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CICom 2021: EAI International Conference on Computational Intelligence and Communications

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截稿日期:
2021-05-01
通知日期:
2021-06-01
会议日期:
2021-09-02
会议地点:
Versailles, France
届数:
2
浏览: 10728   关注: 0   参加: 0

会伴指数 (CP-I)

38.0 / 100
全站第 5,373 名 / 共 5,652 个会议 · 前 96%

人工智能与机器学习 第 697 / 736 网络与通信 第 827 / 862

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
30
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 有据可查的届次：2 · 过去 24 个月打开过本页的研究者：2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-08

征稿

CICom 2021 (EAI International Conference on Computational Intelligence and Communications) is an academic conference held in Versailles, France on 2021-09-02. The paper submission deadline is 2021-05-01. Acceptance notifications are sent on 2021-06-01.

CiCOM 2021 will provide an opportunity to exchange innovative research on Computational Intelligence and Communications. Computational Intelligence makes use of biologically and linguistically motivated computational paradigms including neural networks, fuzzy logic systems, and evolutionary computation, as well as social reasoning, artificial life, ambient intelligence, deep learning, and the likes. New technologies in communications bring new application paradigms for healthcare, transportation, education, agriculture, and social networks. With the arrival of 5G and 6G networks technologies, we need to address new research challenges, including the tight integration of machine intelligence, efficient and reliable networking, interoperability, scalability and security. Track topics include, but are not limited to: Track 1: Computational Intelligence in Automation, Control, and Intelligent Transportation System Intelligent Decision Making and Support Adaptive and Optimal Control Model-Predictive Control Fuzzy Systems and Control Fuzzy Neural Systems and Control Hybrid Intelligent Control Object recognitions, traffic sign detection and recognition Multimodal intelligent transport systems & services Vehicle communications and connectivity Driver and traveller support systems Simulation and forecasting models Driver assistance and automation systems Driver state detection and monitoring Track 2: Computational Intelligence on Big Data, Internet of Things, and Smart Cities Efficient algorithms on processing and analysis of big data Extracting and understanding from distributed, diverse and large-scale data resources Visualisation of big data and visual data analytics Human-computer interaction and collaboration in big data Classification, Clustering, Regression Data mining from nonstationary and drifting environments Novel Architecture and Protocols of AI Integrated with IoT Security, Privacy, Access Control, & Trust Frameworks of IoT Resource Management Techniques of Using AI for IoT Control Schemes in IoT Smart Data Storage in IoT Track 3: Computational Intelligence on Wireless Communication Systems and Cyber Security Machine Learning for Networks Unmanned Aerial Vehicles Wireless Sensor Networks Network Virtualization Software Defined Networks | Blockchain and its security Waveforms and Radio Access Technologies Software Defined Radio Visible Light Communication (VLC) Radio Access Networks Intrusion/malware detection, prediction, and classification Sensor network security, web security, wireless and 4G, 5G media security Self-awareness, auto-defensiveness, self-reconfiguration, and self-healing networking paradigm Modelling adversarial behaviour for threat detection Cloud and virtualization security Track 4: Computational Intelligence on Human/Brain-omputer Interfaces & Image and Pattern Recognition Signal processing for Brain-Computer Interface (BCI) BCI Feature Extraction, Pattern Recognition, and Multiple modalities for BCI Invasive and non-invasive BCIs. Online and offline BCI applications Advances on Human-Computer Interfaces Feature ranking and weighting Feature selection, extraction, construction, and reduction Feature analysis on high-dimensional and large-scale data Evolutionary computation for feature analysis Information theory, statistics, mathematical modelling, etc., for feature analysis Feature analysis in classification, clustering, regression, image analysis, and other tasks Real-world applications of computational intelligence for feature analysis
由 Dou Sun 最后更新于

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