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Stress Detection from Sensor Data using Machine Learning

Stress is commonly defined as a feeling of strain and pressure which occurs from any event or thought that makes you feel frustrated, angry, or nervous. In the present situation, many people have succumbed to stress especially the adolescent and the working people. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. The current technology, using Galvanic skin response (GSR), Heart rate variability (HRV), and Skin temperature are being used individually to detect stress.

In this project data set is created using five features age, gender, body temperature, heartbeat, and blood pressure, and four stages of labels are used for detecting the level of stress.  A decision tree algorithm is used to train the data set and create a model and use the Flask framework to take input data and predict the stress level of the user. 

EXISTING SYSTEM:

 Existing systems were designed to detect stress by taking tweets as input from the Twitter or Facebook data set and machine learning algorithms are applied to detect stress from tweets.

Disadvantages:

  • Most of the existing system works were on social networking stress data not on body-based sensor data.
  • Stress level is calculated based on tweets posted by users.

PROPOSED SYSTEM:

The proposed system is designed by collecting data from sensors and preparing data set on three features (temperature, heartbeat, age, male or female). Using this data set machine learning Decision tree algorithm is applied using and the model is saved. Front end web application is designed to collect new user features and passed them to the model to predict stress stages which are divided into 4 stages.

Advantages: 

  • Data is collected from real-time sensors and a data set is created for different ages and male and female users.
  • Data is trained using machine learning which helps automate the process of stress detection.
  • The web applications can help users to easily check their stress state based on their features.

Data collection:

  • In this state data is collected from real-time sensors and stored in an excel sheet with five features age, gender, temperature, heartbeat, and this data is applied for machine learning, and a model is created.

Data preprocessing:

  • Features are extracted from the data set and stored in the variable as train variable and labels are stored in y train variable. Data is preprocessing by standard scalar function and new features and labels are generated. 

Testing training:

  • In this stage, data is sent to the testing and training function and divided into four parts x test train, and y test train. Train variables are used for passing to the algorithm whereas tests are used for calculating the accuracy of the algorithm. 

Initializing Decision tree Algorithm:

  • In this stage, the decision tree algorithm is initialized and train values are given to the algorithm by this information algorithm will know what are features and label. Then data is modeled and stored as a pickle file in the system which can be used for prediction. 

Predict data:

  • In this stage, new data is taken as input and trained models are loaded using pickle and then values are preprocessed and passed to predict function to find out a result which is shown on the web application.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:           Html, JavaScript,  
  • Development Kit:        Flask Framework
  • IDE:           Anaconda prompt
  • Dataset:          Stress dataset

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Three Layer Security Login Java Project

Security system authentication application is designed to provide a three-layer login procedure for any application. Most of the login security methods have three procedures which are password-based login, OTP-based login, and login and OTP combined authentication procedure. These methods can provide login security for users to a certain level but some application requires more authenticated methods which are mainly useful for banking and payment methods.

In this project, three-layer login methods are developed which have password login, image selection then OTP login.  In this application when a user registers with the application user fills out the registration form with the image selection option which is stored in a database. Whenever the user logins to the application first user need to give a password if that is correct second stage image selection should be done if the image is correct then OTP is given then the user can log in to the application.

OBJECTIVES:

The main objective of the three-level security system is to provide advanced security to web applications and to prevent unauthorized access.

EXISTING SYSTEM:

Text passwords are the most commonly used technique for authentication and have several drawbacks.

Graphical passwords provide a promising alternative to traditional alphanumeric passwords due to the fact that humans can remember pictures better than text. 

PROPOSED SYSTEM:

A simple graphical password authentication system that consists of a sequence of ‘n’ images and the user has to select the click points associated with the correct image for successful login.

The task of selecting weak passwords is more tedious, discouraging users from making such choices.

We proposed this security system as a building platform to access the system.

SOFTWARE REQUIREMENTS:

  • Operating system:  Windows XP/7.
  • Coding Language:  Java
  • Tool: Netbeans
  • Database:  MYSQL

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Platform for Conducting Online Examination through Websites PHP Project

Examinations are part of an education system where every institute is using an online platform for conducting examinations through websites. Existing methods mostly work on manual methods where there is a need for human resources, use of paperwork, and time-consuming process.  

In order to solve this problem and provide an effective platform for institutions and students, an online examination platform provides the best solution. This web Online Examination platform has simple features which cover admin, teachers, and student modules. Admin can view students who are registered with the application and add teachers based on department and view tests and marks of students.

