Online Student Expense Management System PHP MySQL

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BUY NOW Source Code ₹501

The **Student Expense Management System** is a core PHP and MySQL-based web application developed to help students manage their monthly expenses effectively. This system is designed to enable students to track their daily, weekly, and monthly expenses while setting a budget to monitor and control their spending. With user-friendly interfaces, the system provides an easy-to-use platform where students can register, log in, set budgets, and categorize expenses. This way, students gain better insights into their spending habits, which can be crucial for developing good financial management skills.

Features

User Registration and Login

  1.    - Register: Allows students to create an account using personal details (username, email, password).
  2.    - Login: Secure access to the system using credentials.
  3.    - Logout: Ensures secure exit from the system.

Dashboard

  1.    - Displays an overview of the student’s total budget, recent expenses, and remaining balance.
  2.    - Provides graphical representation (optional) of spending across different categories.

Budget Management

  1.    - Set Monthly Budget: Students can set a monthly budget to manage their expenses.
  2.    - Available Budget Display: Shows remaining budget after deducting expenses.

Expense Tracking

  1.    - Add Daily Expenses: Students can input their daily expenses along with a description and category (e.g., food, transport, entertainment).
  2.    - Weekly Expense Summary: Overview of all expenses incurred in a week.
  3.    - Monthly Expense Summary: Detailed summary of all expenses within a month.
  4.    - Filter by Category: View expenses by category to analyze specific spending areas.

Reports and Summaries

  1.    - Today’s Expenses: Overview of all expenses recorded for the current day.
  2.    - Weekly Summary: Shows total spending for each week in a month.
  3.    - Monthly Summary: Comprehensive monthly report that includes total expenses and remaining budget.
  4.    - Export Options (Optional): Ability to export expense reports in formats like PDF or Excel for personal records.

Settings

  1.    - Profile Management: Update personal information such as email or password.
  2.    - Notification Settings (Optional): Students can enable notifications or alerts for budget limits.

Responsive Design

  1.    - Ensures usability across devices, so students can access the system from laptops, tablets, or smartphones.

Brief overview of the technology:

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

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 studentexpense.
Import database db name.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/project folder name/"

Online College Result Management System PHP MySQL

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BUY NOW Source Code ₹501

The Online College Result Management System is a web-based application that simplifies result management and provides a streamlined interface for students and college administrators. This system integrates an admin login and a student result view on the same platform. It offers students easy access to their academic performance details and enables administrators to efficiently manage student records, courses, semesters, and results.

Key Features

Student Section

  1. Student Login (View Result) Panel:
    • Students can access their results by entering their unique Student ID.
    • After entering the Student ID, they can view basic details such as Student ID, Name, Branch, Semester, and Percentage of Results.
  2. Detailed Result View:
    • Upon clicking "View Result," students can see their complete results, which includes:
      • Subject Code and Subject Name
      • Marks Obtained for each subject
      • Total Percentage
    • Students also have the option to print their result as a PDF, making it convenient to save or submit for future reference.

College Admin Panel

  1. Admin Login:
    • The admin logs in using a username and password. After a successful login, the admin is redirected to the dashboard.
  2. Dashboard Overview:
    • The dashboard provides an at-a-glance view of key data points:
      • Total Number of Students
      • Total Semesters
      • Total Subjects added by the admin
  3. Sidebar Navigation:
    • The admin can easily navigate through different sections via a sidebar menu.
  4. Semester Management:
    • View Semesters: View all existing semesters.
    • Add, Update, Delete, and Modify Semesters: Admins can manage semester data, allowing for easy updates and adjustments.
  5. Subject Management:
    • View Subjects: View a list of all subjects currently offered.
    • Add, Update, Delete, and Modify Subjects: Admins have control over the subject list, enabling them to keep course offerings up to date.
  6. Student Management:
    • View Students: Access a list of all students enrolled in the system.
    • Add, Update, Delete, and Modify Student Records: Admins can manage student records, ensuring data accuracy.
  7. Result Management:
    • Generate New Student Results: Admins can input student scores and generate a result based on their performance.
    • Save Results: Generated results are saved in the system, allowing for future access by both students and administrators.

This Online College Result Management System is designed to improve administrative efficiency and provide a simple, accessible way for students to review their academic progress. With a user-friendly interface and essential functionalities, the system caters to both students' need for easy result access and administrators' need for efficient data management.

