Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Browsing Bilgisayar Mühendisliği Bölümü Koleksiyonu by browse.metadata.publisher "Institute of Electrical and Electronics Engineers Inc."

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    Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Kara, E.; Altıntaş, S.; Çakar, T.
    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.
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    Citation - Scopus: 4
    Cost of Guessing: Applications To Data Repair
    (Institute of Electrical and Electronics Engineers Inc., 2020) Arslan, Şuayb Şefik; Haytaoğlu, Elif
    In this paper, we introduce the notion of cost of guessing and provide an optimal strategy for guessing a random variable taking values on a finite set whereby each choice may be associated with a positive finite cost value. Moreover, we drive asymptotically tight upper and lower bounds on the moments of cost of guessing problem. Similar to previous studies on the standard guesswork, established bounds on moments quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of the Rényi's entropy. A new random variable is introduced to bridge between cost of guessing and the standard guesswork and establish the guessing cost exponent on the moments of the optimal guessing. Furthermore, these bounds are shown to serve quite useful for finding repair latency cost for distributed data storage in which sparse graph codes may be utilized.
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    Does Prompt Engineering Help Turkish Named Entity Recognition?
    (Institute of Electrical and Electronics Engineers Inc., 2024) Pektezol, A.S.; Ulugergerli, A.B.; Öztoklu, V.; Demir, Şeniz
    The extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE.
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    Next-Generation Data Storage: Transistor and Dna
    (Institute of Electrical and Electronics Engineers Inc., 2018) Pusane, Ali E.; Arslan, Şuayb Şefik; Ashrafi, Reza A.
    With the generation of diverse data growing at exponential rates, investigating better digital storage media is inevitable. Currently, one solution is the utilization of solid-state based memory devices, which offer several desirable characteristics, including very fast write/read operations, scalability, and reduced fabrication costs. However, with the increased need for long term and large storage space, their data retention capabilities drastically decline. Another emerging storage technology on the horizon is the biotechnological based DNA storage, which renders a phenomenal storage capacities. In this paper, basics of these two promising storage technologies are reviewed and their potential future trends are discussed. © 2018 IEEE.