Author Archive: Yugesh Verma

Android Ludo game project with Source code

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You can find a simple android ludo game developed in android studio. This is just sample project to illustrate canvas in android with threading for game development.

This is 2D game developed for a simple stat of game development in android studio. Please go through code and you can understand yourself. If you still face any issues then do post issues and I will try to solve it.

Ludo game

Ludo written in java, java is very comfortable language for game development but Unity game is perfect game engine to implement high level supported games like pubg, subway surfers, Free fire and more. Mostly used for develop video games, web plugin.

Android Ludo  Game Source Code

Here codes are not explained because spaces are not enough, and online visitors also don’t like the code explanation. They expect only source code which is original on ludo. The full coding is java and xml. If you have good knowledge java then you able to easily make money online. We post android article, projects, applications and more on company website. Get the android applications codes, ideas for that,  to download source code & working from Android Studio IDE.

 

Source code Download Link

 Apk File Download zip

 

GST Billing Project in Python Django with Source Code

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GST invoice is a bill or receipt of items sent or services that a seller or service provider offers to a customer. It specifically lists out the services/products, along with the total amount due. One can check a GST invoice to determine said product or service prices before CGST and SGST are levied on them.

What is a GST bill? As a GST registered dealer, you are required to provide GST Invoices, also known as bills to your clients. An invoice or a bill is a list of goods sent or services provided, along with the amount due for payment. You can create GST compliant invoices FREE of cost using ClearTax Billing Software.

Simplest GST Billing Project in Python Django .

Features :

  • Easily create invoices
  • Manage inventory
  • Keep books and track balances
  • Print Bill.
  • Automatic Tax Calculation.
  • Automatic Gst Calculation.

Installation Steps: 

  • cmd -  virtualenv -p python3 venv
  • cmd-  source venv/bin/activate
  • cmd-  pip install -r requirements.txt
  • cmd  - python manage.py migrate
  • cmd  -python manage.py runserver

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Beauty Parlour Management System using PHP and MySQL

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100 + PHP Projects with Source Code 

Beauty Parlour Management System is essentially a web-based application that's inbuilt Php with CSS, Ajax and js (javascript). And for the backend of the system, SQL server has been used (i.e. Database) in order that it'll be easy to retrieve later. Also, the most aim of the system is to assist the user to book the appointment within the salon for online.
The system is essentially for the users where they will book a meeting in salon/ salon with login. The users of the system include the customers where they can register initially with the minimum details and will be allowed to make an appointment. Moreover, the system has two panels i.e. Admin and User. The user can make a meeting within the parlour and therefore the admin of the system approves it. Besides, the user also can modify this scheme consistent with their requirements. The user can extract a zipper file containing the ASCII text file and may import into Sublime Text 3 for application development.
Besides, they will also choose the service which they're trying to urge within their specific date and time within the system. All these activities of creating appointments like choosing service also as date and time are going to be recorded within the database for all the events. This project integrates a login panel for a more secure system. Moreover, the system also provides contact details in order that the user has no difficulty in searching the parlour. Besides, the user can visit parlour at a selected date and time because the system already records the appointment made by them.

Brief overview of the technology:

HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++

  1. CSS : (Cascading Style Sheets) Create attractive Layout
  2. JavaScript: it is a programming language, commonly use with web browsers.
  3. Bootstrap: responsive designing.
  4. jQuery

Back end: PHP, MySQL

  1. PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software.
  2. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases.

Software Requirement(any one)

  1. WAMP Server
  2. XAMPP Server
  3. MAMP Server
  4. LAMP Server

Installation Steps

1. Download zip file and Unzip file on your local server.
2. Put this file inside "c:/wamp/www/" .
3. Database Configuration
Open phpmyadmin
Create Database named salon_management.sql.
Import database matrimony.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/Folder Name/"

User Login

  1. id-ram@gmail.com
  2. Password-ram12345

Staff Id Password

  1. id-dilu@gmail.com
  2. Password-dilu1234

Admin Id password

  1. id-admin
  2. password-admin

 

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Hospital Management System Java JSP Mysql

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This is a hospital management user interface for managing, monitoring and controlling the system in a Hospital. This application is developed in java, which mainly focuses on basic operations in a hospital like adding new patient information, and updating new information, assigning the doctor for patient. It features a familiar and well thought-out, an attractive online user interface, combined with strong searching Insertion and reporting capabilities. The Backend of the project is designed with Java, MySQL for database connectivity and front end using HTML, CSS, and Bootstrap. This was my Java mini-project for third semester UG.

