GELİŞTİRİLMİŞ YAPAY SİNİR AĞLARI (ANN) VE ÇOKLU DOGRUSAL REGRESYON (MLR) MODELLERİYLE ÇOCUKLARDA BİLGİSAYAR OYUN BAĞIMLILIĞININ TAHMİN EDİLMESİ

Öz

Çocuklarda oyun bağımlılığının tahmini, çocuğun zihinsel ve fiziksel gelişiminde büyük rol oynar. Bu nedenle çocukların oyun bağımlılığını incelemek için çeşitli ölçekler kullanılmış ve ölçeklerde çeşitli girdi parametreleri (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) ile diğer ise Çoklu Doğrusal Regresyon (ÇDR) ile geliştirilmiştir. 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 Kelimeler

• Yapay sinir ağları
• Çoklu Doğrusal Regresyon
• Bilğisayar Oyun Bağımlılığının Tahmini
• Uzman Sistem

PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS

Abstract

Estimation of game addiction in children plays a major role in the mental and physical development of the child. Therefore, Various scales are used to examine game addiction of children and various input parameters (Age, Gender, Daily play time, etc.) are employed 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.

Keywords

• Artificial Neural Network
• Multiple Linear Regression
• Prediction of Computer Game Addiction
• Expert System

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