Topic Areas

Special Session on
Applications of Neural Networks in Smart Structural Health Monitoring

Structural Health Monitoring (SHM) has emerged as a viable tool for damage detection and preventive maintenance of engineering systems. The proposed session at the 17th International Symposium on Neural Networks (ISNN'2020, is meant to engage researchers in relevant engineering disciplines to share their proposed artificial neural networks in health monitoring applications.

We believe that ISNN'2020 is a suitable forum for presenting some new results in this field. Topics of the proposed session include but are not limited to

  • Network architecture
  • SHM system reliability
  • SHM and supervised system interaction
  • Aerospace applications
  • Cultural heritage applications
  • Engine health monitoring
  • SHM of bridges
  • SHM of high rise buildings


Maguid H.M. Hassan (
Dean of Engineering, Professor, Ph.D.
Faculty of Engineering
The British University in Egypt (BUE), Egypt

Papers are solicited for, but not limited to the following tracks:

  • Computational Neuroscience and Cognitive Science
  •   Computational Neural Models
      Spiking Neurons
      Visual and Auditory Cortex
      Neural Encoding and Decoding
      Plasticity and Adaptation
      Brain Imaging (fMRI, MEG, EEG)
      Learning and Memory
      Inference and Reasoning
      Knowledge Acquisition and Language
      Perception, Emotion and Development
      Action and Motor Control
      Attractor and Associative Memory
      Neurodynamics, Complex Systems, and Chaos

  • Models, Methods and Algorithms
  •   Stability and Convergence Analysis
      Neural Network Models (Feedforward/Recurrent/Self-organizing/Cellular/Hybrid Neural Networks)
      Supervised/Unsupervised/Reinforcement Learning/Deep Learning
      Statistical Learning Algorithms (PCA, ICA, Projection Pursuit Methods)
      Kernel Methods, Large Margin Methods and SVM
      Optimization Algorithms / Variational Methods
      Probabilistic and Information-Theoretic Methods
      Mixture Models, Graphical Models, Topic Models and Gaussian Processes
      Ensemble Learning, Committee Algorithms and Boosting
      Bayesian, Belief, Causal and Semantic Networks
      Model Selection and Structure Learning
      Feature Analysis and Clustering
      Sparsity and Feature Selection
      Pattern Analysis and Classification
      Matrix/Tensor Analysis and Factorization
      Temporal Models and Sequence Data
      Structured and Relational Data
      Embeddings and Manifold Learning
      Active Learning

  • Vision and Auditory Modelling
  •   Visual Perception and Modelling
      Visual Selective Attention
      Statistical and Pattern Recognition
      Visual Features Analysis
      Object Recognition
      Motion and Tracking
      Natural Scene Statistics
      Image Segmentation
      Image Coding and Representation
      Auditory Perception and Modeling
      Source Separation
      Speech Recognition and Speech Synthesis
      Speaker Identification
      Audio and Speech Retrieval
      Music Modeling and Analysis

  • Control, Robotics and Hardware
  •   Neuromorphic Hardware and Implementations
      Embedded Neural Networks
      Reconfigurable Systems
      Fuzzy Neural Networks
      Robotics: Neural Robotics, Cognitive Robotics, Developmental Robotics
      Multi-Agent Systems and Game Theory
      Reinforcement Learning
      Markov Decision Processes
      Planning and Decision Making
      Predictive State Representations
      Policy Search
      Action and Motor Control
      Visuomotor Control

  • Novel Approaches and Applications
  •   Brain-Like Systems, Adaptive Intelligent Systems
      Brain-Computer Interfaces
      Granular Computing
      Evolutionary Neural Networks
      Hybrid Intelligent Systems
      Bioinformatics and Biomedical Engineering
      Neuroinformatics and Neuroengineering
      Systems Biology
      Time Series Prediction
      Information Retrieval
      Data Mining and Knowledge Discovery
      Natural Language Processing