Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/13913
  Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification

  Reis, Hatice Catal; Turk, Veysel

  Melanoma, a type of skin cancer, is a severe skin cancer that is critical for early diagnosis but difficult to detect. Deep learning is promising in melanoma diagnosis because it can automatically analyze visual features and perform end-to-end learning. However, achieving the optimum balance between performance and computational efficiency for the effective use of these models in clinical applications is an important research topic. In this study, three innovative methods are proposed for skin cancer detection: PADSCNet, P-DEFF, and M–DEFF. The PADSCNet is an effective neural network developed with CNN and multi-head attention techniques, designed with fusion methods, and stands out with its minimum number of parameters despite being quite deep. The P-DEFF and M–DEFF are based on ensemble learning techniques; P-DEFF was developed with a single deep learning model, and M–DEFF was developed with multiple pre-trained deep learning models. The proposed approaches were tested on the Skin Cancer: Malignant vs. Benign dataset, which was pre-processed using image enhancement techniques. According to the experimental results, PADSCNet achieved accuracy rates of 88.12 %, P-DEFF 93.75 %, and M–DEFF 95.00 %. In additional experiments conducted with the PADSCNet model, 93.12 % accuracy rates were achieved in the CNN for Melanoma Detection Dataset, and 95.00 % accuracy rates were achieved in the PH2 dataset. These findings show that the proposed methods are effective in skin cancer detection and can achieve successful results in clinical applications.

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• MakaleYalnız künye2026/r/gumushane/13784
  Local differentially private multi-criteria recommender system

  Demir Gül, Nurgül Nisa; Batmaz, Zeynep; Yargıç, Alper

  While multi-criteria collaborative filtering (MCCF) enhances recommender systems with more accurate and personalized content, it also introduces significant privacy concerns due to the use of fine-grained personal data. Differential privacy (DP), particularly local DP (LDP), is widely used in single-criterion systems to protect privacy. However, LDP-based mechanisms remain unexplored in MCCF systems.This study introduces the first LDP-enabled MCCF framework that applies a criterion-wise perturbation procedure to preserve the structure of multi-dimensional ratings while providing formal privacy guarantees. Three Laplace-based mechanisms as Standard (SLM), Bounded (BLM), and Truncated (TLM) are integrated and evaluated on two Yahoo!Movies subsets under privacy budgets ε ∈ [0.1, 10]. The results show that BLM and TLM consistently outperform SLM, particularly under stricter privacy conditions. On YM20, the root mean squared error (RMSE) loss compared to the unmasked variant ranges from 3.92–140.62% with SLM, 3.92-69.58% with BLM, and 3.68–88.47% with TLM. For YM10, RMSE loss ranges from 1.72–130.12% with SLM, 2.09–63.25% with BLM, and 1.55–75.64% with TLM. Moreover, minimizing noise via min-max normalization generally enhances accuracy, while rating distributions and sparsity of the datasets, clipping strategies and privacy levels further affect outcomes. Overall, the proposed LDP-based MCCF framework achieves competitive prediction quality while ensuring strong privacy guarantees, advancing privacy-preserving approaches in multi-criteria recommendation.

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• MakaleAçık Erişim2023/r/gumushane/12648
  A comparative study of evolutionary computation and swarm-based methods applied to color quantization

  Pérez-Delgado, María-Luisa; Günen, Mehmet Akif

  Color Quantization (CQ) is a complex and hard problem because selecting the best set of colors from many available colors and using that set to obtain a good quality image is an NP-complete problem. The use of evolutionary computation and swarm-based methods to solve search and optimization problems has increased dramatically in recent years. This article compares some of these methods in order to solve the CQ problem. The following methods were used to generate CQ images: Particle swarm optimization, Artificial bee colony, Adaptive differential evolution, Success-history based adaptive differential evolution (with and without linear population size reduction), Cuckoo search, Firefly algorithm and Shuffled-frog leaping algorithm. For the first two methods, two variants were considered. Thus, a total of ten metaheuristics were compared with four classical CQ methods (Variance-based, Median-cut, Binary splitting and Wu's methods) applying them to a set of benchmark images and considering four different palette sizes (32, 64, 128, and 256 colors). Three error measures were considered to compare the methods: the mean squared error, the mean absolute error and the peak signal-to-noise ratio. Some of the swarm-based methods analyzed include a recently proposed CQ method using ants. Although they have a slow computational speed in the experimental studies, the ant-based methods are significantly better than all other methods according to the Wilcoxon signed rank test. In general, despite their speed, classical methods underperform the other ten methods both qualitatively and quantitatively.

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• MakaleYalnız künye2022/r/gumushane/11439
  A decision support tool for the student–supervisor allocation problem of postgraduate programs

  Bahadır Şimşek, Ahmet

  The stable supervisor-student allocation (SSA) is crucial in the success of the research at the postgraduate education. The difference of SSA from the well-known student-project allocation (SPA) problem is that it moves the determination of the research topic to after the allocation, thus allowing the student and the lecturer to discuss the research topic. In the education systems of developing countries where decision support systems are not prevalent, the student–lecturer allocation is performed manually with a greedy approach, which can result in unstable matches. In this study, we propose a multi-objective binary model that allows decision-makers to shift between three main allocation strategies with a trade-off parameter. We test the model with a real dataset of a postgraduate program in Turkey. Experiments show that the model can successfully implement different strategies, and there can also be more than one breaking point between strategies. Also, we transform the model into a decision support tool that provides easy use to the decision-maker and reports the solutions of the trade-off parameter sensitivity analysis. The tool reports the level of satisfaction for each strategy at different breakpoints, allowing the decision-maker to see its options.

