Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/13716
  Multi-scale deep fusion architecture for brain tumor detection via local-contextual feature integration

  Reis, Hatice Catal; Turk, Veysel

  Early detection of brain tumors is crucial for enhancing treatment strategies and improving clinical prognosis. However, current conventional methods limit diagnostic accuracy and slow down diagnostic processes due to subjective interpretations and dependence on experts. Deep learning-based approaches have the potential to enhance diagnostic accuracy and facilitate early diagnosis by mitigating current limitations in automatic learning techniques for feature extraction and modeling processes. In this study, an advanced deep learning model, Multi-Scale Local-Context Fusion Network (MSLCFNet), is proposed for automatic diagnosis of brain tumors within the scope of oncological diseases. The proposed model evaluates local spatial features, global patterns, and long-range contextual relationships holistically through multi-layered representations, enabling effective identification of complex structures of tumor morphology. This architecture has the potential to increase diagnostic consistency and minimize the risk of misdiagnosis by reducing inter-observer variability, especially in cases where limited contrast and unclear boundaries lead to diagnostic uncertainty. The proposed model achieved accuracy rates of 96.51% and 98.17% on two different MRI brain tumor datasets, respectively. Additionally, a hybrid model based on MSLCFNet+ResNet50 was proposed in the study to obtain high-level feature representations for brain tumor diagnosis. The model offers a significant improvement in the diagnosis process by reducing misdiagnosis rates to 98.86% and 99.50% accuracy. The proposed methods make a significant contribution to enhancing diagnostic accuracy in clinical decision support systems, as they enable the efficient processing of complex morphological structures.

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• MakaleAçık Erişim2024/r/gumushane/14005
  Fusion of transformer attention and CNN features for skin cancer detection

  Reis, Hatice Catal; Turk, Veysel

  Melanoma is a skin cancer that spreads quickly and has serious risks. Early diagnosis is essential, but since the symptoms of skin lesions in the early stages are vague and similar, they can be difficult for specialists to detect. Therefore, machine learning-based alternative diagnostic methods can be developed in addition to existing ones. This study proposes a new deep learning model, a modified lightweight vision transformer (ViT) architecture, and a hybrid framework developed with an integrated deep learning model and an Ensemble Learning (EL) model for the early-stage diagnosis of skin lesions. The proposed deep learning model was developed based on convolution layers and transformers. The model is called multi-head attention block depthwise separable convolution network (MABSCNET). The proposed hybrid framework was developed by combining modern deep learning and EL models pre-trained with the ImageNet dataset along with the MABSCNET model. In the experimental process, the effectiveness of the proposed methods was evaluated on the ISIC 2020 dataset. Additionally, additional experiments were conducted on ISIC 2018 and a Kaggle dataset to analyze the proposed hybrid framework's classification performance. Image enhancement techniques were used in the datasets. In the ISIC 2020 dataset, the MABSCNET model reached 78.63 % accuracy, the ViT model obtained 76.50 %, and the hybrid framework reached 92.74 % accuracy. Moreover, the proposed hybrid framework achieved 100 % on the ISIC 2018 dataset and 94.24 % on the Kaggle dataset.

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• MakaleYalnız künye2023/r/gumushane/12557
  Detection of forest fire using deep convolutional neural networks with transfer learning approach[Formula presented]

  Reis, Hatice Catal; Turk, Veysel

  Forest fires caused by natural causes such as climate change, temperature increase, lightning strikes, volcanic activity or human effects are among the world's most dangerous, deadly, and destructive disasters. Detection, prevention, and extinguishing forest fires is challenging. In addition, forest fires can cause habitat destruction that cannot be controlled in time and cause great material and moral losses. Therefore, fast and accurate Detection of forest fires is vital in emergency response. Here, in solving the problem, the transfer learning method from deep learning sub-topics can be used, which allows the application of pre-trained networks to a new problem. The Fire Luminosity Airborne-based Machine learning Evaluation dataset (consisting of forest fire images) obtained by Unmanned Aerial Vehicle was used in this study. In the Detection of forest fire images in the dataset, InceptionV3, DenseNet121, ResNet50V2, NASNetMobile, VGG-19 deep learning algorithms, transfer learning techniques that can produce more successful results than networks trained from scratch, and hybrid proposed with Support Vector Machine, Random Forest, Bidirectional Long Short-Term Memory, Gated Recurrent Unit algorithms methods have been applied. In the classification study with the Fire Luminosity Airborne-based Machine learning Evaluation dataset in performance measurement, 97.95% accuracy was obtained from the DenseNet121 model, which was started with random weights. In the transfer learning study using ImageNet weights, satisfactory results were obtained with 99.32% accuracy in the DenseNet121 model. We anticipate that working in forest fire detection and response can be entirely satisfactory.

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• MakaleYalnız künye2022/r/gumushane/12872
  Scrutinizing blockchain applicability in sustainable supply chains through an integrated fuzzy multi-criteria decision making framework

  Erol, Ismail; Ar, Ilker Murat; Peker, Iskender

  Sustainability has become increasingly important over the last three decades and has proven to be a key enabler for constructing resilient supply chains. Customers who want their products to be authenticated for sustainability put pressure on Original Equipment Manufacturers and suppliers to become more sustainable on a global scale. Moreover, social sustainability issues have become more challenging to address, and a growing number of stakeholders put emphasis on societal concerns. To this end, decision-makers are becoming increasingly interested in applying disruptive technologies to address societal, environmental, and economic concerns and accomplish sustainability goals. Researchers argue that disruptive technologies such as blockchain may be implemented to assist supply chains towards building sustainability. However, our literature analysis concluded that existing research has not quantitatively examined the critical functions of sustainable supply chain (SSC) for blockchain applicability using a decision framework. Therefore, this research, through Fuzzy SWARA-COPRAS-EDAS and COPELAND-based framework, is aimed at investigating the most feasible functions of a SSC for potential blockchain implementations. Using this framework, the critical functions of a SSC were ranked against the benefits of blockchain. The findings of this study implied that while sourcing, delivery, transformation and product recovery proved to be the most appropriate functions of SSCs for blockchain applications, customers and product use was the least feasible one. This study aids decision-makers in gaining a more thorough understanding of where in a SSC blockchain may create additional value.

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• MakaleYalnız künye2011/r/gumushane/16395
  Weight minimization of trusses with genetic algorithm

  Dede, Tayfun; Bekiroglu, Serkan; Ayvaz, Yusuf

  This study is concerned with encoding types such as value encoding and binary encoding for continuous and discrete optimization with a GA, which is coded in FORTRAN and considers stress and displacement constraints, in view of weight minimization of truss structures. Moreover, when continuous optimization is performed, the challenge of huge search space due to the effort of considering continuous set of design variables is overcame by a mechanism introduced as restricted range approach (RRA) in this study. In comparison with the literature, it is concluded that the program developed in this study can be effectively used in the weight minimization of truss structures. It is also came to the conclusion that value encoding overcomes the adverse effects of Hamming-cliff, and that value encoding requires less computer memory and time, never destroys the fit chromosome. (C) 2010 Elsevier B.V. All rights reserved.

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