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dc.contributor.authorRahman, Mahfuzur
dc.contributor.authorShafi, Fardin Ahsan
dc.contributor.authorKhatun, Halima
dc.contributor.authorOshin, Nabilah Tabassum
dc.contributor.authorSufian, MD Abu Sauri
dc.contributor.authorKabir, Ashraful
dc.contributor.authorUddin, Mohammad Rejwan
dc.contributor.authorShidujaman, Mohammad
dc.contributor.authorHasan, Mahady
dc.date.accessioned2023-11-01T03:39:04Z
dc.date.available2023-11-01T03:39:04Z
dc.date.issued2023-07
dc.identifier.urihttps://ar.iub.edu.bd/handle/123456789/633
dc.description.abstractthis thesis paper presents an elementary approach to food spoilage detection and prediction in a warehouse setting, offering a practical and cost-effective solution to reduce food waste and improve food safety. This project develops a device that can predict and detect food spoilage in a timely and accurate manner by utilizing different gas sensors, temperature, and humidity sensor to predict and detect the presence of spoilage in stored food. The gathered data from the stored food is then processed and analyzed by the system. The output is displayed on an LED screen and a buzzer goes off to alert the food keepers. The project also involves the development of a user-friendly interface to allow for easy monitoring and management of the system. It uses a Bluetooth module to transmit the resulting data from the system. The findings of the study demonstrated the effectiveness of the Food Rotting Prediction and Detection System, with the sensors providing reliable data for predicting and detecting food spoilage. A mixed method analysis including thematic analysis and predictive modeling allows for more robust validation of the findings, as the results can be triangulated across different data sources.en_US
dc.publisherIndependent University, Bangladeshen_US
dc.subjectMicrocontrolleren_US
dc.subjectSensoren_US
dc.subjectCalibrationen_US
dc.subjectRottenen_US
dc.subjectKNN algorithmen_US
dc.subjectEmissionen_US
dc.titleFood Rotting Prediction and Detection System for warehouseen_US
dc.typeArticleen_US


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  • 2023 [67]
    Research articles produced by the CSE department in the year 2023

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