Smart Lost and Found Android Application for University using Machine Learning

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Date

2020

Journal Title

Journal ISSN

Volume Title

Type

Other

Publisher

MSA university Faculty of Computer Science

Series Info

Faculty Of Computer Science Graduation Project 2019 - 2020;

Doi

Scientific Journal Rankings

Abstract

Losing an item of monetary or emotional value can be very hard to take. After the initial scare, you worry if you will never get it back. Between too many intermediates and high costs, the present lost and found offices are rather inflexible and not adjusted for our hectic lives. University students are especially busy and hastened during the academic year, so they frequently find themselves losing or misplacing possessions that they very much want back. Unfortunately, the lost and found system at campus is outdated, and often completely handwritten. This present system makes it increasingly difficult for those in charge of the lost and found department to reunite students with their lost items. The primary goal of this project is the development of an Android Mobile Application that will help students retrieve their missing items on campus more quickly and efficiently. The main features of this Android Application include a registration and login system. Users have the ability to add information about lost and found items by making new posts describing the items they have lost or found in detail,. An admin web page panel will also be implemented, giving admins special privileges like deleting, and modifying post as well as adding, deleting and updating users contact information. A pre-trained CNN classifier model, with a classifier button has also been implemented where students who have pictures of their lost/found items can have those images classified and put into categories to make the search easier and faster.

Description

Keywords

October university for modern sciences and arts, جامعة أكتوبر للعلوم الحديثة والآداب, University of Modern Sciences and Arts, MSA University, Android Application, University using Machine Learning

Citation

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