Brief overview of the technology:

Front end: HTML, CSS, JavaScript

  1. HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++
  2. CSS : (Cascading Style Sheets) Create attractive Layout
  3. Bootstrap : responsive design mobile freindly site
  4. JavaScript: it is a programming language, commonly use with web browsers.

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 csrms.
Import database db name.sql from downloaded folder(inside database)
4. Open Your browser put inside "http://localhost/project folder name/"

AI Recruitment & Staffing Agency Software Solution

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is loaded with everything you need to effectively manage your staffing agency. Create a beautiful and customizable website, receive and manage orders, manage employer and candidate relationships, and recruit new candidates easily with job posts, computer-based tests, and powerful filtering tools.

New! Deep AI Integration

CarePro now ships with powerful AI features that leverage OpenAi’s gpt models. Features include candidate recommendations, candidate bio filters, employer job vacancy filters, contract generation, candidate bio generator, Job vacancy generator and more!

Software Built for YOUR Agency

Do you run a Staffing agency? Then you have come to the right place! CarePro is the Staffing Agency Software built just for agencies like yours!
CarePro is a PHP web application that enables you effectively manage all aspects of your agency’s operations. With features such as Order Management, Employer and Candidate Management, Placements Management, User Records, Candidate Recruitment and lots more, your agency will never be the same again!
Buy our system and install on your server with just a few clicks! With CarePro, you can be up and running in minutes!

Order Management

CarePro comes with a complete order management module that makes it easy for employers to place orders for your Candidates!

Customizable Order Form

comes with a complete form builder that allows you to create custom fields for your order placement. You can define the questions you would like to ask or options you would like to be selected when an employer is placing orders for your candidates.

Powerful AI Features

now ships with deep integration with OpenAI’s gpt models. Our AI features include
1. Candidate Recommendations: Employers and Admins can now find the best candidates for a given role using the power of AI. Our Candidate Recommendation feature is a powerful tool that will change the way you recruit forever!
2. Candidate Bio filter: our candidate bio filter will drastically reduce the manpower needs of your agency. You can configure the AI model to filter each candidate’s bio to remove any text that violates your terms of service.
3. Employer Job Vacancy filter: Our job vacancy filter will ensure that each time an employer creates a job vacancy, it will comply with your policies. You can even have the Gpt model automatically re-write each job posting to enforce quality standards.
4. Contract Generation: Automatically create contracts with the AI contract generation feature. The system will use the signatories you have configured to draft a professional legal document.
5. Candidate Bio Generator: Easily create professional bios for your candidates with the click of a button. The system will gather all the candidate’s data and draft a professional bio.
6. Job vacancy generator: Employers and admins can use AI to easily create professional job listings.
7. Blog post generator: Easily create blog posts with the built-in blog post generator.
8. Email creation: Save time drafting emails with the built-in email template creator

Sell online courses to candidates

ships with a powerful learning management system that enables you offer free and paid courses to your candidates. Create courses with multiple types of content such as text, videos, images, quizes, Zoom meetings and more. You can also issue certificates automatically to successful candidates.

Candidate Shortlisting

Employers can optionally shortlist candidates while placing orders! You get to define candidates that are available for shortlisting on your front end. You can also create orders from your backend and shortlist candidates yourself

Automatic Invoice Generation & Checkout

You can configure the order placement module to generate an invoice when an order is placed automatically. This is especially useful for mobilization fees. Employers will be emailed an invoice immediately after they place the orders and they will be redirected to make an online payment via your selected payment gateways

Multiple Payment Gateways

You can configure the payment methods you would like to use to receive payment for your order invoices. We support multiple methods such as Paypal, Stripe, Paystack, 2Checkout, Rave, Bank Transfer, and many more! All payments go directly through your selected gateway straight to your bank account as you will get to save your gateway credentials directly on your CarePro settings page.

Order Statuses and Notifications

Easily update the status and comment of orders during the course of each placement. Employers will receive email notifications once their order is updated. They can also view the history of their previous orders

Employers & Candidates

comes with powerful features for managing all your employers and candidates. Easily view and manage all employers and candidates from your backend. Shortlist candidates, hotlist employers, and more!

