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A Flower Status Tracker and Self Irrigation System (FloTIS)

Rumeysa KESKİN | Furkan GÜNEY | M. Erdal ÖZBEK

The Internet of Things (IoT) provides solutions to many daily life problems. Smartphones with user-friendly applications make use of artificial intelligence solutions offered by deep learning techniques. In this work, we provide a sustainable solution to automatically monitor and control the irrigation process for detected flowers by combining deep learning and IoT techniques. The proposed flower status tracker and self-irrigation system (FloTIS) is implemented using a cloud-based server and an Android-based application to control the status of the flower which is being monitored by the local ...Daha fazlası

Erişime Açık

A Face Authentication System Using Landmark Detection

Velican ERCAN | M. Erdal ÖZBEK

Biometric data is the key for many security applications. Authentication relies on the individual’s measurable biometric properties collected as features. In this study, a face authentication system is built to be used in opening the entrance door accessing to the apartments and housing estates. The proposed system consists of three stages. In the first stage, landmarks on the face are captured using a deep neural network. Then six selected features from the landmarks are extracted and traditional machine learning algorithms are used to authenticate users. In the last stage, a user interface i ...Daha fazlası

Erişime Açık

Fabric Defect Classification Using Combination of Deep Learning and Machine Learning

Fatma Günseli YAŞAR ÇIKLAÇANDIR

Automatic systems can be used in many areas, such as the production stage in factories, country defense, and traffic control. They provide the opportunity to reach results faster with higher success rates thanks to human-computer vision cooperation. In this study, it is aimed to develop an intelligent system that automatically detects and classifies defects in fabrics. Thanks to the developed system, the cause of the malfunction is eliminated, and the recurrence of the malfunction is prevented. Using deep learning methods in fabric defect classification studies has a disadvantage compared to o ...Daha fazlası

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