Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/13793
  Determining the effect of blockchain technology on the medical waste management process in the circular economy framework using integrated FUCOM-ARAS methods

  Batar, Bilge; Peker, İskender

  Background: The circular economy concept aims to reduce environmental impact by promoting resource reuse, minimizing waste through input reduction, and lowering costs. Rising costs are leading healthcare facilities and other organizations to prioritize medical waste management. Blockchain technology, introduced in 2008, functions as a database of auditable and traceable transactions built on a network of trust-based blocks. Key features such as transparency, reliability, accessibility, flexibility, and automation can significantly enhance the efficiency of medical waste management processes, thereby contributing to the circular economy. Objective: This study aims to evaluate the effectiveness of medical waste management—critical for healthcare institutions and sustainable development—within the framework of the circular economy. Multi-criteria decision-making (MCDM) techniques are used to assess the contributions of blockchain technology to this process. Methods: A literature review was conducted to identify relevant criteria and alternatives. The Full Consistency Method (FUCOM), an MCDM technique, was used to calculate the weight of each criterion. The Additive Ratio Assessment (ARAS) method was applied to rank the alternatives. Conclusion: According to the analysis's findings, transparency, traceability, and dependability are the three most important criteria, respectively. The stages of medical waste storage, disposal, and transportation have the highest priority ratings among the available options. According to these results, incorporating blockchain technology into medical waste management can significantly improve system traceability and reliability, enhancing its role in the circular economy. Notably, the storage phase is the most appropriate for implementing blockchain. However, the other phases’ comparatively high appropriateness scores attest to the technology's capacity to be used efficiently across the waste management cycle. This integration promotes traceable and transparent procedures, guaranteeing dependable waste management procedures in healthcare facilities and supporting the objectives of the circular economy and sustainability. Additionally, the sensitivity analysis shows that when the weighting of criterion is altered, the ranking of alternatives changes as well. This dynamic interaction emphasizes how blockchain technology may be successfully integrated into all phases rather than being restricted to just one because waste management is cyclical and holistic.

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• MakaleYalnız künye2026/r/gumushane/13501
  A multi-scale context-aware deep learning framework for medical disease diagnosis

  Reis, Hatice Catal; Turk, Veysel

  Early and accurate diagnosis of brain tumors, skin lesions, and diabetic retinopathy is essential for effective treatment planning and improving patient prognosis. Nevertheless, the diagnostic process for these conditions remains challenging due to the reliance on conventional methods, which are often subjective, time-intensive, and dependent on expert interpretation. In this context, deep learning-based approaches offer promising opportunities to enhance diagnostic precision and support clinical decision-making. This study introduces BSCRADNet, a novel deep learning model designed for the automated diagnosis of oncological and retinopathic diseases. The proposed architecture employs a multi-layered analytical framework that integrates local and spatial features with long-range contextual dependencies, enabling the effective recognition of complex morphological patterns in medical images. By enhancing diagnostic consistency, BSCRADNet reduces the risk of misclassification—particularly in cases characterized by high inter-observer variability or subtle lesion presentation. The model demonstrated strong performance across multiple datasets, achieving classification accuracies of 94.67% for brain tumors, 89.58% for skin cancer, and 90.40% for diabetic retinopathy. Furthermore, to improve the discriminative power of feature representations across diverse disease categories, a composite hybrid architecture combining BSCRADNet with ResMLP was developed. This hybrid model yielded competitive results, with accuracies of 93.33%, 88.19%, and 87.40% for the respective diagnostic tasks. Experimental findings indicate that the proposed approach is capable of distinguishing intricate patterns in medical imaging data and holds significant potential for integration into clinical diagnostic workflows, contributing to more reliable and efficient disease classification.

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• MakaleYalnız künye2024/r/gumushane/13934
  Identification of wart treatment evaluation by using optimum ensemble based classification techniques

  Balcı, Muharrem; Alkan, Ahmet

  Warts caused by Human Papilloma Virus (HPV) do not disappear spontaneously with the body's natural immunity. This is due to many features of HPV, such as its weak immune effect, not entering the bloodstream and causing an immune response, and permanent infection of skin cells. HPV requires urgent treatment due to these characteristics and the fact that warts are most common on the hands and feet and are painful and disturbing. Choosing the most appropriate Cryotherapy or Immunotherapy treatment methods according to the patient's physiological characteristics is important in order to carry out the treatment process quickly. This study focuses on helping experts choose the most appropriate treatment for the most common plantar and common warts. For the selection of treatment method machine learning have been used. Bagged Tree and Subspace K-Nearest Neighbors (KNN) Ensemble classification algorithms were developed and tested on two data sets. These data sets were used to develop classification algorithms with pre- and post-treatment information of 180 patients receiving cryotherapy and immunotherapy treatments. The Subspace KNN Ensemble algorithm correctly predicted the treatment results of 88 of 90 patients receiving cryotherapy treatment, reaching 97.80% accuracy, and the Bagged Tree Ensemble algorithm correctly predicted 78 out of 90 patients receiving immunotherapy, reaching 86.7% accuracy. It has been shown that algorithms can help experts in terms of both high accuracy values ​​and fast decision making. The algorithms proposed have not been used before, and it has also been seen that they have superior performances than similar studies.

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• MakaleYalnız künye2020/r/gumushane/14731
  An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching

  Manshouri, Negin; Maleki, Masoud; Kayikcioglu, Temel

  Despite knowing the reality of three-dimensional (3D) technology in the form of eye fatigue, this technology continues to be retained by people (especially the young community). To check the influences of the human brain's power spectrum density (PSD) pre and post a 2D and 3D movie watching, a five-member test group was arranged. In this study, electroencephalogram (EEG) was used as a neuroimaging method. EEG recordings of five individuals were taken both before and after watching 2D and 3D movies. The main framework of this study was to analyze the effects of power spectrum density (PSD) of the human brain by testing all EEG frequency bands before and after 2D and 3D movie watching, also to determine effective brain lobes, and consequently, EEG channels. After 2D/3D EEG recording, this record was divided into three stages for analysis. These stages consisted of Relax, Watching, and Rest. This benchmarking analysis included I) before and after watching the 2D movie (R2b and R2a), II) before and after watching the 3D movie (R3b and R3a), and III) after watching the 2D/3D movie (R2a and R3a). In the Relax and Rest stages, the 2D/3D EEG power differences in all channels of brain regions for the five EEG bands, including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), were analyzed and compared. The PSD based on short-time Fourier transform (STFT) was used to select the dominant bands in this study. Feature extraction was performed on the preprocessed EEG signals using STFT and discrete wavelet transform (DWT). In the 2D analysis, δ, θ, α, and β acted as dominant bands, in 3D, δ, θ, and α were dominant bands, and in 2D/3D, δ and α were selected as meaningful and dominant bands. Partial least-squares regression (PLSR) and support vector machine (SVM) classification algorithms were considered in order to classify the obtained R2a and R3a EEG signals. After the dominant band selection, the correct choice of effective channel combinations in these bands resulted in the percentage of high success of classifiers for Stage III. Taking into account the optimum number of channels, the best percentage of classification accuracy for the average of PLSR and SVM classification results by considering the best channels of SVM and DWT feature extraction method is 91.83% and 80.73% respectively.

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