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Band 13974

Brain Informatics 16th International Conference, BI 2023, Hoboken, NJ, USA, August 1–3, 2023, Proceedings

Aus der Reihe Lecture Notes in Artificial Intelligence

78,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.09.2023

Herausgeber

Feng Liu + weitere

Verlag

Springer

Seitenzahl

479

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

744 g

Auflage

1st edition 2023

Sprache

Englisch

ISBN

978-3-031-43074-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.09.2023

Herausgeber

Verlag

Springer

Seitenzahl

479

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

744 g

Auflage

1st edition 2023

Sprache

Englisch

ISBN

978-3-031-43074-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Brain Informatics
  • Produktbild: Brain Informatics
  • Cognitive and Computational Foundations of Brain Science:  Fusing Structural and Functional Connectivity using Disentangled VAE for Detecting MCI.- Modulation of Beta Power as a Function of Attachment Style and Feedback Valence.- Harnessing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry.- A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding.- Measuring Stimulus-Related Redundant and Synergistic Functional Connectivity with Single Cell Resolution in Auditory Cortex.- Fusing Simultaneously Acquired EEG and fMRI via Hierarchical Deep Transcoding.-  Investigations of Human Information Processing Systems:  Decoding Emotion Dimensions Arousal and Valence Elicited on EEG Responses to Videos and Images: A Comparative Evaluation.- Stabilize Sequential Data Representation via Attractor Module.- Investigating the Generative Dynamics of Energy-Based Neural Networks.- Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease.- Brain Big Data Analytics, Curation and Management: Effects of EEG Electrode Numbers on Deep Learning-Based Source Imaging.- Graph Diffusion Reconstruction Model for Addictive Brain-Network Computing.- MR Image Super-Resolution using Wavelet Diffusion for Predicting Alzheimer’s Disease.- Classification of Event-Related Potential Signals with a Variant of UNet Algorithm using a Large P300 Dataset.- Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research.- Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Comparison of Tree-based Machine Learning Algorithms for Survival Analysis.- Predicting Individual Differences from Brain Responses to Music: A Comparison of Functional Connectivity Measure.- Multiplex Temporal Networks for Rapid Mental Workload Classification.- Super-Resolution MRH Reconstruction for Mouse Models.- Bayesian Time Series Classifier for Decoding Simple Visual Stimuli from Intracranial Activity.- Variability of Non-parametric HRF in Interconnectedness and its Association in Deriving Resting State Network.- BrainSegNeT: A Lightweight Brain Tumor Segmentation Model based on U-Net and Progressive Neuron Expansion.- Improving Prediction Quality of Face Image Preference using Combinatorial Fusion Algorithm.- MMDF-ESI: Multi-Modal Deep Fusion of EEG and MEG for Brain Source Imaging.- Rejuvenating Classical Source Localization Methods with Spatial Graph Filters.- Prediction of Cannabis Addictive Patients with Graph Neural Networks.- Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks.- Latent Neural Source Recovery via Transcoding of Simultaneous EEG-fMRI.-  Informatics Paradigms for Brain and Mental Health Research:  Increasing the Power of Two-Sample T-Tests in Health Psychology using a Compositional Data Approach.- Estimating Dynamic Posttraumatic Stress Symptom Trajectories with Functional Data Analysis.- Comparison Between Explainable AI Algorithms for Alzheimer’s Disease Prediction Using EfficientNet Models.- Social and Non-social Reward Learning Contexts for Detection of Major Depressive Disorder using EEG: A Machine Learning Approach.- Transfer Learning-Assisted DementiaNet: A Four Layer Deep CNN for Accurate Alzheimer’s Disease Detection from MRI Images.- Multimodal Approaches for Alzheimer's Detection Using Patients' Speech and Transcript.-  Brain-Machine Intelligence and Brain-Inspired Computing.-  Exploiting Approximate Joint Diagonalization for Covariance Estimation in Imagined Speech Decoding.- Automatic Sleep-Wake Scoring with Optimally Selected EEG Channels from High-Density EEG.- EEG Source Imaging of Hand Movement-Related Areas: An Evaluation of the Reconstruction Accuracy with Optimized Channels.- Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer's and Parkinson's Diseases.- Roe: A Computational-Efficient Anti-Hallucination Fine-Tuning Technology for Large Language Model Inspired by Human Learning Process.-  The 5th International Workshop on Cognitive Neuroscience of Thinking  and Reasoning:  Brain Intervention Therapy Dilemma: Functional Recovery versus Identity.