Browsing by Author "Mahmud, Antara"

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Classification on Educational Performance Evaluation Dataset Using Feature Extraction Approach
(ACM International Conference Proceeding Series, 2020-03-20) Rahmn, Majidur; Mahmud, Antara
With the drastic drive in population and economy of Bangladesh, the urgency of higher education of the mass is also extending. Moreover, due to scarcity of resources, financial support and many other convoluted circumstances, for many of the university going youth, concentrating on study becomes arduous. Such phenomenon implies a repercussion on their academic result; which is absolutely not expected. In order to minimize such unwanted damages, we have carried out an investigation by surveying a number of undergraduate students of various universities of Bangladesh. Through our research, we have tried to excavate the key features which mostly contribute to the aforementioned matter. Our research primarily focuses on the features that have been extracted by the feature selection process we have implemented. Firstly, we have conducted classifier comparison on the original dataset which has provided us an insight about the distribution of the dataset. Furthermore, we have extracted out the most telling features with the help of feature importance ranking measure. Finally, we have once again carried out classifier comparison on the reduced dataset which has demonstrated better accuracy than that of the original dataset.
Classification on Educational Performance Evaluation Dataset Using Feature Extraction Approach
(ACM International Conference Proceeding Series, ACM Digital Library, 2020-01-10) Rahman, Majidur; Mahmud, Antara
With the drastic drive in population and economy of Bangladesh, the urgency of higher education of the mass is also extending. Moreover, due to scarcity of resources, financial support and many other convoluted circumstances, for many of the university going youth, concentrating on study becomes arduous. Such phenomenon implies a repercussion on their academic result; which is absolutely not expected. In order to minimize such unwanted damages, we have carried out an investigation by surveying a number of undergraduate students of various universities of Bangladesh. Through our research, we have tried to excavate the key features which mostly contribute to the aforementioned matter. Our research primarily focuses on the features that have been extracted by the feature selection process we have implemented. Firstly, we have conducted classifier comparison on the original dataset which has provided us an insight about the distribution of the dataset. Furthermore, we have extracted out the most telling features with the help of feature importance ranking measure. Finally, we have once again carried out classifier comparison on the reduced dataset which has demonstrated better accuracy than that of the original dataset.
Prediction of Thyroid Disease (Hypothyroid) in Early Stage Using Feature Selection and Classification Techniques
(International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 2021-04-12) Riajuliislam, Md; Rahim, Khandakar Zahidur; Mahmud, Antara
Thyroid disease is one of the most common diseases among the female mass in Bangladesh. Hypothyroid is a common variation of thyroid disease. It is clearly visible that hypothyroid disease is mostly seen in female patients. Most people are not aware of that disease as a result of which, it is rapidly turning into a critical disease. It is very much important to detect it in the primary stage so that doctors can provide better medication to keep itself turning into a serious matter. Predicting disease in machine learning is a difficult task. Machine learning plays an important role in predicting diseases. Again distinct feature selection techniques have facilitated this process prediction and assumption of diseases. There are two types of thyroid diseases namely 1. Hyperthyroid and 2.Hypothyroid. Here, in this paper, we have attempted to predict hypothyroid in the primary stage. To do so, we have mainly used three feature selection techniques along with diverse classification techniques. Feature selection techniques used by us are Recursive Feature Selection (RFE), Univar ate Feature Selection (UFS) and Principal Component Analysis (PCA) along with classification algorithms named Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Naive Bayes (NB). By observing the results, we could extrapolate that the RFE feature selection technique helps us to provide constant 99.35% accuracy for all four classification algorithms. Thus it's deduced from our research that RFE helps each classifier to attain better accuracy than all the other feature selection methods used.
Social Media Content Categorization Using Supervised Based Machine Learning Methods and Natural Language Processing in Bangla Language
(Scopus, 2020) Alam, Md. Rejaul; Akter, Afsana; Shafin, Minhajul Abedin; Hasan, Md. Mehedi; Mahmud, Antara
Social media has acquired the primary platform for people to connect. Millions of posts generate from social media consistently. The people of Bangladesh are habitually comfortable sharing their opinion on social media in the Bangla language. It is often arduous to place them in distinct categories relying on textual information. Classifying social media posts are challenging. It tends to be complicated to scrutinize when scripted in Bangla language. Our aspiration is to categorize these opinions from social platforms to enable searching, filtering, and organizing based on post sentiment. We employed the Sentiment Analysis to interpret the persuasion of the posts. We introduced a model that will classify the Bangla posts in several categories by using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, Logistic regression algorithms. We adopted the algorithm that provides the most reliable performance to classify the social media post with quite proficient in Bangla Language
Symptom Wise Age Prediction of Cancer Patients Using Classifier Comparison and Feature Selection
(2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, IEEE, 2020-03-19) Rahman, Majidur; Rashid, Sud Mohammad; Khan, Md. Nayem Ferdous; Biswas, Avijit; Mahmud, Antara
Cancer has become one of the most life threatening disease over the past few decades. Especially on Bangladesh the number of people being affected by cancer is increasing in an agitating rate. Again cancer, diagnosed after a certain stage, inevitably leads towards death. To abate this vicious upheaval of cancer, awareness has no other alternative. Our research primarily focuses on detection of certain age group, according to the corresponding cancer diagnosis and relevant factors. In order to do so, we have implemented logistic regression, support vector machine and convolutional neural network on the original dataset. Afterwards, two feature selection methods (Feature Importance Ranking Method and Recursive Feature Elimination) have been applied on the dataset to extract out the most significant features. The three classifier comparison has been implied on both the feature selection methods. It is found that the classifier accuracy on the extracted features is significantly better in case of Recursive Feature Elimination rather than Feature Importance Ranking Method.