Titlebook: Artificial Neural Networks in Pattern Recognition; 11th IAPR TC3 Worksh Ching Yee Suen,Adam Krzyzak,Nicola Nobile Conference proceedings 20

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Machine Learning for?Clinical Score Prediction from?Longitudinal Dataset: A?Case Study on?Parkinson’achine learning approach to predict the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson’s Progression Markers Initiative (PPMI) dataset, we examined the impact of da

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Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networksform traditional machine learning, especially for Brain-Computer Interface (BCI) applications. By processing also other recording modalities (e.g., electromyography, EMG) together with EEG signals, motor decoding improved. However, multi-modal algorithms for decoding hand movements are mainly applie

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VAeViT: Fusing Multi-views for Complete 3D Object Recognitionhallenges. To bridge this gap, we introduce VAeViT, a pioneering hybrid model that seamlessly integrates the strengths of Vision Transformers and Variational Autoencoders (VAE). VAeViT leverages VAE’s efficiency in feature representation to encode the 3D object views into a lower-dimensional latent

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Executive Women and Glass Ceiling in China,ly determined while estimating the parameters. Our suggested approach is utilized in medical settings, specifically to focus medication for individuals with heart disease based on clinical data and analyze breast tissue taking into account histological scans. When it comes to data with strictly boun