Skip to content
Kshitij Sharma edited this page May 9, 2023 · 189 revisions

EasyWay! - Pace University Capstone Project


Project Description

View Project Description as PDF | Download Project Description as Word Document

Project Overview

The "EasyWay" web application is a comprehensive platform designed to aggregate various utility services, including beauty, electrical maintenance, home cleaning, pest control, and more. The primary objective of the application is to provide a convenient and hassle-free experience to the end-user, enabling them to book services, pay for them, and give feedback, all in one place.

Key Features

The key features of the EasyWay web application include:

  • Easy service selection: The end-user can select their preferred service from a list of available options. Convenient appointment booking: The application facilitates easy calendar and time slot booking, allowing the user to schedule services at a convenient time.
  • Seamless payment process: The end-user can pay for their services securely and conveniently.
  • Feedback mechanism: The application enables the end-user to give feedback on the services they have availed, thus ensuring quality control.
  • One-stop-shop: The application serves as a one-stop-shop, catering to all the utility needs of the end-user.

Team Members


Kshitij Sharma
Machine Learning Engineer & Software Developer
kshitij.sharma@pace.edu

Aditya Kadarla
Scrum Master/Project Manager
ak42336n@pace.edu

Vidisha Vijay Sawant
Software Developer & Cloud Engineer
vs10015n@pace.edu

Femina Maheshbhai Baldha
Frontend Developer/Designer
fb59536n@pace.edu

Shubham Pravin Sawant
Machine Learning Engineer & Software Developer
ss97349n@pace.edu

Ravi Kumar Dabbada
Database Administrator
rd83159n@pace.edu

Project Design

Architecture Overview

The EasyWay web application is built using a client-server architecture, with the front-end implemented in Angular JS, the backend in Node JS, and the server in GOLang. The database system used is MySQL.

Front-End Design

The front-end of the EasyWay web application is designed using Angular JS, a popular framework for building single-page applications. The front-end design includes the following components:

Node.js Automated Testing - Front-End Continuous Integration

  • User Interface: The user interface is designed to be intuitive and user-friendly, with clear and concise layouts and color schemes.
  • Navigation: The navigation system is designed to provide easy access to all the features of the application, with clearly labeled menus and icons.
  • Forms and Input Fields: The forms and input fields are designed to be easy to use, with clear instructions and error messages. Interactive Elements: The interactive elements, such as buttons and links, are designed to provide a responsive and smooth user experience.

Back-End Design

The back-end of the EasyWay web application is designed using GOLang, a popular framework for building scalable and performant applications. The back-end design includes the following components:

Go Automated Testing - Back-End Continuous Integration

GOLang
  • RESTful API: The back-end provides a RESTful API for the front-end to communicate with the server.
  • Database Access: The back-end interacts with the MySQL database system to store and retrieve data.
  • Server: The server component of the back-end is implemented in GOLang, a high-performance programming language designed for building scalable and efficient applications.

Object Detection using Deep Learning

The EasyWay web application has the ability to detect objects in images submitted by users using a state-of-the-art deep learning algorithm for object detection.

Algo - Algo Continuous Integration

How Object Detection using Deep Learning Works

Object detection using deep learning typically involves a convolutional neural network (CNN) trained on a large labeled dataset. During training, the network learns to identify object features such as edges and corners.

Once trained, the network can be used to detect objects in new images by predicting bounding boxes and class probabilities for each object. Anchor boxes can be used to increase efficiency by defining boxes of different sizes and shapes.

ObjectDetectionNetworkArchitecture

Implementation in EasyWay

EasyWay uses a neural network framework optimized for GPU computing to implement object detection using deep learning. The model is trained on a custom dataset of images relevant to the application's utility services and fine-tuned using transfer learning on a large-scale dataset of common objects.

Benefits of Object Detection using Deep Learning

Object detection using deep learning offers several benefits for the EasyWay web application, including:

  • Real-time performance: The algorithm is designed for real-time object detection, making it well-suited for the real-time nature of the EasyWay application.
  • High accuracy: Object detection using deep learning is one of the most accurate object detection algorithms available, with state-of-the-art performance on common object detection benchmarks.
  • Easy to use: The algorithm is easy to use and integrate into the EasyWay application, thanks to its well-documented implementation in the neural network framework.
  • Customizability: The algorithm can be easily fine-tuned on custom datasets to improve its accuracy on specific types of objects.

Deployment

The EasyWay web application is deployed on a cloud platform named Amazon Web Services (AWS). The front-end and back-end components can be deployed separately to ensure scalability and reliability.

Deployment Status - Front-End Continuous Deployment

Deployment

Languages and Tools

AWS  Atom  Angular  Cucumber  Cypress  Electron  Figma  FileZilla  Flask  GitHub  git  GO  Jira  Jupyter  Linux  Matlab  MySQL  NodeJS  NumPy  OpenCV  PyCharm  Postman  Python  Selenium  Slack  Tensorflow  TypeScript  VSCode  Yarn 


EasyWay Final Application Artifacts

Project Demo

Watch EasyWay Demo | Click here to download mp4 File
Watch MVP Prototype Of EasyWay | Click here to download mp4 File
Watch Cypress Integration Testing On EasyWay | Click here to download mp4 File
Watch Cucumber Integration Testing On EasyWay | Click here to download mp4 File

MVP Demo

Watch EasyWay MVP Demo | Click here to download mp4 File
Watch Integration Testing On MVP | Click here to download mp4 File

Application Manuals

Deployment Manual

View Deployment Manual | View Deployment Manual as PDF | Download Deployment Manual as Word Document

