Sürdürülebilir Kalkınma Amaçları

• 13
• 4
• 3
• 2
• 2
• 2
• 1
• 1

Araştırmacılar(21)

• Aslan, Muhammed Emin
• Bingöl, Ozkan
• Bozkır, İlknur Kayıkçıoğlu
• Dogan, Ramazan Ozgur
• Eroğlu, Alperen
• Genemo, Musa Dıma
• Gul, Fatih
• Gül, Nurgül Nisa Demir
• Güvercin, Mehmet
• Küçükuğurlu, Büşranur
• Melek, Mesut
• Seyfi, Gökhan
• Sünnetci, Betül Koşmaz
• Şahin, Rıdvan
• Tonyalı, Samet
• Türe, Hayati
• Türk, Salih
• Uçar, Kübra
• Ural, Mustafa Nuri
• Uslu, Fatmatulzehra
• Yaprak, Büşranur

• MakaleYalnız künye2026/r/gumushane/13834
  Machine learning-based multi-class classification of rock mass permeability using integrated geotechnical and geophysical parameters

  Sari, Mahmut; Alemdag, Selcuk; Melek, Mesut; Seren, Aysel

  In engineering applications, machine learning (ML) provides a promising approach when direct characterization of subsurface properties is challenging, time-consuming, and costly. This study developed a multi-class ML framework for classifying rock mass permeability based on Lugeon values using 86 observations from drilling and geophysical investigations at the Kırklartepe dam site in Bayburt, Türkiye. Seven predictors were considered: depth, Rock Quality Designation (RQD), joint frequency, Schmidt hammer hardness, P-wave velocity, S-wave velocity, and electrical resistivity. The observations were assigned to four permeability classes: very low, low, moderate, and high. Six classifiers were evaluated: Polynomial Kernel Support Vector Machine (P-SVM), Linear Discriminant Analysis, k-Nearest Neighbors (k-NN), AdaBoostM2, Bagged Trees, and Radial Basis Function SVM. Sequential Forward Feature Selection and Leave-One-Out Cross-Validation were used for feature selection and model evaluation, respectively. Robustness was further assessed using bootstrap confidence intervals, class-imbalance treatments, feature-selection stability, and learning curves. Among the baseline models, P-SVM using all seven predictors achieved the highest accuracy (79.07%), with a macro-F1 of 0.774 and the lowest Golden Distance (GD = 0.4372). When balanced class-wise performance was prioritized, k-NN with in-fold SMOTE achieved the lowest overall GD (0.4126) and a macro-F1 of 0.777. S-wave velocity and electrical resistivity were the most influential predictors. Overall, the findings demonstrate that the proposed framework effectively integrates geotechnical and geophysical data for rock mass permeability classification, providing a robust, data-driven decision-support approach that supports subsurface characterization and complements conventional permeability assessment in engineering applications.

  İncele
• 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.

  İncele
• MakaleYalnız künye2026/r/gumushane/13770
  TREE TOPOLOGY-BASED RESOURCE ALLOCATION AND DISCOVERY ALGORITHM FOR ONE HOP

  Genemo, Musa Dima; Saroja, Yalamanchili; Gopi, Kistam; Mutyam, Veeramani; Reddy, Bhargavi Peddi; Navatejareddy, Ramireddy; Madala, Srinivasa Rao; Sai, Nimmagadda Yaswanth; Koteswararao, K.

  The components of a distributed system include multiple computing nodes which may use different types of hardware and connect to each other through a communication network that operates without shared memory or clock systems. The primary advantage of distributed systems enables organizations to share resources which results in faster computing times together with increased data availability and system reliability. The process of resource sharing requires organizations to first identify their resources and then assign those resources before anyone can access them. Computer systems implement caching to store frequently used data and program segments. The new caching technique called virtual caching enables a host node to delegate its cache authority for specific pages to nearby nodes who can use its partial cache space. The virtual caching protocol specifies neither the client node's process for accessing virtual cache nor its method for retrieving virtual cache from distant servers. The resource discovery and allocation problem needs us to develop a solution for this particular issue. The resource donation process requires us to find donor nodes who have extra resources while we need to assess which resources will be given to deficient nodes at each connected network point. Our goal is to achieve complete fulfillment of requests which deficient nodes make for resources. The importance of virtual caching lies in the fact that it doesn't require physical movements of cache. The requirements of the system determine when virtual cache allocation can be modified. The proposed heuristics provide efficient performance because they require minimal time and perform limited communication operations. We assess distribution quality through a comparison between heuristic distribution results and the distribution outcomes from the ILP problem solution. We present and assess several heuristics which aim to reduce unfulfilled resource requests that arise from resource-deficient nodes during their resource search process which has a maximum distance of two hops. The paper restricts its analysis to single hop operations only. The paper demonstrates through its research that this algorithm reaches optimal solution status based on our established benchmark criteria. The resource distribution for bounded hops restricts its operations to one-hop resource distribution. Every resource-surplus node transfers additional nodes to other nodes while maintaining its own resource balance without reaching resource-deficient status. Load distribution cannot be divided into infinite parts. Our research focuses on measuring the fulfillment level for each resource-deficient node request.

  İncele
• MakaleYalnız künye2026/r/gumushane/13713
  Burned area detection using Sentinel-1 and Sentinel-2 features and analysis of ensemble models with explainable AI

  Günen, Mehmet Akif; Türk, Salih

  Wildfires are serious environmental disasters that threaten ecosystem sustainability, reduce biodiversity, and disrupt the global carbon cycle. This study aimed to improve the accuracy and interpretability of burned area mapping in the Bodrum, Köyceğiz, and Marmaris districts of Muğla province, Türkiye, by integrating multi-sensor satellite data with ensemble machine learning and explainable artificial intelligence (XAI). Sentinel-1 and Sentinel-2 imagery were processed to derive 50 optical and radar-based features, which were classified using four ensemble methods: AdaBoost.M1, Random Forest, GentleBoost, and RUSBoost. The SHapley Additive exPlanations framework was applied to quantify the contribution of each feature and guide feature selection. Results indicated that Sentinel-2 spectral indices, particularly NBR, NBRSWIR, and bands B12 and B8, were the most decisive variables for burned area classification, while Sentinel-1 features contributed less strongly overall but the IVH_ratio variable emerged as the most informative radar-based indicator. Among the classifiers, AdaBoost.M1 achieved the most stable and accurate performance, Random Forest showed strong sensitivity to spectral variability, GentleBoost performed effectively with reduced feature sets, and RUSBoost addressed class imbalance but showed weaker spatial continuity. Importantly, classification accuracy saturated after approximately 15 features, demonstrating that high accuracy can be achieved with fewer inputs and lower computational demand. Moreover, McNemar’s statistical test confirmed that the observed differences among classifiers were significant, highlighting GentleBoost’s clear superiority over other methods. Overall, the integration of optical and radar data with XAI provides a transparent, efficient, and replicable framework for operational burned area monitoring in Mediterranean ecosystems.

  • 
  • 
  İncele
• BildiriYalnız künye2026/r/gumushane/13691
  Analysis of Temporal Consistency in Video-Based Palmprint ROI Tracking

  Sunnetci, Betul Kosmaz; Bingol, Ozkan; Gedikli, Eyup

  In real-time palmprint recognition systems, operating under unconstrained conditions, maintaining the defined palmprint region of interest (ROI) throughout hand motion presents a significant challenge. Variations in hand position or orientation may cause the ROI to shift unexpectedly or partially or completely disappear, which can adversely affect subsequent processing steps and their computational cost. In this study, the temporal stability and consistency of palmprint ROIs extracted from video data in the MPW-180 dataset were evaluated using two different approaches. Specifically, the behavior of two methods (optical flow based tracking and keypoint-based MediaPipe Hands detection) was quantitatively analyzed over time using corner position shifts and overlap ratios. Experimental results indicate that although the keypointbased method produces spatially accurate ROI estimates, it may exhibit sudden positional jumps in certain frames. In contrast, the optical flow method provides smoother motion transitions but may introduce positional drift over time due to the accumulation of tracking errors.

