Filtreler
A Flower Status Tracker and Self Irrigation System (FloTIS)

Rumeysa KESKİN | Furkan GÜNEY | M. Erdal ÖZBEK

Makale | 2021 | Journal of Artificial Intelligence and Data Science1 ( 1 ) , pp.45 - 50

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 sensor devices. The system detects chang . . .es in the moisture of the soil and provides necessary irrigation for the flower. In order to optimize the water consumption, different classification algorithms are tested. The performance comparisons of similar works for example flower case denoted higher accuracy scores. Then the best generated deep learning model is deployed into the smartphone application that detects the flower type in order to determine the amount of water required for the daily irrigation for each type of flower. In this way, the system monitors water content in the soil and performs smart utilization of water while acknowledging the user Daha fazlası Daha az

A Face Authentication System Using Landmark Detection

Velican ERCAN | M. Erdal ÖZBEK

Makale | 2021 | Journal of Artificial Intelligence and Data Science1 ( 1 ) , pp.28 - 34

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 is built. Face recognition tests achieved . . . an accuracy rate of 89.79% Daha fazlası Daha az

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