Manuscript Peer Review System using ASP.NET Core

The Manuscript Peer Review System is a web-based platform designed to streamline the process of reviewing and evaluating scholarly manuscripts submitted for publication. This system aims to facilitate efficient and transparent peer review, ensuring the quality and credibility of the published content in academic and research journals. It provides a common platform for authors, reviewers, and editorial members to submit, review, and track manuscripts or research papers.

Key Features:

The Manuscript Peer Review System offers a range of features to enhance the peer review process:
» Different Editorial Categories: The system allows for the categorization of manuscripts into different editorial categories, making it easier for reviewers and editorial members to find relevant submissions.
» Reviewer Based on Specialization: Manuscripts are assigned to reviewers based on their specialization, ensuring that experts in the field evaluate the content.
» Double-Blind Review Applied: The system supports double-blind review, where the identities of both the authors and reviewers are kept anonymous, ensuring unbiased evaluations.
» Efficient Submission Management: Authors can easily submit their manuscripts through the system, ensuring a smooth and organized submission process.
» Seamless Reviewer Assignment: Reviewers are assigned to manuscripts seamlessly, ensuring timely evaluations and reducing administrative burden.
» Preliminary Desk Checks: The system allows for preliminary desk checks to ensure that the submitted manuscripts meet the basic requirements and guidelines.
» Revision Management Workflow: Authors can submit revisions to their manuscripts, and the system tracks and manages the revision process efficiently.
» Effective Editorial Decision: Editorial members can make informed decisions based on the evaluations and recommendations provided by the reviewers.
» Automated Notification System: The system sends automated notifications to authors, reviewers, and editorial members at various stages of the peer review process, ensuring timely communication.
» In-app Messaging for Communication: The system provides an in-app messaging feature, allowing authors, reviewers, and editorial members to communicate and discuss the manuscripts within the platform.
» Support for Supplementary Materials: Authors can submit supplementary materials, such as datasets or additional files, to support their manuscripts.
» Comprehensive Search Functionality: The system offers a comprehensive search functionality, allowing users to easily find and access manuscripts based on various criteria.

Tools and Technology Used:

The Manuscript Peer Review System is built using the following tools and technologies:
» Language: C#
» Framework: ASP.NET Core 7
» UI Project Type: ASP.NET Core Razor Pages
» Authentication/Authorization: Identity Core
» ORM: Entity Framework, Dapper
» UI Framework: Bootstrap, AdminLTE
» Database: SQL Server Express 2019
» IDE: Visual Studio 2022

Requirements:

To use the Manuscript Peer Review System, you will need the following:
» ASP.NET Core 7
» SQL Server Express 2019
» Visual Studio 2022
rel="nofollow">User Manual

What You Will Get:

» Full Source Code with Visual Studio Solution
» Database Script in SQL Express 2019
» Project Documentation

Summary:

With the Manuscript Peer Review System, you can streamline the peer review process for scholarly manuscripts, ensuring efficient and transparent evaluations. Experience the benefits of a web-based platform designed specifically for the needs of academic and research journals.

For Live Demo & Enquiry  :

Call / Whatsapp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 6 months support included

Node MySQL Hospital Management Project

Key Features:

  1. Patient Management: The system allows healthcare providers to manage patient records, including their personal information, medical history, and treatment plans.
  2. Appointment Management: The system enables scheduling, rescheduling, and canceling of appointments for patients.
  3. Prescription Management: The system manages the prescription and medication of patients.
  4. Billing Management: The system provides billing management functionalities to manage patient payments, invoices, and receipts.
  5. User Management: The system allows managing the user roles and access permissions of the healthcare providers.
  6. Reports: The system provides a range of reports that help in decision making for the hospital management.

Technologies used:

  1. Node.js – The backend technology used for building the application logic and server-side scripting.
  2. MySQL – The relational database management system used for storing and managing data.
  3. Express.js – The web framework used for building the RESTful API endpoints and middleware.
  4. EJS – The template engine used for generating HTML pages on the server-side.
  5. Bootstrap – The front-end framework used for building responsive and mobile-friendly web pages.
  6. Passport.js – The authentication middleware used for securing the application’s endpoints and managing user sessions.

Conclusion:

The Node MySQL Hospital Management Project is a comprehensive solution for managing hospital operations efficiently. The system provides a user-friendly interface for managing patient records, appointments, prescriptions, billing, and user roles. The use of Node.js and MySQL ensures scalability, reliability, and performance of the system.

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Iris Flower Classification with Decision Trees Web App

Objective:

To build a web application that can accurately classify Iris flower species based on their sepal and petal characteristics using a Decision Tree machine learning algorithm.

Dataset: The Iris flower dataset, which contains 150 samples of Iris flowers, each with measurements for sepal length, sepal width, petal length, and petal width. The dataset is labeled with the species of each flower: Iris setosa, Iris versicolor, and Iris virginica.

Methodology:

  1. Data Preprocessing: Load the dataset and split it into training and testing sets. Perform feature scaling to normalize the data.
  2. Decision Tree Model Building: Train a decision tree model on the training data using scikit-learn library. Tune the hyperparameters of the model to obtain the best performance.
  3. Web App Development: Use Flask web framework to create a web app that allows users to input the sepal and petal measurements of an Iris flower and displays the predicted species using the trained decision tree model.
  4. Model Interpretation: Interpret the decision tree to gain insights into which features are most important in classifying the Iris flower species.

Tools and Technologies:

  1. Python
  2. scikit-learn
  3. Flask
  4. HTML
  5. CSS
  6. pandas
  7. numpy
  8. matplotlib.

Conclusion:

Decision Trees are a simple yet powerful machine learning algorithm for classification tasks. In this project, we have built a decision tree model to classify Iris flower species with high accuracy and developed a web application that allows users to interactively predict the species of an Iris flower based on its sepal and petal measurements. The web app can be used for real-world applications such as plant identification, environmental monitoring, and plant breeding.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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