Since Jun 2020, I have been working with Truelancer as a Developer/Freelancer, where I have been repeatedly recognized for solving/freelancing projects/software,applications and solving a wide range of problems.
Since Sep 2020, I have been working with Blend Group of Companies as a Data Science Instructor and Developer, where i get to build myself as a team member and exposer of myself to the corporate world.
I am Currently Studying in Final Year of,
B.E in Computer Engineering from Advanced College of Engineering and Management, Kathmandu, since 2016.
Truelancer, formerly Elance-oDesk, is a global freelancing platform where businesses or individuals connect to conduct business.
What I preferred about Truelancer and Freelancing is, I get to work in variants of projects. Rather than learning a categorical types of projects, I get to learn and experience different kinds of projects in different languages and frameworks. Truelancer helped me to improve my communication skills and taught me how to showcase my skills to the client, it genuinely edified me on the side a client wants me in.
Great! place to learn.
Fiverr started in 2010, is an online marketplace where freelancers (sellers) from around the world offer all sorts of digital tasks and services to clients starting from $5 (1Gig).
This was a great platform for me to showcase what I am capable of as a computer engineer. Fiverr has contributed lots to amend my communication skills and it taught me how to represent my accommodations to clients, how would they love to see my services through the gigs feature provided by Fiverr.
Blend is a Group of companies started by a group of professionals with vision to excel in the field of Information Technology, Finance and Various Services, like Training, Software Development, Web Development, Consultancy, Placements etc. Blend is a company started by a group of professionals with vision to excel in the field of IT and IT Related Services, like Training, Software Development, Web Development, Consultancy, Placements etc. The group was started way back in 2007 handling major IT training program.
Blend helped me to build myself as a team member and exposer of myself to the corporate world. The only things I missed were teamwork and exposer to the corporate world. Blend was a good source for me to achieve these qualities.
Market Basket Analysis is an Computer Desktop Application that analyze customer buying pattern and provide analysis about the customers Buying behaviour and recommend better approach to the seller.
Read MoreImage Forgery Detection deals with detection COPY-MOVE forgery detection. This can be of great use for document validation checking since must of the documents are now a days used in degital format
Read MoreThis is a simple chat app developed utilizing Socket in python programming language. Tkinter is utilized as the U.I module in this chat app.
Read MoreThis Project endeavors to detect face and agnize persons according to the trained data and detect trained labels. This project has all the different methods that can be habituated to face recognization i.e Utilizing Tensorflow models and training according to required data and labels and the other one is utilizing OpenCV inbuilt face recognization model..
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Digital images are facile to manipulate and edit due to the availability of puissant image processing and editing software. Nowadays, it is possible to integrate or abstract paramount features from an image without leaving any conspicuous traces of tampering. As digital cameras and video cameras supersede their analog counterparts, the desideratum for authenticating digital images, validating their content, and detecting forgeries will only increase. Detection of maleficent manipulation with digital images (digital forgeries) is the topic of this paper. In particular, we fixate on detection of a special type of digital forgery – the facsimile-move attack in which a component of the image is replicated and pasted somewhere else in the image with the intent to cover a consequential image feature. In this paper, we investigate the quandary of detecting the facsimile-move forgery and describe an efficient and reliable detection method. The method may prosperously detect the forged part even when the replicated area is enhanced/retouched to merge it with the background and when the forged image is preserved in a lossy format, such as JPEG. The performance of the proposed method is demonstrated on several forged images.
The real project have in depth and adequate functionalism compared to the above synopsis.
