Browsing by Author "Hossain, Sheikh Mufrad"

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    Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms
    (IEEE, 2023-07-23) Mia, Yousuf; Rahman, Md. Solaimanur; Basak, Animesh; Hossain, Sheikh Mufrad; Zulfiker, Md. Sabab
    We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches.
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    Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study
    (Springer Nature, 2023-01-25) Hossain, Sheikh Mufrad; Rahman, Md. Mahfujur; Barros, Alistair; Whaiduzzaman, Md.
    The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student’s academic performance. In this research, we aim to identify the factors that impact the student’s academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers.