Germany Section Computational Intelligence Society Chapter

Dr. sc. Alexandros Tanzanakis

alexandros_tanzanakis@ieee.org

Officers / Board Members

Chair: Dr. sc. Alexandros Tanzanakis, Friedrich-Alexander-Universität Erlangen-Nürnberg
alexandros_tanzanakis@ieee.org

Vice-Chair: Dr.-Ing. Nikolaos Athanasios Anagnostopoulos, University of Passau
nikolaos.anagnostopoulos@uni-passau.de

Secretary: Mr. Victor Shatov, Friedrich-Alexander-Universität Erlangen-Nürnberg
victor.shatov@fau.de

 

Upcoming events

  • Future technical meetings of the German Chapter of the IEEE Computational Intelligence Society will be announced here.
 

  • Webinar: Learning from imbalanced data using improved data pre-processing procedures and machine learning techniques

     

    Date and Time

    • Date: 8 December 2025
    • Time: 05:00 PM to 06:30 PM
    • All times are (UTC+01:00) Berlin
    •  
     

    Location

    • Virtual attendance.
     

    Hosts

     

    Registration

     

    Speaker

    • Dr. Mario Dudjak

    Description

    The challenge of learning from imbalanced data is well recognised, as standard classification algorithms are often found to struggle with minority instances, which may represent rare but critical events. Improvements have been developed in both data pre-processing and radial basis function networks. Feature selection has been enhanced by storing and combining multiple candidate solutions, reducing overfitting and more reliably identifying relevant features. Oversampling has been refined to determine neighbourhoods and the number of synthetic instances automatically, simplifying its use. RBFN classifiers have been designed incrementally, with knowledge from simpler networks guiding the construction of more complex ones. These approaches have been shown to improve minority class recognition and overall performance, providing practical methods for tackling imbalanced data in real-world problems.
     

    Speaker Biography


    Dr. Mario Dudjak is a postdoctoral researcher at the University of J. J. Strossmayer in Osijek, specialising in machine learning and software engineering. He earned his BSc, MSc and PhD in Computer Science from the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. During his studies, he participated in competitions such as IEEE Extreme and IEEE MADC and received several academic awards for outstanding achievement.

    Mario has worked on numerous research and professional projects involving spatial data processing, high performance computing, optimisation and computer vision, contributing as an expert in machine learning and data science. He has published dozens of papers in scientific journals and international conferences, including a notable first author publication in Expert Systems with Applications (IF 8.665). He also teaches several courses in computer science and machine learning and has supervised and mentored many student thesis projects at both undergraduate and graduate levels.

    He is an active contributor to the research community, serving as a permanent reviewer for several respected Elsevier journals, including Machine Learning with Applications, Expert Systems with Applications and Applied Soft Computing, and as a reviewer for many international conferences. He has also served on program committees for the SST 2024 and CECNet 2025 conferences.

    Alongside his academic work, Mario is an external research and development engineer at Random Red Ltd., where he combines practical software development with advanced machine learning solutions. At his Faculty, he serves as President of the Lifelong Learning Committee. He is a member of the AMA FERIT association, formerly of IEEE, and holds several professional certifications, including Microsoft Data Science and Microsoft Azure Data Scientist Associate. Mario speaks Croatian, English and Slovak.

Recent past events

  • List of past technical meetings of the German Chapter of the IEEE Computational Intelligence Society.
 

  • CALL FOR PAPERS
    Workshop on Artificial Intelligence in Healthcare (AIH 2025)
    Berlin, Germany | 7 September 2025

    To be held in conjunction with the 2025 IEEE 14th International Conference on Consumer Electronics – Berlin (ICCE-Berlin) | 6-8 September 2025

     

    Held at:

    • Hotel Estrel, Berlin
    • Sonnenallee 225
    • 12057 Berlin
    • Germany

     

    Description:

    The rapid advancement of Artificial Intelligence (AI) technologies presents transformative opportunities for healthcare. From predictive analytics and virtual twins to generative AI and deep learning, the integration of AI into healthcare promises significant improvements in diagnostics, treatment personalization, clinical decision-making, patient care, healthcare management, drug discovery, telemedicine, and remote patient monitoring. Additionally, AI-driven methodologies enhance medical research, patient safety, healthcare efficiency, and resource optimization across clinical settings. This workshop aims to bring together researchers, clinicians, healthcare practitioners, data scientists, biomedical engineers, industry experts, and policymakers to discuss state-of-the-art developments, practical implementations, ethical implications, current challenges, and future directions of AI applications in healthcare.

