employees' profile allows the identification of excesses of occurrences of certain morbidities. The early absenteeism research primarily focused on predicting the characteristics and the categories of diseases of employees that make them perform higher absenteeism at workplace. However, predicting the absenteeism time of employees using different machine learning classifiers is able to give the researches a new dimension in line with the intention of revealing the underlying causes and patterns of absenteeism. In this paper, we have applied 4 prominent machine learning algorithms namely Decision Tree, Gradient Boosted Tree, Random Forest, and Tree Ensemble on the absenteeism dataset of a courier company in Brazil in order to predict the absenteeism time of employees at work as well as the best classifier. Based on the 7 evaluation metrics such as True Positive, True Negative, False Positive, False Negative, Sensitivity, Specificity, and Accuracy we found that Gradient Boosted Tree produced the best result with an accuracy rate of 82% whereas Tree Ensemble performed the lowest with the accuracy rate of 79%.
Violent activity detection through surveillance camera using deep learning
(BRAC University, 2023-01) Miah, Parvez; Haque, Abrar Ahbabul; Imran, Abdullah Al; Hassan, MD. Radip; Rahman, Rafiur; Alam, Md. Golam Rabiul
Surveillance camera systems have been implemented in most parts of the world to combat the rising rate of criminal activities. In the hopes of making public places safer for everyone, computer vision has also aided in making these systems more sophisticated but reliable and efficient. However, we are yet to make them better. While modern systems are able to record incidents, they often do not do so intelligently in order to make it easier for law reinforcements to respond quickly enough to aid victims or stop more crimes from occurring. Hence for our thesis project, we intend to use computer vision on a surveillance system so that it is able to identify crimes such as physical altercation, harassment, hijacking, snatching, etc. In this model, an action recognition system will be used, where we will be using extracted images from video feeds from multiple sources, and all those sources (cameras) will be centrally connected to a server. The server will be connected to databases containing information about violent activities. Based on the feeds, a signal will be sent to the respective system if a particular activity is detected. This system is mainly based on image processing concepts using different neural networks like MobileNet-V2, ResNet50, and LSTM to match live images with the existing trained system. This model will specifically use to detect criminal activities such as punching, kicking, slapping, and weapon violence, and all these pieces of information will be previously stored in the database. We have also implemented Grad-CAM in an effort to apply model explain ability.