Real time system for predicting Monkey Pox Disease Using Machine and Deep learning Techniques Test phase, srs, design phase and source code final deliverable

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Real time system for predicting Monkey Pox Disease Using Machine and Deep learning Techniques Test phase, srs, design phase and source code final deliverable

Project Domain / Category

Artificial Intelligence + Desktop/Web Based Application

Abstract / Introduction

Monkey pox is an infectious disease caused by the monkey pox virus that can occur in certain animals, including humans. Symptoms begin with fever, headache, muscle pains, swollen lymph nodes, and feeling tired. An ongoing outbreak of monkey pox was confirmed on 6 May 2022, beginning with a British resident who, after traveling to Nigeria (where the disease is endemic), presented symptoms consistent with monkey pox on 29 April 2022. The resident returned to the United Kingdom on 4 May, creating the country’s index case of the outbreak.In this project, Students are required to create a real-time system to classify monkey pox disease using the Monkey-Pox PATIENTS Dataset. The dataset link has been shared below from which students can download it by visiting it.

The system will consist of Two Modules. Each module will have its own set of requirements. In order to complete this project students are required to complete all the requirements of each module.

Note: To select this project, the students must know the pre-requisite required for the project selection. If any student wants to select this project without any prior basic knowledge of Artificial Intelligence or Machine Learning, he/she must complete the resources and the tutorials mentioned in the pre-requisite section in parallel to SRS and Design Document Submission.

Functional Requirements:

Module 1:

• The system will first Import the dataset.
• System will display the summary statistics, trends, patterns and insights on the data visually by performing the EDA (Exploratory Data Analysis).
• After performing the EDA system will preprocess the data.
• System will Split the data into train and test.
• System will use a Supervised/Unsupervised based Machine and deep learning algorithm to train the data.
  • System needs to use any 4 machine learning algorithm as per his/her choice for training.
  • System needs to use any 2 deep learning algorithms as per his/her choice for training.
• After the training process ends, system will evaluate the trained model on the test data.
• System will generate a confusion matrix to assess the errors in the train model.
• System will save the model for future use.

Module 2:

• System will provide the user an Interface window. Students can create the interface window any GUI Python Library or a Web page using any python web framework like Django or Flask etc.
• System will integrate the trained model from module 1 into the module 2.
• The interface should provide the user an option to interact with the system, by first entering his/her symptoms, and predicting whether the user has monkey pox disease.

Pre-requisite:

• Students need to have understanding of the CS607 (Artificial Intelligence) course.
• Students also need to have basic understanding of the Machine learning techniques.
• Students can learn Machine learning and Artificial Intelligence techniques from the following links:
  • https://www.javatpoint.com/machine-learning
  • https://ocw.vu.edu.pk/Videos.aspx?cat=Computer+Science%2fInformation+Tec hnology+&course=CS607
  • https://www.youtube.com/watch?v=_u-PaJCpwiU&list=PLu0W_9lII9ai6fAMHpacBmJONT7Y4BSG&index=1
  • https://vulms.vu.edu.pk/Courses/CS607/Downloads/AI_Complete_handouts_f pdf Dataset:
• https://www.kaggle.com/datasets/muhammad4hmed/monkeypox-patientsdataset?select=DATA.csv Python Tutorials:
• https://www.programiz.com/python-programming
• https://www.tutorialspoint.com/python/index.htm
• https://www.w3schools.com/python/ EDA Tutorials:
• https://www.analyticsvidhya.com/blog/2021/05/exploratory-data-analysis-eda-astep-by-step guide/#:~:text=EDA%20is%20the%20process%20of,to%20understand%20the%20data %20better.
• https://www.geeksforgeeks.org/what-is-exploratory-data-analysis/ Machine Learning Tutorials:
• https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
• https://www.geeksforgeeks.org/machine-learning-with-python/
• https://www.youtube.com/watch?v=ZftI2fEz0Fw
• https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-toknow-article
• https://www.analyticsvidhya.com/blog/2017/09/common-machine-learningalgorithms/

Deep learning Tutorials:

• https://www.tutorialspoint.com/python_deep_learning/index.htm
• https://deeplizard.com/learn/video/gZmobeGL0Yg
• https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learningalgorithm
• https://www.javatpoint.com/deep-learning-algorithms Tkinter:
• https://www.tutorialspoint.com/python/python_gui_programming.htm
• https://www.geeksforgeeks.org/python-gui-tkinter/ Django:
• https://www.djangoproject.com/start/ https://www.w3schools.com/django/

Tools:

Language: Python (Only python language)

Framework: Anaconda, Tkinter, PyQt5, Django,Flask, etc.

IDE: JupyterNotebook, Colab, Pycharm, Spyder, Visual Studio Code, etc.

Supervisor:

Name: Saad Ahmed