Teachers can log in with a valid username and password and upload test detail with questions and times for each test along with positive and negative marks for answers. Students can register with the application and select a test and take the test. Students can view marks, results, ranking, and history.

PROBLEM STATEMENT:

Conducting online examinations needs a planned process where resources and time are important. It is not possible to conduct exams where students can write exams from any location.

OBJECTIVE:

Developing an online examination system that works on a web-based platform can help in conducting exams from any location in a short span of time and students from any location can write exams.

EXISTING SYSTEM:

Colleges and institutions conduct exams through examination centers where each student is provided with seat allocation and time. Based on that user need to be available at that location and time and take the test.

DISADVANTAGE:

This method needs a user available at that location.

Resources and costs are involved in the examination process.

PROPOSED SYSTEM:

The proposed Online Examination system works on the online web portal where students, admin, and lecturers can be part of this portal by registering with the application.  Admin will add teachers and students can register with the application and view all tests available and take tests and view results and ranks.           

ADVANTAGE:

Students and institutes can save time by conducting exams online.

The entire process is online students from any location can take tests.

SYSTEM REQUIREMENT:

Programming language: PHP

Database: Mysql

Server: Tomcat, xamp server

Front End: Html, CSS, Javascript.

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Securing Data Using DES, RSA, AES And LSB Steganography

ABSTRACT:

Data security is the main concern in different types of applications from data storing in clouds to sending messages using chat. In order to provide security for data in the cloud, there are many types of techniques which are already been proposed like AES, DES, and RSA but in existing methods, most of the time only a single type of encryption was used either AES, OR DES, OR RSA based on user requirement but in this system main problem is each encryption is done using encryption keys if these keys are exposed in any case entire data is lost so we need an effective method which can provide more security so in this project hybrid cryptography is used where existing encryption methods are used but three methods will be used.

When the user uploads data will split into three parts the first part will be encrypted using AES, the second part will be encrypted using DES, the third part will be encrypted using RSA  and these three encrypted files will be stored in the cloud and keys used for AES, DES, and RSA are stored in the image using LSB steganography when users want to download total data from cloud-first keys should be retrieved from the image and these keys are used for decrypting data again by using AES, DES and RSA and final data is combined and stored in the file. This method provides more security for data.

OBJECTIVE:

Data security is the main issue in cloud data management there is a chance of developing effective methods like hybrid cryptography for improving security. In this project, AES, DES, and RSA are used along with LSB.

INTRODUCTION:

The cloud is playing important role in data management and is another type of service that provides a secure way of data handling and remote data accessing where users from anywhere can use the cloud for data access. As the cloud is a third-party application where data uploaded by users must provide security features to reduce risks from data attacks in order to do that encryption techniques here are used like AES, DES, and RSA.

EXISTING SYSTEM:

In the existing system, the cloud is used to use any one of the encryption techniques and key verification is done using the identity of the user. Based on application requirements different encryption techniques are used.

DISADVANTAGES:

Only single encryption techniques are used and if keys are not managed effectively there are chances of leakage of keys. 

PROPOSED SYSTEM:

In order to improve security for cloud data compared to existing techniques where keys are shared security between users new hybrid cryptography technique is proposed where three types of encryption are used AES, DES, and RSA, and the LSB steganography technique is used for secure key sharing.

ADVANTAGES:

Data is split into three parts and each part is encrypted using one encryption technique and keys are shared securely by embedding in the image.

SOFTWARE REQUIREMENTS: 

  • Operating system: Windows 7.
  • Coding Language: python
  • Tool: anaconda, visual studio code
  • Database: SQL lite

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Drowsiness Detection using OpenCV Project

Abstract:

The new way of security system which will be discussed in this project is based on machine learning and artificial intelligence. Passenger security is the main concern of the vehicle’s designers where most accidents are caused due to drowsiness and fatigued driving in order to provide better security for saving the lives of passengers Airbags are designed but this method is useful after an accident accord.

But the main problem is still seeing many accidents happening and many of them are losing their lives. In this project we are using the OpenCV library for image processing and giving input as user live video and training data to detect if the person in the video is closing their eyes or showing any symptoms of drowsiness and fatigue then the application will verify with trained data and detect drowsiness and raise an alarm which will alert the driver.

Existing system:

There are various methods like detecting objects which are near a vehicle and front and rear cameras for detecting vehicles approaching near to vehicle and airbag systems that can save lives after an accident is accorded.

Disadvantages:

Most of the existing systems use external factors and inform the user about the problem and save users after an accident is an accord but from research, most of the accidents are due to faults in users like drowsiness and sleeping while driving.

