Congress Topics

Machine Learning and Neural Networks

Algorithms that enable computers to learn from data and make predictions or decisions without explicit programming; computational models inspired by the structure and function of the human brain, consisting of interconnected nodes that process information and learn complex patterns.

Artificial Neural Networks (Multi-Layer FF) Spiking | Artificial Neural Networks | Convolutional Neural Networks | Deep Learning | Recurrent Neural Networks and Reservoir Computing | Multilayer Perceptrons and Kernel Networks | Learning and Adaptive Systems | Machine Learning | Bayesian and Echo State Networks | Bayesian Models | Deep Learning and Big Data | Deep Learning and Big Data Analytics | Deep Learning and Cybersecurity | Deep Learning and Forensics | Deep Learning and Real-Time Systems | Deep Learning and Social Networks | Machine Learning and Cybersecurity | Machine Learning and Forensics | Machine Learning for Biomedical Systems | Multilayer Perceptrons | Machine Learning and Neural Networks

Applications and Domains

Practical use of artificial intelligence across various fields, including healthcare, finance, gaming, marketing, and robotics; techniques such as machine learning, natural language processing, and computer vision to solve real-world problems, automate tasks, and enhance decision-making processes.

Agriculture | Artificial Intelligence | Biomedical Systems | Building Automation | Construction Automation | Cybersecurity | eBusiness, eCommerce, eHealth, eLearning | Environmental Intelligent Modeling | Finance, Banking and Insurance | Forensic Sciences | Gaming | Generative Design | Healthcare |Infrastructure Management | Marketing | Media Machine Learning in Engineering | Medical Informatics and Biomedical | Parametric Design | Predictive Maintenance | Robotics and Virtual Reality | Safety and Security | Smart Cities | Social Media | Space Optimization | Structural Analysis | Telecommunications - Transportation | Urban Mobility | Urban Planning

Data Analysis and Mining

Extracting meaningful insights and patterns from large datasets; techniques such as data cleaning, preprocessing, statistical analysis, and machine learning to uncover hidden relationships and trends; decision-making, predict future outcomes, and derive actionable insights for businesses and organizations.

Data Fusion | Data Mining and Information Retrieval | Knowledge Acquisition and Representation | Knowledge Management for e-Learning and Enterprise Portals | Text Mining | Time Series and Forecasting | Training and Learning Inference, Recognition Clustering, Mining, and Exploratory Analysis

Natural Language Processing and Communication

Enabling computers to understand, interpret, and generate human language; tasks such as sentiment analysis, machine translation, text summarization, and language understanding; communication between humans and machines, enabling interactions through speech, text, and dialogue systems.

Chatbot | Natural Language Processing | Speech and Natural Language Processing | Speech Synthesis | Ontologies

Computer Vision and Image Processing

Enabling machines to interpret and understand visual information from images or videos, performing tasks like object detection and recognition; manipulating digital images to extract useful information or enhance visual quality, aiding in analysis and decision-making.

Smart Graphics | Computer Vision | Object and Face Recognition | Image Processing | Colour/Image Analysis | Signal and Image Processing

Control and Optimization

Designing systems that achieve desired objectives while accounting for uncertainties and constraints; adaptive control methods for dynamic systems and optimization algorithms for finding optimal solutions.

Adaptive Control | Evolutionary Systems - Optimization | Particle Swarm Optimization | Planning and Resource Management | Planning and Scheduling

Interdisciplinary and Emerging Areas

Intersections with diverse fields like neuroscience, psychology, economics, and sociology; neuromorphic computing, affective computing, and AI ethics; foster collaboration, offering novel perspectives and solutions to complex problems while addressing societal, ethical, and technological challenges in AI development and deployment.

Affective Computing | Biologically Inspired Neural Networks | Crisis and Risk Management | Engineering and Industry | Internet of Things (IoT) | Smart Grids | Swarm Intelligence and Decision-Making | Theoretical Neural Computation

Human-Machine Interaction and User Experience

Designing intuitive interfaces and interactions between humans and AI systems; user experience (UX) research to optimize usability, accessibility, and satisfaction; communication and collaboration, ensuring AI technologies effectively support and augment human capabilities across various domains and applications.

Human-Machine Interaction / Presence Information and Optimization | Intelligent Profiling and Personalization

Bioinformatics

The merging of artificial intelligence with bioinformatics is a powerful combination of computational intelligence and biological data analysis that is revolutionizing research, diagnostics and personalized medicine.

Genomics and Genome Sequencing | Proteomics | Drug Discovery and Development | Systems Biology | Medical Imaging and Diagnostics | Text Mining and Natural Language Processing (NLP) | Ethical and Social Implications | Tools and Techniques | Applications and Future Directions

Biostatistics

The field of biomedical research and healthcare is evolving with the possibilities created by artificial intelligence. With its powerful computational capabilities, more meaningful insights from complex data, improved predictive models, and improved patient outcomes can be derived.

Data Collection and Management | Data Analysis | Predictive Modeling | Clinical Trials | Epidemiology | Genomics and Personalized Medicine | Medical Imaging | Healthcare Analytics | Ethics and Privacy | Real-World Applications | Challenges and Future Directions

Biometrics

The integration of artificial intelligence with biometrics not only enhances security and user experience but also opens up new possibilities for applications in various sectors including finance, healthcare, and law enforcement. However, it also raises important ethical and regulatory considerations related to privacy, data protection, and algorithmic bias that need to be carefully addressed.

Enhanced Security | Improved Accuracy | Personalization | Adaptability | Efficiency | Integration with IoT | Privacy and Ethical Considerations | Continuous Improvement

Econometrics

Transformation of economic research and analysis by combining the statistical methods of econometrics with the powerful computational capabilities of artificial intelligence. Developing the ability to analyze complex economic data, develop predictive models, and derive actionable insights.

Data Analysis and Prediction | Macroeconomic Forecasting | Financial Market Analysis | Policy Evaluation | Consumer Behavior Analysis | Labor Market Analysis | Big Data Integration | Real-Time Economic Indicators | Inequality and Poverty Measurement | Challenges and Future Directions | Key Technologies and Techniques | Real-World Applications

Molecular Biology and Genetics

Artificial intelligence enables the analysis of large and complex data sets; uncovers new insights into biological processes; increases the pace of research and discovery.

Genomic Data Analysis | Gene Expression Analysis | Protein Structure Prediction | Genetic Interactions and Pathways | Drug Discovery and Development | CRISPR and Gene Editing | Personalized Medicine | Evolutionary Biology | Synthetic Biology | Challenges and Future Directions | Key Technologies and Techniques | Real-World Applications

Ethics, Governance, and Policy

Ethical considerations, regulations, and standards; fairness, transparency, accountability, and privacy protection; developing guidelines, laws, and international agreements to mitigate risks and maximize the societal benefits.

Ethical Issues | Social Impacts and Implications | Development, Deployment and Governance

Sustainability

Develop and deploy AI technologies in a manner that minimizes environmental impact, promotes social equity, and ensures long-term viability; optimizing energy efficiency, reducing carbon footprint, addressing biases, and fostering equitable access to AI resources, contributing to a sustainable and inclusive future.

Energy Efficiency and Optimization | Environmental Monitoring | Sustainable Agriculture | Climate Modeling

Creativity

The ability of artificial intelligence systems to generate novel and valuable outputs, such as art, music, literature, or designs; using algorithms like generative adversarial networks (GANs), recurrent neural networks (RNNs), and reinforcement learning to produce original content, often indistinguishable from human-created works.

Art | Music | Design | Literature | Content Generation

*Suggested topics include, but are not limited to the above.