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Welcome to Jai's web resume (start with "help" if you are lost). Download the resume.
Counting his breath sinceHis full name is Jai Singhal. He completed his master's degree in Computer Science from Birla Institute of Technology and Science(BITS), Pilani. He is a developer and programmer with good insights in Data structure and algorithm.
Jai is helping in nature, he always helps others to get their things done in any of the possible ways, which makes him different from others. He considers himself as a constant learner in every field of life that allows him to adapt himself to new technologies in a short period of time.
Jai has expertise in languages like C/C++, Python, Javascript. Precisely he is a Django developer. He helped many of his customers in building their MVP's.
ME Computer Science - 8.22 CGPA
B.Tech Computer Science and Engineering - 71.5%
Class 12th(2015) - 90.2%
Class 10th(2013) - 9.0 CGPA
Abstract: Document verification is the first step whenever we enter any organization or institute. In any organization it is very much important to track, verify and check the background of the person who will become the part of the organization. This process is very time consuming and hectic for both the parties involved. Various governments provide cloud based digital locker service for the citizens storing the public document on a centralized server. But due to its centralized nature this type of service is weak against information breaches and Denial of Service attacks. Also there are some privacy concerns with such centralized digital locker services as the stored documents may contain users’ crucial personal information. In this paper, we propose a blockchain based digital-locker in the form of a decentralized application using Ethereum Blockchain to securely store the personal documents with high availability. The proposed solution also provides verification of documents with ease, confidentiality, access control, data privacy, authenticity and maintaining integrity of document.
Abstract: Removing non-uniform blur from an image is a challenging computer vision problem. Blur can be introduced in an image by various possible ways like camera shake, no proper focus, scene depth variation, etc. Each pixel can have a different level of blurriness, which needs to be removed at a pixel level. Deep Q-network was one of the first breakthroughs in the success of Deep Reinforcement Learning (DRL). However, the applications of DRL for image processing are still emerging. DRL allows the model to go straight from raw pixel input to action, so it can be extended to several image processing tasks such as removing blurriness from an image. In this paper, we have introduced the application of deep reinforcement learning with pixel-wise rewards in which each pixel belongs to a particular agent. The agents try to manipulate each pixel value by taking a sequence of appropriate action, so as to maximize the total rewards. The proposed method achieves competitive results in terms of state-of-the-art.
about, education, publications, projects, experience, skills, contact, download, theme, clear
Email: jaisinghal48@gmail.com
LinkedIn: https://www.linkedin.com/in/jai-singhal/
Github: https://github.com/jai-singhal
Google Scholar: https://scholar.google.com/citations?user=M7oajYQAAAAJ&hl=en
Quora:
https://www.quora.com/profile/Jai-Singhal
StackOverflow
https://stackoverflow.com/users/6899815/jai-singhal
Facebook:
https://www.facebook.com/jaisinghal48
Instagram: https://www.instagram.com/jai_si__/
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| Command | Description |
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| about | Read more about me |
| education | Read about my qualifications |
| projects | Read some of my amazing projects |
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| experience | See my past experience |
| skills | See me skills |
| contact | Find out ways to contact me |
| download | Download my resume |
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