Authors are invited to submit electronically original, English-language research contributions or experience reports not concurrently submitted elsewhere. Papers should be no longer than 12 pages formatted according to the LNCS Springer style. The program committee may reject papers that exceed this length on the grounds of length alone. In the spirit of the previous EANN conferences, the paper should concentrate on the application rather than just the algorithm or the theory.
Camera-ready submissions should be corrected by following the remarks of the referees and submitted in zip format including (1) the camera-ready version of the authors’ work in pdf format, (2) the camera-ready version of the authors’ work in editable sources format as well as (3) the Consent to Publish signed in ink and scanned to image file. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer’s LNCS format and should be, including all text, figures, references and appendices: 12 pages for papers accepted as full and 10 pages for papers accepted as short.
Submitted papers will be refereed by at least three reviewers for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the conference and included in the proceedings, which will be published by SPRINGER CCIS “Communications in Computer and Information Science” and they will be available on site. It is important though to follow the instructions in this page to ensure that your paper will be included in the proceedings.
Topics
- Adaptive AI architectures
- AI and Data analytics
- AI and Image-Video Processing
- AI and Information Retrieval
- AI and Music
- AI application in e-Security
- AI applications in Energy Grids
- AI in Social Media
- AI Prediction
- AI related Hardware development
- Bioinformatics
- Biomedical AI Applications
- Biomedical engineering
- Classification
- Colour, Motion analysis with AI
- Computer vision
- Data mining
- Deep Learning ANN
- Dimensionality Reduction
- Education. Intelligent Tutoring
- Engineering AI Applications
- Environmental Applications of AI
- Evolutionary architectures
- Filtering
- Fusion
- Fuzzy Inference Systems
- Fuzzy logic systems
- General Engineering AI Applications
- Genetic Algorithms
- Hybrid Intelligent systems
- Innovative AI Algorithms
- Inteligent Financial Forecasting
- Inteligent Molecular Biology Engineering
- Intelligent Adaptive Control
- Intelligent Adaptive Educational methods
- Intelligent Agents
- Intelligent Clustering
- Intelligent Decision Making
- Intelligent Financial Engineering
- Intelligent Thermal Engineering
- Intelligent Traffic systems
- Intelligent Transportation Systems
- Internet of Things AI applications
- Learning theory
- Low cost architectures
- Machine Learning
- Machine Learning and Big Data
- Multi agent systems
- Pattern Recognition
- Process Monitoring and Diagnosis
- Real Time Intelligence
- Recommendation systems
- Reinforcement Learning
- Risk Modeling
- Robotics
- Robotics
- Self Organizing Maps
- Signal Processing
- Smart Cities
- Spiking ANN
- Support vectors machines
- Telecommunications and AI
- Time Series Analysis
- Unsupervised Learning
- Unsupervised Learning (in general)
- Theoretical Neural Computation
- Information and Optimization
- From Neurons to Neuromorphism
- Spiking Dynamics
- From Single Neurons to Networks
- Complex Firing Patterns
- Movement and Motion
- From Sensation to Perception
- Object and Face Recognition
- Convolutional Neural Networks
- Deep Learning in Real Time Systems
- Deep Learning and Big Data Analytics
- Deep Learning and Big Data
- Deep Learning and Forensics
- Deep Learning and Cybersecurity
- Deep Learning and Social Networks
- Evolving Systems – Optimization
- Learning
- Machine Learning for BioMedical systems
- Machine Learning and Video – Image Processing
- Machine Learning and Forensics
- Machine Learning and Cybersecurity
- Machine Learning and Social
- Media Machine Learning in Engineering
- Bayesian and Echo State Networks
- Recurrent Neural Networks and Reservoir Computing
- Coding Architectures Interacting with The Brain
- Swarm Intelligence and Decision-Making
- Multilayer Perceptrons and Kernel Networks
- Training and Learning Inference and Recognition Clustering, Mining and Exploratory Analysis
- Time Series and Forecasting