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Author: Eryilmaz, MeltemScopus Q: Q4

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    A New E-Commerce Model Suggestion of Agricultural Products
    (Springer international Publishing Ag, 2024) Eryilmaz, Meltem; Briman, Mohammed Khalid Hilmi; Yakut, Gokce
    This study aims to create an e-commerce application that enables farmers to sell their products directly to their customers without resorting to intermediaries by a smart cargo box. It presents a method by which the agricultural industry can gain momentum in e-commerce. The main contributions of the study are an e-commerce application that enables farmers to sell their products directly to the customer without the need for an intermediary and the design and implementation of a smart cargo box that is tailored to carry agricultural items throughout the delivery process that locks and unlocks only via QR code generated by the application after order. Django FrameworkNext.js, React.js, and Redux are used for the system development. The database is created with PostgreSQL using migrations, and AmazonWeb Services is used for this database. The "ESP32 Cam" is used to read the QR Code.
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    Citation - WoS: 4
    Citation - Scopus: 3
    Using Artificial Intelligence Methods to Predict Student Academic Achievement
    (Springer international Publishing Ag, 2022) Al-Khafaji, Mustafa; Eryilmaz, Meltem
    This study applies two artificial intelligence methods represented by both the neural network and fuzzy logic to predict student achievement in the exam. The dataset used in this study was taken from an Iraqi engineering college and it represents data of 200 students who have enrolled in the computer science course. Gender, age, resources downloaded, videos viewed, discussion chat joined, exam scores used as the data set. The type of artificial neural network used was pattern neural network. Levenberg-Marquardt's algorithm was used to train the neural networks. On the other hand Sugeno fuzzy inference system was used for the fuzzy logic. The study results showed that the students who spend more time on the learning system have the most success rate. According to the results the neural network accuracy rate 73% and the fuzzy was 88%. This high accuracy rates support that artificial intelligence methods can be used to predict student academic achievement.