As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions.

Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

The easiness of reaching information through the internet and social media and the expansiveness of opportunities for searching, copying, and spreading data have caused some problems in identifying an author for a specific text. A text carries the characteristic features of the person who wrote it, and these features can be used to identify its author. For this study, we are offering a method that is based on an approach using ensemble learning algorithm (ELA) and genetic algorithm (GA) for author identification in Tur-kish texts. The raw data set, which includes 40 authors and 3269 texts, was created from Turkish news websites and analyzed in pre-processing step. After, syntactic and structural analyses were done on the data and, in total, 6 different data sets were created. Each of the data sets was subjected to the feature selection process by using GA and ELA approach together. Each of the obtained data sets from the previous step was classified by using the ELA's bagging method which contains 5 different classifiers, namely, Naive Bayes, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machine, and Decision Tree. After applying the aforementioned processes to the raw data, the author identification approach reached 89% accuracy. The combination of ELA and GA has a strong potential to identify the author of a text.

Yüzyıllardır süregelen yazarı belirsiz metinler sorunu, internet çağının başlamasıyla oldukça artmıştır. Bu durumun en büyük sebebi internetteki verilerin çok yüksek oranını yapısal olmayan verilerin oluşturması ve bu yapısal olmayan verilerin de büyük bir bölümünü sınıflandırılmamış, yazarları belirsiz metinlerin oluşturmasıdır. Son yıllarda yapılan sınıflandırma işlemlerinde makine öğrenmesi yöntemlerinin kullanılması, yazar tanıma problemlerine yeni bir bakış açısı getirmiştir. Bu çalışmada makine öğrenmesi yöntemleri kullanılarak yazar tanıma problemi için web tabanlı arayüze sahip uçtan uca bir uygulama geliştirilmiştir. Sınıflandırma işlemi için 37 yazarın köşe yazılarından oluşturulmuş 46715 metin verisi içeren bir derlem kullanılmıştır. Bu derlemden TF-IDF yöntemi kullanılarak öznitelikler çıkarılmış ve bir veri kümesi elde edilmiştir. Daha sonra veri kümesi, Destek Vektör Makineleri (DVM), NB (NB) ve RO (RO) gibi makine öğrenme algoritmaları ile eğitilmiş ve test edilmiştir. Test sonucunda, DVM %90 doğruluk oranıyla en iyi performansı gösteren sınıflandırıcı model olmuştur. Elde edilen DVM modeline, Python programlama dilinin kütüphanelerinden olan Flask kullanılarak bir web arayüzü geliştirilmiştir. Son olarak uygulama, kararlı ve dağıtıma uygun bir halde çalıştırılması amacıyla Docker konteynerına dönüştürülmüştür. Sonuç olarak, uçtan uca geliştirilen bir yazar tanıma uygulaması doğrudan son kullanıcı tarafından kullanılabilir biçimde sunulmuştur. Makine öğrenmesi desteğiyle web tabanlı böyle bir uygulamanın oluşturulması, yazar tanıma çalışmasını daha anlamlı ve kullanılabilir hale getirmiştir.

Large Language Models (LLMs) have markedly progressed natural language processing. Nevertheless, owing to the restricted availability of training data, they may prove insufficient in generating current and precise information, particularly for low-resource languages. The Retrieval-Augmented Generation (RAG) methodology, designed to resolve this challenge, improves the precision and dependability of models' outputs by leveraging external information sources. This study comparatively evaluated four distinct LLMs (Qwen-14B, Gemma3-12B, LLaMA3.1-8B, and DeepSeek-R1-14B) within the RAG framework using a Turkish question-answer dataset. Experimental results demonstrate the RAG methodology markedly enhances information precision, response uniformity, and contextual relevance in Turkish question-answering systems. Moreover, the LLaMA3.1-8B model had the best equitable performance regarding precision and recall. The findings illustrate the relevance of RAG-based applications for Turkish and offer significant insights for advancing knowledge-assisted generation methods. This study addresses a significant gap in the literature by illustrating the viability of RAG-based systems in morphologically rich and low-resource languages, including Turkish. It serves as a foundational reference for subsequent Turkish natural language processing research.

Disaster events significantly affect communication network performance by causing spatially localized degradations that may not be captured by conventional monitoring approaches. Traditional network assessment relies primarily on average Key Performance Indicators (KPIs), which may obscure localized anomalies and fail to reveal region-specific disruptions. To address this limitation, this study proposes a multi-stage statistical and spatial analysis framework that integrates non-parametric statistical testing with density-based spatial clustering to detect localized performance degradation patterns under disaster conditions. The proposed framework combines statistical significance testing and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify abnormal regions and quantify performance variations across network infrastructure. Experimental results demonstrate that the proposed approach effectively detects spatially localized anomalies and provides improved insight into network behavior during abnormal operational conditions. The findings indicate that the proposed framework enables more reliable identification of performance degradation patterns and supports more effective network monitoring and decision-making processes in disaster scenarios.