Modules and their requirements :-

User Module :

  1. Login
  2. Registration
  3. View Menu
  4. Book Appointment.

Admin Module :

  1. Login
  2. View Menu
  4. View All User
  5. View All Doctors
  6. View All Appointment

Doctor Module :

  1. Login
  2. Registration
  3. View Appointment.
  4. Profile .

 

Software Requirements

Technology used:

  1. Front end – JSP
  2. Backend – MYSQL

Software:

  1.  IDE - Netbeans 8.2
  2.  Database support - MySQL 5.7
  3.  Operating system – Windows 8 and above
  4.  Server deployment - Glassfish server

Technology:

  • HTML is integrated in JSP. It provides a means to structure text based information in a document. It allows users to produce web pages that include text, graphics and
    hyperlinks.
  •  Javascript is a scripting language which supports the development of both client and server applications. It is preferred at client side to write programs that can be
    executed by a web browser within the context of a web page.
  •  CSS(Cascading Style Sheets) is a style sheet language used for describing the presentation of a document written in a markup language. Although most often used to set the visual style of web pages and user interfaces written in HTML and XHTML, the language can be applied to any XML document,
  •  SQL is the language used to manipulate relational databases. It is tied closely with the relational model. It is issued for the purpose of data definition and data
    manipulation.
  • Java Server pages is a simple yet powerful technology for creating and maintaining dynamic-content web pages. It is based on the Java programming language. It can be thought of as an extension to servlet because it provides more functionality than servlet A JSP page consists of HTML tags and JSP tags. The jsp pages are easier to maintain than servlet because we can separate designing and
    development.

We require a JDBC connection between the front end and back end components to write

to the database and fetch required data.

Download Link 

 

Covid-19 Hospital Management python django Project with Source Code

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Covid-19 Hospital, Patient Management, Beds Management, Python Django Project with Source Code . Covid-19 Hospital patient management , system will full and advanced features. Hospital Management System is a web application for the hospital which manages doctors and patients. In this project, we use Python Django  and SQLite database. A responsive web-app with aesthetic and accessible UI for managing COVID patients of a certain hospital built using Django and Bootstrap framework

Features: Covid-19 Hospital Management python django

 

  1. Clean aesthetic User Interface, which changes dynamically as per the status of patient changes
  2. In bed availability grid the red color indicates that bed is occupied else available
  3. It is one of the two pages available for public to view
  4. Here you can add patient for storing it to the db.
  5. Patient's relative contact details are also taken to check if the relative has contacted COVID virus
  6. Bed numbers which are available are only shown
  7. Information filled here, will make changes in dashboard dynamically
  8. Since there are relatively more COVID patients than any other viruses/diseases, a checklist for COVID symptoms only is present
  9. Here you can search patients wrt to name, bed no. doctor assigned and status
  10. You can also find update button to update the patient details.
  11. This is where the actual updates for individual patients are done
  12. Seat Management.
  13. Equipment Management.
  14. Oxygen Management.
  15. Doctor Management.
  16. Daily Patient Routine Checkup .
  17. Hospital capacity, including information on ICU capacity and available ventilators
  18. Staffing levels, including any shortages
  19. How many patients are coming into the hospital with confirmed or suspected COVID-19 cases
  20. Many other relevant details that public health officials need to properly coordinate COVID responses

Technical Specification:

  1. Frontend
  2. HTML5
  3. CSS3
  4. JQuery
  5. Bootstrap4
  6. Backend
  7. Django framework
  8. Database
  9. SQLite

Installation

  • Setup virtual environment
  • Exceute pip install -r requirements.txt.
  • run python manage.py runserver.

Download Link 

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Live Face Mask Detection Project in Machine Learning

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Face Mask Detection web applicaion built with Flask, Keras-TensorFlow, OpenCV. It can be used to detect face masks both in images and in real-time video.

The goal is to create a masks detection system, able to recognize face masks both in images, both in real-time video, drawing bounding box around faces. In order to do so, I finetuned MobilenetV2 pretrained on Imagenet, in conjunction with the OpenCV face detection algorithm: that allows me to turn a classifier model into an object detection system. Live Face Mask Detection Project in Machine Learning.

Technologies

  • Keras/Tensorflow
  • OpenCV
  • Flask
  • MobilenetV2

Installation:

You have to install the required packages, you can do it:

  • via pip pip install -r requirements.txt
  • or via conda conda env create -f environment.yml

Once you installed all the required packages you can type in the command line from the root folder:

python app.py

and click on the link that the you will see on the prompt.

Datasets

The dataset used for training the model is available here.

Loan Eligibility Prediction Python Machine Learning Project

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Loan Eligibility Prediction Python Machine Learning Project. Loan approval is a very important process for banking organizations. The system approved or reject the loan applications. Recovery of loans is a major contributing parameter in the financial statements of a bank. It is very difficult to predict the possibility of payment of loan by the customer. In recent years many researchers worked on loan approval prediction systems. Machine Learning (ML)techniques are very useful in predicting outcomes for large amount of data.