  İncele
• Kitap BölümüYalnız künye2026/r/gumushane/13568
  Rethinking IoT Security in the Era of Quantum Computing: Literature Reviews

  Hanna, Yacoub; Tonyali, Samet; Harrilal-Parchment, Ricardo; Akkaya, Kemal

  As cryptographically relevant quantum computers get scaled up and stabilized, traditional cryptographic systems are one step closer to being obsolete, necessitating the development of robust and future-proof alternatives. This chapter investigates the landscape of quantum-based algorithms and protocols, with a focus on their applicability to the Internet of Things (IoT) ecosystem, ranging from low-power edge sensors to more capable embedded systems. The theoretical basis and practical applications of Quantum Resistant Cryptography (QRC) and Quantum Key Distribution (QKD) are analyzed, examining their computational requirements, energy consumption, scalability, and integration possibilities in IoT architectures with limited resources. In particular, how various Post-Quantum Cryptography (PQC) schemes, such as lattice-based, code-based, and hash-based cryptography, perform across different classes of IoT hardware is discussed. The chapter further investigates ongoing standardization efforts, implementation obstacles, and innovative solutions specifically designed for heterogeneous IoT environments. Moreover, the feasibility of QKD in resource-constrained scenarios is examined, including low-power QKD implementations for embedded and edge devices in IoT networks. Also, the trade-offs between security and efficiency are discussed, considering real-world deployment scenarios and resource constraints. Furthermore, a comprehensive threat model is presented, and the security of current literature is analyzed based on this model. By combining insights from recent literature, a comprehensive understanding of how quantum-based security methodologies can be integrated across the IoT spectrum is provided to guarantee long-term security resilience in the era of quantum computing. This chapter aims to serve as a foundational reference for researchers and practitioners seeking to build quantum-resistant or quantum-enabled IoT infrastructures.

  • 
  İncele
• Derleme MakaleYalnız künye2026/r/gumushane/13408
  A systematic and evolutionary analysis of software design pattern detection and recommendation approaches (2015–2025)

  Turk, Salih; Coskuncay, Ahmet

  Context: Automated detection and recommendation of software design patterns is crucial for software maintenance, reverse engineering, and architectural quality. Despite decades of research, the field has undergone a significant transformation with the emergence of deep learning and large language models (LLMs), leading to a fragmented landscape of methodologies and evaluation settings. Objective: This study aims to provide a systematic and evolutionary synthesis of design pattern detection and recommendation approaches published between 2015 and 2025, categorizing them into distinct technological paradigms and identifying open research challenges. Method: Following Kitchenham’s evidence-based software engineering guidelines and PRISMA principles, a systematic literature review was conducted. We executed a structured search on the Web of Science database, identifying 212 initial records. After rigorous multi-stage screening and quality assessment, 66 primary studies were selected for final synthesis and analysis. Results: The evolution of the field is characterized through six distinct paradigms: Rule-Based, Graph-Based, Feature-Driven Machine Learning, Representation Learning (Deep Learning), Hybrid, and Contextual Intelligence (LLM). Findings reveal a clear methodological shift from structural exactness to semantic and contextual understanding. However, the analysis also identifies a high concentration on specific datasets (JHotDraw and P-MARt) and a lack of standardized benchmarks for variant detection and multi-modal analysis. Conclusions: While LLM-based approaches show promise in handling pattern variants and semantic context, the field requires more diverse benchmarks and explainable models. This review provides a comprehensive research roadmap, emphasizing the need for multi-modal frameworks and industry-scale validation to bridge the gap between academic research and practical software engineering.