API Documentation

View API Documentation | View API Documentation as PDF | Download API Documentation as Word Document

EasyWay Technical Paper

View Technical Paper as PDF | Download Technical Paper as Word Document


CS691 - Fall 2022 Deliverables

Presentations (Sprint Reviews)

Sprint 1

Sept 07, 2022 - Sept 27, 2022

  1. Watch Deliverable 1 Presentation Video | Click here to download mp4 File
    1a. View Deliverable 1 Presentation Slides as PDF
    1b. Download Deliverable 1 Presentation Slides as PowerPoint

Sprint 2

Sept 28, 2022 - Oct 25, 2022

  1. Watch Deliverable 2 Presentation Video | Click here to download mp4 File
    2a. View Deliverable 2 Presentation Slides as PDF
    2b. Download Deliverable 2 Presentation Slides as PowerPoint
    2c. MVP Prototype on Figma (TIP - Use the ▶ on the right top to run the prototype app.) | Download MVP Prototype as FIG
    2d. Watch Demo | Click here to download mp4 File

Sprint 3

Oct 26, 2022 - Nov 15, 2022

  1. Watch Deliverable 3 Presentation Video | Click here to download mp4 File
    3a. View Deliverable 3 Presentation Slides as PDF
    3b. Download Deliverable 3 Presentation Slides as PowerPoint
    3c. View Technical Paper as PDF | Download Technical Paper as Word Document
    3d. Watch Demo | Click here to download mp4 File

Sprint 4

Nov 16, 2022 - Dec 14, 2022

  1. Watch Deliverable 4 Presentation Video | Click here to download mp4 File
    4a. View Deliverable 4 Presentation Slides as PDF
    4b. Download Deliverable 4 Presentation Slides as PowerPoint
    4c. Watch Integration Testing On MVP | Click here to download mp4 File
    4d. Watch MVP Demo | Click here to download mp4 File

Sprint Burndown Charts and Completed Tasks

  1. Sprint 1 Completed Tasks
  2. Sprint 2 Burndown Chart and Completed Tasks
  3. Sprint 3 Burndown Chart and Completed Tasks
  4. Sprint 4 Burndown Chart and Completed Tasks

Retrospectives

  1. Sprint 1 Retrospective
  2. Sprint 2 Retrospective
  3. Sprint 3 Retrospective
  4. Sprint 4 Retrospective

Team Working Agreement

Team Working Agreement as PDF | Download Team Working Agreement as Word Document


CS692 - Spring 2023 Deliverables

Presentations (Sprint Reviews)

Sprint 5

Jan 24, 2023 - Feb 07, 2023

  1. Watch Deliverable 5 Presentation Video | Click here to download mp4 File
    5a. View Deliverable 5 Presentation Slides as PDF
    5b. Download Deliverable 5 Presentation Slides as PowerPoint
    5c. View Product Description as PDF | Download Product Description as Word Document
    5d. Watch Demo | Click here to download mp4 File

Sprint 6

Feb 08, 2023 - March 07, 2023

  1. Watch Deliverable 6 Presentation Video | Click here to download mp4 File
    6a. View Deliverable 6 Presentation Slides as PDF
    6b. Download Deliverable 6 Presentation Slides as PowerPoint
    6c. View Technical Paper as PDF | Download Technical Paper as Word Document
    6d. Watch Demo | Click here to download mp4 File

Sprint 7

March 08, 2023 - April 04, 2023

  1. Watch Deliverable 7 Presentation Video | Click here to download mp4 File
    7a. View Deliverable 7 Presentation Slides as PDF
    7b. Download Deliverable 7 Presentation Slides as PowerPoint
    7c . View Project Description as PDF | Download Project Description as Word Document
    7d. Watch Demo | Click here to download mp4 File

Sprint 8

April 05, 2023 - May 02, 2023

  1. Watch Deliverable 8 Presentation Video | Click here to download mp4 File
    8a. View Deliverable 8 Presentation Slides as PDF
    8b. Download Deliverable 8 Presentation Slides as PowerPoint
    8c. Watch Project Demo | Click here to download mp4 File

Sprint Burndown Charts and Completed Tasks

  1. Sprint 5 Burndown Chart and Completed Tasks
  2. Sprint 6 Burndown Chart and Completed Tasks
  3. Sprint 7 Burndown Chart and Completed Tasks
  4. Sprint 8 Burndown Chart and Completed Tasks

Retrospectives

  1. Sprint 5 Retrospective
  2. Sprint 6 Retrospective
  3. Sprint 7 Retrospective
  4. Sprint 8 Retrospective

Team Working Agreement

Team Working Agreement as PDF | Download Team Working Agreement as Word Document


Diagrams

Architectural Design | Conceptual Architecture Diagram| Control Flow Diagram | Data Flow Diagram Level 0 | Data Flow Diagram Level 1 | Diagram Representing Modular Design | Sequence Diagram | Admin Sequence Diagram | User Sequence Diagram | Diagram Representing Modular Design | Entity Relationship Diagram (ERD) | Use Case Diagram | Object Detection - Network Architecture | Object Detection - Working of Architecture


Additional Project Artifacts

Product Personas

Persona 1 - Victor Carlos
Persona 2 - Angela Mathew
Persona 3 - Prathna De

User Stories

View User Stories Spreadsheet as PDF | Download User Stories as Excel Workbook

Acceptance Criteria

View Acceptance Criteria as PDF | Download Acceptance Criteria as Excel Workbook

Application Test Cases

View Test Cases as PDF | Download Test Cases as Excel Workbook