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Call for Papers

Scope

Computational intelligence is a computer science field by which a computer can learn a specific task from data or experimental observation. It addresses complex real-world problems in which mathematical or traditional modelling are not the preferred solution given that (i) the involved computational process might be too complex for mathematical reasoning; (ii) it might contain some uncertainties during the process, or (iii) by nature, the computational process is a randomly determined one (heuristic). EAI International Conference on Computational Intelligence and Communications (CICom 2020) provides solutions to various problems of this nature.

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Topics

Track topics include, but are not limited to:

Track 1: Computational Intelligence in Automation, Control, and Intelligent Transportation

• Intelligent Decision Making and Support
• Adaptive and Optimal Control
• Model-Predictive Control
• Fuzzy Systems and Control
• Expert and Decision Support Systems
• System Identification and Learning
• Fault Detection and Diagnosis
• Complex System Modelling
• Fuzzy Evolutionary Systems and Control
• Fuzzy Neural Systems and Control
• Hybrid Intelligent Control
• Advanced transportation information, communication and management systems
• Object recognitions such as pedestrian detection, traffic sign detection and recognition
• Multimodal intelligent transport systems and services
• Vehicle communications and connectivity
• Personalized driver and traveller support systems
• Air, road, and rail traffic management
• 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 reading, processing and analysis of big data
• Integration of multi-modal, multi-fidelity, and structured and unstructured 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
• Applications of big data, such as industrial process, business intelligence, healthcare, bioinformatics and security.
• Classification, Clustering, Regression
• Feature learning and feature engineering
• Data mining from nonstationary and drifting environments
• Novel Architecture and Protocols of AI Integrated with IoT
• Security, Privacy, Access Control, and Trust Frameworks of IoT
• Resource Management Techniques of Using AI for IoT
• Control Schemes in IoT
• Smart Data Storage in IoT
• Software-Defined Networking in IoT
• Applications in Industrial IoT, in Smart Energy Efficiency, in Smart Grids, in Smart City, in Green Networks, in Connected Health, etc.

Track 3: Computational Intelligence on Wireless Communication Systems and Cyber Security

• Machine Learning for Networks
• V2X Communications
• Unmanned Aerial Vehicles
• Wireless Sensor Networks
• Low Power Wireless Area Networks
• Network Virtualization
• Quality of Service (QoS)
• Resources Management
• Embedded systems
• Software Defined Networks
• Blockchains
• Waveforms and Radio Access Technologies
• Software Defined Radio
• Visible Light Communication (VLC)
• Edge/Fog computing/networking
• RFID, NFC
• MIMO-based Networks
• Radio Interference Management
• Radio Access Networks
• Intrusion/malware detection, prediction, classification, and response
• Models for survivable, resilient, and self-healing systems
• Sensor network security, web security, wireless and 4G, 5G media security
• Digital forensics, security information visualization
• Self-awareness, auto-defensiveness, self-reconfiguration, and self-healing networking paradigm
• Modelling adversarial behaviour for insider and outsider threat detection
• Cloud and virtualization security
• Block-chain security

Track 4: Computational Intelligence on Brain-Computer Interfaces, and Image and Pattern Recognition

• Signal processing for Brain-Computer Interface (BCI)
• BCI Feature Extraction
• BCI Pattern Recognition
• BCI multi-view learning
• Multiple modalities for BCI
• Invasive and non-invasive BCIs.
• Online and offline BCI applications
• Feature ranking and weighting
• Feature selection, extraction, construction, and reduction
• Feature analysis on high-dimensional and large-scale data
• Evolutionary computation for feature analysis
• Neural networks for feature analysis
• Fuzzy logic for feature analysis
• Hybrid evolutionary computation and machine learning, 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

Publication

All registered papers will be submitted for publication by Springer and made available through SpringerLink Digital Library.

Proceedings will be submitted for inclusion in leading indexing services, Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL).

Authors of selected best accepted and presented papers will be invited to submit an extended version to:

All accepted authors are eligible to submit an extended version in a fast track of:

Additional publication opportunities:

Community Review

Community Review is a service offered to Program Committees and submitting Authors of all EAI conferences designed to improve the speed and the quality of the review process.

Abstracts of all authors who opt in to Community Review during submission will be published and available for Bidding here.

Learn more about the Community Review process

Paper submission

Papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section).

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