    We invite submissions of original, unpublished papers addressing topics including, but not limited to:

    • Predictive Analytics and Decision Support
      • Clinical decision-making and AI-driven support systems
      • Predictive modeling for disease progression and early intervention
      • Risk stratification, prognosis, and personalized patient management
      • AI for precision medicine and treatment optimization
    • Virtual Twins Models in Healthcare
      • Development and deployment of virtual twins in healthcare
      • Personalized medicine through virtual twin technology
      • Virtual twin modeling for chronic disease management (e.g., diabetes, cardiovascular conditions)
    • Generative AI in Healthcare
      • Synthetic data generation for clinical research and clinical trials
      • Generative models for medical image synthesis, enhancement, and reconstruction
      • Natural Language Processing (NLP) for clinical documentation, medical literature summarization, and chatbot interactions
      • Generative AI for drug discovery and molecular design
    • Deep Learning Applications
      • Medical imaging analysis (radiology, pathology, dermatology, ophthalmology, etc.)
      • Time-series analysis and forecasting for health monitoring and disease management
      • Biomarker discovery and multi-omics data integration using deep learning
      • Real-time diagnostic systems and anomaly detection
    • Explainable AI (XAI) in Healthcare
      • Methods for interpretability and transparency of AI models
      • Ethical implications and trustworthiness of black-box models
      • Regulatory considerations and compliance (FDA, EMA, GDPR guidelines)
      • Human-in-the-loop approaches to enhance clinical acceptance
    • Federated and Privacy-preserving AI
      • Federated learning for multi-institutional collaborations and data privacy
      • Privacy-preserving AI for secure health data sharing and collaboration
      • Differential privacy and homomorphic encryption in healthcare applications
      • Secure multi-party computation and distributed analytics
    • AI-driven Healthcare Operations and Management
      • Resource allocation, staffing, and operational optimization
      • AI applications for hospital and healthcare system management
      • Patient flow modeling, scheduling optimization, and capacity planning
      • Real-time monitoring and healthcare infrastructure management
    • Ethics, Fairness, and Policy
      • Ethical challenges and potential solutions for AI deployment in healthcare
      • Bias detection, fairness evaluation, and equitable AI-driven healthcare
      • Policy frameworks, regulations, and governance models for AI integration
      • Societal impacts, patient rights, and ethical responsibilities
    • Healthcare Robotics and Autonomous Systems
      • AI-driven robotic surgery and intervention
      • Autonomous patient monitoring and care assistance systems
      • Robotics for rehabilitation, assisted living, and elderly care
      • Human-robot collaboration in clinical settings
    • Telemedicine and Remote Patient Monitoring
      • AI-powered telemedicine applications and virtual consultations
      • Remote patient monitoring technologies and predictive health analytics
      • Mobile health applications and wearable device integration
      • AI-enhanced patient engagement and remote care
    • AI and Public Health
      • AI-driven epidemic forecasting, surveillance, and outbreak management
      • Population health analytics and interventions
      • Health informatics, epidemiological modeling, and health policy decision-making
      • AI applications in health education, promotion, and preventive medicine
     

    Important Dates:

    • Paper Submission Deadline: 03 August 2025
    • Notification of Acceptance: 17 August 2025
    • Camera-Ready Submission: 24 August 2025
    • Workshop Date: 7 September 2025
     

    Submission Guidelines:

     

    Please submit your paper electronically in the PDF format using the following submission link: https://edas.info/newPaper.php?c=33570&track=132820.

    Authors are invited to submit original, unpublished manuscripts of 2- to 6-page length, including figures and references, with a 200-word abstract, written in English, using the 10-point font, double-column, single-space IEEE conference paper format. Previously published papers or papers under review for other conferences/journals should not be submitted for consideration. All papers must be submitted through the EDAS Conference and Journal Management System website. No more than two (2) additional printed pages (10-point font) over the 6-page limit may be included in your submission, and the extra pages will incur an over-length page charge of US$100 per page, if accepted for publication.

    For the final submission, please verify your paper with IEEE PDF eXpress® (conference ID: 67488X). Only IEEE-compliant papers that have been accepted, registered, and presented (including 2-page, regular, special session, and research forum papers) will be published in the ICCE Berlin 2025 Proceedings and submitted to IEEE Xplore for indexing. A selected set of papers of the ICCE Berlin 2025 program will be invited for re-submission to special issues of peer-reviewed journals (IEEE CE Magazine, IEEE Transactions on Consumer Electronics) based on the reviewers’ feedback and quality of conference presentation.

    We look forward to welcoming you to AIH 2025 and exploring the exciting intersection of AI and healthcare.

     

    Workshop Chair:

    • Dr. sc. Alexandros Tanzanakis, Friedrich-Alexander-Universität Erlangen-Nürnberg
     

    Publication Chairs:

    • Prof. Dr. Tolga Arul, Universität Passau
    • Dr.-Ing. Nikolaos Athanasios Anagnostopoulos, Universität Passau
     

    More information:

    More information can be found at the dedicated page!

     

  • Annual IEEE CIS Germany Chapter meeting to take place in September during the ICCE-Berlin 2025, in Berlin

    https://events.vtools.ieee.org/m/491635

    Date and Time

    • Date: 07 September 2025
    • Time: 18:15-19:00 PM
    • All times are (UTC+02:00) Berlin
     

    Location

    • Room C3+C4
    • Hotel Estrel, Berlin
    • Sonnenallee 225
    • 12057 Berlin
    • Germany

     

    Host

    • Germany Section Chapter, CIS11
     

    Registration