Ajay2521/Lottery-smartContract

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License
MIT License
Created almost 4 years ago
Updated almost 4 years ago

Ajay2521/Lottery-smartContract

Ajay2521

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In this lottery smart contract has been designed

Complete-C-Programming

LU-DataScience

LetsUpgrade data science essential program

Jupyter Notebook

Python

In this major python concept has been covered.

LetsUpgrade-Python-Essential

LetsUpgrade Python Essential Assignment

Jupyter Notebook

Facial-landmark-detection-using-Keras

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 5 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat. Facial keypoint detection is a regression problem.

VH-21-Maayon-13

Problem Statement : Solving the problem of traditional land registry system and making land registry system in blockchain in which the record will be digitalized and stored permanently for whenever needed Abstract : Traditional land registry system need lot of paper works and human interaction in which the data can be lost or mis-entred and there is lot of chance for the data to be changed by the unauthorised persons thus our proposed system will ensure all the records and transaction which is happening in the land registry system will be recorded and data will be stored in the blockchain network permanently once the data stored in the blockchain then there is no way to change tha data value thus it provide a huge support to tamper proof data. The records or transaction in the Blockchain network can be verified by the hash value which is generated at the every instance of time thus accountability of the data will be ensured. Only authorised user will able to make a transaction only if the user satifics the condition of the smart contract.

Face-Recognition-using-Siamese-Network

Deep learning algorithms fail to work well if we have only one training example. One-shot learning is a classification or object categorization task in which one or a few examples are used to classify many new examples. The principle behind one-shot learning is Humans learn new concepts with very little supervision. ### Problem in COnvNet - A small training set is really not enough to train a robust neural network for this task. The feature vectors trained do not contain important information that can be used for the recognition of future images. - Retraining the ConvNet every time the number of classes or dataset is increased is way too time and resource consuming. ### Solutions to one-shot learning #### 1. Siamese network for one-shot learning ![image-2.png](attachment:image-2.png) #### Siamese networks are based on a similarity function, which does not require extensive training #### Takes input two images—one being the actual image and other the candidate image—and outputs the similarity between the two. The two input images, if very similar, output = lower value The two input images, if not similar, output = Higher value degree of difference between the two images is compared with the threshold value(𝜏) (which is a hyperparameter), if degree of difference between 2 image < threshold -> output = same person if degree of difference between 2 image > threshold -> output = different person #### Both the images to be compared are passed through the same networks, called sister networks, having the same parameters and shared weights. images are passed through a sequence of convolutional, pooling, and fully connected layers, and end up with a feature vector of a fixed size, say, 128 denoted by f(x1) as an encoding of the input image x1.

Jupyter Notebook

AgriFoodSupply.sol

Agriculture Food Supply chain using smart contracts in ethereum blockchain

face_mask_detection_using_mobileNetv2

HTML

In this complete HTML tags and concepts will be covered from scratch.

Ajay2521

Config files for my GitHub profile.

LU-Linux

Linux administration

Tutorial-on-CPlusPlus-

Competitive-Programming

This contains the competitive coding from the famous sites like LeetCode, GeeksforGeeks, HackerRank and HackerEarth

Python-Projects

My python projects.

Shellscript

In this Shellscript programming language which is used in Unix / Linux has been explained.

JS-Assignment

LetsUpgrade JavaScript Assignment.

AgriFoodSupply

Game-Of-Life-In-Go-lang

Java-Essential-LU

Java essential course assignment from LetsUpgrade.

Student-Registration-System-using-Java-and-MySql

Student Registration System using Java and MySql

REDIS-HANDS-ON

Here you can able to get the hands on experience in REDIS.

Analog-to-digital-clock-using-opencv-python

The application will read the analog clock image from the specified image path and then the it process the image to convert the analog clock image data into the digital values and display the digital reading using tkinter

Jupyter Notebook

Emoji-using-python-gui

I have used the python tkinter module and python emojis module to list some emojis name and allows user to choice any one option from the above list. Then the function is used to create a secondary GUI windows which is used to print the selected emoji.

todo_list_with_django

I have create a simple todo list application with the django framework using python

facial-landmark-detection-using-MTCNN

Facial keypoints (also called facial landmarks) are the small blue-green dots shown on each of the faces in the image above - there are 15 keypoints marked in each image. They mark important areas of the face - the eyes, corners of the mouth, the nose, etc. Facial keypoints can be used in a variety of machine learning applications from face and emotion recognition to commercial applications like the image filters popularized by Snapchat.

RateLimiter-using-Sliding-Window

Create a rate limiter program using a sliding window which allows/blocks request based on timestamps. Program will receive sequence of timestamps via arguments. For each timestamp it has to output allowed/blocked based on the sliding window. Sliding window to have 2 configurations, R: maximum number of requests allowed in sliding window S: sliding window time period in seconds