From February 17th to February 21st, 2025, the National and Kapodistrian University of Athens (NKUA) hosted two training activities, Schools 5 & 6, in Athens, Greece.
The fifth school, entitled “Artificial Intelligence/Machine Learning Offering Full Network Automation for Healthcare 4.0,” provided essential training on AI/ML tools and their applications for full 6G network automation in healthcare. Various techniques were discussed, targeting automatic network orchestration and the “AI everywhere” principle while ensuring high robustness. Special focus was given to Explainable AI (XAI) algorithms and their benefits in terms of interpretability, transparency, and trustworthiness, with real-life medical examples demonstrating XAI’s importance to Healthcare 4.0 stakeholders.
The sixth school, entitled “Security and Blockchain for Healthcare 4.0,” focused on different methods for secure information handling, trusted data sharing, and data management in Healthcare 4.0. Special emphasis was placed on ensuring secure and incentivized collaboration among various stakeholders within the healthcare ecosystem. To achieve this, blockchain techniques were analyzed.
Both schools, delivered by our academic and industrial partners alongside invited experts, provided valuable skills to professionals and researchers working towards the future of healthcare technology.
All presented material can be obtained here.
Machine Learning, Neural Networks and Hands-on
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Time |
Speaker |
Title |
Syllabus |
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1st Day: Monday 17th of February 2025 |
Suite5 Dr. Minas Pertselakis |
Machine Learning (ML): Common methods and techniques |
· Most commonly used algorithms, history, and evolution · Types of ML: Supervised, Unsupervised, Reinforcement Learning · Hyperparameter fine-tuning · Introduction to Python and relevant libraries (e.g., scikit-learn) · Interesting Use Cases from Projects |
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Suite5 Mr. Giorgos Raptis |
Deep Learning and Neural Networks |
· The basics of neural networks: layers, activation functions, backpropagation · Convolutional Neural Networks (CNNs) for image data · Advanced models · Introduction to relevant python libraries (e.g. tensorflow keras) |
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Suite5 Dr. Minas Pertselakis |
Hands-on with ML/DL in a Notebook Environment (1/2) |
· Guided exercises on a chosen ML task (e.g., classification, regression) · Data exploration, preprocessing, and feature engineering · Model training, testing and evaluation · Experimentation with different algorithms and hyperparameters (tuning) |
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Suite5 Mr. Giorgos Raptis |
Hands-on with ML/DL in a Notebook Environment (2/2) |
· Guided exercises on a chosen ML task (e.g., classification, regression) · Data exploration, preprocessing, and feature engineering · Model training, testing and evaluation · Experimentation with different algorithms and hyperparameters (tuning) |
Blockchain and Security Fundamentals
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Time |
Speaker |
Title |
Syllabus |
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2nd Day: Tuesday 18th of February 2025 |
NKUA Prof. Thanasis Papaioannou
Sponsoring Project: NGI TrustChain |
Blockchain Fundamentals (1/2) |
· Introduction to Blockchain Technology · Definition, key characteristics (decentralization, transparency, immutability). · Historical evolution (from Bitcoin to modern systems). · Components of a Blockchain · Nodes, transactions, blocks, consensus mechanisms. · Public vs. Private Blockchains · Differences, examples, and use cases. · Blockchain trilemma |
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NKUA Prof. Thanasis Papaioannou
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Blockchain Platforms and Ecosystems |
· Bitcoin ˗ Key features, scripting language. · Ethereum ˗ Smart contracts, ERC standards, Ethereum Virtual Machine (EVM). · Other Platforms ˗ Hyperledger Fabric, Solana, Cardano. · Blockchain Interoperability ˗ Cross-chain solutions and bridges. · Advanced operations · Sharding, state channels, oracles |
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University of Piraeus Mr. Aggelos Sideris
Sponsoring Project CHRISS |
Blockchain Fundamentals (2/2) |
· Introduction to Blockchain Technology · Definition, key characteristics (decentralization, transparency, immutability). · Historical evolution (from Bitcoin to modern systems). · Components of a Blockchain · Nodes, transactions, blocks, consensus mechanisms. · Public vs. Private Blockchains ˗ Differences, examples, and use cases. o Blockchain trilemma |
