Browsing by Author "Hasan, Md. Mehadi"

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A Decision Support System for Early Prediction of Brain Stroke Disease in Bangladesh
(Daffodil International University, 2019-04) Alam, Md. Mahabur; Hasan, Md. Mehadi
Brain Stroke is a Neurological disease that occurs when the blood supply to brain is interrupted or reduce, depriving brain tissue of oxygen and nurturance. This can lead to brain damage to possibly death. Brain Stroke are a medical emergency and prompt treatment is essential because the sooner a person receives treatment stroke, the less damage is likely to happen. Brain Stroke is the second leading cause of death in worldwide and third in Bangladesh. Information gain from health data may lead to innovative solution of better treatment plan for patients. In order to gain knowledge intelligently from brain stroke data, some machine learning technique and utilized to process data and generated data model that can be used to predict brain stroke disease or extract valuable information about brain stroke disease. In this study a data mining model has been build using a few machine learning algorithm, which can find out the important features of brain stroke as well as efficiently predict brain stroke.
An Efficient Modified Bagging Method for Early Prediction of Brain Stroke
(Scopus, 2019-07-12) Alam, Md. Mahabur; Hasan, Md. Mehadi; Hasan, Md. Zahid
Brain stroke become a serious cardiovascular and cerebral disease causes of human death. Precisely predicting stroke effect from a set of predictive attributes may classify high-risk patients and guide cure approaches, leading to reduce relative incidence. In respect to, we have collected the information regarding brain stroke patient's data from five renowned hospitals in Bangladesh with connectivity in patients with acute thalamic ischemic stroke (melanoma), Atypical Nevus (cancer risk) and Common Nevus (No cancer risk). In this work, we propose an ensemble based Modified Bootstrap Aggregating (Bagging) technique for pattern classification. Existing bagging algorithm, can usually progress the performance of a single classifier. However, they typically need larger space as well as quite time-consuming predictions. However, our proposed accuracy based pruning bagging method can improve the classification performance and reduce ensemble size. In general, our proposed modified bagging technique is more appropriate than traditional bagging technique for the prediction of brain stroke disease patients with greater accuracy of 96%.
Bangla Handwritten
(Daffodil International University, 2023-11-07) Jaman, Sadia; Sovon, Mehadi Hassan; Raihanuzzaman, Syed; Hasan, Md. Mehadi; Nabi, Nusrat; Ahamed, Md. Sazzadur
The difficulty of handwritten character identification varies by language, owing to differences in shapes, lines, numbers, and size of characters. There are several studies for the identification of handwritten characters accessible for English in comparison with other significant languages like Bangla. In their recognition procedures, existing technologies use multiple techniques such as classification tools and feature extraction. CNN has recently been shown to be proficient in handwritten character recognition in English. A Handwritten Bangla character identification system based on CNN has been examined in this research. Using CNN, the suggested approach for feature, labelling and normalizing the handwritten character of images, as well as categorizing different characters. It doesn't use a feature extraction approach like previous research in the field. This research used almost 4,50,000 unique handwritten characters in a variety of styles. The recommended model has been proved to have a high recognition accuracy level and outperforms some of the most widely used methods already in use. In this research, identify the Bangla handwritten character and digits with the use of 189 classes consisting of 50 fundamental characters, 119 compound characters, 10 numerals, and 10 modifiers. The accuracy rate of basic characters is 84.62%, numerals 94%, modifiers 96.46%, compound characters 77.60% using created new model.
Web Based Online Shopping
(East West University, 1/20/2016) Hasan, Md. Mehadi
The business-to-consumer aspect of electronic commerce (e-commerce) is the most visible business use of the World Wide Web. The primary goal of an e-commerce site is to sell goods and services online. This project deals with developing an e-commerce website for online shopping. It provides the user with a catalog of different product available for purchase in the store. In order to facilitate online purchase a shopping cart is provided to the user. The system is implemented using a 3-tier approach, with a backend MySQL database, a middle tier apache server and a web browser as the front end client. In order to develop online shopping application use HTML, CSS, JAVA Script, server side scripting language PHP and relational database MySQL. This is a project with the objective to develop a basic website where consumer is provided with a shopping cart application and also to know about the technologies used to develop such an application