Browsing by Author "Aydin, M.N."

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Citation - Scopus: 0
Big data analytics and models
(IGI Global, 2019) Sönmez, F.; Perdahçi, Z.N.; Aydin, M.N.
When uncertainty is regarded as a surprise and an event in the minds, it can be said that individuals can change the future view. Market, financial, operational, social, environmental, institutional and humanitarian risks and uncertainties are the inherent realities of the modern world. Life is suffused with randomness and volatility; everything momentous that occurs in the illustrious sweep of history, or in our individual lives, is an outcome of uncertainty. An important implication of such uncertainty is the financial instability engendered to the victims of different sorts of perils. This chapter is intended to explore big data analytics as a comprehensive technique for processing large amounts of data to uncover insights. Several techniques before big data analytics like financial econometrics and optimization models have been used. Therefore, initially these techniques are mentioned. Then, how big data analytics has altered the methods of analysis is mentioned. Lastly, cases promoting big data analytics are mentioned. © 2020, IGI Global.
Citation - Scopus: 0
Forecasting the Short-Term Electricity in Steel Manufacturing for Purchase Accuracy on Day-Ahead Market
(Institute of Electrical and Electronics Engineers Inc., 2022) Koca, A.; Erdem, Z.; Aydin, M.N.
Forecasting electricity consumption in the most accurate way is crucial for purchase on the day-ahead market in steel manufacturing. This study is aimed to predict short-term electricity consumption regarding the day-ahead market purchase by employing important features of electricity consumption time-series data. We utilize Random Forest (RF), Gradient-Boosted Trees (GBT), and Generalized Linear Models (GLM), as they are appropriate for the given problem and widely used regression algorithms for prediction purposes. This study leverages the regression algorithms in the Apache Spark Machine Learning library. The performance of the prediction models is evaluated based on the standard deviation of the residuals (RMSE) and the proportion of variance explained (R-squared). We additionally discuss the distribution of prediction errors of the models. Experiments show that the RF model outperforms the GBT and GLM. It is considered that the results can contribute to accurate forecasting of short-term electricity demand for purchasing on the day-ahead. © 2022 IEEE.
Citation - Scopus: 0
Issues Regarding Deployment of Ipv6 and Business Model Canvas for Ipv6
(Peter Lang AG, 2019) Aydin, M.N.; Dilan, Ebru; Dilan, E.
[No abstract available]
Citation - Scopus: 0
A Machine Learning Approach To Steel Sheet Production Surface Quality
(Institute of Electrical and Electronics Engineers Inc., 2024) Öztürk, A.; Aydin, M.N.
This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE.