Browsing by Author "Md. Muktadir Hossain"

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    Open Access
    Major Crops Yield Prediction For Bangladesh
    (North South University, 2020-09) Akhtaruzzaman Khan; Md. Muktadir Hossain; Nurun Naima Tuly; Dr. Tanzilur Rahman; 1611319042; 1610597042; 1620617042
    Bangladesh is predominantly an agricultural country where agriculture sector plays a vital role in accelerating the economic growth. Climate and other environmental changes has become a major threat in the agriculture field. In the present paper, we have considered Max-Temp, Min-Temp, Rainfall, Humidity, Wind Speed, Bright Sunshine, Cloud Coverage and Altitude from the weather dataset and Districts Name, Crop Name, Crop Category, Area, Production and Year from Crop dataset for 18 districts of Bangladesh and combined these two into one for 45 years from 1969 to 2013. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. The proposed research work pursues to produce prediction model using machine learning algorithms on 6 types of crops ( Aman, Aus, Boro, Jute, Potato and Wheat ) based on weather data. For crop yield assessment and prediction 9 algorithms( Linear Regression, Lasso Regression, Ridge Regression, Bayesian Ridge, Random Forest, K-Nearest Neighbor, Decision Tree, SVR, Artificial Neural Network) these algorithms provided acceptable values and higher accuracy rate. Linear Regression gave highest score for Aman (R2= 0.79), Aus (R2= 0.88), Boro (R2= 0.95), Jute (R2= 0.96) and Wheat crop (R2= 0.93) and for Potato Random Forest(R2= 0.87). The main purpose of this research work for helping to the farmer to predict the yield of the crop before cultivating onto the agriculture field.The crop yield prediction model discussed in the present paper will further improve in future with the use of long period dataset. Similar model can be developed for different crops of other locations. Keywords: Crop yield, Aman, Aus, Boro, Jute, Potato, Wheat, Prediction, Linear Regression, Random Forest, K-Nearest Neighbor, Decision Tree, SVR, Artificial Neural Network
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    Embargo
    Smart Speed Control and Road Safety System
    (North-south University, 2017-12-31) Md. Porag Sarkar; Samiha Lubaba; Md. Muktadir Hossain; Dr. Md. Shahriar Karim; 1320570042; 1510806645; 1530219045
    This report presents the design and the implementation of the project “Smart Speed Control and Road Safety System”. Road way incidents have become one of the leading causes of unfortunate death tolls in our country in recent years which are mostly occur because of over speeding, unusual hard braking, carelessness of drivers, bad condition of the vehicles, poor services, not following the road regulations etc. Therefore, this project has come up with a solution which provides an efficient vehicle speed, hard brake and location monitoring system. The system enables the administrators and it’s users to track those parameters of the vehicle in real time. It has also the feature to rate and review about the whole service’s quality and safety.