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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-138222038-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-138222038-1');
</script>
<title>Bhavin Dhedhi</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<link href="https://fonts.googleapis.com/css?family=Poppins:300,400,700" rel="stylesheet">
<link rel="icon" href="images/bd-winb.png" title="Bhavin Dhedhi">
<link rel="stylesheet" href="css/open-iconic-bootstrap.min.css">
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</head>
<body onload="myfunc()">
<div class="KW_progressContainer">
<div class="KW_progressBar">
</div>
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<div class="page">
<nav id="colorlib-main-nav" role="navigation">
<a href="#" class="js-colorlib-nav-toggle colorlib-nav-toggle active"><i></i></a>
<div class="js-fullheight colorlib-table">
<div class="colorlib-table-cell js-fullheight">
<div class="row no-gutters">
<div class="col-md-12 text-center">
<!-- <h1 class="mb-4"><a href="index.html" class="logo">Bhavin Dhedhi</a></h1> -->
<ul>
<li><a href="index.html"><span><small>01</small>Home</span></a></li>
<li class="active"><a href="about.html"><span><small>02</small>Resume</span></a></li>
<!-- <li><a href="services.html"><span><small>03</small>Services</span></a></li>
<li><a href="portfolio.html"><span><small>03</small>Portfolio</span></a></li>
<li><a href="blog.html"><span><small>04</small>Blog</span></a></li>
<li><a href="contact.html"><span><small>06</small>Contact</span></a></li> -->
</ul>
</div>
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</nav>
<div id="colorlib-page">
<header>
<div class="container">
<div class="row">
<div class="col-md-12">
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<a class="colorlib-logo" href="index.html">
<span class="logo-img" style="background-image: url(images/bd-binw.png);"></span></a>
</div>
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</div>
</div>
</div>
</header>
<section class="ftco-section about-section">
<div class="container">
<div class="row d-flex justify-content-end mt-5">
<div class="col-md-12">
<div class="profile ftco-animate">
<h2>Profile</h2>
<a download href="images/CV Bhavin Dhedhi.pdf">Download PDF</a>
<div class="exp-wrap py-4">
<div class="desc_resume_name">
<p><strong>Name:</strong> <span>Bhavin Dhedhi</span></p>
<p><strong>Address:</strong> <span>C - 210, Shivram Apartment, Malad - West, Mumbai</span></p>
<ul class="ftco-footer-social list-unstyled mt-4">
<li><a href="https://www.linkedin.com/in/thisisbhavin/" target="_blank"><span class="icon-linkedin"></span></a></li>
<li><a href="https://github.com/thisisbhavin" target="_blank"><span class="icon-github"></span></a></li>
<li><a href="https://twitter.com/thisisbhavind" target="_blank"><span class="icon-twitter"></span></a></li>
<li><a href="https://www.facebook.com/thisisbhavin" target="_blank"><span class="icon-facebook"></span></a></li>
<li><a href="https://www.instagram.com/thisisbhavin/" target="_blank"><span class="icon-instagram"></span></a></li>
</ul>
</div>
</div>
</div>
<!-- <h4 class="mb-4">I'm a UI/UX Designer & Frontend Developer from London, UK. I aim to make a difference through my creative solution.</h4>
<p>Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.</p> -->
<div class="exp mt-5 ftco-animate">
<h2 class="mb-4">Experience</h2>
<!-- <a class="button" data-modal="modalthink"> -->
<div class="accordion">
<div class="exp-wrap py-4">
<div class="desc">
<h4>Associate <span> - Think Analytics</span> </h4>
<p class="location">Mumbai, IN</p>
</div>
<div class="year">
<p>August 2018 - Present</p>
</div>
</div>
</div>
<!-- </a> -->
<!-- <div id="modalthink" class="modal">
<div class="modal-content">
<div class="contact-form">
<a class="close">×</a>
<h4>Associate</h4>
<div class ="desc">
<p class="location">Think Analytics</p>
</div>
<div class="col-md-12">
<ul>
<li><p>Working on a time-series data of petroleum industry to detect various anomalies using different ML and statistical techniques such as SVC, Random Forest, PCA, Hotelling T-Square, Q-Statistic</p></li>
<li><p>R</p></li>
<li><p>C++</p></li>
<li><p>SQL</p></li>
<li><p>Tensorflow</p></li>
<li><p>Pandas</p></li>
<li><p>OpenCV</p></li>
<li><p>Keras</p></li>
<li><p>Leadership</p></li>
</ul>
</div>
</div>
</div>
</div> -->
<div class="panel">
<ul>
<li>
<p>Working on a time-series data of petroleum industry to detect various anomalies using different
ML and statistical techniques such as SVC, Random Forest, PCA, Hotelling T-Square, Q-Statistic
</p>
</li>
<li>
<p>Created a POC for a Deep Learning based solution to the problem of uncalled No-Ball in cricket,
We used YOLO - v3 to detect foot of the bowler and an Analytical algorithm to detect the landing
