Call for Papers

Original contributions from researchers describing their original, unpublished, research contribution which is not currently under review by another conference or journal and addressing state-of-the-art research are invited to share their work in all areas of Smart and Innovative trends. Accepted papers will be published in the proceedings and submitted to the IEEE-Digital Library and EI Index. At least one author of each accepted paper is required to register and present their work at the conference; otherwise, the paper will not be included in the proceedings. Best Paper/Demo Awards will be presented to high-quality papers/demos. The ICBDCI 2019 Organizing Committee also invites proposals for workshops associated with the conference, addressing research areas related to the conference. Accepted workshop papers will be included in the proceedings. Please send workshop proposals to

Paper Submission Link:

Track 1: Computational Intelligence in Big Data

Novel CI methods of big data acquisition, CI in distributed computing of big data, Memory efficient CI algorithms relating to reading, processing or analysing big data, Data mining in big data, Deep learning in big data, Integration of big data, such as multi-modal, multi-fidelity, structured and unstructured data, Big data in industry, Big data in healthcare, Big data and the internet of things, Big data in the future of media and social media, Big data in finances and economy, Big data in public services, Big data in intelligent robotics, Big data driven business or industry, Extracting understanding from distributed, diverse and large-scale data resources, Real time analysis of large data streams, Predictive analysis and in-memory analytics, Dimensionality reduction and analysis of large and complex data, New information infrastructures, Visualisation of big data and visual data analytics, Semantics technologies for big data, Scalable learning in big data, Optimisation of big data in complex systems, Data governance and management, CI in curation of big data, Human-computer interaction and collaboration in big data, Big data and cloud computing, Applications of big data, such as industrial process, business intelligence, healthcare, bioinformatics and security

Track 2: Computational Intelligence for Wireless Systems

Radar architectures and systems; Advanced and 5G communications systems and networks, Automotive wireless systems and devices, Electromagnetic imaging and diagnostics, Remote sensing, Radio-frequency identification, Wireless power transmission, Wireless sensor networks, Wireless green networks, Antennas, MIMO, Software defined radio, Smart antennas, Direction finding

Track 3: Computational Intelligence in Internet of Everything

Data Analytics middleware for Edge computing, Cloud-based intelligent analytics, Edge-node-driven data analytics, Intelligent data synchronization and updating between Edge nodes or Cloud nodes, Data metering for Edge nodes and Cloud nodes, Intelligent pricing mechanisms for Edge nodes and Cloud nodes, Data-driven privacy and security solutions in Edge computing and Cloud computing, Case studies for data analytics using Edge nodes or Cloud nodes, Internet of Everything for Biomedical/Healthcare Data and Imaging, Internet of Everything for Brain Computer Interface/Human Machine Interaction, Internet of Everything for Robotics/Humanitarian Science and Technology

Track 4: Computational Intelligence in Vehicles and Transportation Systems

Automated driving and driverless cars, Big data learning algorithms from connected vehicle data: V2V, V2I, V2P and V2X communications, Cloud computing and big data in transportation and vehicle systems, Computational intelligence in advanced transportation information, communication and management systems, Computational intelligence in air, road, and rail traffic management, Computer vision and machine learning in collision detection and avoidance, Deep learning algorithms and applications, Driver assistance and automation systems, Driver state detection and monitoring, Multimodal intelligent transport systems and shared services, Object recognitions such as pedestrian detection, traffic sign detection and recognition, Machine learning algorithms for personalized driver and traveler support systems, Simulation and forecasting models using computational intelligence, Spatio-temporal traffic pattern recognition, Trip modeling and driver speed prediction, Vehicle fault diagnostics and health monitoring, Vehicle energy management and optimization in hybrid vehicles.

