Evaluation of optimization algorithms for power and voltage in solar power generation system: FOPID controller and MPPT-based P&O algorithm

International Journal of Information Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Язык: Английский

---

Advanced Deep Learning Models for Improved IoT Network Monitoring Using Hybrid Optimization and MCDM Techniques

Symmetry, Год журнала: 2025, Номер 17(3), С. 388 - 388

Опубликована: Март 4, 2025

This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm (HGWOPSO) Hybrid World Cup Harris Hawks (HWCOAHHO)—designed to symmetrically global exploration local exploitation, thereby enhancing model training adaptation environments. These methods leverage complementary search behaviors, where symmetry processes enhances convergence speed accuracy. The proposed approaches are validated using real-world datasets, demonstrating significant improvements accuracy, scalability, adaptability compared state-of-the-art techniques. Specifically, HGWOPSO combines hierarchy-driven leadership Wolves velocity updates Optimization, while HWCOAHHO synergizes strategies competition-driven algorithm, ensuring balanced decision-making processes. Performance evaluation benchmark functions data highlights superior precision, recall, F1 score traditional methods. To further enhance decision-making, Multi-Criteria Decision-Making (MCDM) framework incorporating Analytic Hierarchy Process (AHP) TOPSIS employed evaluate rank Results indicate that achieves most optimal accuracy followed closely by HGWOPSO, like FFNNs MLPs show lower effectiveness detection. symmetry-driven approach these algorithms ensures robust, adaptive, scalable monitoring solutions networks characterized traffic patterns evolving anomalies, thus stability integrity. findings have substantial implications smart cities, industrial automation, healthcare applications, performance efficiency crucial reliable monitoring. work lays groundwork research techniques learning, emphasizing role resilience systems.

Язык: Английский

---

A novel hyperparameter tuning method for enhanced intrusion detection in network security

Turkish Journal of Engineering, Год журнала: 2025, Номер 9(3), С. 519 - 534

Опубликована: Март 9, 2025

Intrusion Detection Systems (IDS) are essential for ensuring the security of enterprise networks and cloud-based systems, as they defend against sophisticated evolving cyberattacks. Machine learning (ML) techniques have emerged effective tools to enhance IDS performance, addressing limitations traditional methods. This study proposes a novel hyperparameter tuning method ML-based IDS, leveraging NSL-KDD dataset with extensive feature selection preprocessing address data imbalance redundancy. The method, integrating adaptive refinement stochastic perturbation, optimizes classifiers such Random Forest (RF), Gradient Boosting (GB), Extreme (XGB), achieving both higher detection accuracy (99.90% RF) improved computational efficiency. approach excels due its dynamic adjustment parameter ranges controlled randomness, converging faster than Grid Search by reducing iterations up 87.5%. experimental results demonstrate that tree-based models, particularly RF, outperform others their ability model complex, non-linear patterns, enhanced proposed method. Measured in terms convergence speed, CPU time, memory usage, this proves suitable deployment real-time, resource-constrained environments, offering scalable efficient solution network security.

Язык: Английский

---

Power Quasi-XLindley probability distribution: Its properties, inference and application on engineering data

Alexandria Engineering Journal, Год журнала: 2025, Номер 122, С. 190 - 204

Опубликована: Март 12, 2025

Язык: Английский

---