Tag: Python

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Latest CSE Python Projects on ML & AI – 2022

These are the Latest CSE Python Projects on Machine Learning, Deep Learning, Artificial Intelligence, Big Data, Blockchain Technology, Cloud Computing, Data Mining, Networking, Network Security, and Cyber Security domains.

Download the Projects List Here – Python Projects on ML & AI – 2022

Python Projects List – 2022

These are the latest Python Machine Learning & Deep Learning projects for the year 2022.

  1. Characterizing And Predicting Early Reviewers For Effective Product Marketing On Ecommerce Websites
  2. Semi-Supervised Machine Learning Approach For DDoS Detection
  3. 5g-Smart Diabetes Toward Personalized Diabetes Diagnosis With Healthcare Big Data Clouds
  4. Credit Card Fraud Detection Using Random Forest & Cart Algorithm
  5. Driver Drowsiness Monitoring System Using Visual Behaviour And Machine Learning
  6. E-Assessment Using Image Processing In ∞Exams
  7. Automating E-Government Using Ai
  8. Eye Ball Cursor Movement Using OpenCV
  9. Filtering Instagram Hashtags Through Crowdtagging And The Hits Algorithm
  10. Converging Blockchain and Machine Learning for Healthcare
  11. Suspicious Activity Detection
  12. Use Of Artificial Neural Networks To Identify Fake profiles
  13. Video-Based Abnormal Driving Behaviourdetection Via Deep Learning Fusions
  14. Crop Yield Prediction And Efficient Use Of Fertilizers
  15. Fake Images Detection
  16. Opinion Mining For Social Networking Sites
  17. Image Classification Using Cnn (Convolution Neural Networks) Algorithm
  18. Cyber Threat Detection Based On Artificial Neural Networks Using Event Profiles
  19. Analysis Of Women’s Safety In Indian Cities Using Machine Learning On Tweets
  20. Construction Site Accident Analysis Using Text Mining And Natural Language Processing Techniques
  21. Performance Comparison Of SVM ,Random Forest, And Extreme Learning Machine For Intrusion Detection
  22. Accident Detection System
  23. A Data Mining-Based Model For Detection Of Fraudulent Behaviour In Water Consumption(Standalone)
  24. Detection Of Lung Cancer From Ct Image Using Svm Classification And Compare The Survival Rate Of Patients Using 3d Convolutional Neural Network(3d Cnn)On Lung Nodules Data Set
  25. Automatic Facial Expression Recognition Using Features Extraction Based On Spatial & Temporal Sequences Using Cnn & Rnn Algorithm
  26. Facial Expression Recognition And Their Temporal Segments From Face Profile Image Sequences Using Yolo Object Detection Algorithm
  27. Cartoon Of An Image
  28. Bird Species Identification Using Deep Learning
  29. A Deep Learning Facial Expression Recognition Based Scoring System For Restaurants
  30. Seer Cancer Incidence Using Machine Learning With Data Analysis
  31. Loan Prediction Dataset Using Machine Learning With Data Analysis
  32. User-Centric Machine Learning Framework For Cyber Security Operations Center
  33. Recolored Image Detection
  34. Robust Malware Detection For IoT Devices Using Deep Eigenspace Learning
  35. Modeling And Predicting Cyber Hacking Breaches
  36. Image-Based Appraisal Of Real Estate Properties
  37. Heart Disease Prediction
  38. Crop prediction using machine learning
  39. Face mask detection using artificial intelligence
  40. Facial attendance system using artificial intelligence
  41. Skin disease prediction using deep learning
  42. Fruit disease prediction using deep learning
  43. Malaria disease prediction using deep learning
  44. Phishing email detection using convolutional neural network
  45. Type 2 diabetes prediction using machine learning
  46. Suicidal tweets detection using machine learning
  47. web community questions and answering
  48. currency recognize system using artificial intelligence
  49. Fake news Detection using machine learning
  50. Detection of fake online reviews using semi-supervised and supervised learning
  51. mobile price prediction using machine learning
  52. Vigorous malware detection for the internet of things devices using machine learning
  53. Estimating the price of houses using machine learning
  54. Predicting the strength of the concrete pillars used in industrial infrastructure
  55. Passive Aggressive Classifier for Detection of Encrypted VPN
  56. speech emotion recognition using mlp algorithm
