In the first stage, an improved ICA is proposed to generate scheduling, and CNN is used to train the collected scheduling information data. The second stage is to predict the robustness of scheduling based on th
In article [19], a multi-objective optimization method for power systems is proposed, which is based on Deep Q-Network (DQN) guiding the Multi-Objective Bacterial Foraging Optimization Algorithm (MBFO). This method aims to address the issue of traditional heuristic algorithms losing selection ...
Sahu, Prakash Chandra, Reetimukta Baliarsingh, Ramesh Chandra Prusty, and Sidhartha Panda. 2022. “Novel DQN Optimised Tilt Fuzzy Cascade Controller for Frequency Stability of a Tidal Energy-Based AC Microgrid.”International Journal of Ambient Energy43 (1): 3587–3599. https://doi.org/10.1080...
The obtained F1 scores are illustrated in Table 15, which demonstrates how data augmentation techniques have improved the classification performances across all ML algorithms. KNN and Random Forest, in particular, have outperformed all the others and have generally provided superior results when using SM...
The NB method performs comparably well relative to other popular ML algorithms for classifica- tion, according to a variety of experiments carried out on real-world datasets (see for ex- ample the work by Osisanwo et al. [57]). This method takes the assumption that the class- given ...
Training DL algorithms are known for being computationally expensive. There exist in both Academia and Industry many efforts to accomplish efficient DL training in EC-like layouts (see Federated Learning). Thanks to the offloading, the hardware in the vehicles would need less processing power, ...
However, to the best of our knowledge, there is no research combines these two algorithms together to solve the two-stage hybrid flow shop scheduling problem on parallel-batching machines considering deteriorating effect and non-identical job sizes simultaneously. An extensive study of different models...
In addition, although deep reinforcement learning algorithms have begun to be tried in solving such AL balancing problems [6,7], they are currently used to optimize simulation models and resource allocation. Therefore, this paper carries out the study on load balancing of TAL based on deep ...
To address these limitations, this research proposes a novel SDN-IoT framework integrating picture-based authentication, game theory, and advanced AI algorithms to enhance security, scalability, and system reliability. One approach utilized deep learning technology for intrusion detection in SDN-based IoT...
To address these limitations, this research proposes a novel SDN-IoT framework integrating picture-based authentication, game theory, and advanced AI algorithms to enhance security, scalability, and system reliability. One approach utilized deep learning technology for intrusion detection in SDN-based IoT...