Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Publisher: search.filters.publisher.Institute of Electrical and Electronics Engineers Inc.Subject: search.filters.subject.Neural Networks

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  • Conference Object
    Developing Autonomous Steering Algorithm To Improve Cornering Slip Performance of a Four-Wheel Car Using Neural Network Tools
    (Institute of Electrical and Electronics Engineers Inc., 2025) Çakar, Tuna; Emeryan B.J.; Barbaros B.; Cakar T.; Kilic N.; Emeryan, Burak Jirayr; Kilic, Namik; Alatciyan, Diran Robin; Barbaros, Bugra; Cakar, Tuna; 01. MEF University; 02.02. Department of Computer Engineering; 02. Faculty of Engineering
    This study investigates a neural network-based predictive steering control using simulation data generated from ADAMS Car. A Long Short-Term Memory (LSTM) architecture is employed to estimate steering angle and longitudinal velocity from sequential input features, with the goal of analyzing the model's behavior in cornering scenarios. The experimental setup includes multiple simulation runs under varying configurations, particularly exploring the effect of different sliding window sizes on prediction performance. Results show that the proposed model can effectively capture temporal patterns in the input data and produce consistent estimations across test conditions. While the study is limited to a simulation environment, it provides initial insights into how AI-based models may support steering control tasks and lays the groundwork for future extensions involving additional vehicle dynamics inputs. © 2025 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Artificial 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 University
    The 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.