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University of Piraeus Mr. Anastasios Voudouris
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Cryptographic Foundations |
· Core Cryptographic Concepts ˗ Hashing ˗ Public and private key cryptography. ˗ Digital signatures. · Merkle Trees ˗ Structure, purpose in blockchains. ˗ Security Challenges |
Security and Blockchain for Healthcare 4.0
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Time |
Speaker |
Title |
Syllabus |
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3rd Day: Wednesday 19th of February 2025 |
NKUA Prof. Thanasis Papaioannou & Prof. Dionysis Xenakis |
Consensus Mechanisms |
· Proof of Work (PoW) ˗ Mechanism, energy concerns, and mining. · Proof of Stake (PoS) and Variants ˗ Staking mechanics, ˗ Delegated PoS, Practical Byzantine Fault Tolerance (PBFT). · Emerging Consensus Mechanisms ˗ Proof of Authority (PoA), Proof of Space and Time, etc. · Comparative Analysis ˗ Strengths, weaknesses, and use-case suitability. |
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NKUA Prof. Thanasis Papaioannou
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Smart Contracts (1/2) |
· What are Smart Contracts? ˗ Definition, characteristics, execution. · Smart Contract Development ˗ Tools: Solidity. ˗ Hands-on: Write and deploy a basic contract. · Common Vulnerabilities · Reentrancy, integer overflow, gas limit issues |
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University of Piraeus Mr. Aggelos Sideris
Sponsoring Project: CHRISS
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Smart Contracts (2/2) |
· What are Smart Contracts? ˗ Definition, characteristics, execution. · Smart Contract Development ˗ Tools: Solidity. ˗ Hands-on: Write and deploy a basic contract. · Common Vulnerabilities ˗ Reentrancy, integer overflow, gas limit issues |
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NKUA Prof. Thanasis Papaioannou & Prof. Dionysis Xenakis |
Blockchain Applications |
· Healthcare · Supply Chain · Decentralized Finance · Telecoms / Content Sharing · Other applications |
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University of Piraeus Mr. Anastasios Voudouris
Sponsoring Project: AIAS |
Blockchain and Research |
· State-of-the-Art Research Topics ˗ Privacy-preserving technologies (zk-SNARKs, zk-STARKs). ˗ Multiparty computation ˗ Game theory · Open Challenges · Opportunities for Innovation · Identifying gaps for academic contributions |
Graph ML and Reinforcement Learning
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Time |
Speaker |
Title |
Syllabus |
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4th Day: Thursday 20th of February 2025 |
Suite5 Mr. Panagiotis Chronopoulos |
Graph Deep Learning |
· The basics of Graph Machine Learning, how and when it can be applied. · Introduction to Graph Neural Networks · Introduction to relevant python libraries |
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Suite5 Mr. Panagiotis Chronopoulos |
Hands-on with Graph NN in a Notebook Environment (1/2) |
· Guided exercises on a chosen Graph NN task · Simulated environment creation · Model training, testing and evaluation |
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Suite5 Dr. Minas Pertselakis |
Reinforcement Learning and Optimization |
· The basics of RL, how and when it can be applied. · Introduction to relevant python libraries (e.g. stable_baselines3, gym) · Using Reinforcement Learning and Model optimisation in projects |
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Suite5 Dr. Minas Pertselakis |
Hands-on with RL in a Notebook Environment (2/2) |
· Guided exercises on a chosen RL/FL optimization task · Simulated environment creation · Model training, testing and evaluation |
Explainable AI and Applications to Healthcare
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Time |
Speaker |
Title |
Syllabus |
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5th Day: Friday 21st of February 2025 |
Suite5 Ms. Erifili Ichtiaroglou |
Methods and Techniques for Explainable AI |
· Explainability techniques: LIME, SHAP, feature importance · Introduction to relevant libraries. · The use of XAI in different projects · Open discussion on the future of XAI in Healthcare |
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Suite5 Ms. Erifili Ichtiaroglou |
Hands-on XAI in a Notebook Environment |
· Guided exercises on a chosen ML task (e.g., classification, regression) · Data exploration, preprocessing, and feature engineering · Model training, testing and evaluation · Experimentation with different algorithms and hyperparameters (tuning) |