Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - Scopus: 1Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction(Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Çakar, Tuna; Altıntaş, S.; Çakar, T.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R2). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service. © 2024 IEEE.Conference Object Analyzing Consumer Behavior: the Impact of Retro Music in Advertisements on a Chocolate Brand and Consumer Engagement(IEEE, 2023) Girişken, Yener; Soyaltın, Tuğçe Ezgi; Çakar, Tuna; Çakar, Tuna; Türkyılmaz, Ceyda Aysuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study presents research utilizing binary classification models to analyze consumer behaviors such as chocolate consumption and retro music ad viewing. Retro music, with its potential to evoke nostalgic feelings in consumers, is used in advertisements, which can have a significant impact on brand perception and consumer engagement. Firstly, a model focusing on chocolate consumption was developed and tested. The model yields significant outcomes. Secondly, a model based on retro music ad viewing status was developed and tested. Significant potential findings were obtained. This study emphasizes the applicability of effective classification models that can be used to understand and predict consumer behaviors, yielding significant outcomes.