point of bowler's foot. </p>
</li>
<li>
<p>Responsible for analyzing sales data of top US drug manufacturers. Segmented prescribers using
various clustering techniques based on prescribing patterns of doctors to maximize revenue.</p>
</li>
<li>
<p>Built R - Shiny Applications that helped major pharma companies to better contract with
insurance providers. Using these applications companies could make decisions within minutes
which used to take weeks and save millions of dollars with the accuracy of predictions. </p>
</li>
<li>
<p>Built Credit score models with 85% accuracy currently serving 100,000 customers to get instant
loans within minutes. </p>
</li>
</ul>
</div>
<!-- <a class="button" data-modal="modalantenna"> -->
<div class="accordion">
<div class="exp-wrap py-4">
<div class="desc">
<h4>Researcher <span> - KJ Somaiya College of Engineering</span> </h4>
<p class="location">Mumbai - IN</p>
</div>
<div class="year">
<p>May - June 2016</p>
</div>
</div>
</div>
<!-- </a> -->
<!-- <div id="modalantenna" class="modal">
<div class="modal-content">
<div class="contact-form">
<span class="close">×</span>
<h2>Haters Gonna Hate</h2><br>
</div>
</div>
</div> -->
<div class="panel">
<ul>
<li>
<p>Designed wide-band microstrip patch antenna, (2 GHz to 10 GHz) using IE3D software.</p>
</li>
<li>
<p>Compared different antenna designs on the basis of different parameters (gain, radiation
pattern, beamwidth) and proposed two antenna designs with better characteristics.</p>
</li>
<li>
<p>Fabricated both proposed antenna design</p>
</li>
</ul>
<div class="block-3-resume d-md-flex ftco-animate" data-scrollax-parent="true">
<a class="image-resume d-flex justify-content-center align-items-center"
style="background-image: url('images/antenna_pentagon.jpg'); ">
</a>
<div class="text-resume">
<h2 class="heading-resume">Pentagon Microstrip</h2>
<ul>
<li>
<p>Bandwidth : 5.2578 GHz (1.39 GHz to 6.65 GHz)</p>
</li>
<li>
<p>VSWR: 1.68 (port 1,port 2 at 5.42 GHz)</p>
</li>
<li>
<p>Gain : 4.56 dB</p>
</li>
<li>
<p>Impedance: 82 - j10.7 ohm (at 5.42 GHz)</p>
</li>
<li>
<p>Radiation Efficiency: 60 %</p>
</li>
</ul>
</div>
</div>
<div class="block-3-resume d-md-flex ftco-animate" data-scrollax-parent="true">
<a class="image-resume order-2 d-flex justify-content-center align-items-center"
style="background-image: url('images/antenna_comb.jpg'); ">
</a>
<div class="text-resume order-1">
<h2 class="heading-resume">Comb Microstrip</h2>
<ul>
<li>
<p>Bandwidth : 3.02 GHz (3.65 GHz to 6.67 GHz)</p>
</li>
<li>
<p>VSWR: 1.33 (at 5.228 GHz, Port 1), 1.6 (at 5.228 GHz, Port 2)</p>
</li>
<li>
<p>Gain : 3.08 dB (at 5.228 GHz)</p>
</li>
<li>
<p>Impedance: 54.6241 + j13.9 ohm</p>
</li>
<li>
<p>Radiation Efficiency: 74.9 %</p>
</li>
</ul>
</div>
</div>
</div>
</div>
<div class="exp mt-5 ftco-animate">
<h2 class="mb-4">Research papers</h2>
<div class="accordion">
<div class="exp-wrap py-4">
<div class="desc">
<h4>Automatic license plate recognition using Deep Learning <span> - Springer</span> </h4>
<p class="location">Chennai, IN</p>
</div>
<div class="year">
<p>December 2018</p>
</div>
</div>
</div>
<div class="panel">
<p class="mb-4" style="size: 10px;"><a
href="https://link.springer.com/chapter/10.1007%2F978-981-13-3582-2_4" target="_blank">Read paper
here</a></p>
</h2>
<p><span style="font-weight:bold; font-style: italic">Abstract: </span>Automatic License Plate
Recognition (ALPR) has been a topic of research for many years now due to its real-life application
but hasn’t been any significant breakthrough due to limitations in image processing algorithms to
satisfy all the real-life scenarios such an illumination, moving cars, background etc. This paper
presents a robust and efficient ALPR system using a combination of the ‘You only Look Once’ (YOLO)
neural network architecture and standard Convolutional Neural Network (CNN). In total 3 stages of
YOLO and 1 stage of CNN has been used in the proposed system. The last stage of YOLO and CNN have
been specifically designed to perform detection (segmentation) and recognition of characters,
respectively. We have built our own dataset of 604 car images in natural settings with different
lighting conditions and viewing angles for the YOLO stages. In addition, a computer-generated
dataset of 42237 characters has been used to train CNN. The resulting system has been tested on 50
random test images not part of training or validation datasets. The validation accuracies of all 4
stages exceed 90% whereas, the overall final accuracy on 50 test images comes to 82% with some fault
tolerance. The use of deep learning instead of Image Processing also enabled to detect skewed
license plates. The accuracy of stages 1 and 2 of YOLO were 100% on both validation and test sets.
