Page 131 - Book of Abstracts 2023

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More Than Food
SE-2-1
By: Haim Rubin haimrubin1@gmail com com Matan Fadida fadida7@gmail com com Advisor: Dr Alexander Churkin Shamoon College of Engineering Beer-Sheva
Restaurants can use our application to to to allow their diners to to to open the the the the restaurant menu with a a a a a a a a a a a a a a a a QR code read reviews and and and ratings on on on each dish place an an an an an an an an order directly to to to to the the the the the the kitchen and and and see the the the the the the preparation status at at at at at at at any given moment and and and and at at at at at at at the the the the the the the end end of the the the the the the the meal to to pay pay receive a a a a a a a a a a a a a a a a a a a a a a a tip recommendation and and and and also divide the the the the the the the the payment between the the the the the the the the diners Also the the the the the the the the application will help the the the the the the staff manage the the the the the the restaurant and and and allow the the the the the the kitchen to receive orders in in a a a a a a a a a a a a a a a a a a pleasant and and and convenient way Keywords: food order order kitchen management menu orders payment QR rating restaurants review service tips waiters
Skin disease AI
SE-2-2
By: Nadav Ishai nadavis@ac sce sce ac ac ac ac il il Dolev Peretz peretpe@ ac ac ac ac sce sce ac ac ac ac il Advisors: Dr Dr Marina Litvak Dr Dr Natalia Vanetik Shamoon College of Engineering Beer-Sheva
Dermatological issues are increasingly common in in in in in in family clinic visits and severe facial skin problems can have negative impacts on on on on mental health Dermatologists play a a a a a a a a a a a a a a a a a a a a a a a a a crucial role in in in in in in in in in accurately diagnosing skin conditions since accurate accurate diagnosis is is is is essential for successful treatment outcomes To address this concern we aim to to to create create a a a a a a a a a a a a a a a a a a a a a a a a a system utilizing machine learning and and image image processing analysis to to to to automatically diagnose skin skin diseases We created a a a a a a a a a a a a a a a a a a a a a a a a a a large image image dataset dataset for six skin skin lesion types then trained and and and evaluated up to to to 40 models on on this dataset dataset Our best model model achieves 87% accuracy and and provides website links explaining the the detected lesion type through a a a a a a a a a user-friendly interface Keywords: CNN computer vision image processing machine-learning skin disease Book of Abstracts | 2023