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MLUS 2027: International Conference on Machine Learning and Unmanned Systems

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MLUS
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投稿締切日:
2027-03-13 残り 175 日
通知日:
2027-04-13
開催日:
2027-05-28
開催地:
Hangzhou, China
開催回数:
閲覧: 1899   フォロー: 0   参加: 0

会伴インデックス (CP-I)

43.4 / 100
全 5,682 件中 第 3,596 位 · 上位 64%

ロボティクス・制御 分野 469 件中 第 247 位 人工知能・機械学習 分野 739 件中 第 456 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
30
コミュニティの注目度 (10%)
17
公開情報の充実度 (15%)
55

使用した入力: 確認できる開催回数：2 · 過去 24 か月にこのページを開いた研究者：9 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-19

論文募集

MLUS 2027 (International Conference on Machine Learning and Unmanned Systems) is an academic conference held in Hangzhou, China on 2027-05-28. The paper submission deadline is 2027-03-13. Acceptance notifications are sent on 2027-04-13.

The topics of interest for submission include, but are not limited to: Track 1: Machine Learning Theory & Algorithms Supervised and unsupervised learning Deep learning architectures Reinforcement learning Transfer learning and domain adaptation Federated learning Graph neural networks Generative adversarial networks Explainable and interpretable AI Large-scale model training Model compression and distillation Multimodal learning Few-shot and zero-shot learning Probabilistic and Bayesian methods Online and incremental learning Benchmarking and evaluation methodologies Track 2: Perception & Sensing for Unmanned Systems Image recognition and classification Object detection and tracking Scene understanding and semantic segmentation 3D vision and reconstruction LiDAR and depth sensing Multispectral and infrared imaging Point cloud processing Visual navigation and localization Sensor fusion and integration Multimodal perception systems Real-time perception and edge processing Anomaly and fault detection Event-based and neuromorphic vision Adverse-condition perception Synthetic data and simulation for perception Track 3: Communication & Networking UAV and ad-hoc communication networks Signal processing and optimization Image and video transmission Multi-channel data fusion Cooperative communication protocols Low-power and energy-efficient communication Self-organizing and mesh networks Interference suppression and robustness Integrated space-air-ground networks Cognitive radio and dynamic spectrum access 5G/6G-enabled communication Delay-tolerant networking Network security and resilience Communication-aware path planning Quantum communication and security Track 4: Control & Autonomous Systems Intelligent and adaptive control Distributed and decentralized control Path planning and obstacle avoidance Trajectory optimization Swarm intelligence and cooperative control Robust and fault-tolerant control Model predictive control Real-time decision-making and scheduling Human-machine collaborative control Multi-agent systems and coordination Learning-based control Game-theoretic decision-making Event-triggered and hybrid control Control system security Digital twin for control and validation Track 5: System Integration & Applications Intelligent transportation and autonomous logistics Environmental monitoring and disaster response Industrial inspection and automation Smart manufacturing Healthcare robotics and medical monitoring Smart cities and infrastructure inspection Precision agriculture and forestry Marine and underwater exploration Energy systems and grid monitoring Security, surveillance, and defense Modular and reconfigurable systems Hardware-software co-design Field deployment and operational experience Open-source platforms and benchmarking Standardization and regulatory frameworks Track 6: AI Frontiers & Interdisciplinary Exploration Embodied AI and simulation-to-real transfer Large language models for unmanned systems Edge AI and on-device intelligence Brain-computer interfaces Human-robot interaction Quantum machine learning Metaverse and virtual simulation Cross-domain data fusion Novel sensors and materials Green AI and sustainable computing AI safety and robustness Ethical AI and responsible innovation Privacy and trust in unmanned systems Causal inference and reasoning Interdisciplinary methodologies and paradigms
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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
BMachine Learning2.9Springer0885-6125
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
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IEEE Access3.6IEEE2169-3536

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