Confirmed Workshops

We are pleased to advise that the following Workshops are confirmed for the 2024 programme and we would like to thank the organisers for the time they took to put forward and manage these sessions.

No.Workshop TitleRuntime (hours)Special Session Organiser
1Rhythms in the Brain - Workshop on Deep Oscillatory Neural Network to solve sequential problems and model brain dynamics3V Srinivasa Chakravarthy
2Advancements in Renewable Energy Optimization through Artificial Intelligence4Reza Enayatollahi
3Privacy Compliant Health Data As A Service For AI Development4Mufti Mahmud
4Evolutionary Information Processing5Amir H Gandomi
5Generative Frontiers in Healthcare: Empowering Medical Innovations with GANs3Samiya Khan

Rhythms in the Brain - Workshop on Deep Oscillatory Neural Network to solve sequential problems and model brain dynamics

Led by Prof. V. Srinivasa Chakravarthy (Indian Institute of Technology Madras)

The workshop presents theoretical and practical aspects of understanding brain rhythms and their modulation, using Deep Oscillatory Neural Network (DONN). It is organised so that theory and practical sessions are interspersed.

Advancements in Renewable Energy Optimization through Artificial Intelligence

Led by Dr. Reza Enayatollahi (Toi Ohomai Institute of Technology)

Abstract:
Renewable energy sources are becoming increasingly vital in the global pursuit of sustainable energy solutions. As the world transitions towards a low-carbon future, optimizing the generation, distribution, and consumption of renewable energy becomes paramount. Artificial Intelligence (AI) has emerged as a powerful tool in achieving this optimization, offering innovative solutions to complex challenges. This session aims to explore the latest developments, challenges, and opportunities in applying AI techniques to optimize renewable energy systems.

Session Objectives:

  • To showcase cutting-edge research and practical applications of AI in optimizing renewable energy sources such as solar, wind, hydro, and geothermal.
  • To discuss the impact of AI on enhancing the efficiency, reliability, and cost-effectiveness of renewable energy systems.
  • To identify key challenges and potential solutions in integrating AI technologies with renewable energy infrastructure.
  • To facilitate knowledge exchange and collaboration among researchers, industry professionals, policymakers, and practitioners in the field.

Topics of Interest:

  • Machine learning algorithms for predicting renewable energy generation and demand.
  • Optimization techniques for maximizing energy production from renewable sources.
  • AI-driven strategies for grid management and balancing renewable energy fluctuations.
  • Smart control systems for improving the efficiency of renewable energy conversion devices.
  • AI applications in energy storage and grid integration of renewable resources.
  • Case studies and real-world applications demonstrating the effectiveness of AI in renewable energy optimization.
  • Ethical considerations and societal implications of AI deployment in the renewable energy sector.

Privacy Compliant Health Data As A Service For AI Development

Led by Dr. Mufti Mahmud (Nottingham Trent University), Dr. Antti Airola (University of Turku)

Artificial intelligence (AI) enables data-driven innovations in health care. AI systems, which process vast amounts of data quickly and in detail, show promise both as a tool for preventive health care and clinical decision-making. However, the distributed storage and limited access to health data form a barrier to innovation, as developing trustworthy AI systems require large datasets for training and validation. Furthermore, the availability of anonymous datasets would increase the adoption of AI-powered tools by supporting health technology assessments and education. Secure, privacy compliant data utilization is key for unlocking the full potential of AI and data analytics. In this project we have been developing a solution that enables analyst to utilize encryption-in-use technologies (secure multi-party computation, fully homomorphic encryption and federated learning) to run analytics and build better machine learning models by accessing more data. We have been working on advancing the current state-of-the-art data synthesis methods towards a more generalized approach of synthetic data generation, and also developing metrics for testing and validation, as well as protocols that enable synthetic data generation without access to real-world data (through multi-party computation). These have been put together as a combined effort from 20 partners from 10 European countries and funded by the European Commission under the Horizon Europe Programme.

The workshop will introduce the audience to the project and its approaches to achieving a next-generation healthcare ecosystem in Europe through secure, privacy-preserving AI models as a service and synthetic healthcare data as a service.

Evolutionary Information Processing

Led by Prof. Amir H. Gandomi (University of Technology Sydney)

The one-day workshop on ""Evolutionary Information Processing"" offers an intensive and focused exploration into the integration of evolutionary computation with advanced information processing techniques. This event aims to provide attendees with a comprehensive understanding of the latest advancements and applications in this interdisciplinary field, emphasizing hybrid models with artificial neural networks (ANNs) and other machine learning (ML) methodologies, as well as evolutionary population-based optimization.

Morning Session: Fundamentals and Hybridization with ANNs

Keynote Presentation: An introduction to evolutionary computation, covering key concepts, methodologies, and their significance in information processing.
Hybrid Models with ANNs: Detailed exploration of how evolutionary algorithms can optimize ANN architectures and learning processes. This session includes case studies demonstrating the successful application of these hybrid models in various industries.
Midday Break: Networking and discussion opportunities for participants to exchange ideas and experiences.

Afternoon Session: Broader ML Integrations and Population-based Optimization

Evolutionary Computation with ML Techniques: A dive into the integration of evolutionary algorithms with diverse ML techniques, such as reinforcement learning and support vector machines, showcasing their enhanced capabilities in complex problem-solving.
Population-based Optimization Methods: An examination of how evolutionary strategies can be applied to population-based optimization, featuring interactive discussions on particle swarm optimization and ant colony optimization.
Closing Panel Discussion: A forum featuring experts in evolutionary computation and information processing, discussing future trends, potential research directions, and practical implications of the day's topics.

This one-day workshop is designed to be dynamic and interactive, offering a mix of presentations, case studies, and discussions to ensure a comprehensive and engaging experience for all participants.

Generative Frontiers in Healthcare: Empowering Medical Innovations with GANs

Led by Dr. Samiya Khan (University of Greenwich), Dr. Khursheed Aurangzeb (King Saud University)

This workshop invites academicians, researchers, and practitioners to explore the transformative potential of Generative Adversarial Networks (GANs) within the healthcare sector. As we stand on the cusp of a new era in medical technology, the applications of GANs are proving to be remarkably diverse, ranging from synthetic data generation for research purposes to enhancing diagnostic accuracy and personalizing patient care. This workshop aims to foster a multidisciplinary dialogue, showcasing the latest advancements and discussing the future direction of GAN applications in healthcare. Through a mix of keynote speeches and paper presentations, participants will gain insights into the challenges and opportunities that lie ahead in integrating artificial intelligence with medical science to improve health outcomes worldwide.

The topics covered in this workshop include, but are not limited to -
(1) Synthetic Data Generation for Clinical Research: Exploring how GANs can create realistic, anonymized patient data to support medical research.
(2) Personalized Treatment Plans: Discussing the role of GANs in simulating patient responses to various treatment modalities, aiding in the development of personalized medicine.
(3) Predictive Analytics for Patient Outcomes: Examining the use of GANs in predicting disease progression and patient outcomes, facilitating early interventions and better resource allocation.

This workshop proposal aims to create a comprehensive platform for discussing the innovative applications of GANs in healthcare, highlighting both their potential and the challenges they bring.

Conclusion:
The integration of AI technologies holds immense promise for advancing the optimization of renewable energy systems, paving the way for a more sustainable and resilient energy future. This session provides a platform to explore groundbreaking research, exchange ideas, and foster collaborations that will accelerate progress toward achieving global energy sustainability goals.