The topics of interest include (but are not limited to)

Track 1

Artificial Intelligence (A.I.) for Health

  • Data Mining and Knowledge Discovery in Healthcare

  • Machine and Deep Learning approaches for Health Data

  • Explainable A.I. models for Health, Biology, and Medicine

  • Regression and Forecasting for Medical, and Biomedical Signals

  • Medical Signal and Image Processing and Techniques

  • Diagnosis and Therapy Support Systems

  • Applications of A.I. in Healthcare and Wellbeing Systems

  • Neural Networks in Medicine

  • A.I. for Healthcare Social Networks

  • Healthcare Devices and Circuits for Artificial Intelligence

Track 2

Energy Web & Informatics

  • Energy web

  • Smart grid technologies

  • Sustainable Energy

  • High-performance computing

  • Biomedical informatics

  • Grid computing

  • Translation research informatics

  • Imaging informatics

  • Health informatics

  • Chemo-informatics

Track 3

Machine Learning (ML) and Artificial Intelligence (A.I.) for the Internet of Things, 5G, and Beyond

  • Machine Learning for control, planning, and slicing off 5G networks.

  • Machine Learning for signal intelligence in IoT.

  • Experimental data collection campaigns to create Artificial Intelligence datasets for 5G.

  • Artificial Intelligence for wireless sensing operations using IoT waveforms.

  • Machine Learning for next-generation MIMO networking in 5G

  • Experimental evaluation of Artificial Intelligence techniques for IoT

  • Machine Learning for mm-Wave and Terahertz (THz) beyond 5G networking

  • Machine Learning for Open Radio Access Network (O-RAN) Beyond 5G control

  • Artificial Intelligence for reconfigurable reflecting surfaces for IoT

  • Artificial Intelligence for next-generation MIMO networking in IoT

  • Machine Learning-based channel modeling for 5G.

Track 4

Machine Learning empowered computer networks

  • Concepts, methodologies, solutions for modeling, managing, mining, and understanding computer networks

  • Algorithmic accountability and explainable algorithms for network services and applications

  • Ethical issues of data collection, storage, and exchange in network security and privacy

  • Machine-learning empowered the Internet of Things

  • Artificial Intelligence solutions to mobile and edge computing

  • Machine Learning algorithms to support green networking

  • Machine learning approaches to QoE monitoring of encrypted video traffic

  • Reinforcement learning enables autonomous computer networks operation

  • Artificial intelligent, human-centric, and human-driven computer networks

Track 5

Survivability Analysis of Wireless Networks with Performance Evaluation

  • Transmission Reliability Evaluation for Wireless Sensor Networks

  • Simulation tools for wireless network performance evaluation

  • Improving Wireless Sensor Network Survivability using Human-Inspired Deep Learning

  • Reliability and dependability of mobile and wireless communication networks

  • Reliable Machine-to-machine (M2M) communication for future networks

  • Energy-efficient real-time wireless networks

  • Safety, security, and privacy for network survivability

  • Availability Evaluation of Wireless Sensor Networks for Industrial Applications;

  • Reliability of Software Defined Wireless Sensor Network;

  • Fuzzy Inference System for Increasing of Survivability in Wireless Sensor Networks

  • Survivability techniques for IoT