Detecting Impersonators in Examination Centres using AI

 

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency. In order to solve this problem, an effective method is required with less manpower. With the advancement of machine learning and AI technology, it is easy to solve this problem. In this project we are developing an AI system where images of students are collected with names and hall ticket numbers are pre-trained using the KDTree algorithm and the model is saved. Whenever a student enters the classroom, the student should look at the camera and enter class, after the given time or class is filled with student’s information will store in a  video file with student name and hall ticket no . The video will have a user with hall ticket no and name on each face. If admin finds any unknown user tag on face admin can recheck and trace impersonators . 

Problem statement:

Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers.  According to the latest news reports, 56 JEE candidates who are potential impersonators were detected by a national testing agency.

Existing system:

                  Information given in the hall ticket is used as verification to check if the student is the impersonator or not.  Manual security checks are performed with are not perfect and sometimes students can even change images from the hall ticket.    

Advantages:

           Manual verification methods are used for checking personally for each student which is not possible to check each student personally.

             Chances of changing images from hall tickets are possible which doesn’t have a verification method.

Proposed system:

  • In the proposed system initially, images of each student are collected and each dataset consists of 50 images of each student. These images are trained using kdtree algorithm using the image processing technique and the model is saved in the system this model can be used for automatic prediction of students in exam halls from live video or images. 

Advantages:

  • The student verification process is fast and accurate with the least effort. Reduces impersonator’s issue with live verification.
  • Time taken for prediction and processing is less and prediction done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting students at exam centers and display in the video.  

SOFTWARE REQUIREMENT: 

  •  Operating system:           Windows XP/7/10
  • Coding Language:           python

  • Development Kit             anaconda

  • Library:     Keras, OpenCV

  • Dataset:   any student’s dataset

Movie Character Recognition From Video And Images

Live tracking of characters from movies is important for automating the process of classification for user-friendly information management systems like online platforms where characters in a movie can be seen before watching the movie. At present manual method is used which can be automated using this movie character classification method. The objective of this work is to collect a dataset of any movie characters and train a model which captures the facial features of all characters and the model is saved for prediction.  For testing purposes, a real-time live video can be used to track characters. This application also works for images where users can give input as images of trained movie characters and get results with character names on the image as output. In this project for training dataset KDTree, the algorithm is used which takes images from a given folder and train each image and save model a dump file in the system. In the second stage using this trained model input image or input video is predicted with the model and the result is shown as video or image.

Problem statement:

Classification of character for each movie manually is a time taking process and the database should be managed.

Objective:

The objective of this project is to develop an automatic classification of characters after training from the dataset. If the one-time model is created it can be used for prediction any time from images of video

Existing system:

In the existing system movie characters are managed in the database and which are used for displaying when required in this process database is the important to the time taken for processing is more.

Disadvantages:

  • Time taken for processing is more and the database should be managed and integrated with the required system whenever required.
  • This method includes the manual process of data collection and updating and deleting data. 

Proposed system:

In the proposed system initially dataset of respected move characters is collected and each dataset consists of 50 images. These images are trained using KDDTree algorithm using the image processing technique and the model is saved in the system this model can be used for automatic prediction of characters from live video or images.

Advantages:

  • Time taken for prediction and processing is less and prediction done automatically using a trained model.
  • A trained model can be used to track live video and automates the process of detecting characters and displays on screens.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:           python
  • Development Kit               anaconda
  • Library:                                 TensorFlow, Keras, OpenCV
  • Dataset:                               Any movie dataset

Canteen Automation system using NLTK and Machine Learning

The canteen automation system project is designed to select the food items from a web application with cost, time of cooking, and give rating for products. This application is designed to help students to order food items without giving orders to waiters or going to the counter and give orders. Most of the colleges don’t have order-taking system students should directly reach the counter and give an order which is time taking process in order to solve this problem this online order booking system is designed. As there will be many students who will be giving orders from different departments as a web application is designed with multiple admins, each department will have one admin who will take request and process request. Another problem is best food from today’s canteen menu can be known by checking ratings given by other users based on that students can give orders. Students can also give reviews for each food item along with ratings. NLT is used to calculate the sentiment of each review by taking the yelp dataset and applying machine learning and NLTK to calculate sentiment and store it in the database.

Proposed system:

  • In the proposed system food ordering is done online and each department has its own admin who handles requests and on daily basis, users can give a rating of food items which will help other students to select the food item from the list. Sentiment analysis using Yelp data set and NLTK and Machine learning are used to store sentiment of each review given by the student.

Advantages:

  • Helps students to give orders from any location inside the campus and save time by reaching the canteen based on the given cooking time from the application.
  • Sentiment analysis is done for reviews using NLTK and Machine Learning. Sentiment and Rating is useful for students to select food items.

SOFTWARE REQUIREMENTS:

 Operating system:           Windows XP/7/10

  • Coding Language:           Html, JavaScript,  
  • Development Kit:        Flask Framework
  • Database:           MySQL
  • Dataset:           YELP
  • IDE:           Anaconda prompt

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

COVID-19 Data Analysis And Cases Prediction Using CNN

ABSTRACT:

Corona virus ( COVID-19 ) is creating panic all over the world with fast growing cases. There are various datasets available which provides information of world-wide effected information. Covid has affected all counties with large number of cases with variation of numbers under death, survived, effected. In this project we are using data set which has county wise details of cases with various combined features and labels. Covid data analysis and case prediction project provide solution for data analysis of various counties on various time and data factors and creating model for survival and death cases and prediction cases in future. Machine learning provides deep learning methods like Convolution neural network which is used for model creation and prediction for next few months are done using this project.  

PROBLEM STATEMENT:

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

OBJECTIVE:

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

EXISTING SYSTEM:

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

Disadvantages:

  • Data set used for predicting disease is different compare to one we are using for this project.
  • Image processing techniques are used.

PROPOSED SYSTEM:

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

Advantages:

  • Data analysis and prediction is 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