Browsing by Author "Podder, Shuvo"

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    ONLINE AUCTION SYSTEM
    (Daffodil International university, 2017-03-01) Podder, Shuvo; Sumi, Shamima Rashid
    present, people need to go to the auction place where auction is held, to buy product by bidding. This type of manual system is time consuming. Moreover, in the traditional system people have to stay physically when auction is held. Now a day internet users are increasing day by day of our country. Considering this situation, we have built an online based auction system. The primary goal of our project is to reduce complexity of manual system and introduce automated governance. In our system an auctioneer can easily perform auction and others user can bid and buy this item without problem using internet from staying home comfortably. In order to develop this system we need to consider and implement a number of technologies. The development tools is used for this project include HTML5, CSS3, Bootstrap, codeIgniter,
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    Violence Activity Recognition Using Computer Vision
    (Daffodil International University, 2021-06) Podder, Shuvo
    I proposed an intelligent system algorithm to address real-time violence activity using computer vision. Sometimes in our absent different violence activity occurs in our daily life. As a part of a smart surveillance system detecting real-time violent activity plays a key role. A video is several frames of the pixel so analyzing and classify them is a challenging research topic in the field of computer vision. Deep learning nevertheless CNN is the key part of computer vision. In previous research action recognition mostly focus on real-life activities but not enough for predicting violence. Considering all possible situation to recognize real-life violence more accurately in this research I follow Convolutional Long Short-Term Memory (CONVLSTM). The model finds spatial features from video and analysis the correlation. Datasets collected from various source and comparatively I get an adequate accuracy result. The research project finished with several experiments using different deep video analyzing algorithms. I compared and differentiated different deep learning model and finalize the best one which about 90% accuracy result. Finally, realtime video footage set to classify with my trained model. The model returns the relevant output whether the scenario is violent or not at the same time the result sent through the cloud to my developed mobile application for further action.