</p>
</div>
<div class="accordion">
<div class="exp-wrap py-4">
<div class="desc">
<h4>Detection of bird in the wild using Deep Learning methods <span> - IEEE</span> </h4>
<p class="location">Mangalore - IN</p>
</div>
<div class="year">
<p>October 2018</p>
</div>
</div>
</div>
<div class="panel">
<p><span style="font-weight:bold; font-style: italic">Abstract: </span>
Object detection and localization is one of the
prominent applications of the computer vision. The paper
presents comparative study of state of the art deep learning
methods - YOLOv2, YOLOv3 and Mask R-CNN, for detection of
birds in the wild. Detection of birds is an important problem
across multiple applications including the aviation safety, avian
protection and ecological science of migrant bird species. Deep
learning based methods are very pre-eminent at detecting and
localizing the birds in the image as it can tackle the conditions
wherein the birds shown are diverse in shapes and sizes and most
importantly the complex backgrounds they are in. We used the
training and testing dataset provided by the NCVPRIG (BROID)
conference which contained 325 and 275 images respectively. For
training, we used the pre-trained models on the VOC 2012 and
COCO dataset and trained them on the 325 images. We used F –
score as one of the performance metrics, and F-Scores were
0.8140, 0.8721, 0.8688 for the YOLOv2, YOLOv3 and Mask R-
CNN respectively. The results show that YOLOv3 out performs
YOLOv2 and is a marginal improvement over Mask R-CNN.</p>
</div>
</div>
<div class="exp mt-5 ftco-animate">
<h2 class="mb-4">Education</h2>
<div class="exp-wrap py-4">
<div class="desc">
<h4>Bachelor of Technology | ECE Engineering <span> - KJ Somaiya College of Engineering</span> </h4>
<p class="location">8.64 GPA</p>
<p class="location">Mumbai -IN</p>
</div>
<div class="year">
<p>2014 - 2018</p>
</div>
</div>
<div class="exp-wrap py-4">
<div class="desc">
<h4>High School<span> - TP Bhatia Junior College of Science</span> </h4>
<p class="location">85.85%</p>
<p class="location">Mumbai - IN</p>
</div>
<div class="year">
<p>2012 - 2014</p>
</div>
</div>
</div>
</div>
</div>
<div class="row mt-5 flex-column ftco-animate">
<div class="col-md-8">
<h2 class="mb-4">My Skills</h2>
</div>
<div class="col-md-12">
<ul>
<li>
<h5>Python</h5>
</li>
<li>
<h5>R</h5>
</li>
<li>
<h5>C++</h5>
</li>
<li>
<h5>SQL</h5>
</li>
<li>
<h5>Tensorflow</h5>
</li>
<li>
<h5>Pandas</h5>
</li>
<li>
<h5>OpenCV</h5>
</li>
<li>
<h5>Keras</h5>
</li>
<li>
<h5>Leadership</h5>
</li>
</ul>
</div>
<!-- Rating bar -->
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<div class="progress-wrap">
<h4>Python</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="75"
aria-valuemin="0" aria-valuemax="100" style="width:75%">
<span>75%</span>
</div>
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</div>
</div>
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<div class="progress-wrap">
<h4>jQuery</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="60"
aria-valuemin="0" aria-valuemax="100" style="width:60%">
<span>60%</span>
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<h4>HTML5</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100" style="width:85%">
<span>85%</span>
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<div class="progress-wrap">
<h4>CSS3</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="90"
aria-valuemin="0" aria-valuemax="100" style="width:90%">
<span>90%</span>
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<h4>WordPress</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="70"
aria-valuemin="0" aria-valuemax="100" style="width:70%">
<span>70%</span>
</div>
</div>
</div>
</div>
<div class="col-md-6 animate-box" data-animate-effect="fadeInRight">
<div class="progress-wrap">
<h4>SEO</h4>
<div class="progress">
<div class="progress-bar color-1" role="progressbar" aria-valuenow="80"
aria-valuemin="0" aria-valuemax="100" style="width:80%">
<span>80%</span>
</div>
</div>
</div>
</div> -->
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