  57. face emotion recognition system using artificial intelligence
  58. Data Analysis by Web Scraping using Python
  59. A Decision Tree-based Recommendation System for Tourists
  60. A Machine Learning Model for Average fuel consumption in Heavy Vehicles
  61. Density-Based Smart Traffic Control System UsingCanny Edge Detection Algorithm for CongregatingTraffic Information
  62. Image Classification Using CNN (Convolution Neural Networks) Algorithm
  63. Prudent Fraud Detection in Internet Banking
  64. Twitter sentimental analysis
  65. Android malware detection using Machine learning techniques
  66. Missing Child Identification System using DeepLearning and Multiclass SVM
  67. Emotion-Based Music recommendation system
  68. Stress Detection in IT Professionals
  69. Cryptocurrency Price Analysis with Artificial Intelligence
  70. Object detection using artificial intelligence
  71. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
  72. Human Activity Recognition
  73. Deep Learning Model for Detecting COVID-19 on Chest X-Ray Using Convolutional Neural Networks
  74. Encryption And Decryption Algorithm Based On Neural Network
  75. FAMD: A Fast Multi-feature Android Malware Detection Framework, Design, and Implementation
  76. Image Super-Resolution using Convolution Neural Networks and Auto-encoders
    Lip Reading using Neural Networks and Deep learning
  77. Image Segmentation with Mask R-CNN
  78. Open Pose: Real-time Multi-Person 2D Pose Estimation using Part Affinity Fields
  79. Tuning Malconv: Malware Detection With Not Just Raw Bytes
  80. A Hybrid Fuzzy Logic-based Deep Learning Approach for Fake Review Detection and Sentiment Classification of Amazon Food Reviews
  81. News Text Summarization Based on Multi-Feature and Fuzzy Logic
  82. An Android Malware Detection Approach Based on SIMGRU
  83. Access Control and Authorization in Smart Homes: A Survey
  84. Spam Detection for Youtube Comments
  85. Animal Classification using Facial Images with Score-Level Fusion
  86. An Enhanced Anomaly Detection in Web Traffic Using a Stack of Classifier Ensemble
  87. Automatic Corpus Creation and Annotation for Natural Language Processing of Telugu
  88. Blockchain for Secure EHRs Sharing of Mobile Cloud-Based E-Health Systems
  89. CaptionBot for Assistive Vision
  90. A joint multi-task CNN for cross-age face recognition
  91. Cyber security Tools for IS Auditing
  92. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles
  93. A Deep Learning Approach for Effective Intrusion Detection in Wireless Networks using CNN
  94. Digital Image Encryption Algorithm Based on Elliptic Curve Public Cryptosystem
  95. Toward Universal, Word Sense Disambiguation Using Deep Neural Networks
  96. An Expert System for Insulin Dosage Prediction
  97. An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions
  98. Self-Diagnosing Health Care Chatbot using Machine Learning
  99. Machine learning Models for diagnosis of the diabetic patient and prediction insulin dosage
  100. An Improved Approach to Movie Recommendation System
  101. Efficient Privacy-Preserving Machine Learning for Blockchain Network
  102. Sentiment Analysis to Classify Amazon Product Reviews Using Supervised Classification Algorithms
  103. A Deep Learning Approach for Effective Intrusion Detection in Wireless Networks using CNN
  104. SE-Enc: A Secure and Efficient Encoding Scheme Using Elliptic Curve Cryptography
  105. Secure Image Transmission Using Chaotic-Enhanced Elliptic Curve Cryptography(JAVA)
  106. Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data a Comparative Analysis
  107. Multimedia summarization
  108. Quality Risk Analysis for Sustainable Smart Water Supply Using Data Perception
  109. Android Permission Control App(ANDROID)
  110. Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset
  111. Fake Image Identification
  112. Non-Binary Image Classification using Convolution Neural Networks
  113. Robust Intelligent Malware Detection Using Deep Learning
  114. Construction site accident analysis using text mining and natural language processing techniques
  115. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles
  116. Data Recovery
  117. Context-Based Image Processing Using Machine Learning Approaches
  118. A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning
