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  • The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.

  • Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.

Last update from database: 3/13/26, 4:15 PM (UTC)

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