PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS
Yıl: 2021 Cilt: 23 Sayı: 2 Sayfa Aralığı: 551 - 570 Metin Dili: İngilizce DOI: 10.26468/trakyasobed.789767 İndeks Tarihi: 22-09-2022
PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS
Öz: Game addiction in children plays a major role in the mental and physical development of the child. Therefore, various scales are used to examine computer game addiction of children and various input parameters (age, gender, daily play time, etc.) are utilized in scales. The purpose of this study is to project a system that estimates whether the child is addicted to the game when looking at the input parameters. Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) techniques were used to design this system. In order to measure the predictive performance of the developed models, the Root Mean Squared Error (RMSE), and Correlation Coefficient (R) criteria were examined respectively and it was observed that the model developed by ANN predicted CGA with high accuracy.
Anahtar Kelime: GELİŞTİRİLMİŞ YAPAY SİNİR AĞLARI (ANN) VE ÇOKLU DOGRUSAL REGRESYON (MLR) MODELLERİYLE ÇOCUKLARDA BİLGİSAYAR OYUN BAĞIMLILIĞININ TAHMİN EDİLMESİ
Öz: Çocuklarda oyun bağımlılığı, çocuğun zihinsel ve fiziksel gelişiminde büyük rol oynar. Bu nedenle çocukların bilgisayar oyun bağımlılığını incelemek için ölçek ve ölçeklerde çeşitli parametreler (yaş, cinsiyet, günlük oyun süresi vb.) kullanılmıştır. Bu çalışmanın amacı, girdi parametrelerine bakıldığında çocuğun oyuna bağımlı olup olmadığını tahmin eden bir uzman sistemi tasarlamaktır. Bu sistemin tasarlanması amacıyla iki model kullanılmıştır. Bu modellerden biri Yapay Sinir Ağları (YSA) diğeri ise Çoklu Doğrusal Regresyon (ÇDR)’dur. Modellerin performansı, Kök Ortalama Kare Hatası (KOKH) ve Korelasyon Katsayısı (R) kriterleri kullanılarak değerlendirilmiştir. Bu kriterler analiz edildiğinde, YSA yüksek tahmin performansı gösterirken, MLR düşük tahmin performansı göstermiştir. Sonuç olarak, YSA ile geliştirilen sisteme farklı girdi değerleri verildiğinde, çocuklardaki oyun bağımlılığı ile ilgili en doğru tahminlerin elde edildiği görülmüştür.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | uzunhisarcıklı e, Kavuncuoglu E, AKGÜL H (2021). PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. , 551 - 570. 10.26468/trakyasobed.789767 |
| Chicago | uzunhisarcıklı esma,Kavuncuoglu Erhan,AKGÜL Hanife PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. (2021): 551 - 570. 10.26468/trakyasobed.789767 |
| MLA | uzunhisarcıklı esma,Kavuncuoglu Erhan,AKGÜL Hanife PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. , 2021, ss.551 - 570. 10.26468/trakyasobed.789767 |
| AMA | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. . 2021; 551 - 570. 10.26468/trakyasobed.789767 |
| Vancouver | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. . 2021; 551 - 570. 10.26468/trakyasobed.789767 |
| IEEE | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H "PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS." , ss.551 - 570, 2021. 10.26468/trakyasobed.789767 |
| ISNAD | uzunhisarcıklı, esma vd. "PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS". (2021), 551-570. https://doi.org/10.26468/trakyasobed.789767 |
| APA | uzunhisarcıklı e, Kavuncuoglu E, AKGÜL H (2021). PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. Trakya Üniversitesi Sosyal Bilimler Dergisi, 23(2), 551 - 570. 10.26468/trakyasobed.789767 |
| Chicago | uzunhisarcıklı esma,Kavuncuoglu Erhan,AKGÜL Hanife PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. Trakya Üniversitesi Sosyal Bilimler Dergisi 23, no.2 (2021): 551 - 570. 10.26468/trakyasobed.789767 |
| MLA | uzunhisarcıklı esma,Kavuncuoglu Erhan,AKGÜL Hanife PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. Trakya Üniversitesi Sosyal Bilimler Dergisi, vol.23, no.2, 2021, ss.551 - 570. 10.26468/trakyasobed.789767 |
| AMA | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. Trakya Üniversitesi Sosyal Bilimler Dergisi. 2021; 23(2): 551 - 570. 10.26468/trakyasobed.789767 |
| Vancouver | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS. Trakya Üniversitesi Sosyal Bilimler Dergisi. 2021; 23(2): 551 - 570. 10.26468/trakyasobed.789767 |
| IEEE | uzunhisarcıklı e,Kavuncuoglu E,AKGÜL H "PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS." Trakya Üniversitesi Sosyal Bilimler Dergisi, 23, ss.551 - 570, 2021. 10.26468/trakyasobed.789767 |
| ISNAD | uzunhisarcıklı, esma vd. "PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS". Trakya Üniversitesi Sosyal Bilimler Dergisi 23/2 (2021), 551-570. https://doi.org/10.26468/trakyasobed.789767 |
