Computer Graphics App In C++

Last Updated on May 3, 2021

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This project was made in 3rd sem, I applied all concept (data structure, graphics , oopl) taught in 3rd sem .This project includes drawing structures and applying transformations on it.

Varieties of data structure , algorithm are used for drawing , selecting, transformations ,etc. 

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Government Fund Tracking System Using Blockchain

Last Updated on May 3, 2021

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The main idea behind the project is to track the funds hierarchically i.e from central government to the common man including in this chain. We have considered four hierarchical components which are: Central government, state government, Contractor, resource provider/dealer. In the beginning, the budgets which would get finalized in the house will be uploaded according to their respective category. After funds allocation state government will instigate the required projects by documenting them and will send the document to the central government. Now the Central government will verify the project details and if satisfied, they will grant the project funds to the state government else they can reject the project. After receiving funds from the central government, the state government will open the tenders for the contractor and by proper bidding system the contractor will be chosen for the specific project. As bidding and tender allocation will be carried out by an automation bidding system with no human intervention involved, it would reduce corruption. Government committee will check the amount of work done synchronously and will mark every progress by submitting a brief report to the hierarchical officer, who will add it to the blockchain. In this report the progress can be portrayed in the form of images, videos, written plan of the building or structure, etc. To get the payment the contractor will have to submit a form of his total spendings with proper distribution over the duration. This form details will then be checked by the respective authority of the state government and then will initiate the payment to the contractor. In this way doing work over a period gets paid, this process will repeat until a particular work is being done completely.

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Wafer Sensors Faulty Detection

Last Updated on May 3, 2021

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Project Description :

Detecting faulty sensors in wafers by using K means, Random forest, Decision tree algorithms


Problem Statement

To build a classification methodology to predict the quality of wafer sensors based on the given training data. 

Architecture

1.Data Description

2.Data validation

3.Data Insertion in Database

4.Model Training

5.Prediction Data Description

6.Data Validation

7.Data Insertion in Database

8.Prediction

9.Cloud Deployment

Data Description

The client will send data in multiple sets of files in batches at a given location. Data will contain Wafer names and 590 columns of different sensor values for each wafer. The last column will have the "Good/Bad" value for each wafer.

"Good/Bad" column will have two unique values +1 and -1. 

"+1" represents Bad wafer.

"-1" represents Good Wafer.

Apart from training files, we also require a "schema" file from the client, which contains all the relevant information about the training files such as:

Name of the files, Length of Date value in File Name, Length of Time value in File Name, Number of Columns, Name of the Columns, and their datatype.

Data Validation 

In this step, we perform different sets of validation on the given set of training files. 

a. Name Validation

b. Number of columns

c. Name of columns

d. Data type of columns

e. Null values in columns

If all the sets are as per requirement in schema file, we move such files to "Good_Data_Folder" else we move such files to "Bad_Data_Folder."

Data Insertion in Database

 1) Database Creation and connection -- Create a database with the given name passed.

2) Table creation in the database -- Table with name - "Good_Data", is created in the database for inserting the files in the "Good_Data_Folder" based on given column names and datatype in the schema file.

3) Insertion of files in the table -- All the files in the "Good_Data_Folder" are inserted in the above-created table. If any file has invalid data type in any of the columns, the file is not loaded in the table and is moved to "Bad_Data_Folder

Model Training

1) Data Export from Db - The data in a stored database is exported as a CSV file to be used for model training.

2) Data Preprocessing  

  a) Check for null values in the columns. If present, impute the null values using the KNN imputer.

  b) Check if any column has zero standard deviation, remove such columns as they don't give any information during model training.

3) Clustering --- KMeans algorithm is used to create clusters in the preprocessed data. The optimum number of clusters is selected by plotting the elbow plot, and for the dynamic selection of the number of clusters, we are using "KneeLocator" function. The idea behind clustering is to implement different algorithms

  To train data in different clusters. The Kmeans model is trained over preprocessed data and the model is saved for further use in prediction.

4) Model Selection --- After clusters are created, we find the best model for each cluster. We are using two algorithms, "Random Forest" and "XGBoost". For each cluster, both the algorithms are passed with the best parameters derived from GridSearch. We calculate the AUC scores for both models and select the model with the best score. Similarly, the model is selected for each cluster. All the models for every cluster are saved for use in prediction.

 Prediction Data Description


Client will send the data in multiple set of files in batches at a given location. Data will contain Wafer names and 590 columns of different sensor values for each wafer.

Apart from prediction files, we also require a "schema" file from client which contains all the relevant information about the training files such as:

Name of the files, Length of Date value in FileName, Length of Time value in FileName, Number of Columns, Name of the Columns and their datatype.


Then again we repeat steps 2,3

 Data Validation  

Data Insertion in Database 


Finally we go for


 Prediction

 

1) Data Export from Db - The data in the stored database is exported as a CSV file to be used for prediction.