Key Features

  • Interface to predict loan application approval
  • data insights withhin Jupyter Notebook
  • Trained Model
  • multiple machine learning algorithms.

Technology :

  • Flask==1.1.1
  • html5lib==1.0.1
  • json5==0.8.5
  • jsonify==0.5
  • numpy==1.16.5
  • pandas==0.25.1
  • scikit-image==0.15.0
  • scikit-learn==0.21.3
  • scipy==1.3.1
  • gunicorn==19.9.0
  • requests==2.22.0

Loan Defaulter Prediction Machine Learning Projects

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Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

 

Goals/Outcome

  • Determining probability of user liability
  • Creating an interactive UI that will take users input and return an output
  • To determine if a neural network vs logistic regression is the better model for classification

Models Created

  • Logistic Regression
  • Random Forest Model
  • Deep Neural Network

About

Probability of Credit Card Default, Machine Learning

Technologies Used : -

  • beautifulsoup4==4.6.0
  • certifi==2018.4.16
  • chardet==3.0.4
  • click==6.7
  • Flask==1.0
  • gunicorn==19.8.0
  • idna==2.6
  • itsdangerous==0.24
  • Jinja2==2.10
  • MarkupSafe==1.0
  • numpy==1.14.3
  • pandas==0.22.0
  • python-dateutil==2.7.2
  • pytz==2018.4
  • requests==2.18.4
  • scikit-learn==0.19.1
  • scipy==1.0.1
  • six==1.11.0
  • SQLAlchemy==1.2.7
  • urllib3==1.22
  • Werkzeug==0.14.1

 

Used Car Price Prediction Using Machine Learning

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Car Price Prediction is a really an interesting machine learning problem as there are many factors that influence the price of a car in the second-hand market. In this competition, we will be looking at a dataset based on sale/purchase of cars where our end goal will be to predict the price of the car given its features to maximize the profit.

Datasets Link - Kaggle Data 

Technologies Used : -

  1. Python 3.7
  2. Pandas
  3. Numpy
  4. Flask

Running the web app

Locally

  • Install requirements
    pip install -r requirements.txt
  • Run flask web app
    python app.py

Skin cancer Detection using Machine learning

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Skin cancer Detection using Machine learning .The purpose of this project is to create a tool that considering the image of a mole, can calculate the probability that a mole can be malign.

Skin cancer is a common disease that affect a big amount of peoples. Some facts about skin cancer:

Every year there are more new cases of skin cancer than the combined incidence of cancers of the breast, prostate, lung and colon.

An estimated 87,110 new cases of invasive melanoma will be diagnosed in the U.S. in 2017.

The estimated 5-year survival rate for patients whose melanoma is detected early is about 98 percent in the U.S. The survival rate falls to 62 percent when the disease reaches the lymph nodes, and 18 percent when the disease metastasizes to distant organs.

Development process and Data

The idea of this project is to construct a CNN model that can predict the probability that a specific mole can be malign.

Data: Skin cancer Detection using Machine learning

To train this model I'm planning to use a set of images from the International Skin Imaging Collaboration:

Mellanoma Project ISIC https://isic-archive.com.

The specific datasets to use are:

ISICUDA-21: Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.

Benign: 23

Malign: 37

ISICUDA-11 Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and

benign lesions are included.

Benign: 398

Malign: 159

ISICMSK-21: Benign and malignant skin lesions. Biopsy-confirmed melanocytic and non-melanocytic lesions.

Benign: 1167 (Not used)

Malign: 352

ISICMSK-12: Both malignant and benign melanocytic and non-melanocytic lesions. Almost all images confirmed by histopathology. Images not taken with modern digital cameras.

Benign: 339

Malign: 77

ISICMSK-11: Moles and melanomas. Biopsy-confirmed melanocytic lesions, both malignant and benign.

Benign: 448 Malign: 224

As summary the total images to use are:

Benign ImagesMalign Images
1208849

Some sample images are shown below: 1. Sample images of benign moles:

Sample images of malign moles:

Preprocessing:

The following preprocessing tasks are going to be developed for each image: 1. Visual inspection to detect images with low quality or not representative 2. Image resizing: Transform images to 128x128x3 3. Crop images: Automatic or manual Crop 4. Other to define later in order to improve model quality

CNN Model:

The idea is to develop a simple CNN model from scratch, and evaluate the performance to set a baseline. The following steps to improve the model are: 1. Data augmentation: Rotations, noising, scaling to avoid overfitting 2. Transferred Learning: Using a pre-trained network construct some additional layer at the end to fine tuning our model. (VGG-16, or other) 3. Others to define.

Model Evaluation:

To evaluate the different models we will use ROC Curves and AUC score. To choose the correct model we will evaluate the precision and accuracy to set the threshold level that represent a good tradeoff between TPR and FPR.

python 3.6.8