Yazar "Birogul, Serdar" seçeneğine göre listele

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    Effect the Number of Reservations on Implementation of Operating Room Scheduling with Genetic Algorithm
    (Springer International Publishing Ag, 2020) Timucin, Tunahan; Birogul, Serdar
    In this paper, the problem of the most efficient use of the Operating Rooms (ORs) which one of the most important departments of hospitals, was tackled. Efficient use of operating rooms is a scheduling problem with many constraints. This type of problem is defined as NP-Hard. Complex problems involving multiple constraints are defined as NP-Hard type problems. As the NP-Hard type problem does not consist of polynomial values, the solution of such problems becomes complicated. Such problems cannot be solved by classical mathematical methods. For the solution of NP-Hard type problems which have high level of complexity and many constraints, heuristic and meta-heuristic algorithms such as Genetic Algorithm (GA), tabu search algorithm, simulated annealing algorithm and partical swarm optimization algorithm have emerged. In this paper, the operating room scheduling problem is solved by the genetic algorithm. When coding the program, the C# programming language was preferred because of the visual advantages and user-friendliness of the language.
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    Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain
    (Mdpi, 2023) Gunduz, Fatih; Birogul, Serdar; Kose, Utku
    Blockchain systems are popular technologies that have recently emerged. As a decentralized system, blockchain technology has provided many solutions and many problems associated with these solutions. One of its most important problems is that while performing hash calculations very intensively to create a new consensus block, it reduces its efficiency depending on the duration. In this study, a new model to avoid Proof of Work (PoW), which directs the computations made to create blocks to optimization algorithms, is proposed. The proof mechanism proposed in this study is called Proof of Optimization (PoO). A traveling salesman problem (TSP) is entered into the designed system to solve the optimization algorithms. Nodes are asked to solve the TSP in certain iterations and populations. As a result, nodes are asked to create blocks with the fitness, density and time values obtained. PoO and PoW consensus algorithms were subjected to an experimental comparison in the system. The test results show that the block generation time of the PoO consensus model varies between 2 s according to the dataset solution with the least cities (ulysses22) and 60 s according to the dataset solution with the most cities (gr666). Additionally, as a result of experimental analyses, it was determined that decentralization, which is the percentage of block creation among miners in the blockchain, reached a more stable value and the fairness index rose above 0.90 on average. When the obtained values were compared with PoW, it was observed that the block time was more stable and the decentralization of the blockchain was higher. In this way, high-equipped nodes in the blockchain system are prevented from dominating the network. Thus, it is ensured that low-equipped nodes have the right to create blocks in the blockchain. The difficulty levels of the problems can be adjusted by changing the number of cities in the TSP evaluated in this study. In this way, the problem of creating blocks in the network can be made more difficult or easier at any time.
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    A survey: Making Smart Contracts really smart
    (Wiley, 2021) Timucin, Tunahan; Birogul, Serdar
    Blockchain technology is a distributed data recording system developed to monitor and secure all encrypted transactions with the shortest identification. Blockchain technology made a name for itself in 2008. However, the real development of this technology has been realized with use of smart contracts in Blockchain technology. In the cryptocurrency world, a smart contract can be defined as an application or program running on the Blockchain. They operate as digital deals that have to comply with certain rules. These rules are predetermined by computer codes and then copied and implemented by the servers in the entire network. The use of the word smart as a term in smart contracts comes from the fact that it is not made manually and is realized digitally. In this study, it is aimed to contribute to future studies in the transformation of smart contracts into real smart contract structures by applying artificial intelligence algorithms.
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    YOLO Object Recognition Algorithm and & x201C;Buy-Sell Decision & x201D; Model Over 2D Candlestick Charts
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Birogul, Serdar; Temur, Gunay; Kose, Utku
    Earning via real-time predictions with the experience in the visible trend directions of an investment instrument in the past requires a different perspective on charts. Indicators and formations within the scope of technical analysis constitute the most significant basis of this perspective. Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way. In this study, a model which had never been encountered in the literature before, was designed through a different perspective on the same data, enabling the movements of an investment element over the 2D candlestick chart to be recognized as a & x201C;Buy-Sell & x201D; object respectively and to decide on the trend direction as a result. The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa & x0130;stanbul (BIST) between 2000 & x2013;2018 were used. The model, which can make a & x201C;Buy-Sell & x201D; decision without the need for an additional time series except for the views on the visual candlestick charts, is promising in terms of its successful predictions. Its ultimate aim is to provide a foresight strengthening the & x201C;Buy-Sell & x201D; decisions to be made in the decision-making process following the other basic and technical analyses in addition to its stand-alone use in making investment decisions. The effect of this foresight on the success can clearly be seen on the test results received. In the results, the model was found to be successful by 85 & x0025; while a 100 & x0025; profit was generated. Besides, the model can be used for all the time series for which candlestick charts can be created.