Customizable Employer & Candidate Profiles

allows you to define fields for Employers and Candidates. Set attributes for each user type that employers or candidates can fill in during registration.

User Registration and Confirmation

Optionally enable or disable registration for Employers or Candidates. You can also enable email confirmation for employers or candidates. Social login is also available for Employers and Candidates.

User Profile Management

Employers and Candidates can easily manage their account details by logging into their account area. Candidates can also upload profile pictures, resumes, and any other type of files you may require e.g. Identification documents

Employer and Candidate Records

You can view and create different types of records for candidates and employers. Such records include Placement History, Invoice Payments, Comments, and Attachments. Easily search through all types of records using our powerful search feature

Placements

Easily manage placements with CarePro! Create placement relationships and define start and end dates. Specify salary and also attach documents for placements.
You can also create comments for each placement. Employers can log in and view all comments for a placement. They can also create comments for placement and attach documents.

Contract Management

comes with a powerful contract management feature that helps you streamline the process of managing contracts between employers and candidates. Please note the following regarding this feature:

  1. Each contract is unique and is created specifically for the signing parties. A contract is only visible to admins and signatories assigned to it.
  2. A contract requires a minimum of one signatory and can include an unlimited number of signatories.
  3. Signatories can sign contracts online using the web-based signature capture feature.
  4. Signatories are not allowed to modify contracts.
  5. Signatories can download PDF versions of their contracts only after signing.
  6. Other signatories are notified via email whenever another signatory signs the contract.

Invoicing

comes with a powerful invoicing feature. Easily create invoices and send them to employers or candidates. Invoices are sent as a PDF attachment and a link is provided in the email for online payments. The link sent via email enables automatic login for easy payment. Users can also view all their invoices by logging into their accounts. Outstanding invoices can be paid from this area.

Vacancies & Recruitment

Easily post vacancies on your portal! Receive applications for each vacancy from your candidates. Download resumes for each applicant. CarePro also provides powerful filtering features for selecting the right candidates for each position.

Testing

comes with a powerful Computer Based Testing feature! You can create online examinations for your Candidates. Our tests are highly secure and immune to cheating or hacking. You can create time-limited examinations and specify if multiple attempts are allowed. Easily filter results to get candidates with the best scores!
You can create an unlimited number of Tests.

Messaging

comes with a powerful messaging feature that enables you to easily send Email and SMS messages to your users.

Email Messaging

Our Email messaging feature is designed to make it easy to send documents to Employers and Candidates. Some features include

  1. Resume Attachment: Easily attach the resume of candidates to an email and send it to an employer with the click of a button! Resumes are automatically created by the system and all have a uniform format. You can also specify the resumes as partial in order to restrict the data included in the resume.
  2. Email Resources: Easily create resources that can be reused in emails. Items such as Guarantor forms, Agreements, and Terms of Service can be uploaded as email resources.
  3. Invoice Attachment: Easily send invoices as PDF attachments and clickable links.
  4. Email Templates: Create Email templates that can be easily loaded onto the text editor. This saves you the trouble of having to recompose common emails.

SMS Messaging

Easily send SMS broadcast messages to Employers and Candidates. CarePro supports multiple SMS gateways. Simply save your credentials on the settings page and start sending messages easily!

Website Builder

comes with a powerful and easy to use website builder. With this, you can easily install one of our multiple templates and have your site looking gorgeous! All templates are free to use. If you are using CarePro as your main website or adding it to an existing website, we have a template for you!
Our website builder comes with powerful tools for customizing your selected template. Easily upload images, change colors, text, and much more!

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Script Come With :

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

Document Management System Project php mysql

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Document Management is a self-hosted, web-based document management system designed to help organizations store, track, modify, and manage documents on a centralized platform. Its features include document sharing, reminders, user management, bulk permissions, document download, document preview, sending documents via email, document audit tracking, document versioning, document comments, and multilingual support

It allows you to upload multiple documents and share them with an unlimited number of system users. Additionally, it provides the option to share documents for a specific period and allows for the download option.

New Feature Added:

  • Deep Document Content Search
  • Share document via secure link
  • Quality verified by Envato
  • Complete Source Code
  • Regular updates
  • Free future updates
  • Welcome for suggestions

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Script Come With :

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

Garments ERP – Apparel & Textile Industrial ERP Software

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Welcome to the world of Garments ERP , where precision meets efficiency in the garments and apparel industry. Our tailored Enterprise Resource Planning software is your key to streamlining and optimizing every facet of clothing and textile management.

