Loepso, local optimum escape particle swarm optimization, an algorithm for traffic forecasting in software-defined networking using deep-learning models

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

Language: Английский

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Intrusion Detection with Federated Learning and Conditional Generative Adversarial Network in Satellite-Terrestrial Integrated Networks

Mobile Networks and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Language: Английский

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Graph Neural Networks for Routing Optimization: Challenges and Opportunities

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9239 - 9239

Published: Oct. 24, 2024

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional protocols, such as OSPF or Dijkstra algorithm, often fall short handling complexity, scalability, and dynamic nature modern network environments, including unmanned aerial vehicle (UAV), satellite, 5G By leveraging their ability to model topologies learn from complex interdependencies between nodes links, GNNs offer a promising solution distributed scalable optimization. This paper provides comprehensive review latest research on GNN-based methods, categorizing them into supervised learning modeling, optimization, reinforcement tasks. We also present detailed analysis existing datasets, tools, benchmarking practices. Key challenges related real-world deployment, explainability, security are discussed, alongside future directions that involve federated learning, self-supervised online techniques further enhance GNN applicability. study serves first survey aiming inspire practical applications

Language: Английский

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Intra- and Interpatient ECG Heartbeat Classification Based on Multimodal Convolutional Neural Networks with an Adaptive Attention Mechanism

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(20), P. 9307 - 9307

Published: Oct. 12, 2024

Echocardiography (ECG) is a noninvasive technology that widely used for recording heartbeats and diagnosing cardiac arrhythmias. However, interpreting ECG signals challenging may require substantial time from medical specialists. The evolution of artificial intelligence has led to advances in the study development automatic arrhythmia classification systems aid diagnoses. Within this context, paper introduces framework classifying arrhythmias on basis multimodal convolutional neural network (CNN) with an adaptive attention mechanism. signal segments are transformed into images via Hilbert space-filling curve (HSFC) recurrence plot (RP) techniques. developed evaluated using MIT-BIH public database alignment AAMI guidelines (ANSI/AAMI EC57). evaluations accounted interpatient intrapatient paradigms, considering variations input structure related number leads (lead MLII V1 + MLII). results indicate competitive those state-of-the-art studies, particularly two leads. accuracy, precision, sensitivity, specificity F1 score 98.48%, 94.15%, 80.23%, 96.34% 81.91%, respectively, paradigm 99.70%, 98.01%, 97.26%, 99.28% 97.64%, paradigm.

Language: Английский

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Adaptive genetic algorithm-optimized temporal convolutional networks for high-precision ship traffic flow prediction

Evolving Systems, Journal Year: 2024, Volume and Issue: 16(1)

Published: Nov. 16, 2024

Language: Английский

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Network simulation tools for unmanned aerial vehicle communications: A survey

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: 37(15)

Published: June 17, 2024

Summary Unmanned aerial vehicle (UAV) communication has been proposed as an effective solution in both military and civilian scenarios, with low cost, high efficiency, flexibility, on‐demand deployment. Network simulation is economically efficient method for validating new ideas UAV communication. While some tools have communications, there a lack of state‐of‐the‐art review to guide newcomers this research field. In existing surveys, the discussion network simulators not comprehensive content discussed outdated. There also no open‐source tools. To fill these gaps, survey presents updated tools, including unique collection Research challenges opportunities inspire follow‐up studies.

Language: Английский

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Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM)

Sensors, Journal Year: 2024, Volume and Issue: 24(15), P. 5016 - 5016

Published: Aug. 2, 2024

Assessing sleep posture, a critical component in tests, is crucial for understanding an individual's quality and identifying potential disorders. However, monitoring posture has traditionally posed significant challenges due to factors such as low light conditions obstructions like blankets. The use of radar technolsogy could be solution. objective this study identify the optimal quantity placement sensors achieve accurate estimation. We invited 70 participants assume nine different postures under blankets varying thicknesses. This was conducted setting equipped with baseline eight radars-three positioned at headboard five along side. proposed novel technique generating maps, Spatial Radio Echo Map (SREM), designed specifically data fusion across multiple radars. Sleep estimation using Multiview Convolutional Neural Network (MVCNN), which serves overarching framework comparative evaluation various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, Swin Transformer. Among these, DenseNet-121 achieved highest accuracy, scoring 0.534 0.804 nine-class coarse- four-class fine-grained classification, respectively. led further analysis on ensemble For radars head, single left-located proved both essential sufficient, achieving accuracy 0.809. When only one central head used, omitting side retaining three upper-body resulted accuracies 0.779 0.753, established foundation determining sensor configuration application, while also exploring trade-offs between fewer sensors.

Language: Английский

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MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 231, P. 103981 - 103981

Published: July 30, 2024

Language: Английский

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Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

This paper addresses the challenges of exponentially growing traffic in cellular networks by proposing a novel predictive model, HGCRN, which combines static graph convolutional recurrent neural network and meta-graph learning. The model is designed to effectively capture complex spatio-temporal dependencies traffic, enhancing prediction accuracy operational efficiency. By constructing adjacency matrices that go beyond mere geographical proximity, HGCRN offers deeper understanding dynamic interactions within network. Tested on real-world datasets from Telecom Italia China Mobile, demonstrates significant improvements over traditional state-of-the-art methods terms reliability.

Language: Английский

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Zero-shot sim-to-real transfer using Siamese-Q-Based reinforcement learning

Information Fusion, Journal Year: 2024, Volume and Issue: 114, P. 102664 - 102664

Published: Sept. 6, 2024

Language: Английский

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