Sanatkumar Dhobi

About Me


Hello, I am Sanatkumar Dhobi. Currently, I am doing my Master's Degree at the University of Windsor.

I am an Enthusiastic Software engineer who uses different trending technology to create the best software products. I am genuinely open to any opportunity where I can use my skillset to develop great software that make people’s lives easier.

Email
dhobi@uwindsor.ca
Address
Windsor
Canada - N9A2G7
Mobile
+1 226-975-7427

Education


Master's Of Applied Computing

University of Windsor, Windsor, Canada

CGPA

85.33/100

B.TECH INFORMATION TECHNOLOGY

BVM ENGINEERING COLLEGE,VVNAGAR,GUJARAT,INDIA

CPI

8.11

H.S.C(SCIENCE)

KNOWLEDGE HIGH SCHOOL,NADIAD,GUJARAT,INDIA

PERCENTAGE (PCM)

81%

S.S.C

ST.MARY'S HIGH SCHOOL,PETLAD,GUJARAT,INDIA

PERCENTAGE

91%

Skills And Experience


HTML/CSS ADVANCED
Javascript & php ADVANCED
C & java language ADVANCED
Bootstrap Intermediate
Python ADVANCED

Coding

IN 2017 CODEVITA CONTEST I AM CLEARED A FIRST ROUND WITH 552 RANK IN ALL INDIA.

ACHIEMENT

SECURED FIRST RANK IN SMART GUJARAT FOR NEW INDIA HACKATHON

Database

I HAVE KNOWLEDGE ABOUT THE SQL/MS SQL/MySQL/MongoDb DATABASE

Work Experience

I worked as a Systems Engineer at Tata Consultancy Services, where I worked on multiple client-based projects. [July 2019 – November 2021]

Projects


Panzer Clash AR Game

• Sketched three objects and devised scenes using the Unity tool and played role of scrum master in a team of seven students

• Generated a sprint report, conducted daily stand-up meetings to update scrum board and tested five different game features

Web Search Engine simulation

• Led a team of 4 students with varying skill levels in developing a web search engine. Prioritized and organized tasks using tools such as JIRA, allowing software to be completed by the semester deadline

• Implemented desired functionality in Java using the Trie data structure, was more efficient than the Hash Table. Regular expressions were used to code for Domain Extraction

Disease Detection in cotton Plant

• Collected around 10,000 original cotton plant images data by visiting farms with a drone or a mobile camera, then uploaded them to a server; gaussian blur and other smoothing techniques were used to clean up the dataset

• Classified four different diseases (Alternaria, Bacterial Blight, Cercosporin, and Anthracnose) by deploying Python to implement a neural network Rectifier function (ReLU), return to the end user notification of plant diseases

• Predicted diseases with 80% accuracy; send result as a notification