Special Session #23

Advances in Machine Learning, Deep Learning and Artificial Intelligence to Meet Complex Systems, Health and Emerging Applications: Real-World Problems

 

Chair:

Assoc. Prof. Dr. Mehmet Akif Çifçi, Bandırma Onyedi Eylül University, Balıkesir, Türkiye;  e-mail: meh.cifci@kvk.lt

Dynamics and attributes of current healthcare systems encounter multiple challenges ranging from rising burden of illness to multimorbidity and disability. To effectively tackle such challenges and achieve accuracy and timely intervention, fundamental transformation of health systems is critical and essential. The employment of machine learning and Artificial Intelligence (AI) techniques has ensured facilitation with diagnostic processes, evaluation, optimization of medication dosages, enhancement of image scanning and segmentation, prediction of risk of diseases besides supporting of decision-making processes and reduction of healthcare costs. Since learning is an integral and natural process in human behavior, it has also become a vital part of machines, which has led to the emergence of deep learning and deep neural networks for enabling the analysis and implementation of various applications with remarkable outcomes for further real-world applications. With AI algorithms continuously examining factors including disease prevalence, geographical distribution, population demographics, and so on, identification of patients at a higher risk of some conditions can be facilitating in terms of prevention and treatment processes. In addition to these points, edge analytics are capable of detecting irregularities so that potential healthcare incidents could be predicted. The generation of datasets of high resolution and interdisciplinary efforts oriented toward efficacy and productivity are concerned with the production of machine learning regarding human and computer hybrid systems. These transformative aspects and advances in healthcare systems and other ones are expected to have significant impacts on improving effectiveness, equity, efficiency as well as responsiveness. 

Given the fact that AI has been transforming how we model, analyze and understand complex systems, this Special Session welcomes contributions on machine learning, deep learning, reinforcement learning and hybrid AI methods that address real-life challenges in domains like science, engineering and healthcare. We are particularly interested in work that combines technical innovation with real-world applications ranging from healthcare and cybersecurity to mobility, energy and environmental systems.

The topics include but will not be limited to:

  • Advanced ML/DL architectures for prediction, classification and control
  • AI in healthcare, diagnostics, medical imaging and bioinformatics
  • AI for cybersecurity, smart mobility and renewable energy systems
  • Reinforcement learning and autonomous decision-making
  • Explainable and trustworthy AI in high-stake applications
  • Large-scale, distributed, and high-performance AI systems
  • Hybrid AI techniques combined with quantum / wavelets / fractional methods