.- PharML, Machine Learning for Pharma and Healthcare Applications. .- CORKI: A Correlation-driven Imputation Method for Partial Annotation Scenarios in Multi-Label Clinical Problems. .- Neuro-Symbolic Artificial Intelligence for Patient Monitoring. .- Direct One-to-all Lead Conversion on 12-Lead Electrocardiogram. .- Unveiling Driver Modules in Lung Cancer: A Clustering-Based Gene-Gene Interaction Network Analysis. .- Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation. .- Predicting Sepsis Onset with Deep Federated Learning. .- A Workflow for Creating Multimodal Machine Learning Models for Metastasis Predictions in Melanoma Patients. .- Molecular Fingerprints-based Machine Learning. .- Simplification, Compression, Efficiency and Frugality for Artificial intelligence. .- Neural Networks comprising Sequentially Semiseparable Matrices with one dimensional State Variable are Universal Approximators. .- TinyMetaFed: Efficient Federated Meta-Learning for TinyML. .- On The Potentials of Input Repetition in CNN Networks for Reducing Multiplications. .- The Quest of Finding the Antidote to Sparse Double Descent. .- Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data. .- Towards Comparable Knowledge Distillation in Semantic Image Segmentation. .- Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers. .- Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence (Position Paper). .- Leveraging low rank filters for efficient and knowledge-preserving lifelong learning. .- Learning when to observe: A frugal reinforcement learning framework for a high-cost world. .- Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making. .- Exploiting causal knowledge during CATE estimation using tree based metalearners. .- A Parameter-Free Bayesian Framework for Uplift Modeling - Application on Telecom Data. .- A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling. .- 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL) . .- Semi-Supervised Co-Teaching for Monitoring Parkinson's Disease Patients. .- Explainable Artificial Intelligence in Medical Diagnostics: Insights into Alzheimer's Disease. .- Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment. .- Multimodal Sensor Fusion for Daily Living Activities Recognition in Active Assisted Living for Older Adults. .- Modeling and Detecting Urinary Anomalies in Seniors from Data obtained by Unintrusive Sensors. .- Assessing Frailty Using Behavioral and Physical Health Data in Everyday Living Settings. .- Synthesizing Diabetic Foot Ulcer Images with Diffusion Model. .- Engaging Older Adults at Meal-time through AI-empowered Socially Assistive Robots. .- Investigating the Dynamics of Cardio-metabolic Comorbidities and their Interactions in Ageing Adults through Dynamic Bayesian Networks. .- Adapting to Change: Reliable Multimodal Learning Across Domains. .- Harnessing Error Patterns to Estimate Out-Of-Distribution Performance. .- HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues. .- CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification. .- EMG subspace alignment and visualization for cross-subject hand gesture classification. .- Adapting Classifiers To Changing Class Priors During Deployment. .- AI4M: AI for Manufacturing. .- Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance. .- Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains. .- Reinforcement Learning for Segmented Manufacturing. .- Automatic tool wear inspection by cascading sensor and image data.

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