Proposed system:

To deal with this problem and provide an effective system a drowsiness detection system can be developed which can be placed inside any vehicle it will take live video of the driver as input and compare it with training data and if the driver is showing any symptoms of drowsiness system will automatically detect and raise an alarm which will alert the driver and other passengers.

Advantages:

This method will detect a problem before any problem accord and inform the driver and other passengers by raising an alarm.

In this OpenCV-based machine learning techniques are used for the automatic detection of drowsiness.

SOFTWARE REQUIREMENTS: 

  • Operating system: Windows 7.
  • Coding Language: python
  • Tool: anaconda, visual studio code
  • Libraries: OpenCV
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Students Marks Prediction Using Linear Regression

Abstract:

Education institutions use new technologies to improve the quality of education but most of the applications which are used in colleges are related to service and development there are web applications that are helping students to take online training and tests. There are very few methods that can help teachers to know about student’s performance. Considering this problem machine learning techniques are used to predict students’ marks based on previous marks and predict results. Linear regression models are used to predict student performance and predict the next subject’s marks.

Problem statement:

Education institutions use web applications for training students and checking performance based on marks but there are no specific steps followed for predicting students’ performance and taking measures to improve performance.

Objective:

Design a machine learning model for the prediction of students’ marks and take measures to improve student performance. The linear regression algorithm is used to train the model and prediction.

Existing system:

Researchers had done work on the automation of grading techniques in which previous marks were used to give grades to students.

Algorithms like association rule mining and apriori algorithms are used for classifying students’ marks.

Disadvantages:

Existing methods mostly work based on marks obtained from exams.

Algorithms are used for classifying students based on marks. 

Proposed system:

The dataset of other subject marks is taken as input and the data set is processed with labels and features then test split is performed on the dataset and then the machine learning model is applied to the dataset then the prediction is performed.

Advantages:

Before the final marks of all subjects are evaluated prediction can be performed.

Using a machine learning process automation of marks prediction can be done. 

SOFTWARE REQUIREMENTS:

  • Operating system: Windows XP/7/10
  • Coding Language: python  
  • Development environment: anaconda, Jupiter 
  • Dataset: students mark the dataset
  • IDE: Jupiter notebook
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Student Coding Assignment Evaluation Using API Project

Abstract:

Data mining in educational institutions is helping to analyze students’ details and provide an effective evaluation system in a short time. With the advancement of new technologies, the student evaluation procedure has changed from manual correction to automating the process of correction and analysis. This student coding assignment evaluation system using API is designed to evaluate students coding correction process through the automation process.

When a student submits an answer to a student’s question online faculty will evaluate coding by sending data to API and get results or error messages. By checking these messages faculty will give marks to students. This process is done through a web application that is developed in a python programming language.  

Problem statement:

Students assignment evaluation is a time taking process for faculty which required a manual process of checking each line of code and giving marks to students. 

Objective:

The coding evaluation process can be automated by using available code-checking API which can be integrated into the college assignment assigning website. Using this process evaluation is completed with just in a click and faculty can give marks based on results.

Existing system:

  • A manual process was used for checking assignments and evaluating results.
  • Data mining techniques were used for evaluation which uses previous coding datasets and predicts results that are not accurate.

Disadvantages:

  • Faculty must check each line of code to evaluate coding and give grading.
  • The time taken for the evaluation process is high.

Proposed system:

The student online coding evaluation system provides an automatic coding checking process through which faculty can assign coding assignments and get results from students and compile code in click and check results and give marks.

Advantages:

  • The entire process of assigning to evaluation is done online and coding evaluation is done in one click.
  • API is used for checking errors in code and giving grading.

System requirement: 

Programing language: python

Framework: Flask

Database: MYSQL

API: for compiling code

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Cyber Bullying Detection Using Machine Learning Project

Abstract:

Cyberbullying is the process of sending wrong messages to a person or community which causes heated debate among users. Cyberbullying is mostly seen on social networking sites where users reply to post with bullying words to threaten or insult other users. Cyberbullying is considered a misuse of technology. According to the latest survey done all over the world data day by day, cases are increasing on cyberbullying.

In order to solve this problem many natural language processing techniques are proposed by various authors which are time taking and not automatic. With the advancement of machine learning and artificial intelligence, models can be created and automatic detection can be implemented. To show this scenario live chat application is developed in python programming with multiple clients and one server and the Naive Bayes algorithm is used to train the model on a Twitter dataset using this model live detection of cyberbullying is predicted and alert messages are shown on the chat application.

Problem statement:

Social networking and online chatting applications provide a platform for any user to share knowledge and talent but few users take this platform to threaten users with cyberbullying attacks which causes issues in using these platforms.