Track 5: Computational Intelligence in Remote Sensing

CI based processing - Image registration, Image enhancement, Band selection, SAR speckle filtering, Spectral unmixing, Image classification methods, Image clustering methods, Image segmentation, Spectral-spatial methods, Spatio-spectral fusion, Regression techniques RS Application - Short/long term change detection in hyperspectral/multispectral images, Land-surface phenology using AVHRR/MODIS/VIIRS data, Disaster monitoring using SAR image, Forest monitoring by LIDAR, Land use and land cover mapping, Oil spill detection, Ocean surface RS, Land surface temperature, Land surface dynamics, Target detection, Numerical weather modeling, Agriculture monitoring, Road extraction, Forest fire mitigation, Urban sprawl, Power line monitoring

Track 6: Computational Intelligence for Engineering solutions

Complex engineering systems, structures and processes, Intelligent analysis, control and decision-making, Management and processing of uncertainties, Problem solution in uncertain and noisy environments, Reliable computing, Sustainable solutions, Infrastructure security, Climate change, Environmental processes, Disaster warning and management, Lifecycle analysis and design, Automotive systems, Monitoring, Smart sensing, System identification, Decision-support and assistance systems, Visualization methods, Prediction schemes, Classification methods, cluster analysis, Response surface approximations and surrogate models Sensitivity analysis, Robust design, reliability-based design, performance-based design, Risk analysis, hazard analysis, risk and hazard mitigation, Optimization methods, evolutionary concepts, Probabilistic and statistical methods, Simulation methods, Monte-Carlo and quasi Monte-Carlo, Bayesian approaches / networks, Artificial Neural Networks, Imprecise probabilities, Evidence theory, p-box approach, Fuzzy probability theory, Interval methods, Fuzzy methods, Convex modeling, Information gap theory

Track 7: Computational Intelligence in Image and Pattern Recognition

Feature ranking/weighting, Feature selection, Feature extraction, Feature construction, Dimensionality reduction, Multi-objective feature selection, construction or extraction, Feature analysis on high-dimensional and large-scale data, Analysis on computational intelligence for feature selection, construction, and extraction algorithms, Evolutionary computation for feature analysis, Neural networks for feature analysis, Fuzzy logic for feature analysis, Hybridisation of evolutionary computation, neural networks, and fuzzy logic for feature selection, construction, and extraction, Hybridisation of 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, e.g. image sequences/analysis, face recognition, gene analysis, biomarker detection, medical data classification, diagnosis, and analysis, handwritten digit recognition, text mining, instrument recognition, power system, financial and business data analysis, etc.

Track 8: Computational Intelligence Applications in Smart Grid

Algorithms for modeling, control and optimization, Communication and control, Cyber security, Demand side management, Distributed energy resources, Dynamic equivalents, Emission Reductions, FACTS, Markets and economics, Methods and algorithms for real-time analysis, Optimization, Planning, operation and control, Plug-in electric vehicles, Renewable energy, Smart grid education, Smart homes, Smart micro-grids, Smart nano-grids, Smart sensing, Synchrophasors, Wide area monitoring, control and protection, Visualizations for control centers

Track 9: Computational Intelligence in Control and Automation Control and Decision:

Neural Networks Control, Fuzzy Systems and Control, Evolutionary Control, Intelligent and AI Based Control, Model-Predictive Control, Adaptive and Optimal Control, Large-scale Systems and Decentralized Control, Intelligent Control Systems, Industrial Automations, Intelligent Decision Making and Support, Expert and Decision Support Systems, System Modeling and Learning: System Identification and Learning, Fault Detection and Diagnosis, Complex System Modeling, Dynamic Systems Modeling, Time Series and System Modeling, Hybrid Control: Fuzzy Evolutionary Systems and Control, Fuzzy Neural Systems and Control, Neural Genetic Systems and Control, Hybrid Intelligent Control, Granular Computing and Control, Hierarchical Systems and Control

Track 10: Computational Intelligence in E-governance

Integration of structured, semi-structured and unstructured data, Data fusion of diverse data resource, Data mining, Computer vision, Intelligent analytics of big data, Workflow scheduling, Resource scheduling in cloud environment, Knowledge discovery, fusion and service, Case based reasoning, Optimization of complex systems, Decision support system, Emergency management, Smart city, Human-computer interaction and collaboration

Track 11: Computational Intelligence in Cyber Security

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, new sensor fusion and decision support for mobile device security, Self-awareness, auto-defensiveness, self-reconfiguration, and self-healing networking paradigm, Modeling adversarial behavior for insider and outsider threat detection, Cloud and virtualization security, Internet of Things (IoT), wearable device security, Identity Science, Authentication and Access Control, Social network security and privacy, Electronic Healthcare security, privacy and compliance, BigData and Application Security, Blockchain Security