I am Shivesh Narain

Name: Shivesh Narain

Profile: Industrial Automation Engineer

Email: narainshivv@gmail.com

Download My Resume.

Skills

Industrial Automation 90%
3D Modeling/printing 90%
Prototyping 90%
Python 90%
ML/DL/CV 90%
C++ 80%
About me

Hi, I’m Shivesh. I completed my Master’s in Mechatronics from the University of Limerick and currently work as a Research Assistant. I focus on industrial automation and related research projects, applying my academic and practical experience in the field.

1

Bachelor’s in Mechanical Engineering

Foundation in core engineering principles, design, and problem-solving.

2

Associate ML Engineer (CV / NLP / Cloud)

Transition into Machine learning with focus on computer vision, natural language processing, and cloud deployment.

3

Master’s in Mechatronics Engineering

Advanced studies integrating mechanical, electrical, and software systems for intelligent industrial automation.

4

Research Assistant - University of Limerick

Applied research in automation and intelligent systems, bridging academia and industry.

Projects

Below are my Projects and accomplishments.

Automated Plastic Sorting system.

Industral Automation

Object detection for recycling.

Industrial Automation & AI

Catheter Valve orienter system.

Industrial Automation & AI

MY Conferences

Check out my Conference presentations

Sustainability

Sorting methods for used CGM Applicators for Sustainable Waste Management.

In this paper, we looked into a few other ways to sort plastics either before or after shredding. We focused on three methods: using computer vision (CV), using near-infrared (NIR) sensors, and triboelectric separation. These methods give us alternative ways to sort four types of plastics.

MY Blogs

Below are my blogs.

Random Forest

Random Forest Algorithm

Random Forest is a powerful and one of the most used supervised machine learning algorithm that works on bagging approach. It can be used both for classification and regression problems.

Decision Tree

Decision Tree Algorithm

If the data we are dealing with is qualitative or categorical , then also decision trees are proved to be powerful. Decision Tree Classifiers make use of a series of calculated questions regarding the attributes