Advances in Neural Networks - Computational and Theoretical Issues

Advances in Neural Networks - Computational and Theoretical Issues

Simone Bassis / Anna Esposito / Francesco Carlo Morabito
星期一, 六月 1, 2015


This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and bio-inspired memristor-based networks.
Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive and context-aware Information Communication Technologies.


Part I: Introductory Chapter
Recent Advances of Neural Networks Models and Applications: An Introduction
Part II: Models
Simulink Implementation of Belief Propagation in Normal Factor Graphs
Time Series Analysis by Genetic Embedding and Neural Network Regression
Significance-Based Pruning for Reservoir’s Neurons in Echo State Networks
Online Selection of Functional Links for Nonlinear System Identification
A Continuous-Time Spiking Neural Network Paradigm
Online Spectral Clustering and the Neural Mechanisms of Concept Formation
Part III: Pattern Recognition
Machine Learning-Based Web Documents Categorization by Semantic Graphs
Web Spam Detection Using Transductive–Inductive Graph Neural Networks
Hubs and Communities Identification in Dynamical Financial Networks
Video-Based Access Control by Automatic License Plate Recognition
Part IV: Signal Processing
On the Use of Empirical Mode Decomposition (EMD) for Alzheimer’s Disease Diagnosis
Effects of Artifacts Rejection on EEG Complexity in Alzheimer’s Disease
Denoising Magnetotelluric Recordings Using Self-OrganizingMaps
Integration of Audio and Video Clues for Source Localization by a Robotic Head
A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data
Part V: Applications
Application of Bayesian Techniques to Behavior Analysis in Maritime Environments
Domestic Water and Natural Gas Demand Forecasting by Using Heterogeneous Data: A Preliminary Study
Radial Basis Function Interpolation for Referenceless Thermometry Enhancement
A Grid-Based Optimization Algorithm for Parameters Elicitation in WOWA Operators: An Application to Risk Assesment
An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks
Fuzzy Measures and Experts’ Opinion Elicitation: An Application to the FEEM Sustainable Composite Indicator
Algorithms Based on Computational Intelligence for Autonomous Physical Rehabilitation at Home
A Predictive Approach Based on Neural Network Models for Building Automation Systems
Part VI: Emotional Expressions and Daily Cognitive Functions
Effects of Narrative Identities and Attachment Style on the Individual’s Ability to Categorize Emotional Voices
Cogito Ergo Gusto: Explicit and Implicit Determinants of the First Tasting Behaviour
Coordination between Markers, Repairs and Hand Gestures in Political Interviews
Making Decisions under Uncertainty Emotions, Risk and Biases
Influence of Induced Mood on the Rating of Emotional Valence and Intensity of Facial Expressions
A Multimodal Approach for Parkinson Disease Analysis
Are Emotions Reliable Predictors of Future Behavior? The Case of Guilt and Other Post-action Emotions
NegativeMood Effects on Decision Making among Potential Pathological Gamblers and Healthy Individuals
Deep Learning Our Everyday Emotions: A Short Overview
Extracting Style and Emotion from Handwriting
Part VII: Memristor and Complex Dynamics in Bio-inspired Networks
On the Use of Quantum-inspired Optimization Techniques for Training Spiking Neural Networks: A New Method Proposed
Binary Synapse Circuitry for High Efficiency Learning Algorithm Using Generalized Boundary Condition Memristor Models
Analogic Realization of a Non-linear Network with Re-configurable Structure as Paradigm for Real Time Analysis of Complex Dynamics
A Memristive System Based on an Electrostatic Loudspeaker
Memristor Based Adaptive Coupling for Synchronization of Two Rössler Systems
Author Index