The Fifth Elephant 2016

India's most renowned data science conference

The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.

Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.

We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.

Tracks

We are looking for application level and tool-centric talks and tutorials on the following topics:

  1. Deep Learning
  2. Text Mining
  3. Computer Vision
  4. Social Network Analysis
  5. Large-scale Machine Learning (ML)
  6. Internet of Things (IoT)
  7. Computational Biology
  8. ML in healthcare
  9. ML in education
  10. ML in energy and ecology
  11. ML in agriculrure
  12. Analytics for emerging markets
  13. ML in e-governance
  14. ML in smart cities
  15. ML in defense

The deadline for submitting proposals is 30th April 2016

Format

This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:

  • Full-length 40 minute talks.
  • Crisp 15-minute talks.
  • Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on Workshop sessions, 3 and 6 hour duration.

Selection process

Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.

We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.

We will notify you about the status of your proposal within three weeks of submission.

Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).

Commitment to open source

HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.

Key dates and deadlines

  • Revised paper submission deadline: 17 June 2016
  • Confirmed talks announcement (in batches): 13 June 2016
  • Schedule announcement: 30 June 2016
  • Conference dates: 28-29 July 2016

Venue

The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.

Contact

For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Amit Doshi

@amitdoshi

Sensor Analytics for IoT and Embedded Systems

Submitted Apr 30, 2016

Analytics-driven embedded systems are here!

We’ll show this in action by classifying human activity in real-time using sensor data from a smartphone accelerometer. The demo will show a complete workflow:
– pre-processing with digital filtering and frequency analysis,
– exploring different classification algorithms (such as decision trees, support vector machines, or neural networks), and
– automatically generating C/C++ from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.

The ability to create analytics that process massive amounts of business and engineering data is enabling designers in many industries to develop intelligent products and services. We’ll show you how to use analytics to describe and predict a system’s behavior, and further combine analytics with embedded control systems to automate actions and decisions.

Outline

MATLAB for analytics-driven embedded systems and an end-to-end workflow:
1. Data access from sensors and historical data
2. Pre-processing, including filtering and feature selection
3. Predictive modeling with machine learning
4. Integrating with enterprise IT and embedded systems
5. Scaling the solution with Big Data

Speaker bio

Amit Doshi, MathWorks India
Amit Doshi is an application engineer at MathWorks in the area of technical computing. He focuses on data acquisition, data presentation, statistical analysis, and parallel computing. Amit has over 9 years of experience in experimental test setup development, testing and validation, workflow automation, and system simulations. He previously worked at Suzlon Energy Limited in Pune and Germany, Texas Instruments in Germany, and IIT Bombay. Amit holds a bachelor’s degree in mechanical engineering and a master’s degree in mechatronics.

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more