10th Industry Symposium 2020
Call for Chapters on
Machine Learning – Theoretical Foundations and Practical Applications.
The 10th industry symposium will held during 09-12th January 2020 in conjunction with 16th edition of ICDCIT. Since its inception in 2011, the symposium means to provide a forum for researchers and practitioners from industry concerning the challenges, findings, encountered obstacles and lessons learned on recent global trends in technology. Keeping the symposium objective in mind this edition of Industry Symposium has picked of Machine Learning – Theoretical Foundations and Practical Applications.
A subset of Artificial Intelligence (AI), Machine Learning aims to provide computers the ability of independent learning without being explicitly programmed with ability to take intelligent decisions without human intervention. The stream of research is proceeding towards enabling machines to grow and improve with experiences referred to as learning by machines making them more intelligent. There are numerous advantages of Machine Learning like usefulness for large scale data processing, large scale deployments of machine learning is beneficial for improved speed and accuracy in processing, understanding of non-linearity in the data and generation of function mapping input to output as in supervised Learning providing recommendations for solving classification and regression problems, ensuring better customer profiling and understand of their needs and many more
In order to invigorate discussion on the topics, the symposium plans for presentations by selected participants during symposium. It also plans for bringing out a book (Like previous years, Springer will be approached for publication of the proposed book) covering the following topics but not limited to.
- Machine Learning and its applications
- statistical learning
- neural network learning
- knowledge acquisition and learning
- knowledge intensive learning
- machine learning and information retrieval
- machine learning for web navigation and mining
- learning through mobile data mining
- text and multimedia mining through machine learning
- distributed and parallel learning algorithms and applications
- feature extraction and classification
- theories and models for plausible reasoning
- computational learning theory
- cognitive modelling
- hybrid learning algorithms
Chapters from practitioners, technology developers as well as researchers are solicited on the above topics but not limited to as long as it is related to machine learning. The submitted chapters will go through reviews and the selected ones will be included in the book.
Authors are requested to send abstract and chapter to [email protected]
Chapter Abstract: July 8, 2019
(in 250-300 words, Author’s bio, affiliation and email address)
Full Chapter: September 8, 2019
Review result: November 18, 2019
Camera Ready: December 2, 2019