Our Garments ERP keeps you in control by offering real-time insights into raw materials, fabric management, production progress, and finished products. Say goodbye to inefficiencies and waste, and welcome efficient quality management.

It aids in planning production schedules, resource allocation, and order monitoring, leading to optimized production processes and reduced lead times. Your operations become smoother, more efficient, and more cost-effective.

With Garments ERP, you can process buyer orders with ease, from entry to fulfillment and delivery. It ensures accuracy and punctuality, satisfying your customers and bolstering your market reputation. Efficient supplier management guarantees a steady supply chain, reducing production delays and boosting your competitive edge.

Our software doesn’t stop at production – it’s equipped with financial modules that manage accounts payable, receivable, cost tracking, and budget management. It empowers you to make informed financial decisions and generates reports to aid in your strategic planning.

Garments ERP offers apparel businesses heightened operational efficiency, reduced costs, greater customer satisfaction, and sharper decision-making capabilities. It’s your essential tool for thriving in the fast-paced and ever-evolving garment industry. Step into the future with Garments ERP and see the difference for yourself.”

What You Get :

Key Features:

Dashboard
  • Access real-time company data from the dashboard.
Order Management
  • Create orders for buyers, including Booking, Budget, Costing, Sample, Production, and Shipments.
Manage Inventory
  • Create Accessory Units and Accessories.
  • Place accessory orders with suppliers.
User Management
  • Administer user accounts and assign roles.
Bank & Accounts
  • Conduct financial transactions.
  • Access Bank Accounts, Cash in Hand, Cheques, Income, Expense, Credit Voucher, Debit Voucher, Monthly Transactions, Daily Transactions, Party Ledger, and Daily Cashbook.
Party List
  • Create and manage two types of parties: Suppliers and Buyers.
  • Purchase accessories from Suppliers.
  • Sell orders to Buyers.
HRM Management
  • Define designations and manage employee data.
  • Handle full or partial salary payments.
Party Due List
  • Monitor dues for parties (Suppliers and Buyers).
  • Process payments to Suppliers.
  • Receive payments from Buyers.
Loss Profit
  • Analyze the company’s financial situation.
  • Review expenses, incomes, and the overall Loss/Profit.
Reports
  • Generate Order, Transactions, Production, Sales, and Purchase Reports.
Settings
  • Configure Currency settings.
  • Manage Notifications.
  • Update core system settings.
  • Adjust website settings.
Roles, Permissions & Others
  • Control user visibility and permissions.

For Live Demo & Enquiry  :

WhatsApps : +916263056779

Email : official@projectworlds.in

Script Come With :

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

Lung Cancer Detection Using CNN Project with Source Code

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Lung cancer is one of the most prevalent and deadly forms of cancer worldwide. According to the World Health Organization (WHO), lung cancer accounts for approximately 2.1 million new cases annually and is responsible for 1.8 million deaths. The high mortality rate is largely due to the fact that lung cancer is often detected at an advanced stage when treatment options are limited, and survival rates are low. Early detection of lung cancer significantly improves the chances of successful treatment and long-term survival. However, traditional diagnostic methods, such as X-rays, CT scans, and biopsies, are time-consuming, require significant expertise, and are not always effective at detecting early-stage tumors.

This project aims to detect lung cancer using a Convolutional Neural Network (CNN) model deployed with Flask. It includes a Jupyter notebook (lung_cancer_detection.ipynb) for model training and a Flask app (app.py) for making predictions. Additionally, an HTML template (index.html) is provided for the web interface.

Data Preprocessing

  • Data Acquisition: The CT-Scan  images and their labels were obtained from this Kaggle dataset. It provides a diverse set of chest cancer images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in CT-Scan images, leading to accurate classification of Lung Cancer.

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 3.8, 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 3.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Brain Tumour Detection Project Using Deep Learning From MRI Image

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Brain tumors are one of the most critical and life-threatening conditions affecting the human brain. Early and accurate detection of brain tumors is crucial for effective treatment planning and improving patient outcomes. However, manual detection and diagnosis from MRI (Magnetic Resonance Imaging) scans can be time-consuming, prone to human error, and require expert radiologists.