2) Data Preprocessing   

  a) Check for null values in the columns. If present, impute the null values using the KNN imputer.

  b) Check if any column has zero standard deviation, remove such columns as we did in training.

3) Clustering - KMeans model created during training is loaded, and clusters for the preprocessed prediction data is predicted.

4) Prediction - Based on the cluster number, the respective model is loaded and is used to predict the data for that cluster.

5) Once the prediction is made for all the clusters, the predictions along with the Wafer names are saved in a CSV file at a given location and the location is returned to the client.



Deployment


We will be deploying the model to the Pivotal Cloud Foundry platform. Not only Pivotal but also we can use Heroku, AWS, Azure, GCP Platforms


Among the above all platforms Heroku is only a free open source platform for Deployment with unlimited storage.


Pivotal is a free open source before september 2020 ,Now it became a paid paltform


AWS, Azure, GCP these are all have free deployment source but with limited access


More Details: WAFER SENSORS FAULTY DETECTION

Scholastic

Last Updated on May 3, 2021

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Problem Statement

Develop tools that would increase Productivity for students and teachers. In the past 10-15 years we have seen the transition of things around us from offline to online, whether it's business, entertainment activities, daily needs, and now even education. Productivity tools have been a success with businesses and firms. Develop productivity tools for students and teachers in any domain of your choice that can achieve the same success in the educational field in the future.


Problem Solution

In this post - covid era, the education sector has erupted, with a plethora of new opportunities. Scholastic provides a complete and comprehensive education portal for students as well as staff.

  • The USP of the application are lab sessions simulated using Augmented Reality.
  • Other features include usage of virtual assistants like Alexa to provide reminders, complete timetable and file integration
  • A blockchain based digital report card system where teachers can upload report cards for students & send it to parents.
  • Plagiarism checker for assignments. It is a one - stop solution to all needs such as announcements and circulars from institution or a staff member, fee payment and even a chatbot for additional support.


Tech Stack

  • Google Assistant For Chatbot
  • Via the Actions Console
  • Python3 for Plagiarism Checker
  • Gensim
  • NumPy
  • NLP Models ( Word Embedding)
  • Heroku (For Deployment & making API Calls)
  • Android Studio with Java For Main Android App
  • AR Foundation For Simulated Lab Sessions with Blender & Unity
  • Ethereum, Solidity & React.js For Blockchain Based Storage for Report Cards (Along with Ganache & Truffle Suite)


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Personal Assistance

Last Updated on May 3, 2021

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PERSONAL COMPUTER ASSISTANT

This project work same as siri in iphone google assistance in android.

It will take input from user voice and will manage many things like youtube, google, stackoverflow, date, time

can play music and can shut-down your pc as well.

.


Necessary pip installation requires are


1. pip install pyttsx3

2. pip install speechRecognition

3. pip install wikipedia

4. pip install webbrowser

5. pip install pipwin

6. pip install PyAudio


What is it capable of doing ?


     Input format :

       This piece of code tak input from the voice of user


     Output :

     

     1. strongly assist and open Google,Youtube,Tell date, time, open stackoverflow,gmail and play music for user.


     2. This piece of code can also shut down your pc if you ask it 

     to shutdown and give premission of 'yes'.


Module installation instruction:


    1. Please install the modules written at the beggining of the 

    code in case it throw error.(in mine pc it's working smoothly).

     

    2. Use pip install module_name to install packages or modules.


NOTE:

  1. Please run this code on jupyter notebook or any offline text edidtor ie (VS Code, Pycharm, Atom, Sublime)
  2. Please install the module in case not installed using pip install modulename.
  3. Module list are written at the begging of the code

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Social Distance Monitoring System(Python, Deep Learning And Opencv)(Research Paper)

Last Updated on May 3, 2021

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Social distancing is one of the community mitigation measures that may be recommended during Covid-19 pandemics. Social distancing can reduce virus transmission by increasing physical distance or reducing frequency of congregation in socially dense community settings, such as ATM,Airport Or market place .

Covid-19 pandemics have demonstrated that we cannot expect to contain geographically the next influenza pandemic in the location it emerges, nor can we expect to prevent international spread of infection for more than a short period. Vaccines are not expected to be available during the early stage of the next pandemic (1), a Therefore, we came up with this system to limit the spread of COVID via ensuring social distancing among people. It will use cctv camera feed to identify social distancing violations

We are first going to apply object detection using a YOLOv3 model trained on a coco dataset that has 80 classes. YOLO uses darknet frameworks to process incoming feed frame by frame. It returns the detections with their IDs, centroids, corner coordinates and the confidences in the form of multidimensional ndarrays. We receive that information and remove the IDs that are not a “person”. We will draw bounding boxes to highlight the detections in frames. Then we use centroids to calculate the euclidean distance between people in pixels. Then we will check if the distance between two centroids is less than the configured value then the system will throw an alert with a beeping sound and will turn the bounding boxes of violators to red.

Research paper link: https://ieeexplore.ieee.org/document/9410745

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