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• MakaleYalnız künye2021/r/gumushane/14776
  A reduced variance unsupervised ensemble learning algorithm based on modern portfolio theory

  Ünlü, Ramazan; Xanthopoulos, Petros

  Unsupervised ensemble learning or consensus clustering has gained popularity due to its ability to combine multiple clustering solutions into a single solution that is robust and often performs better than the individual ones. There have been several approaches to consensus clustering including voting and weighted voting algorithmic schemes. Although there have been several algorithms for adjusting the weights of a consensus clustering all of them are tuned based on some performance characteristic associated with clustering accuracy. In this paper, we propose a method for incorporating weights by taking into consideration the intra algorithmic variability i.e. algorithms that provide solutions with very different performance upon multiple runs. The methodology is inspired by modern portfolio theory and more specifically from Markowitz model for asset allocation where one is trying to identify the most efficient portfolio through the solution of a convex optimization problem. Here, efficiency is defined as the minimum amount of risk for an expected return. We apply this method to different datasets and compare with respect to performance and robustness. The proposed scheme appears to achieve competitive average performance with very low variability.

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• MakaleYalnız künye2020/r/gumushane/14586
  Evaluating the feasibility of blockchain in logistics operations: A decision framework

  Ar, Ilker Murat; Erol, Ismail; Peker, Iskender; Ozdemir, Ali Ihsan; Medeni, Tunc Durmus; Medeni, Ihsan Tolga

  The main purpose of this study is to investigate the feasibility of blockchain technology in logistics industry using a quantitative approach. To this end, a decision framework is proposed based on a multi-criteria decision structure that incorporates AHP into VIKOR under Intuitionistic Fuzzy Theory. This integration presents different solutions and rankings based on different decision-making strategies and also captures uncertainty in the evaluation process. While Intuitionistic Fuzzy AHP calculates the importance weights of the proposed criteria indicated as scalability, privacy, interoperability, audit, latency, visibility, trust, and security, Fuzzy VIKOR ranks the logistics operations demonstrated as materials handling, warehousing, order processing, transportation, packaging, fleet management, labeling, vehicle routing and product returns management. The proposed decision framework was applied in a large-scale logistics company located in Turkey. The findings of this study suggest that while the most important criteria are security, visibility and audit, the most feasible logistics operations proved to be transportation, materials handling, warehousing, order processing and fleet management in a possible blockchain implementation. The decision framework in this study may enable decision makers to evaluate the feasibility of blockchain in logistics operations, which is one of the main research gaps in the current blockchain research. Furthermore, this is the first study that integrates AHP and VIKOR methods under Intuitionistic Fuzzy Theory in the context of blockchain.

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• MakaleYalnız künye2020/r/gumushane/11498
  Symbiotic Organisms Search Algorithm for multilevel thresholding of images

  Küçükuğurlu, Büşranur; Gedikli, Eyüp

  Thresholding is a frequently used method in image processing because of its consistency and low computational cost. Otsu's and Kapur's methods are two important techniques that were proved to be best thresholding methods. However, they have high computational complexity when extended to multilevel thresholding because of their exhaustively search. Recently, meta-heuristic algorithms have been successfully applied for thresholding problems. In this study, six different meta-heuristic algorithms based on Otsu's and Kapur's functions; Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Symbiotic Organisms Search (SOS), Artifical Bee Colony (ABC), Genetic Algorithm (GA) and grey Wolf Optimizer (GWO) were used for multilevel thresholding problem and compared. Experimental results suggest that SOS, PSO and FA algorithms often have higher fitness values than other algorithms. Especially when more than two threshold values are determined, SOS algorithm mostly gives higher fitness values. PSNR and SSIM results of the algorithms are similar. In terms of computational complexity, the GWO algorithm has the fastest convergence. For standard deviations of objective functions; more stable results were obtained with SOS based on Kapur's function, SOS and PSO based on Otsu's function. Also, SOS based on Kapur's function was found to be the most successful algorithm in the Friedman test. As a result, although the GWO approached faster, the SOS algorithm produced more consistent results for both objective functions.

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• MakaleYalnız künye2019/r/gumushane/13354
  Estimating the number of clusters in a dataset via consensus clustering

  Ünlü, Ramazan; Xanthopoulos, Petros

  In unsupervised learning, the problem of finding the appropriate number of clusters -usually notated as k- is very challenging. Its importance lies in the fact that k is a vital hyperparameter for the most clustering algorithms. One algorithmic approach for tacking this problem is to apply a certain clustering algorithm with various cluster configurations and decide to use the one that maximizes a certain internal validity measure. This is a promising and computationally efficient approach since the independent runs are parallelizable. In this paper, we attempt to improve over this estimation approach by incorporating a consensus clustering approach into k estimating scheme. The weighted consensus clustering scheme employs four different indices namely Silhouette (SH), Calinski–Harabasz (CH), Davies–Bouldin (DB), and Consensus (CI) indices to estimate the correct number of cluster. Computational experiments in a dataset with clusters ranging from 2 to 7 show the profound advantages of weighted consensus clustering for correctly finding k in comparison to individual clustering method (e.g, k-means) and simple consensus clustering.

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• MakaleYalnız künye2017/r/gumushane/11823
  Stereo-based palmprint recognition in various 3D postures

  Bingöl, Özkan; Ekinci, Murat

  In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.

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