General Track Sessions
Papers may be submitted to one of the following General Tracks, or to an Invited Session. Only make one submission and select the track or session which best matches the topic of the paper.
The General Tracks are as follows:-
Generic Tracks
G1: Machine Learning, Artificial Neural Networks and Deep Learning
This track will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning in different application fields with especial emphasis on the design of those systems, are particularly encouraged.
The topics of interest include (but are not limited to):
- computational learning theory
- cooperative learning
- federated Learning and distributed IA
- distributed and parallel learning algorithms and applications
- feature extraction and classification
- hybrid learning algorithms
- inductive learning
- instance-based learning
- knowledge discovery in databases
- knowledge intensive learning
- learning through mobile data mining
- machine learning and information retrieval
- machine learning for web navigation and mining
- multi-strategy learning
- neural network learning
- online and incremental learning
- reinforcement learning
- scalability of learning algorithms
- statistical learning
- text and multimedia mining through machine learning
- machine learning for natural language processing
G2: Knowledge Based and Expert Systems
This track covers knowledge-based systems and expert systems, both theoretical research and its applications. Contributions using techniques in knowledge-based systems and expert systems applied to real-world problems, as well as interdisciplinary research involving knowledge-based systems and expert systems in different application fields, are particularly encouraged.
The topics of interest include (but are not limited to):
- Knowledge-Based Systems and Expert systems
- Knowledge Management and Decision-Support Systems
- Knowledge Engineering and Modelling
- Knowledge Acquisition, Knowledge Elicitation and Negotiation
- Knowledge Representation and Decision Making, Knowledge Preferences, Causality, Decision/Utility Theory, Probabilistic Inference, Relational Probabilistic Models, Sequential Decision Making, Stochastic Optimization,
- Uncertainty Representations, Uncertainty in AI, Bayesian Networks
- Reasoning and Reasoning strategies: Ontologies Reasoning, Automated Reasoning, Case-Based Reasoning, Common-Sense Reasoning, Computational Complexity of Reasoning, Description Logics, Diagnosis and Abductive Reasoning, Nonmonotonic Reasoning, Qualitative Reasoning, and Reasoning with Beliefs
- Tools, and Methodologies for Representing, Managing, and Visualizing contents and decision-making
- Visualization and Graphical Models
Other topics of interest are (but are not limited to):
- E-Learning, E-Business, E-Commerce Web Intelligence, Intelligent Web-based Systems
- Web Personalization and User Modeling AI Web systems: Data Analytics, Multimedia and Multimodal Web Applications
- Web Services: Semantic Descriptions, Planning, Matching, and Coordination, Web-Based Collaboration and Cooperation Crowdsourcing Techniques and Methodologies Web Search and Information Retrieval
- Open Data Human Language Technologies for Web Systems
- Text Summarization and Machine Translation Intelligent User Interfaces for Web Systems
- Knowledge Acquisition from the Web Languages
- Semantic Web Data Searching, Querying
- Visualizing, and Interpreting the Semantic Web Social Networking and Community Identification
- Web-Based Recommendation Systems, Web-Based Opinion Extraction and Trend Spotting.
G3: Intelligent Information and Engineering Systems
G3 track covers both intelligent information and engineering systems (G3a) and Cybersecurity (G3b), theoretical research and applications.
Intelligent information and engineering systems is a broad topic and includes contributions describing techniques handling real-world problems. It also includes research involving intelligent information and engineering in different application fields.
The topics of interest include (but are not limited to):
- Natural Language Processing
- Agent and Multi-Agent Systems
- Bio-inspired Systems
- Nature Inspired Methods and Optimization
- Image Processing and Signal Processing
- Machine & Computer Vision
- Monitoring and Prediction
- Speech Processing and Synthesis
G4: Industry Applications
This track covers Industry Applications. Contributions describing industry application techniques applied to real-world problems and interdisciplinary research involving industry applications in different application fields, with especial emphasis on industry, are particularly encouraged.
- Multi-Sensor Information Systems
- Dimensionality Reduction and Interactive Multivariate Data Visualization/Analysis
- Organisation Memories
- Industrial Control
- Fault Diagnosis
- Image Processing
- Medical & Diagnostic Systems
- Environmental Monitoring
- Power Electronics & Drives, High Voltage Systems
- Robotics
- Engine Control and Vehicle Applications, Smart Vehicles and AGVs, Signal and Time Series Processing
- Wavelets
- Industrial communication standards (OPCUA, MqTT, DDS), Minifactories
- Industry 4.0 applications, Financial & Stock Market
- Dashboards, real time networking
- Time Sensitive Networks, Industrial Wireless (5G, ZigBee)
** PLEASE NOTE:
- Do not submit the same paper to more than one General Track or Invited Session as they may be deleted from the conference.
- We may re-allocate papers to more appropriate tracks if we feel it necessary.