Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(4)

• Anaç, Halil
• Doruk, Emre
• Gökgöz, Baki
• Sahin, Yasin

• MakaleAçık Erişim2026/r/gumushane/19374
  A New Caputo-Fabrizio Hybrid Method for the Numerical Solution of Caputo-Fabrizio Fractional Epidemic Model

  Anaç, Halil; Alkan, Asli

  This paper presents a novel Caputo–Fabrizio hybrid numerical method for examining the dynamics of fractional epidemic models defined by memory effects and non-singular kernels. The proposed method combines the Caputo–Fabrizio fractional derivative with a hybrid transform framework, facilitating the derivation of accurate and convergent approximate solutions for nonlinear epidemic systems. The model's parameters are analyzed in relation to infection transmission and recovery dynamics, whereas the fractional order α regulates the magnitude of memory and genetic influences. Numerical simulations indicate that the novel hybrid method exhibits remarkable concordance with established results for integer-order scenarios, while showcasing enhanced precision, computing efficiency, and robustness in fractional contexts. The suggested method offers a versatile and effective analytical instrument for investigating real-world epidemic occurrences exhibiting long-term memory traits.

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• MakaleAçık Erişim2025/r/gumushane/12316
  The analysis of Caputo-Fabrizio fractional plastic waste model with a new numerical method; [Caputo-Fabrizio kesirli mertebeden plastik atık modelinin yeni bir sayısal yöntemle analizi]

  Bingöl, Sait; Anaç, Halil

  In this paper, the new numerical solutions for Caputo-Fabrizio fractional plastic waste model are derived utilizing a novel hybrid approach known as the Caputo-Fabrizio fractional q-formable homotopy analysis transform method. A novel hybrid method combining the Caputo-Fabrizio transform and the q-homotopy analysis method is introduced. A plastic waste model generally denotes a conceptual or mathematical framework employed to comprehend, simulate, or forecast the movement, accumulation, and effects of plastic trash within a system, spanning from local surroundings to the global scale. The plastic waste model is analyzed in the Caputo-Fabrizio fractional framework. The solutions for the Caputo-Fabrizio fractional plastic waste model are illustrated in the Maple program.

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• MakaleAçık Erişim2024/r/gumushane/14428
  A new study on the Newell-Whitehead-Segel equation with Caputo-Fabrizio fractional derivative

  Alkan, Aslı; Anaç, Halil

  In this research, we propose a new numerical method that combines with the Caputo-Fabrizio Elzaki transform and the q-homotopy analysis transform method. This work aims to analyze the Caputo-Fabrizio fractional Newell-Whitehead-Segel (NWS) equation utilizing the Caputo-Fabrizio q-Elzaki homotopy analysis transform method. The Newell-Whitehead-Segel equation is a partial differential equation employed for modeling the dynamics of reaction-diffusion systems, specifically in the realm of pattern generation in biological and chemical systems. A convergence analysis of the proposed method was performed. Two-dimensional and three-dimensional graphs of the solutions have been drawn with the Maple software. It is seen that the resulting proposed method is more powerful and effective than the Aboodh transform homotopy perturbation method and conformable Laplace decomposition method in the results.

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• MakaleAçık Erişim2024/r/gumushane/11801
  The novel numerical solutions for time-fractional Fornberg-Whitham equation by using fractional natural transform decomposition method

  Alkan, Aslı; Anaç, Halil

  The time-fractional partial differential equations were solved by the fractional natural transform decomposition method. Fractional derivatives are Caputo sense. The Fornberg-Whitham equation is a generalization of the Korteweg-de Vries (KdV) equation, which describes the propagation of long waves in shallow water. It includes higher-order dispersion terms, making it applicable to a wider range of dispersive media the fractional natural transform decomposition method (FNTDM) was also used to examine applications, and the solutions obtained by this method have been compared to those obtained by the variational iteration method, fractional variational iteration method, and homotopy perturbation method. In addition, the MAPLE package drew graphs of the solutions of nonlinear time-fractional partial differential equations, taking into account physics. The method described in this study exhibited a notable degree of computational precision and straightforwardness when used to the analysis and resolution of intricate phenomena pertaining to fractional nonlinear partial differential equations within the domains of science and technology.

