Profilbillede af dinesh4nitb
@dinesh4nitb
Flag for India Indore, India
Medlem siden 10. september 2017
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dinesh4nitb

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I'm Full Stack Developer with 7+ years of experience, I have built on 15+ Android Apps, 5+ iOS Apps and 5+ websites using PHP, MySQL, AngularJS. I worked on Map/GPS Apps like Uber, Video Photosharing App, Chatting App, Dating App, Fitness App, E-commerce App, Event Sharing App, School App, Job portal App and many apps. My key skills are: Android iOS Java PHP MySQL Firebase Mongo WordPress Angular IONIC CodeIgniter Unity game I have worked as Research Scholer with the machine learning over 2 years in National Institute of Technology ( NIT ) Bhopal, Madhya Pradesh, India where I developed 3+ Advanced Computing Machine Learning Algorithms that analyze the big data and gives the future prediction for classification and ensemble pruning using variants of extreme machine learning.
$13 USD/hr
1 bedømmelse
2.7
  • 100%Jobs udført
  • 100%Indenfor Budget
  • 100%Til Tiden
  • 25%Genansættelsesfrekvens

Portfolio

Nylige bedømmelser

  • billede af Amit27644 Android App ₹4000.00 INR

    “yes he did very good job. Thank you for your all time support”

Erfaring

Android Developer

Dec 2015

I'm Full Stack Developer, developed 10+ apps and 4+ websites using Android, Java, PHP, MySQL, AngularJS, MongoDB, NodeJS, Python, Matlab for ML Algorithms, C, C++ I want to choose the career as a free launcher and I want to do excellent work to my client with min range of price and want to see happy with our work. I have an excellent team for doing that jobs I hope, I'll achieve soon.

Uddannelse

M Tech

2014 - 2016 (2 years)

Kvalifikationer

CyberSecurity Expert (2014)

National Institute of Technology Bhopal Madhya Pradesh, India

I have awarded the certification of the cybersecurity expert from NIT Bhopal of the duration of 1 year of research in many security attacks and protection of attacks, Cryptography, Encryption, Decryption, Digital Signature and many security related things.

Publikationer

Gain Based VELM

It is gain based voting based extreme learning machine. In this, I have achieved the Higher accuracy and G - mean for classification problems and Ensemble Pruning by reducing the size of Ensemble

Verifikationer

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