NIT Machine learning, Deep learning and Computational Intelligence for wireless communication

National Institute of Technology Tiruchirappalli Machine learning, Deep learning and Computational Intelligence for wireless communication

Objective of the workshop

Due to the feasibility of collecting huge data from mobile and wireless networks, there are many possibilities of using Machine learning, Deep-learning and the Computational Intelligence to interpret and to hunt knowledge from the collected data. The workshop aims in consolidating the experimental results integrating the Machine learning, Deep learning and Computational intelligence for wireless communication. The workshopfocus on the following applications.

Mobile data analysis, Mobility analysis, Network control and security, Wireless sensor networks, User localization, Mobile Network and Signal processing. Also those applications are implemented using one or more of the following ML, DL and Computational intelligence algorithms like the following.

Machine learning: Multiple input Multiple output regression, Probabilistic discriminative approach, Multi-class logistic regression, Probabilistic generative model, Support Vector Machine, Dimensionality reduction techniques. Deep learning: Multilayer perceptron, Boltzmann Machine, Auto-Encoders, Convolutional Neural Network, Recurrent Neural Network, Generative Adversarial Network, Deep Reinforcement Learning. Computational Intelligence: Particle Swarm Optimization, Bacterial Foraging, Simulated Annealing, Ant colony technique, Genetic algorithm, Social Emotional Optimization Algorithm (SEOA), Social Evolutionary Learning Algorithm (SELA).

The papers are expected in the following data driven Wireless Communication Applications (Not limited to)

Network prediction, Traffic classification, Call detail record mining.

Mobile health care, Mobile pattern recognition, Natural language processing, Automatic Speech Processing

Mobility analysis, Indoor localization

Wireless Sensor Networks (WSN)

Energy minimization, Routing, Scheduling, Resource allocation, Multiple access, Power control

Malware detection, Cyber security , Flooding attacks detection, Mobile apps sniffing

MIMO detection, Signal detection in MIMO-OFDM, Modulation recognition, Channel estimation, MIMO nonlinear equalization, Super -resolution channel and direction-of-arrival estimation, NOMA, mm-wave channel model,Full duplex,OFDM/FBMC,NB-IOT

Scope of the workshop: The workshop acts as the platform to disseminate the new discovery to reach the world through high quality publications for the research scholars. This also helps to see others contribution, which in further helps in finding new directions in their research.

Who should submit and present?

Beginner research scholars those who are doing research in Machine Learning, Deep learning and Computational intelligence for wireless communication

Post Graduate students who are doing project as the part of the curriculum.

Who should participate?
All UG, PG, research scholars and faculty who are interested in machine learning, deep learning, computational intelligence and their applications in wireless communications can register for this workshop as the participants.

Participants will be allowed to attend all the following sessions of the workshop.

Guest lectures
Invited talks
Paper presentation by the authors
Machine learning, Deep learning and Computational intelligence Tutorial on 23rd October 2020 .

About the Tutorial on 23rd October 2020

The tutorial consists of series of lectures on Machine learning, Deep learning and Computational intelligence. All UG, PG, research scholars and faculty who are interested in machine learning, deep learning, computational intelligence and their applications will be benefited by this tutorial. The lectures will be based on the book titled “Pattern Recognition and Computational Intelligence Techniques Using Matlab” 2019, Transactions on computational Science and intelligence, Springer publications authored by the Co-ordinator for the event MDCWC 2020. He is also the Guest speaker for the IEEE Training School on Machine Learning for Wireless Communication during 20 to 23rd September 2020.