This Tutorial is designed to introduce and deepen the understanding of dynamic data retrieval techniques for literature
reviews. It will cover the usage of Python programming to automate and streamline the process of extracting relevant
information from various academic and research publication databases specifically Google Scholar and Pubmed. Participants
will engage in hands-on sessions, and interactive discussions to apply the learned skills in real-world scenarios.
Understand the importance of literature reviews in academic and research contexts.
Learn how to automate the process of data retrieval from multiple publication databases using Python.
Explore Python libraries and tools that are useful for parsing, analyzing, and organizing retrieved data.
Develop skills to effectively synthesize and present findings from a comprehensive literature review.
The tutorial is ideal for researchers, academic professionals, students, and anyone involved in conducting literature reviews and research with
an ample knowledge in Python. It is particularly beneficial for those who wish to enhance their research efficiency and
accuracy through the use of automated tools.
Dr Janaka Wijekoon
VIT, Australia and Keio , Japan
Ms. Rashini Liyanarachchi
# Tutorial 2
Title : Machine Learning for Wireless Communications
Artificial intelligence (AI) and machine learning (ML) stand out as some of the most pivotal technologies today.
Notably, deep learning has spearheaded numerous breakthroughs across diverse domains, including computer vision,
natural language processing, and speech recognition. Therefore, it is a natural question to ask: what role will AI/ML
play in the domain of wireless communications? This tutorial tries to shed some light on this question by introducing
fundamental concepts of deep learning and illustrating their applications to wireless communications.
Introduction to ML basics
What is ML/AI?
Advanced Neural Network Architectures
Applications of deep learning for communications
Deep learning for detection
Deep learning for channel estimation
This tutorial mainly targets students, researchers, and practitioners who are new to the respective fields of study.