Sürdürülebilir Kalkınma Amaçları

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• BildiriYalnız künye2025/r/gumushane/11748
  A Novel BPA Derivation Method Using Linguistic Fuzzy Sets in Dempster-Shafer Theory

  Başkan, Elif; Şahin, Rıdvan

  In this study, we present a new method for developing Basic Probability Assignment (BPA) using Linguistic Fuzzy Sets (LFS). Dempster-Shafer Theory (DST) is used to efficiently combine different data sources. The use of LFS provides a robust structure for managing uncertainty in decision making by representing knowledge in linguistic terms. However, effectively integrating multiple uncertain sources requires a strong probabilistic approach. To overcome this challenge, we propose a method to derive BPA values directly from linguistic terms, ensuring compatibility with the DST fusion rule. We then use the Dempster-Shafer combination rule to combine information from various sources, thereby improving the reliability of the decision-making process.

  İncele
• BildiriAçık Erişim2024/r/gumushane/12561
  Extension of TODIM Method Based on Distance Measures of Decomposed Fuzzy Sets

  Köseoğlu, Ali; Şahin, Rıdvan

  Fuzzy sets allow elements to have partial membership in a set, represented by a membership value between 0 and 1. This feature enables fuzzy sets to model imprecise or vague concepts. Decomposed fuzzy sets, an extension of fuzzy sets, have introduced a novel perspective in the literature by incorporating the Kano model into risk assessment. These sets have also paved the way for new approaches in decision-making methods, highlighting their functional and dysfunctional elements. In this paper, we propose a distance measure for decomposed fuzzy sets and subsequently extend the TODIM method based on this distance measure to DFSs. Then, we provide a numerical example to illustrate its effectiveness and practicality.

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• BildiriYalnız künye2023/r/gumushane/14497
  Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection

  Genemo, Musa

  Agricultural research is essential in addressing the challenges of food production and meeting the needs of a growing population. Severe crop disorders can lead to food insecurity, potentially causing disastrous effects on agricultural production, such as yield losses, reduced crop quality, and even complete crop failures. By leveraging computer vision techniques, it is possible to develop systems that detect signs of crop diseases at an early stage, facilitating prompt intervention and management. Environmental changes, such as rainfall and biosolids, can cause plants to become inflamed by microorganisms. While manual observation and assessment of leaf conditions have traditionally been used for crop disease detection, there is increasing interest in leveraging technology to automate and enhance the process. Manual detection of crop diseases based on leaf conditions is subjective, time consuming, and vulnerable to errors. Utilizing computer vision methods to detect plant diseases helps improve plant health. The concept of applying quantum convolutional neural networks (QCNNs) for agricultural mechanization and plant disease detection is an interesting area of research that combines quantum computing and deep learning techniques. However, it is important to note that quantum computing is still in its early stages, and practical implementations of QCNNs for real-world applications, including agriculture, are not yet widespread. The proposed work of paper focuses on the detection of leaf blight disease by classifying leaves into late blight, early blight, and healthy leaves. This work is divided into two stages: segmentation and classification. For segmentation, MobileNetV2 is used, and an emerging quantum machine learning technique named QCNN is used for classification. The main objective is to provide and propose an effective and fast automated technique to detect and classify blight disease, which will help farmers identify blight disease easily. The proposed model achieved a classification accuracy of 0.969. Furthermore, the results were further validated against recent findings in the field. The presented outcome implies that the model’s performance is remarkable.

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• BildiriYalnız künye2022/r/gumushane/14850
  Assessment of Rapid Urbanization Effects with Remote Sensing Techniques

  Yagmur, Nur; Dervisoglu, Adalet; Bilgilioglu, B. Baha

  Istanbul is the most populous city in Turkey. The population, which was approximately 5.5 million in 1985, has reached 15.5 million in 2020. Population growth is the most important factor behind human activities that put pressure on the environment. An increasing population means depletion of limited resources, increasing environmental problems, and rapid urbanization. In parallel with the increase in population and urbanization, there has also been an increase in demand for housing, leading to new residential areas in almost every district of Istanbul. This study examined the transformation from vegetation areas to residential areas between 1985 and 2020 in a selected region in Buyukcekmece, one of the 39 districts of Istanbul. The relationship between land use and land cover (LULC) change in the area and Land Surface Temperature (LST) change caused by urbanization was analyzed. It is seen that the built-up area has increased from 57.1 ha to 781.4 ha in 35 years. In every five years, an increase in surface temperatures was determined in parallel with increasing urbanization, and this increase was determined as about 5.4℃ from 1985 to 2020. Also, when the temperature data of the Buyukcekmece Meteorological station is analyzed, it is seen that there has been an increase of approximately 2 ºC in air temperatures in the last five years. In addition, movements were observed in the stability of structures in rapid urbanization areas after analyzing with the PSI time series InSAR method. The main causes were determined as construction sites around the buildings and geological conditions of the ground, which are triggered by urbanization.

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