INAIT 2019 - 2019 Industry 4.0 and Artificial Intelligence Technologies

INAIT 2019 will offer oral, poster sessions, tutorials, and professional meetings. The program of the INAIT 2019 Conference intends to foster interaction so as to open the way to future cooperation between participants. All researchers and teams who develop research or recently became interested in the domains of Industry 4.0 and Artificial Intelligence Technologies. Submitted papers are expected to cover state-of-the-art technologies, theoretical concepts, standards, products implementation, ongoing research projects and innovative applications of Industry 4.0 and Artificial Intelligence Technologies.
INAIT 2019 Conference is a peer-reviewed conference which publishes articles with original research, extended version of previous papers, abstracts, review, commentary, opinion, rapid communication, case report etc. on all aspects of Industry 4.0 and Artificial Intelligence Technologies. Content areas include, but not limited to:
Topics:
Industry 4.0:
Advanced Diagnostics, Prognostics and Asset Health Management
Advanced Techniques and Technologies for Energy Saving
Big Data
Cloud Computing
Cybersecurity
Embedded Systems
Grid Computing
Internet of Things
Machine-To-Machine Communication Standards for Smart Manufacturing
Manufacturing Intelligence and Manufacturing Informatics
Radio Communications Technologies
Remote Access and Control
Remote Maintenance
Signal Processing
Simulations and Modelisations
Smart Applications
Smart Grid
Smart Manufacturing
Smart Security and Data Applications
Smart Systems
Survey Papers, Reports, Case Studies Work in Progress, and Applications
Trends and Challenges
Virtual Machines
Wearable Technologies
Wireless Sensor Network
Artificial Intelligence:
Hot topics, but not limited to:
AI-optimized Hardware
Biometrics
Deep Learning Platforms
Machine Learning
Natural Language Generation
Robotic Process Automation
Speech Recognition
Text Analytics and NLP
Virtual Agents
Others, but not limited to:
Clustering and Classification
Data Interpretation and Analysis
Evolutionary computations
Features Selection
Fuzzy approaches
Genetic algorithms
Graph-based Data Analysis
Information Retrieval
Knowledge Representation and Reasoning
Machine Learning
Multi-task Learning
Neural Networks
Pattern Recognition
Sequential Data Processing
Signal and Image Processing
Support Vector Machines
Survey Papers, Reports, Case Studies Work in Progress, and Applications
Visualization