Yazar "Topaloglu, Fatih" seçeneğine göre listele

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    A hybrid approach based on k-means and SVM algorithms in selection of appropriate risk assessment methods for sectors
    (Peerj Inc, 2024) Topaloglu, Fatih
    Every work environment contains different types of risks and interactions between risks. Therefore, the method to be used when making a risk assessment is very important. When determining which risk assessment method (RAM) to use, there are many factors such as the types of risks in the work environment, the interactions of these risks with each other, and their distance from the employees. Although there are many RAMs available, there is no RAM that will suit all workplaces and which method to choose is the biggest question. There is no internationally accepted scale or trend on this subject. In the study, 26 sectors, 10 different RAMs and 10 criteria were determined. A hybrid approach has been designed to determine the most suitable RAMs for sectors by using k-means clustering and support vector machine (SVM) classification algorithms, which are machine learning (ML) algorithms. First, the data set was divided into subsets with the k-means algorithm. Then, the SVM algorithm was run on all subsets with different characteristics. Finally, the results of all subsets were combined to obtain the result of the entire dataset. Thus, instead of the threshold value determined for a single and large cluster affecting the entire cluster and being made mandatory for all of them, a flexible structure was created by determining separate threshold values for each sub-cluster according to their characteristics. In this way, machine support was provided by selecting the most suitable RAMs for the sectors and eliminating the administrative and software problems in the selection phase from the manpower. The first comparison result of the proposed method was found to be the hybrid method: 96.63%, k-means: 90.63 and SVM: 94.68%. In the second comparison made with five different ML algorithms, the results of the artificial neural networks (ANN): 87.44%, naive bayes (NB): 91.29%, decision trees (DT): 89.25%, random forest (RF): 81.23% and k-nearest neighbours (KNN): 85.43% were found.
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    Analytic network process (ANP) based decision support tool for nuclear power plant location and reactor type selection
    (Korean Nuclear Soc, 2025) Topaloglu, Fatih
    The nuclear energy industry has seen a resurgence due to interest in sustainable energy. Regulatory agencies are evaluating innovative technology and facility designs for the first time in a long time. The nuclear energy business needs a workable instrument for decision-making in light of the shift in public and regulatory opinion. This study has two main motivations. The first is to determine the set of criteria to be used in determining suitable locations for a nuclear power plant (NPP) to be established in Turkey and to determine suitable regions with Analytic Network Process (ANP) based analyzes in the light of these criteria. The second is to select the most suitable nuclear reactor type for the specified location. Plant efficiency and lifetime vary depending on the reactor type used. For this reason, in the study, both the location of the nuclear power plant and the selection of the appropriate reactor type for the plant were evaluated together. In this context, a criterion set consisting of 3 main criteria and 20 sub-criteria to be used in NPP installation location and reactor selection was determined. In the study, site selection was made by focusing on economic, social and technical factors, and reactor selection was made by focusing on technical factors. Giresun, Trabzon, Rize and Artvin provinces located in the Eastern Black Sea region were evaluated for NPP installation. For reactor selection, we focused on reactor types according to their coolants and in this context, PWR, PHWR, GCR and BWR reactors were examined. NPP location and reactor selection in the study; It was realized as Giresun (PWR) > Rize (PWR) > Trabzon (BWR) > Artvin (BWR).