  İncele
• MakaleAçık Erişim2024/r/gumushane/11788
  Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning

  Gökgöz, Baki; Aydin, Tolga; Gül, Fatih

  The traditional Von Neumann computing architecture, which necessitates data transfer between external memory and the processor, incurs significant energy and time costs when running deep learning (DL) and machine learning (ML) architectures. The primary issue with the energy and time efficiency of this architecture stems from the frequent and intensive data transfers between memory and the processor. Therefore, memristive synaptic devices are utilized to overcome this energy and time inefficiency while performing cognitive tasks. The fundamental working principle of memristive devices is to reduce the need for data transfer by combining memory and processing in the same location, thereby significantly decreasing both energy consumption and the time required for operations. However, to achieve the desired level of efficiency in terms of energy and time consumption from neuromorphic systems, the performance of these systems needs to be further improved with respect to accuracy and test error rates for classification applications. Achieving high accuracy performance in such deep learning or machine learning models necessitates optimization processes not only at the hardware level but also at the algorithmic level. In this context, this paper presents a comprehensive examination and comparison of the frequently used SGD and its momentum variants for deep learning and machine learning applications in memristor-based neuromorphic computing systems. The study thoroughly investigates the performance of critical metrics such as the learning properties, energy efficiency, and accuracy rates of the nano-scale titanium dioxide (TiO2) based synaptic device. The experimental results for the MNIST dataset showed AdaDelta 89.48%, AdaGrad 79.00%, Adam 79.13%, AdaMax 79.68%, Momentum 88.55%, Nadam 81.20%, RMSprop 84.91% and SGD 89.47% accuracy. The experimental results for the CIFAR dataset showed AdaDelta 90.51%, AdaGrad 82.08%, Adam 83.10%, AdaMax 81.76%, Momentum 91.25%, Nadam 82.45%, RMSprop 88.11% and SGD 90.21% accuracy. 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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• Derleme MakaleYayıncıda açık erişim2024/r/gumushane/11691
  An overview memristor based hardware accelerators for deep neural network

  Gökgöz, Baki; Gül, Fatih; Aydın, Tolga

  The prevalence of artificial intelligence applications using artificial neural network architectures for functions such as natural language processing, text prediction, object detection, speech, and image recognition has significantly increased in today's world. The computational functions performed by artificial neural networks in classical applications require intensive and large-scale data movement between memory and processing units. Various software and hardware efforts are being made to perform these operations more efficiently. Despite these efforts, latency in data traffic and the substantial amount of energy consumed in data processing emerge as bottleneck disadvantages of the Von Neumann architecture. To overcome this bottleneck problem, it is necessary to develop hardware units specific to artificial intelligence applications. For this purpose, neuro-inspired computing chips are believed to provide an effective approach by designing and integrating a set of features inspired by neurobiological systems at the hardware level to address the problems arising in artificial intelligence applications. The most notable among these approaches is memristor-based neuromorphic computing systems. Memristors are seen as promising devices for hardware-level improvement in terms of speed and energy because they possess non-volatile memory and exhibit analog behavior. They enable effective storage and processing of synaptic weights, offering solutions for hardware-level development. Taking into account these advantages of memristors, this study examines the research conducted on artificial neural networks and hardware that can directly perform deep learning functions and mimic the biological brain, which is different from classical systems in today's context.

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• MakaleAçık Erişim2023/r/gumushane/11545
  The new numerical solutions of conformable time fractional generalized Burgers equation with proportional delay; [Oransal gecikmeli uyumlu zaman kesirli mertebeden genelleştirilmiş Burgers denkleminin yeni sayısal çözümleri]

  Kartal, Abdullah; Anaç, Halil; Olgun, Ali

  The conformable time-fractional partial differential equations with proportional delay are studied using two new methods: the conformable fractional q-homotopy analysis transform method and the conformable Shehu homotopy perturbation method. The numerical solutions to this equation are graphed. Numerical simulations show that the proposed techniques are effective and trustworthy.

  İncele
• MakaleYayıncıda açık erişim2022/r/gumushane/11910
  A Local Fractional Elzaki Transform Decomposition Method for the Nonlinear System of Local Fractional Partial Differential Equations

  Anac, Halil

  In this paper, the nonlinear system of local fractional partial differential equations is solved via local fractional Elzaki transform decomposition method. The local fractional Elzaki decomposition transform method combines a local fractional Elzaki transform and the Adomian decomposition method. Applications related to the nonlinear system of local fractional partial differential equations are presented.

  İncele
• MakaleAçık Erişim2017/r/gumushane/11854
  Developing web based decision support system for evaluation occupational risks at shipyards

  Cebi, Selcuk; Akyuz, Emre; Sahin, Yasin

  In this study, a web based decision support system (WBDSS) has been developed to provide decision aid for risk analysis on occupational health and safety at shipyards. The system has been constructed as a modular structure to make its application easy for all kind of firms. The WBDSS is rule based and its database includes safety measures and risks for each tool and machine used in shipbuilding process. In addition, probability and hazard values have been involved in the case of violated measures. The frequencies of the violations that occurred in the work environment are entered to the system by the user. Hence, the system provides a decision support about the risks related to facilities and the cautions to be taken in the workplace. The developed WBDSS has the capability of determining accident, almost accident, and threat formation factors at workplace according to the changing parameters. In this aspect, it enables one to constantly monitor, check and evaluate risk preventive measures. Then, the capabilities of the suggested decision support system have been tested with different scenarios for shipyard processes. When the developed WBDSS is applied to all workplaces, it is aimed at providing a decrease in the number of accidents which cause death, occupational illness, injury, and temporary incapability.

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