  İncele
• MakaleAçık Erişim2026/r/gumushane/12307
  A Personalized Target Placement Optimization Framework for VR-Based Upper Extremity Rehabilitation

  Türe, Hayati; Kalfa, Eren; Aslan, Muhammed Emin; Işık, Buket Özdemir; Topçu, Osman; Özdemir, Erhan; Sarıhan, Köksal

  Featured Application: The proposed framework can be directly integrated into existing VR rehabilitation platforms to provide real-time personalized target placement for post-stroke upper extremity therapy, requiring no additional hardware beyond a standard CPU. Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ( (Formula presented.) ) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon (Formula presented.), Cohen’s (Formula presented.) ); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ( (Formula presented.) ). Ablation confirmed both PSO and ACO are individually necessary ( (Formula presented.) pp, (Formula presented.) ). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization.

  • 
  İncele
• BildiriYalnız künye2026/r/gumushane/12245
  Advancing Digital Wallet Security Through Multi-Factor Authentication and Homomorphic Encryption-Based Privacy Preservation

  Genemo, Musa Dima; Kumar, Kotha Anil; Sadat, Quazi Taif; Santosh, Kathari; Sreeram, G.; Raj, I. Infant

  Digital wallets have been provided by FinTech businesses thus reengineering the financial services but nonetheless there is a possibility of keyloggers, bogus personas and assaults of the server with conventional systems using PIN authentication. The current multi-factor authentication configuration usually leaves sensitive biometric information exposed as the logins are processed. To address these security issues, the paper proposes the MHESecAuth that employs Multi-Factor Homomorphic Encryption and Smart Contracts to authenticate. The permission to make transactions is secure since the system operates with PINs, OTPs and no one can view the biometrics. As per experimental research, the system can be accurate at 96.4 percent, false acceptance rate is 0.08 percent, false rejection rate is 0.6 percent and encryption consumes 160ms. The framework optimizes security, privacy and performance against both MFA, encryption-only and combined solutions and is therefore suitable in the development of quantum-secure digital wallets and still blockchain-based audit and transparency.

  İncele
• MakaleAçık Erişim2026/r/gumushane/12191
  Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes

  Bozkır, İsa; İbişoğlu, Merve Seda; Kayıkçıoğlu Bozkır, İlknur; Güler, Halil İbrahim

  The discovery of dual acetylcholinesterase (AChE) and β-secretase (BACE1) inhibitors remains a promising strategy against multifactorial Alzheimer’s disease. Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure–activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (≥ 0.80), and physicochemical outliers were filtered prior to modeling. Multiple classifiers (including Light Gradient-Boosting Machine, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, k-Nearest Neighbors and Gradient Boosting Decision Trees) and fingerprints (e.g., RDKit fingerprints, Extended Connectivity Fingerprint) were benchmarked under scaffold-based nested cross-validation to prevent data leakage. Class imbalance was handled with SMOTETomek applied strictly within training folds. Model selection relied on F-Score, Area Under the Precision–Recall Curve, Matthews Correlation Coefficient (MCC), and Recall, and performance was accompanied by bootstrap confidence intervals, calibration curves, and Y-randomization controls. In classification, the top model (GBDT + ECFP6) achieved strong generalization (Recall ≈ 1.00, PR-AUC ≈ 0.84, MCC ≈ 0.81, F1 Score ≈ 0.84). Shapley Additive Explanations (SHAP) analysis highlighted aromatic and hydrogen-bonding substructures as key positive contributors. Prospective candidates (e.g., CHEMBL5082250, CHEMBL1651126, CHEMBL1651127) were evaluated by active-site-focused docking against AChE (PDB: 4EY7) and BACE1 (PDB: 2G94) with essential waters retained; docking scores (ΔG, kcal·mol⁻1) were used for relative ranking of the ligands. SwissADME/pkCSM profiling suggested CNS-relevant properties (e.g., MPO, logBB, P-gp liability) and acceptable oral drug-likeness. Collectively, the workflow provides a reproducible and transparent pipeline for prioritizing dual AChE/BACE1 chemotypes and nominates testable scaffolds for experimental validation.

  • 
  İncele