Magnesium alloys are popular in the aerospace and automotive industries due to their light weights and high specific strengths. The major disadvantages of magnesium alloys are their weak wear and corrosion resistances. Surface coating is one of the most efficient methods of making material surfaces resistant to wear. Experimental determination of wear loss is expensive and time-consuming. These disadvantages can be eliminated by using machine learning algorithms to predict wear loss. This study used experimentally obtained wear loss data for AZ91D magnesium alloy samples coated via two different spray coating methods (plasma and high velocity oxy-fuel spraying) using various parameters. Support vector regression (SVR) and extreme learning machine (ELM) methods were used to predict wear loss quantities. In models tested using 10-k cross-validation, R² was calculated as 0.9601 and 0.9901 when the SVR and ELM methods were applied, respectively. The ELM method was more successful than SVR. Thus, the ELM method has excellent potential to support the production of wear-resistant parts for various applications via spray coating.

Tool wear is critical to quality, productivity, and sustainability in manufacturing processes. Therefore, accurately monitoring and predicting wear is one of the primary goals of smart manufacturing systems. While AI-based approaches have achieved significant success in this area in recent years, issues such as physical inconsistency, limited generalizability, and low interpretability associated with solely data-driven methods have necessitated the development of hybrid approaches. This study systematically examines the literature published between 2020 and 2025 and comprehensively analyzes hybrid AI systems used in tool wear monitoring. Hybrid systems are categorized into four main groups: physics-based hybrids, knowledge-driven hybrids, transfer learning-based hybrids, and heterogeneous model hybrids. This classification holistically evaluates the synergistic effects and performance gains achieved by combining different methods. The findings demonstrate that the combined use of physical models, expert knowledge, and data-driven learning approaches provides significant advantages in terms of both accuracy and explainability. However, challenges such as data shortage, model complexity, and computational cost remain limitations to widespread industrial use of hybrid systems. The study demonstrates that hybrid AI systems represent a new research direction enabling the development of more reliable, transparent, and efficient solutions in smart manufacturing.

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work.

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential.

Sağlık sektöründe son yıllarda büyük veri kümelerinin artmasıyla birlikte, makine öğrenimi yöntemleri diyabet veri setlerinin keşfi, analizi ve tahmin süreçlerinde önemli bir rol oynamaya başlamıştır. Bu çalışmada, diyabetin erken teşhisine odaklanılmış ve çeşitli makine öğrenimi modellerinin performansı ile Açıklanabilir Yapay Zeka (XAI) tekniklerinin etkisi incelenmiştir. Diyabet veri seti üzerinde, K-En Yakın Komşular (KNN), Destek Vektör Makineleri (SVM), Naive Bayes, Yapay Sinir Ağları (YSA), Karar Ağaçları, Rastgele Orman ve XGBoost algoritmaları kullanılarak modellerin performansları karşılaştırılmıştır. Modellerin değerlendirilmesi, veri temizleme ve ön işleme aşamalarının ardından eğitim ve test süreçleriyle yapılmıştır. Bu değerlendirme sırasında doğruluk, F1 skoru, hassasiyet ve özgünlük gibi başarı kriterleri göz önünde bulundurulmuştur. En iyi sonuç veren modelin çıktılarına SHAP (Shapley Additive Explanations) ve LIME (Local Interpretable Model-Agnostic Explanations) gibi açıklanabilir yapay zeka yöntemleri uygulanarak, modelin verdiği kararların anlaşılabilirliği sağlanmıştır. Bu yöntemler, modelin en önemli özelliklerini belirleyip karar sürecini daha şeffaf hale getirir. Gerçek dünya uygulamalarında bu modellerin potansiyelini anlamak adına uzman görüşleriyle analiz sonuçları desteklenmiştir. KNN, SVM, Naive Bayes, YSA, Karar Ağaçları, Rastgele Orman ve XGBoost algoritmaları sırasıyla %81.18, %75.38, %75.49, %74.83, %76.91, %91.68 ve %98.91 doğruluk oranlarıyla değerlendirilmiştir.