Información de la conferencia
MLNN 2026: International Conference on Machine Learning and Neural Networks
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Día de Entrega: |
2026-03-20 |
Fecha de Notificación: |
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Fecha de Conferencia: |
2026-04-10 |
Ubicación: |
Chengdu, China |
Años: |
3 |
Vistas: 5808 Seguidores: 0 Asistentes: 0
Solicitud de Artículos
The topics of interest for submission include, but are not limited to:
l Machine Learning Algorithms and Techniques
· Supervised and Unsupervised Learning: Methods and Applications
· Deep Learning Architectures and Their Applications
· Reinforcement Learning for Autonomous Systems and Robotics
· Transfer Learning and Domain Adaptation
· Evolutionary and Hybrid Algorithms for Machine Learning Optimization
· Semi-supervised Learning and Meta-learning Approaches
· Ensemble Learning and Online Learning for Dynamic Data
· Probabilistic Models: Bayesian Networks and Markov Models
· Computational Complexity and Optimization in Machine Learning
· Real-world Applications of Machine Learning in Industry and Society
l Machine Learning and Neural Networks in Communication Systems
· Machine Learning for Wireless Communication and 5G Networks
· AI-based Optimization and Resource Allocation in Communication Networks
· Deep Learning for Signal Processing, Channel Estimation, and Spectrum Managemen
· Cognitive Radio Networks and Machine Learning for Dynamic Spectrum Access
· Neural Networks for MIMO Systems, Beamforming, and Network Security
· Machine Learning for Network Traffic Prediction and Intrusion Detection
· AI Techniques for Quality of Service (QoS) and Quality of Experience (QoE) in Networks
· Data-driven Approaches for IoT and Autonomous Communication Systems
· Deep Reinforcement Learning for Communication Network Optimization
l Neural Networks and Deep Learning
· Convolutional and Recurrent Neural Networks in Computer Vision and Time Series
·Generative Models: GANs and Autoencoders in Data Generation and Dimensionality Reduction
· Neural Networks in Natural Language Processing and Speech Recognition
· Deep Reinforcement Learning for Robotics, Automation, and Network Optimization
· Neural Networks in Healthcare: Medical Imaging, Diagnostics, and Anomaly Detection
· Neural Networks for Financial Forecasting and Cybersecurity
· Transfer Learning and Few-shot Learning in Deep Learning
· Deep Learning for Recommender Systems and Data Augmentation
l Other related topics
l Machine Learning Algorithms and Techniques
· Supervised and Unsupervised Learning: Methods and Applications
· Deep Learning Architectures and Their Applications
· Reinforcement Learning for Autonomous Systems and Robotics
· Transfer Learning and Domain Adaptation
· Evolutionary and Hybrid Algorithms for Machine Learning Optimization
· Semi-supervised Learning and Meta-learning Approaches
· Ensemble Learning and Online Learning for Dynamic Data
· Probabilistic Models: Bayesian Networks and Markov Models
· Computational Complexity and Optimization in Machine Learning
· Real-world Applications of Machine Learning in Industry and Society
l Machine Learning and Neural Networks in Communication Systems
· Machine Learning for Wireless Communication and 5G Networks
· AI-based Optimization and Resource Allocation in Communication Networks
· Deep Learning for Signal Processing, Channel Estimation, and Spectrum Managemen
· Cognitive Radio Networks and Machine Learning for Dynamic Spectrum Access
· Neural Networks for MIMO Systems, Beamforming, and Network Security
· Machine Learning for Network Traffic Prediction and Intrusion Detection
· AI Techniques for Quality of Service (QoS) and Quality of Experience (QoE) in Networks
· Data-driven Approaches for IoT and Autonomous Communication Systems
· Deep Reinforcement Learning for Communication Network Optimization
l Neural Networks and Deep Learning
· Convolutional and Recurrent Neural Networks in Computer Vision and Time Series
·Generative Models: GANs and Autoencoders in Data Generation and Dimensionality Reduction
· Neural Networks in Natural Language Processing and Speech Recognition
· Deep Reinforcement Learning for Robotics, Automation, and Network Optimization
· Neural Networks in Healthcare: Medical Imaging, Diagnostics, and Anomaly Detection
· Neural Networks for Financial Forecasting and Cybersecurity
· Transfer Learning and Few-shot Learning in Deep Learning
· Deep Learning for Recommender Systems and Data Augmentation
l Other related topics
Última Actualización Por Dou Sun en 2026-02-04
Conferencias Relacionadas
Revistas Relacionadas
| CCF | Nombre Completo | Factor de Impacto | Editor | ISSN |
|---|---|---|---|---|
| b | Neural Networks | 6.3 | Elsevier | 0893-6080 |
| Interactive Learning Environments | 5.3 | Taylor & Francis | 1049-4820 | |
| IEEE Transactions on Learning Technologies | 4.9 | IEEE | 1939-1382 | |
| IEEE Transactions on Signal and Information Processing over Networks | 4.9 | IEEE | 2373-776X | |
| Nano Communication Networks | 4.7 | Elsevier | 1878-7789 | |
| Language Learning & Technology | 4.1 | University of Hawaii Press | 1094-3501 | |
| b | Machine Learning | 2.9 | Springer | 0885-6125 |
| c | Wireless Networks | 2.1 | Springer | 1022-0038 |
| Engineering Computations | 1.9 | Emerald | 0264-4401 | |
| Optical Memory and Neural Networks | 1.000 | Springer | 1060-992X |