Maisha Farzana

Miah Mohammad Asif Syeed, Maisha Farzana, Ishadie Namir et al.

Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.

Abu Saleh Md. Abir, Sanjana Rahman, Samia Ellin et al.

In a world of digitization, optical character recognition holds the automation to written history. Optical character recognition system basically converts printed images into editable texts for better storage and usability. To be completely functional, the system needs to go through some crucial methods such as pre-processing and segmentation. Pre-processing helps printed data to be noise free and gets rid of skewness efficiently whereas segmentation helps the image fragment into line, word and character precisely for better conversion. These steps hold the door to better accuracy and consistent results for a printed image to be ready for conversion. Our proposed algorithm is able to segment characters both from ideal and non-ideal cases of scanned or captured images giving a sustainable outcome. The implementation of our work is provided here: https://cutt.ly/rgdfBIa