Payroll Management System

Last Updated on May 3, 2021

About

• Open Source Project for various organizations for Payroll Management


• Platform Independent works on Windows , Linux , IOS, Android .


• Can collaborate with technologies like ReactJs.


• Implementing Django Rest framework for creating Restful API’s



Starting URL : http://127.0.0.1:8000/

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Go Alexa Go

Last Updated on May 3, 2021

About

GO ALEXA GO was created with millennials in mind. Why drive and text when you can just text and have Alexa drive?

Inspiration

We were inspired by Millennials' dangerous texting and driving habits, so we developed a driving system to allow them to text and still drive at the same time.

What it does

Our HTC Vive virtual reality experience allows the user to issue commands to our taxi driver, Alexa, and explore Sponsorville.

How we built it

We built our HTC Vive VR experience in Unity using C# and our Amazon backend with node.js and the Alexa skillset. The Amazon Alexa is able to take a user's directional input voice command through Amazon's unique browser-based web services built with node.js, and notifies Unity of the user's input with a web API hosted on Microsoft Azure.

Challenges we ran into

The first challenge we ran to was division of work. Charlie became our Unity/C#/HTC-Vive programmer, Randy became our impromptu Scrum Master/Front-End Designer/3D-modeler, and Caleb and Colin worked on node.js/Azure-IoT/Amazon Web Services. After we had a better sense of everyone's skill-set and strengths, we were able to snowball each other consistently throughout the course of the hackathon. Regarding Unity and C#, we ran into rigidbody and trigger debugging issues early on. With Alexa, we had troubles getting the browser based web service to work with node.js/Azure but by the middle of the second day, we were able to create a working prototype.

Accomplishments that we're proud of

Getting an Amazon Alexa to take voice commands and convert them to directional output in a Unity VR environment.

What we learned

Make sure you go into a hackathon with your division of work ready between your teammates. Additionally, make sure you teammates actually have a solid background in coding the work that is handed to them. Get together with your teammates every few hours, AGILE style, and see what progress has been made and if anyone needs help. Make sure everyone on your team can at some point handle paperwork because there will be a good amount of it throughout the course of the hackathon from the gathering of your teammates, to the final 12 hours before showtime. There needs to be a HUGE sense of trust between you and your teammates. Without some form of solid workflow (we used 2-hour scrums), you can run into problems like people just going off and coding who knows what for 3-4 hours of your hackathon before you realize you have issues.

What's next for Go Alexa Go

We plan on buying our own private islands and moving there with our solid-gold rocket ships from the amount of sponsorship money we've made from our amazing SponsorVille sponsors at Spartahack 2017.

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Tic-Tac-Toe Game

Last Updated on May 3, 2021

About

This project Tic Tac Toe game against a simple artificial intelligence. An artificial intelligence (or AI) is a computer program that can intelligently respond to the player’s moves. This game doesn’t introduce any complicated new concepts. The artificial intelligence that plays Tic Tac Toe is really just a few lines of code.

Two people play Tic Tac Toe with paper and pencil. One player is X and the other player is O. Players take turns placing their X or O. If a player gets three of their marks on the board in a row, column or one of the two diagonals, they win. When the board fills up with neither player winning, the game ends in a draw.

This chapter doesn’t introduce many new programming concepts. It makes use of our existing programming knowledge to make an intelligent Tic Tac Toe player. The player makes their move by entering the number of the space they want to go. These numbers are in the same places as the number keys on your keyboard's keypad


First, you must figure out how to represent the board as data in a variable. On paper, the Tic Tac Toe board is drawn as a pair of horizontal lines and a pair of vertical lines, with either an X, O, or empty space in each of the nine spaces.

In the program, the Tic Tac Toe board is represented as a list of strings. Each string will represent one of the nine spaces on the board. To make it easier to remember which index in the list is for which space, they will mirror the numbers on a keyboard’s number keypad.

The strings will either be 'X' for the X player, 'O' for the O player, or a single space ' ' for a blank space.

So if a list with ten strings was stored in a variable named board, then board[7] would be the top-left space on the board. board[5] would be the center. board[4] would be the left side space, and so on. The program will ignore the string at index 0 in the list. The player will enter a number from 1 to 9 to tell the game which space they want to move on.


Creating a program that can play a game comes down to carefully considering all the possible situations the AI can be in and how it should respond in each of those situations. The Tic Tac Toe AI is simple because there are not many possible moves in Tic Tac Toe compared to a game like chess or checkers.

Our AI checks if any possible move can allow itself to win. Otherwise, it checks if it must block the player’s move. Then the AI simply chooses any available corner space, then the center space, then the side spaces. This is a simple algorithm for the computer to follow.

The key to implementing our AI is by making copies of the board data and simulating moves on the copy. That way, the AI code can see if a move results in a win or loss. Then the AI can make that move on the real board. This type of simulation is effective at predicting what is a good move or not.

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Real Time Object Detection Using Tensorflow

Last Updated on May 3, 2021

About

Object detection is a computer vision technique in which a software system can detect, locate, and trace the object from a given image or video. The special attribute about object detection is that it identifies the class of object (person, table, chair, etc.) and their location-specific coordinates in the given image. The location is pointed out by drawing a bounding box around the object. The bounding box may or may not accurately locate the position of the object. The ability to locate the object inside an image defines the performance of the algorithm used for detection. Face detection is one of the examples of object detection.

These object detection algorithms might be pre-trained or can be trained from scratch. In most use cases, we use pre-trained weights from pre-trained models and then fine-tune them as per our requirements and different use cases.