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    Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization
    (Nature Portfolio, 2025) Topaloglu, Fatih; Kiziloluk, Soner; Sert, Eser; Yildirim, Muhammed
    Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic species in water samples are often time-consuming, require intensive manual effort, are prone to subjective errors, and can lead to delays in necessary water management interventions. This emphasizes the pressing need for a quick and precise automated method. Both aquatic and terrestrial environments include cyanobacteria, and under some circumstances, poisonous cyanobacteria can grow in large numbers and form harmful blooms called harmful cyanobacterial blooms (Cyano-HABs). In addition, cyanoHABs cause hypoxia, ecological imbalances, the generation of toxins, and other detrimental phenomena that put people, animals, and plants in danger of illness. Climate change is expected to cause these situations to increase in frequency and globally. This study presents a novel approach for the automatic detection of harmful cyanobacteria genera by utilizing a newly introduced and publicly available dataset, TCB-DS. In the initial stage, discriminative features are extracted using two powerful deep Convolutional Neural Network (CNN) models: ShuffleNet and ResNet-50. Subsequently, feature fusion is applied to the extracted features to enhance the representation. Then, to select the most relevant features, feature selection is performed using the Artemisinin Optimization (AO) algorithm, a robust meta-heuristic algorithm inspired by the mechanisms of malaria treatment and recently proposed in 2024. This step aims to reduce feature redundancy and improve the overall efficiency of the model. In classifying microscopic images of cyanobacteria species with the proposed method, GoogleNet, MobileNetV2, EfficientNetb0, DarkNet53, ShuffleNet, and ResNet101 models were used. Among these, the proposed method obtained the highest accuracy, with a mean accuracy of 97.471% and max accuracy of 97.683%. Since these results are the highest accuracy values obtained in the TCB-DS dataset, our proposed method significantly improves water quality monitoring in our world.
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    Development of a new hybrid method for multi-criteria decision making (MCDM) approach: a case study for facility location selection
    (Springer Heidelberg, 2024) Topaloglu, Fatih
    Facility location selection is a difficult and very costly task to change after the business is established, so it is a very important decision for businesses. Numerical methods help make important decisions, such as determining the region where businesses will operate for a long time. For this purpose, multi-criteria decision making (MCDM) methods are widely used. Some practical disadvantages of existing MCDM methods are; In cases where the number of alternatives and criteria is high, more pairwise comparison matrices are required, matrix consistency becomes more difficult, and as the number of elements in the hierarchy increases, the problem becomes more complex and causes loss of time. The aim of this study is to develop a new hybrid method for the MCDM approach, which has a simpler mathematical infrastructure, is not affected by the increasing number of criteria, and does not require an additional consistency analysis. The proposed hybrid method includes Analytic Hierarchy Process (AHP) and Standard Scoring Method (SSM) methods. The method includes a single pairwise comparison matrix of the AHP method and the simple mathematical infrastructure of the SSM method, which makes calculations based on real values and standard formulas. In the study, the hybrid method was applied and analyzed to determine the most suitable geographical region of Turkey for the facility location of companies operating in the manufacturing sector in Turkey, in the light of 6 main criteria determined by the expert team as a result of a comprehensive literature review. It has been observed that classification, ranking and selection are possible with the AHP-SSM based hybrid method, which is recommended as the MCDM method, and the most suitable geographical region for the factory location in Turkey is the Black Sea Region. In addition, a comparative analysis was carried out between the proposed method and other MCDM methods.
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    Machine Learning-Based Approaches and Comparisons for Estimating Missing Meteorological Data and Determining the Optimum Data Set in Nuclear Energy Applications
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Topaloglu, Fatih
    Good data analysis is required for the optimal design of nuclear energy projects. However, due to financial or technical reasons, data cannot be collected regularly, which leads to missing data problems. Missing values in data sets can seriously affect research results. There are two main motivations for the study. The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. Its second motivation is to determine the optimum set/number of meteorological data required for nuclear energy projects using the best-performing ML algorithm. For this purpose, 31 years of meteorological data regarding the wind speed, rainfall amount, snowpack and air temperature required for nuclear energy projects by the nuclear policy board in Turkey were analyzed. In this way, some difficulties such as processing and organizing the data created by unnecessary and large data due to its volume and speed have been prevented. In this study, which is based on incomplete meteorological measurement data, the mechanism belongs to the MCAR type. Linear Regression method reached the highest performance with 91.6%. Additionally, by normalizing the data set using Standardization and Normalization scaling techniques, this performance increased to 93.3% and 98.9%, respectively. On the other hand, it has been observed that a 14-year training set is sufficient as a data set in nuclear energy applications.