Browsing by Author "Dip, Sadia Tamim"

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A Smart Polluted Water Overload Drainage Detection and Alert System
(2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC),IEEE, 2021-06-09) Salehin, Imrus; Islam, Baki-Ul-; Noman, S. M.; Hasan, Md. Mehedi; Dip, Sadia Tamim; Hasan, Mehedi
A smart city constructed through the Internet of Things is a great and best medium nowadays. In our study, we have designed an advanced and automated device that can identify overloaded polluted drainage, which is responsible for water-borne disease and unexpected floods. We are using a smart ultrasonic sensor with an Ethernet shield integrated Arduino UNO. This proposed model's most vital side is the remote data access system using IP address and the webserver. This research is adequate for city corporations to advance their city, Develop their city to be more delighter, and reduce their fund used for extra human resources of city corporations cleaner. For data access methods, we are using the city Wi-Fi router and a monitoring station. We also proposed a separate web page designed to show the report. Materials used here are very uncomplicated and inexpensive but adequate to make an advanced automation intelligence system. In this modern scientific era, to lead a comfortable life, an automation system has helped develop a city more than the old system.
An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network
(IEEE, 2020-12) Salehin, Imrus; Talha, Iftakhar Mohammad; Hasan, Md. Mehedi; Dip, Sadia Tamim; Saifuzzaman, Mohd.; Moon, Nazmun Nessa
The most difficult task of meteorology is to predict rainfall. In our study, we proposed an amount of rainfall prediction model that can be easily determined using artificial intelligence and LSTM techniques. This is an advanced method to find out the rainfall. The deep learning approach is most valuable for this type of method implementation and its accuracy finds out. A long short-term memory algorithm is applied to memory sequence data measurement and calculate previous data very fast and create the best prediction. The people of this country are mostly dependent on agriculture so that this prediction system is very necessary. Timely rainfall assessment will increase crop yields and reduce costs in agriculture. Considering all these factors, we have created our model which will help us to determine the amount of rainfall. We have collected data from 6 regions to do this. To predict, we have taken 6 parameters (temperature, dew point, humidity, wind pressure, wind speed, and wind direction). After analyzing all our data, we got 76% accuracy in our work. We also focus on a vast dataset in long time weather for the better result.
Heart Disease Prediction Using Data Mining
(Daffodil International University, 2021-05-31) Dip, Sadia Tamim; Nammi, Kanij Fatema; Rayhan, Ibrahim
CVDs(Cardiovascular disorders) are the primary health problem, with 17.9 million deaths every year (World Health Organization). Heart disease has been the primary cause of death on a global scale for the last 20 years. It has become more difficult to diagnose illness and have adequate care at the right time as the population and disease have grown. However, medical research has advanced to the point that we can see a glimpse of hope. We primarily address it in this article. We looked at various data mining approaches, including Decision Tree Classification, Random Forest Classification, and K-Nearest Neighbor Classification, and we used a good data set of random attributes and values to achieve the highest accuracy. We are only attempting to forecast the progression of heart disease in this article. These Data Mining techniques require less time and have higher accuracy. It is used to monitor and examine the outcome of heart disease patients, with a current diagnosis ranging from in decent form to good shape. Using various data mining methods, the proposed study forecasts the likelihood of Heart Disease and classifies patients' risk levels. As a result, this report provides a comparative analysis of the success of various Data mining algorithms. As opposed to other data mining algorithms, the trial results suggest that the Random Forest and Decision tree algorithms have the best accuracy.