SS01: Use Cases of Machine learning & Deep learning for IoT Applications


NAMES OF PROPOSERS:
1)Dr. Sandeep Kautish,
Dean-Academics LBEF Campus (In Collaboration with Asia Paci!c University Malaysia) Kathmandu Nepal
E-mail: dr.skautish@gmail.com

2)Dr. Pradeep N,
Associate Professor and PG Head (CSE)
Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India

E-mail: nmnpradeep@gmail.com

Description of the Special Session:

There is a growing number of internet-connected sensors around our daily life which are integrated into variety of mechanical, household and automobile devices i.e. cars, PDAs, trains and even civil constructions. As a result, huge amount of data is being generated by the use of sensors and eventually there is a high need of understanding different patterns of such data. Extraction of data and produce useful information is the biggest challenge that's starting to be met using the pa5ern-matching and comparing abilities of Machine Learning and Deep Learning. Industry and academic researchers are required to feed data of IoT sensors into machine-learning models. Further, the resulting information is used to perceive about how these sensors operate and how related products and services can be enhanced in terms of operations and customer experiences. This special session aims to provide a plaform for research directions and novel ideas pertaining to machine learning and deep learning methods for implementing IoT technology in businesses. Conceptual, technical and application-oriented contributions are pursued within the scope of this theme.

Recommended Topics:

Topics to be discussed in this special session include (but are not limited to) the following:
Real-time machine learning
Iterative machine learning
Multi-target learning
Automated video surveillance and tracking
Machine learning and/or Deep learning models tailored to sensor data
Data extraction from sensor networks
Data conversion and calibration issues
Meta-learning, e.g., learning to adjust the analysis pipeline
Interpretable models
Generating high-quality features from sensor data
Application in various areas like e-health, smart city, intelligent transportation system
Generating high-quality data sets
Data quality issues
Gesture and object recognition
Feature based Engineering with a focus on sensor data features

Important Instructions:

All papers that conform to submission guidelines will be peer reviewed and evaluated based on originality, technical and/or research content/depth, correctness, relevance to conference, contributions, and readability.