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BigComp 2027: International Conference on Big Data and Smart Computing

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BigComp
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投稿締切日:
2026-09-30 残り 41 日
通知日:
2026-11-30
開催日:
2027-01-25
開催地:
Fukuoka, Japan
開催回数:
閲覧: 30496   フォロー: 12   参加: 6

論文募集

BigComp 2027 (International Conference on Big Data and Smart Computing) is an academic conference held in Fukuoka, Japan on 2027-01-25. The paper submission deadline is 2026-09-30. Acceptance notifications are sent on 2026-11-30.

Topics of Interest We invite the submission of original research contributions in the following areas, but also welcome any original contributions that may cross the boundaries among areas or point in other novel directions. In addition, IEEE BigComp 2027 encourages submissions about products, practices, and services in industry: AI for big data and smart computing Big data analytics and social media Big data applications / big data as a service Big data ecosystem Big data in the era of LLMs Bioinformatics, multimedia, smartphones, etc. Cloud and grid computing for big data Crowdsourcing and human-in-the-loop Data mining and data science Data and information quality Disaster analysis Infrastructure and platform for big data and smart computing Knowledge graph, graph data management and mining Machine learning and AI theory and fundamentals Mobile communications and smart location-based services Recommender systems Search/retrieval and generation of big data Security and privacy for big data Smart computing models, tools, and devices Techniques, models and algorithms for big data Tools and systems for big data Understanding multi-modal data
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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
Journal of Big Data6.4Springer2196-1115
Big Data Research4.2Elsevier2214-5796
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
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203

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