This project focuses on automating the process of brain tumor detection using deep learning techniques. By leveraging the power of Convolutional Neural Networks (CNNs), we aim to create a model that can accurately classify MRI images into tumor and non-tumor categories. The trained model is then integrated into a user-friendly web application developed using Flask, allowing healthcare professionals and researchers to easily upload MRI images and receive real-time predictions.

Data Preprocessing

  • Data Acquisition: The MRI images and their labels were obtained from this Kaggle dataset. It provides a diverse set of brain images, crucial for training a robust model.

  • Data Augmentation: To enhance the model's ability to generalize and to mitigate overfitting, I used TensorFlow's ImageDataGenerator. This tool allowed for augmenting the images in various ways (like rotation, zoom, flip) to artificially expand the dataset.

  • Normalization and Resizing: Each image was resized to a standard dimension and normalized to ensure uniformity in the input data, which is important for effective training of the CNN.

  • Train-Test Split: The dataset was split into training and test sets. The training set is used to train the model, while the test set helps in evaluating its performance on unseen data.

Model Structure

The CNN model used in this project is a sequential model composed of several Convolution2D and MaxPooling2D layers, carefully structured for effective image classification:

  • Convolution Layers: The model includes multiple Conv2D layers with 128 and 256 filters. These layers are responsible for extracting features from the images. The first two convolution layers have 128 filters each, followed by another set of two with the same number. The next four convolution layers have 256 filters each.

  • Pooling Layers: MaxPooling2D layers are used after certain convolution layers to reduce the spatial dimensions (width and height) of the output volume, helping to reduce the number of parameters and computation in the network.

  • Output Layer: The final layer of the model is a Dense layer with neurons equal to the number of classes, using a 'softmax' activation function for multi-class classification. This allows the model to output a probability distribution over the classes.

  • Optimization and Loss Function: The model is compiled with the Adamax optimizer and categorical cross-entropy loss function. This combination is chosen for effective learning and generalization in multi-class classification tasks.

  • Parameters and Size: The total number of parameters in the model is 3,763,940 (14.36 MB). All these parameters are trainable, ensuring that the model can learn complex patterns in the data.

This structure is designed to effectively capture the intricate patterns in MRI brain images, leading to accurate classification of brain tumors.

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 3.8, 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 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1501

Sleep Disorder Prediction Using Machine Learning Project

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The purpose of this project is to analyze factors that affect sleep health and predict which sleep disorder a person may have based on various factors such as Sleep Quality, Stress Level, BMI Index, Blood Pressure, etc. The dataset used for this project was sourced from Kaggle. The project involved data preprocessing to fill null values, exploratory data analysis (EDA), and the implementation of machine learning models to make predictions.

Key Observations

  1. Individuals aged over 43 years are more likely to experience sleep disorders.
  2. On average, females tend to have better sleep quality compared to males.
  3. Engineers generally have better sleep quality, while Sales Representatives have poorer sleep quality.
  4. Higher stress levels are associated with an increased likelihood of sleep disorders.
  5. Individuals with sleep disorders have lower sleep quality ratings compared to those without disorders.
  6. People in the Obese and Overweight BMI categories tend to experience more sleep disorders.
  7. Individuals who sleep more than 7 hours have a significantly lower chance of having a sleep disorder.

Models Used

Three machine learning models were used:

  1. Logistic Classification
  2. K-Nearest Neighbors (KNN) Classifier
  3. Random Forest Classifier

The same training and testing sets were used for all models.

Model Performance

  • Random Forest Classifier: Best performing model with an accuracy of 89%.
    • Accuracy: 89%
    • Recall: 89%
    • Precision: 90%
    • F1-Score: 89%
  • KNN Classifier: Performed well but not as good as Random Forest.
  • Logistic Classification: Achieved 86% accuracy, which was the lowest among the three models.

Important Features

Based on the Random Forest Classifier, the top three important features for detecting sleep disorders are:

  1. Blood Pressure
  2. BMI Category
  3. Age

Conclusion

 

The Random Forest Classifier was identified as the best model for predicting sleep disorders in the given dataset, achieving an accuracy of 89%. The top three contributing features to the prediction are Blood Pressure, BMI Category, and Age.

Dataset

The dataset for this project was obtained from Kaggle.

