The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network’s achievable rate while satisfying IoT devices’ energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6DChOA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts.

The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches still face challenges related to poor generalization, weak temporal modeling, and degraded accuracy under heterogeneous and imbalanced real-world conditions. To overcome these limitations, a hybrid time series transformer–deep belief network (HTST-DBN) is introduced, integrating the sequential modeling strength of TST with the hierarchical feature representation of DBN, while an improved orchard algorithm (IOA) performs adaptive hyper-parameter optimization. The framework also embodies the concept of symmetry and asymmetry. The IOA introduces controlled symmetry-breaking between exploration and exploitation, while the TST captures symmetric temporal patterns in network traffic whose asymmetric deviations often indicate anomalies. The proposed method is evaluated across four benchmark datasets (ToN-IoT, 5G-NIDD, CICDDoS2019, and Edge-IoTset) that capture diverse network environments, including 5G core traffic, IoT telemetry, mobile edge computing, and DDoS attacks. Experimental evaluation is conducted by benchmarking HTST-DBN against several state-of-the-art models, including TST, bidirectional encoder representations from transformers (BERT), DBN, deep reinforcement learning (DRL), convolutional neural network (CNN), and random forest (RF) classifiers. The proposed HTST-DBN achieves outstanding performance, with the highest accuracy reaching 99.61%, alongside strong recall and area under the curve (AUC) scores. The HTST-DBN framework presents a scalable and reliable solution for anomaly detection in next-generation mobile networks. Its hybrid architecture, reinforced by hyper-parameter optimization, enables effective learning in complex, dynamic, and heterogeneous environments, making it suitable for real-world deployment in future 5G/6G infrastructures.

Given the importance of detecting the ferroresonance phenomenon and distinguishing it from other transient events in active distribution networks, this paper proposes the use of Long Short-Term Memory (LSTM) recurrent neural networks to achieve this goal. In the proposed LSTM network, the optimization of learning period parameters, hidden layers, and corresponding weight coefficients is performed using a Genetic Algorithm (GA) to enhance the detection accuracy and speed. To create a suitable time-sequence database for LSTM training, an active distribution network comprising wind turbines, photovoltaic systems, and synchronous diesel generators is modeled in PSCAD software. By simulating various transient events, such as phase disconnections, capacitor bank switching, and load switching, the calculated detail and approximation coefficients up to six levels of the three-phase voltage waveforms of feeders are used as input features during the LSTM network training phase. Subsequently, to validate and confirm the effectiveness of the proposed LSTM-GA model, numerical studies are conducted on this active distribution network for the identification and distinction of the ferroresonance phenomenon. Simulation results confirm that optimizing and tuning the LSTM network parameters with GA significantly improves performance metrics, including precision, accuracy (P), recall (R), and others.

The increasing complexity of multi-scale energy systems makes robust anomaly detection essential to ensure system resilience and operational continuity. Recent advances in DL enable effective modeling of high-dimensional, non-linear energy data by capturing latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.

With the emergence of information and communication technologies, manufacturing industries have shifted from centralized production architectures to architectures based on global services. This shift is highly beneficial for identifying emerging customer needs in the market, such as rapid response to custom production, autonomy in organizational network resources, flexibility, and efficiency in manufacturing. Consequently, several leading countries have given special attention to cloud manufacturing in their national development programs. Notable examples include the Industrial Internet program in the United States, the Industry 4.0 framework in Germany, and the Made in China 2025 initiative. Cloud manufacturing adopts the cloud computing paradigm as a foundation for providing shared manufacturing services on demand. The result is customer-centric supply chains that can be tailored based on cost, quality, speed, and customization. In this study, considering cloud manufacturing systems, the economic, social, and environmental sustainability components were evaluated and analyzed. A sustainable mathematical model was developed and extended in this field. In the conducted analysis, using a small-scale approach with a metaheuristic algorithm, optimal solutions for economic, social, and environmental sustainability were evaluated. Finally, using the NSGA-II metaheuristic algorithm, larger dimensions of the problem were studied and analyzed. In the performed analysis, the algorithm parameters were initially tuned using the Taguchi method, followed by an evaluation and analysis of the problem dimensions based on exact and metaheuristic solutions. It was demonstrated that the development of cloud manufacturing systems is highly practical and executable within analytical models.

The rapid evolution of multi-scale energy systems (spanning electricity, hydrogen, and renewable integration) has introduced unprecedented complexity, making robust anomaly detection a critical challenge. The vast heterogeneity and dynamic nature of these systems expose them to faults and cyber–physical risks, where timely detection is vital to ensure resilience, safety, and uninterrupted operation. Nowadays, deep learning (DL) techniques have emerged as powerful tools for modeling large-scale, non-linear, and high-dimensional energy data, enabling the extraction of latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.