A variety of domains (Ambiental, Tactile, Language Processing, Tracking, Healthcare, Ecology, etc.) expanded in the last years based on practical advances provided by Artificial Intelligence (AI). Machine learning AI-based discovery and learning allow deep-learning (and unlearn obsolete knowledge), accurate forecasts, fault prevention and detection, as well as prediction of special diseases. Practical AI-based services in Internet of Things (IoT), Transportation systems, Cyber-systems, Citizen-centric systems, and others reached new levels of usability and quality.

AI-based solutions for monitoring, control, decision making are expected to increase the capability of systems providing mechanism for predictions, optimization, risk minimization by interpreting situations and large volumes of data.

We solicit both academic, research, and industrial contributions. We welcome technical papers presenting research and practical results, position papers addressing the pros and cons of specific proposals, such as those being discussed in the standard fora or in industry consortia, survey papers addressing the key problems and solutions on any of the above topics short papers on work in progress, and panel proposals.

Industrial presentations are not subject to the format and content constraints of regular submissions. We expect short and long presentations that express industrial position and status.

Tutorials on specific related topics and panels on challenging areas are encouraged.

The topics suggested by the conference can be discussed in term of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.

All topics and submission formats are open to both research and industry contributions.

AISyS 2024 conference tracks:

Core AI-features
- AI concepts, methodology and algorithms (agents, environments, expert systems, neural networks, deep thinking)
- AI support (Big Data, Machine learning, Deep learning)
- AI stages (narrow, general, super)
- AI types (reactive machines, limited memory, theory of mind, self-awareness)
Generative AI
- AI Uncertainty
- (Trustability, Responsibility, Uncertainty) in AI
- Transparency and interpretability of AI-based applications
- AI-based system explainability
- Responsible AI ( fairness, transparency, explainability, security, privacy)
- Ethics and personalized data

AI-based systems
- AI-based policies for designing, deploying, and maintaining complex systems
- AI for Industry 4.0/5.0
- AI-based for precision agriculture
- AI-based for precision medicine
- AI-based tracking systems
- AI-driven transportation systems
- AI-predicting emergency events in 5G and 6G systems
- AI-based cyber-systems resiliency
- AI-human-centered cybersecurity
- AI-based natural language processing systems

AI-based societal services
- Impact of AI on society and humans: services, advantages, and risks
- Governmental regulations over the AI development lifecycle
- Application/corporation certification
- Economic paradigms
- Financial services, (investment optimization, transactional fraud detection)
- Education services for society awareness
- Human-in-the-loop decisions; decide vs. assist paradigms
- Manipulations and bias in AI-based services
- Data governance and citizens data sharing
- AI-planed regulations in USA and EU