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 3.8, 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 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Buy Now Source Code ₹1001

 

Oral Cancer Detection Using Image Processing with Source code

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Oral cancer is cancer that develops in the tissues of the mouth or throat. It belongs to a larger group of cancers called head and neck cancers. Most develop in the squamous cells found in your mouth, tongue, and lips.

Oral cancer is a major health problem worldwide accounting for 177,384 deaths in 2018 and is most prevalent in low- and middle-income countries. Enabling automation in the identification of oral cancer can lead to the prevention and early diagnostic of disease. Therefore, regular oral check-ups are very important. The focus of transfer learning is to enhance the performance of target learners on target domains by inheriting knowledge from various conceptually related source domain. This project implies a novel approach for the early diagnosis and detection of one of the leading diseases, cancer in most sensory body organ i.e., mouth. In addition to this, deep neural networks are used to build automated systems, where complex patterns are derived to track with this difficult task. Various Transfer Learning architectures has been improvised and comparative analysis has been derived to focus the best learning rate. Further analysis is reported in relation to the classification of the referral decision. Our introductory results shows that deep learning has the power to tackle this challenging task.

In the enlargement of cells that provide with harmed nearby tissues, oral cancer has been documented to be intractable [1]. Oral cancer can be detected in the early stages of mouth cancer, known as ulcers, by a tiny number of sterile cells in the oral tissue. When it comes to metabolism, dead cells can be found in remote places of the geographical region or inside the body. There are several types of cancer, however 90 percent of crab cells are classified as oral cancer [2]. Biological models, as well as clinical forms of related and lesion-free tumour models, can be detected in various parts of the body using appearance models and stereotypes that do not require staining. Machine learning techniques were utilised to predict several biological models for oral cancer, which were used to classify non-cancerous and cancerous samples, which were then analysed for the oral cancer stage [3]. The predictor will use three justification test kits as well as different stages of cancer to determine the accuracy of the interaction. Sampling with sample justification can predict different tumour volumes and the emergence of ulcers in the tissues, assisting in the prediction of different stages of oral cancer [4].

Without the need of any special instruments, the oral cavity can be easily viewed. During clinical practise, specialists use visual inspection to make suspected diagnoses of oral cancer based on their own knowledge and experience with the visual appearances of malignant tumours [5,6]. Oral cancer lesions typically appear as white patches followed by red patches, or as mixed white-red patches in rare cases. The mucosal surface frequently becomes progressively uneven, grainy, and ulcerated [7,8]. Non-specialist medical workers, however, sometimes misinterpret these visual patterns as indicators of ulceration or other types of oral mucous membrane illnesses [8]. For the detection of oral cancer, there is no established vision based technique. Oral cancer is diagnosed through oral biopsy, which takes a long time and is not always available in primary care or community settings, especially in developing countries [9, 10]. As a result, Oral Cancer patiens are frequently unable to acquire prompt diagnosis and referrals [11,12].

There is a lot of evidence that Deep learning algorithms can match, and in some cases outperform, human experts when it comes to identifying minute or miniscule visual patterns from photographs,[13], classifying skin lesions, [14], detecting diabetic retinopathy, [15], and identifying facial phenotypes of genetic disorders [16]. These findings lead us to anticipate that deep learning could capture fine-grained aspects of oral cancer lesions, which would be useful in the early diagnosis of the disease.

The concept of transfer learning is founded on the premise that when knowledge or information from a related domain is transferred to it, it improves an idea in that domain. Consider the case of two people who want to learn to play the flute. One of the participants has no prior musical experience, while the other has a strong understanding of music as a result of playing the sitar. By applying previously learned music knowledge to the process of learning to play the flute, a person with a good music background will be able to learn the flute more quickly [17].

A deep learning system was built using photographic images for entirely automated oral cancer detection when it was assumed that deep neural networks could quickly identify certain visual patterns of oral cancer just like any human expert. On both internal and external validation datasets, we calculated algorithmic performance and compared the model to the average result of seven oral cancer specialists on a clinical validation dataset. Our findings demonstrated that oral cancer lesions have discriminative visual patterns that can be discovered using a deep learning algorithm. The potential to identify oral cancer at the point of care in a less expensive, non-invasive, and effective method has substantial clinical implications.

Datasets Download Link - Kaggle 

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 3.8, 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 3.8.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py