Browsing by Author "Kaiser, Md. Salman"

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Predicting Online Extreme Religious Discourse Using Natural Language Processing Technique
(Daffodil International University, 2021-01-14) Kaiser, Md. Salman; Mahin, Montasir Mahfuz; Jisan, Jihad Azad
In the current age innovation has become an indistinguishable piece of our life. Interpersonal interaction Site is an incredible advancement of current occasions. Facebook, Twitter and so forth have become an ordinary piece of people groups' lives. Everyone utilizing the web these days utilizes long range informal communication destinations. Online media has become a stage for each sort of correspondence. Presently-a-days one can scarcely discover any individual who isn't a client of any online media. Web-based media calculations, today, work in a way where one as a rule sees the sort of posts one prefers or is lined up with, making the scope of discussions smaller and, frequently, and their unnecessary utilization risky. Just as the different employments of interpersonal interaction locales, individuals in some cases end up engaged with genuine viciousness, incited by some online media posts or exercises. One of them is strict viciousness. Strict viciousness is a term that covers marvels where religion is either the subject or the object of rough conduct. Strict viciousness is brutality that is inspired by, or in response to, strict statutes, messages, or the principles of an objective or an aggressor. It incorporates viciousness against strict foundations, individuals, items, or occasions. Strict savagery doesn't solely allude to acts which are submitted by strict gatherings, all things being equal, incorporates acts which are submitted against strict gatherings. Strict savagery is going through a restoration. The spike in exacting mercilessness is worldwide and impacts fundamentally every severe social affair. Nowadays individuals are utilizing interpersonal interaction destinations for posting or remarking their talks about religion which have positive or pessimistic effect and may be answerable for religion brutality. The study focused on religion discourses from some popular social media sites and would have predicted extreme religious discourse data among them.
Primary Stage of Diabetes Prediction Using Machine Learning Approaches
(International Conference on Artificial Intelligence and Smart Systems (ICAIS), IEEE, 2021-04-12) Emon, Minhaz Uddin; Keya, Maria Sultana; Kaiser, Md. Salman; islam, Md. Ariful; Tanha, Tabassum; Zulfiker, Md. Sabab
As per the report of the World Health Organization (WHO), diabetes has become one of the rapidly expanding chronic diseases that has affected the life of 422 million people all over the world. The number of deaths in Bangladesh due to diabetes has reached 28,065, which is 3.61% of the total deaths of Bangladesh, according to the latest data published by the WHO in 2018. So we need to be concerned about the risks of diabetes disease. If we cannot take proper steps to diagnose diabetes at an early stage, eventually we have to face serious health issues. In this paper, we have shown the relation of different symptoms and diseases that cause diabetes so that we can help a person to diagnose diabetes at an early stage. Nowadays, machine learning classification approaches are well accepted by researchers for developing disease risk prediction models. Therefore eleven machine learning classification algorithms such as Logistic Regression (LR), Gaussian Process (GP), Adaptive Boosting (AdaBoost), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Bernoulli Naive Bayes (BNB), Bagging Classifier (BC), Random Forest (RF), and Quadratic Discriminant Analysis (QDA) have been used in this study. Among all these machine learning classifiers, Random Forest (RF) classifier has showed the best accuracy of 98%. And its Area Under Curve(AUC) is also the highest.