Objective:

To provide a better platform for users to share knowledge on social networking sites there is a need for an effective detection system that can automate the process of cyberbullying detection and take decisions.

Existing system:

  • Techniques like unsupervised labeling methods which use N-gram, and TF-IDF methods to detect cyberbullying are used which use the youtube dataset to detect attacks.
  • A support vector classifier is used to train models for detection.

Disadvantages:

Techniques that are used in the existing system are not automated they need time to process requests and update responses.

Social networking and chatting sites require automated detecting and processing methods.

Proposed system:

Cyberbullying detection is designed using machine learning techniques. The Twitter data set is collected with features and labels and the mode is trained using the Naive Bayes algorithm the trained model is applied to a live chatting application that has multiple clients and a single server. For each message, cyberbullying is detected using the model and then alert messages are posted on chat boards.

Advantages:

The cyberbullying detection process is automatic and time taken for detection is less and it works in a live environment. 

The latest machine learning models are used for training models that are accurate.

Software Requirement:

Programming language: python

Front End GUI : tkinter

Dataset: Twitter cyberbullying dataset

Algorithm: Naive Bayes

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Students Marks Prediction Using Linear Regression Project

Abstract:

Analyzing and predicting academic performance is important for any educational institution. Predicting student performance can help teachers to take steps in developing strategies for improving performance at early stages. With the advancement of machine learning and supervised and unsupervised techniques developing these kinds of applications are helping teachers to analyze students in a better way compared to existing methods. In this student marks prediction using Linear regression project students’ academic performance is predicted considering input as previous students’ marks and predicting next subject marks and the accuracy of the model is calculated.

Problem statement:

Analyzing and prediction of marks for students was done based on guesses and students’ personal marks details are not considered for academic evaluation.

Objective:

Machine learning-based data mining techniques are used to automate the process of student performance prediction using linear regression techniques.

Existing system:

  • Researchers have done work on Grading systems in which final examination marks are used for giving grades to students and evaluation of each student is done.
  • Association rule mining and apriori algorithms are used for classifying students based on their marks

Disadvantages:

  • Most of these methods work on data mining techniques that are based on complete data.
  • Early-stage evaluation is not possible in these methods.

Proposed system:

  • Students’ marks in other subjects are taken as input for the evaluation of students’ performance. The data set is pre-processed and features and labels are extracted from the dataset then the dataset is split into test and train sets then linear regression is applied to the dataset for prediction.

Advantages:

  • Before the final marks of all subjects are evaluated prediction can be performed.
  • Using a machine learning process automation of marks prediction can be done.

SOFTWARE REQUIREMENTS:

  • Operating system: Windows XP/7/10
  • Coding Language:            python 
  • Development environment: anaconda, Jupiter
  • Dataset: students mark the dataset
  • IDE :           Jupiter notebook

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COVID-19 Data Analysis And Cases Prediction Using CNN Project

ABSTRACT:

Coronavirus ( COVID-19 ) is creating panic all over the world with fast-growing cases. There are various datasets available that provide information on the world affected information. Covid has affected all counties with a large number of cases with a variety of numbers under death, survived, and affected. In this project, we are using a data set that has county-wise details of cases with various combined features and labels.

Covid data analysis and case prediction project provide solutions for data analysis of various counties on various time and data factors and creating models for survival and death cases and prediction cases in the future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for the next few months done using this project.  

PROBLEM STATEMENT:

With the increase of COVID-19 cases all over the world daily predictions and analysis are required for effective control of pandemic all over the world

OBJECTIVE:

By collecting data from Kaggle and new York datasets data preprocessing is performed and data analysis is performed on the dataset and a machine learning model is generated for future prediction of cases.

EXISTING SYSTEM:

  • The prediction was performed on COVID-19 cases based on different machine learning techniques which are based on an x-ray data set collected from COVID-19 patients.
  • Disease prediction from x-ray images is done using deep-learning techniques.

Disadvantages:

  • The data set used for predicting disease is different compared to the one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

Using the data set pre-processing is performed on the collected data set and various steps for the deep learning model are performed and prediction of cases is done then data analysis is done on various factors.

Advantages:

  • Data analysis and prediction are performed on textual data
  • Deep learning models are generated for predicting future cases.
  • Data analysis is performed for various factors.

SOFTWARE REQUIREMENTS

  • Operating system:  Windows XP/7/10
  • Coding Language:  python
  • Development Kit:  anaconda 
  • Programming language: Python
  • IDE : Anaconda prompt