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MMLDS 2026: International Conference on Multimodality, Machine Learning and Data Science

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MMLDS
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투고 마감일:
2026-10-16 66일 남음
통보일:
개최일:
2026-10-30
개최지:
Zhengzhou, China
개최 횟수:
1
조회: 660   팔로우: 0   참가: 0

논문 모집

MMLDS 2026 (International Conference on Multimodality, Machine Learning and Data Science) is an academic conference held in Zhengzhou, China on 2026-10-30. The paper submission deadline is 2026-10-16.

About ICDLA 2026 In an era where information technology is advancing at an unprecedented pace, data has emerged as the cornerstone of societal progress and scientific discovery. However, the capacity of single-modality data—such as plain text or static images—to convey complex information is increasingly approaching its limits. The next paradigm shift in artificial intelligence is widely seen to lie in the deep integration and cross-disciplinary innovation of multimodal learning, machine learning, and data science. To convene global expertise in exploring the future of this interdisciplinary domain, the 2026 International Conference on Multimodality, Machine Learning and Data Science(MMLDS 2026) will be held in Zhengzhou, China, from October 30 to November 1, 2026. This conference aims to establish an international platform for scholars, engineers, and industry leaders worldwide to engage in in-depth discussions on core topics, including multimodal perception and understanding, machine learning theory and methodologies, data science and intelligent systems, and cutting-edge applications. The topics of interest for submission include, but are not limited to: ◕Track1: Multimodal Learning & Artificial Intelligence Image Understanding Video Analysis Speech Recognition & Processing Text & Language Modeling Cross-Modal Retrieval Multimodal Fusion Techniques Affective Computing & Cognitive Analysis Medical Image Analysis Natural Language Processing Generative Models Intelligent Human-Computer Interaction Intelligent Recommendation Systems Autonomous Driving & Perception Visual Question Answering Modality Transformation & Synthesis ◕Track2: Data Science & Big Data Analytics Data Preprocessing & Cleaning Data Mining Techniques Big Data Management Data Visualization Data Integration & Modeling Data-Driven Decision Making Spatiotemporal Data Analysis Network Data Analysis Social Computing & Behavioral Analysis Business Intelligence Data Security & Privacy Cloud Computing & Data Processing High-Performance Data Analytics Data Science Methodology Data-Driven Scientific Research ◕Track3: Machine Learning & Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning Self-Supervised Learning Deep Neural Networks Graph Neural Networks Sequence Modeling Model Compression & Optimization Transfer Learning Federated Learning Explainable AI Anomaly Detection Time Series Prediction Model Evaluation & Selection Automated Machine Learning (AutoML) Publication All accepted full papers will be published in the conference proceedings and will be submitted to EI Compendex / Scopus for indexing.
최종 수정: Dou Sun ()

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CCF정식 명칭영향력 지수출판사ISSN
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 Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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