Generally, the object detection task is carried out in three steps:

  • Generates the small segments in the input as shown in the image below. As you can see the large set of bounding boxes are spanning the full image

  • Feature extraction is carried out for each segmented rectangular area to predict whether the rectangle contains a valid object.

  • Overlapping boxes are combined into a single bounding rectangle (Non-Maximum Suppression)

Tensorflow is an open-source library for numerical computation and large-scale machine learning that ease Google Brain TensorFlow, the process of acquiring data, training models, serving predictions, and refining future results.

  • Tensorflow bundles together Machine Learning and Deep Learning models and algorithms. 
  • It uses Python as a convenient front-end and runs it efficiently in optimized C++.
  • Tensorflow allows developers to create a graph of computations to perform. 
  • Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.

The TensorFlow Object Detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.

  • There are already pre-trained models in their framework which are referred to as Model Zoo. 
  • It includes a collection of pre-trained models trained on various datasets such as the 
  • COCO (Common Objects in Context) dataset, 
  • the KITTI dataset, 
  • and the Open Images Dataset.

As you may see below there are various models available so what is different in these models. These various models have different architecture and thus provide different accuracies but there is a trade-off between speed of execution and the accuracy in placing bounding boxes.

Tensorflow bundles together Machine Learning and Deep Learning models and algorithms. It uses Python as a convenient front-end and runs it efficiently in optimized C++.

Tensorflow allows developers to create a graph of computations to perform. Each node in the graph represents a mathematical operation and each connection represents data. Hence, instead of dealing with low-details like figuring out proper ways to hitch the output of one function to the input of another, the developer can focus on the overall logic of the application.

The deep learning artificial intelligence research team at Google, Google Brain, in the year 2015 developed TensorFlow for Google’s internal use. This Open-Source Software library is used by the research team to perform several important tasks.

TensorFlow is at present the most popular software library. There are several real-world applications of deep learning that makes TensorFlow popular. Being an Open-Source library for deep learning and machine learning, TensorFlow finds a role to play in text-based applications, image recognition, voice search, and many more. DeepFace, Facebook’s image recognition system, uses TensorFlow for image recognition. It is used by Apple’s Siri for voice recognition. Every Google app that you use has made good use of TensorFlow to make your experience better.

Here mAP (mean average precision) is the product of precision and recall on detecting bounding boxes. It’s a good combined measure for how sensitive the network is to objects of interest and how well it avoids false alarms. The higher the mAP score, the more accurate the network is but that comes at the cost of execution speed which we want to avoid here.

As my PC is a low-end machine with not much processing power, I am using the model ssd_mobilenet_v1_coco which is trained on COCO dataset. This model has decent mAP score and less execution time. Also, the COCO is a dataset of 300k images of 90 most commonly found objects so the model can recognise 90 objects.

This brings us to the end of this project where we learned how to use Tensorflow object detection API to detect objects in images 

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Foreign Direct Investment (Fdi)

Last Updated on May 3, 2021

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I have used "TABLEAU" as my data visualization tool in the project. I have taken the dataset of foreign direct investment and perform various operations to produce interactive dashboard with the help of the functions provided by the tableau. I think we should have some knowledge of a business intelligence tool like tableau , PowerBI because to build these amazing dashboard we can't use programming language . It will be very complex to draw such visualization with the help of programming languages. That's why I have taken this project so that I can improve my Knowledge in these BI tools. Here I have tried to understand the trend of investments through the years with the help of some unique and different graphs and charts. I have concluded some of the observations like the sectors which are getting the heigest investment and which sectors are getting very minimum investments as well as the growth levels of the different sectors. I have also visualized that how the magnitude of investment is changing across the years. I have also tried to scale the future trend of investment for the upcoming years. I can't explain my all observation here because it will go too lengthy I will mention my project link below But If I want to conclude my project in a line , what I have done is in this project I have covered all the aspects of investments over the years and produced a interactive and eye-catching dashboard.

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Student Staff Management System

Last Updated on May 3, 2021

About

This project was a minor project done by me in my B.Tech 3rd year which was submitted to my department in the same year only. The project was completely done using VB.Net as it's front end and MYSQl as its database for the purpose of data storing and management.

This was a small project which was solely prepared to focus on issues regarding performing basic operations swiftly on data of the staff and the students present in the university such as CRUD(create, retrieve, update, delete) operations on data which could be managed easily and was fast in terms of retrieval and provided to cause less hassle. The languages used in the project were as follows :

1) VB. Net : For front end purposes

2) MYSQL : For database purposes

The database prepared for the project was totally normalized up to 3-NF so that the data stored in the database could be optimized and stored in a effective manner. There are nearly 4-NF to create a relational schema for data storing in which 1-NF being the least optimized to 4-NF be the max. Here I tend to chose the 3-NF as because it could provide me with the max optimization and no data loss. The 4-NF instead optimizes the data base better than the 3-NF but could also provide with lossy data. Hence the optimal choice here was to go with the 3-NF and I chose the same option as I didn't wanted to lose any data in the process .

Anyways after designing the database I went forward with the designing of frontend and did it with the help of .Net in the process . Here I tend to keep the user interface as simple as possible so that a simple person could also use it regardless of the knowledge of computer systems . So I chose a very simple user interface which only focuses on the work in hand and doesn't carry any unnecessary details like designing ,coloring etc etc.

So after completing both these operations I then tried to link my data base with the program so that my front end could access the database running in the background and store and retrieve the data easily and in a efficient way. After linking those two my project was almost complete and was ready to be deployed.

So in short the in my total project I :- Successfully managed to create a centralized management system for the students and the staff of the university which helped to manage and store data more efficiently as compared to the previous model.

P.S : I don't currently have the project link to 2 of my projects. Sorry for that

Thank You

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