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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.
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.
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 |
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.
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:
- 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.
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:
- Back-End Continuous Integration
- 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.
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.
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.
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.
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.
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.
- Front-End Continuous Deployment
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
Watch EasyWay MVP Demo | Click here to download mp4 File
Watch Integration Testing On MVP | Click here to download mp4 File
View Deployment Manual | View Deployment Manual as PDF | Download Deployment Manual as Word Document
View API Documentation | View API Documentation as PDF | Download API Documentation as Word Document
View Technical Paper as PDF | Download Technical Paper as Word Document
Sept 07, 2022 - Sept 27, 2022
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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
Sept 28, 2022 - Oct 25, 2022
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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
Oct 26, 2022 - Nov 15, 2022
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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
Nov 16, 2022 - Dec 14, 2022
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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 1 Completed Tasks
- Sprint 2 Burndown Chart and Completed Tasks
- Sprint 3 Burndown Chart and Completed Tasks
- Sprint 4 Burndown Chart and Completed Tasks
Team Working Agreement as PDF | Download Team Working Agreement as Word Document
Jan 24, 2023 - Feb 07, 2023
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Watch Deliverable 5 Presentation Video | Click here to download mp4 File
5a. View Deliverable 5 Presentation Slides as PDF
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5c. View Product Description as PDF | Download Product Description as Word Document
5d. Watch Demo | Click here to download mp4 File
Feb 08, 2023 - March 07, 2023
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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
March 08, 2023 - April 04, 2023
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Watch Deliverable 7 Presentation Video | Click here to download mp4 File
7a. View Deliverable 7 Presentation Slides as PDF
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7c . View Project Description as PDF | Download Project Description as Word Document
7d. Watch Demo | Click here to download mp4 File
April 05, 2023 - May 02, 2023
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Watch Deliverable 8 Presentation Video | Click here to download mp4 File
8a. View Deliverable 8 Presentation Slides as PDF
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8c. Watch Project Demo | Click here to download mp4 File
- Sprint 5 Burndown Chart and Completed Tasks
- Sprint 6 Burndown Chart and Completed Tasks
- Sprint 7 Burndown Chart and Completed Tasks
- Sprint 8 Burndown Chart and Completed Tasks
Team Working Agreement as PDF | Download Team Working Agreement as Word Document
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
Persona 1 - Victor Carlos
Persona 2 - Angela Mathew
Persona 3 - Prathna De
View User Stories Spreadsheet as PDF | Download User Stories as Excel Workbook
View Acceptance Criteria as PDF | Download Acceptance Criteria as Excel Workbook
View Test Cases as PDF | Download Test Cases as Excel Workbook