  119. Robust Intelligent Malware Detection Using Deep Learning
  120. Rossman Stores Sales Prediction
  121. A Driving Decision Strategy (DDS) Based on Machine learning for an autonomous Vehicle
  122. A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things
  123. A Model for prediction of consumer conduct using a machine learning algorithm
  124. An automatic garbage classification system based on deep learning
  125. Detection of fake online reviews using semi-supervised and supervised learning
  126. Forensic Scanner Identification Using Machine Learning
  127. Use of Artificial Neural Networks to Identify Fake Profiles
  128. Analysis and Prediction of Industrial Accidents Using Machine Learning
  129. Credit Card Fraud Detection Using Random Forest & Cart Algorithm
  130. Identification of covid-19 spreaders using multiplex networks approach
  131. Object Tracking Using Python from Video
  132. Software Defect Estimation Using Machine Learning Algorithms
  133. MamaBot: A System Based on ML and NLP for Supporting Women and Families during Pregnancy
  134. Deep Texture Features for Robust Face Spoofing Detection
  135. Prediction of loan eligibility of the customer
  136. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
  137. A Malware Detection Method for Health Sensor Data Based on Machine Learning
  138. Blood Cell Types Classification Using CNN
  139. Feature extraction for classifying students based on their academic performance
  140. Hazard Identification and Detection using Machine Learning Approach
  141. Effective Heart Disease Prediction using Hybrid ML Algorithms
  142. Noise Reduction in Web Data A Learning Approach Based on Dynamic User Interests
  143. Online Book Recommendation System by using Collaborative filtering and Association Mining
  144. Sentiment Analysis Using Telugu SentiWordNet
  145. Soil Moisture Retrieval using Groundwater Dataset using Machine Learning
  146. A Time-Series prediction model using long-short-term memory networks for the prediction of Covid – 19 data
  147. Detection of Cyber Attacks in Network using Machine Learning Techniques
  148. Duplicate Question Detection with Deep Learning in Stack Overflow
  149. Machine Learning Techniques Applied To Detect Cyber Attacks On Web Applications
  150. Performance Analysis and Evaluation of Machine Learning Algorithms in Rainfall Prediction
  151. A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles
  152. Accident Detection
  153. BAT Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset
  154. Online Book store
  155. Data Poison Detection Schemes for Distributed Machine Learning
  156. Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
  157. Detection of Possible Illicit Messages Using Natural Language Processing and
    Machine Learning-Based Approaches for Detecting COVID-19 using Clinical Text Data
  158. Personalized effective feedback to address students’ frustration in an intelligent tutoring system
  159. Research on Recognition Model of Crop Diseases and Insect Pests Based on Deep Learning in Harsh Environments
  160. Smart contract-based access control for health care data vehicle Pattern Recognition using Machine & Deep Learning to Predict Car Model
  161. Analysis of Women’s Safety in Indian Cities Using Machine Learning on Tweets
  162. Analyzing and estimating the IPL winner using machine learning
  163. Characterizing and predicting early reviewers for effective product marketing on eCommerce websites
  164. Crime Data Analysis Using Machine Learning Models
  165. Detection of fake online reviews using semi-supervised and supervised learning
  166. Detection of Malicious Code Variants Based on Deep Learning
  167. Detection and classification of fruit diseases using image processing & cloud computing
  168. Grape Leaf Disease Identification using Machine Learning Techniques
  169. Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
  170. Missing Child Identification System using Deep Learning and Multiclass SVM
  171. Music & Movie Recommendation
  172. Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection
  173. Prediction of Hepatitis Disease Using Machine Learning Technique
  174. Skin Disease Detection and Classification Using Deep Learning Algorithms
  175. Spammer Detection and Fake User Identification on Social Networks
  176. Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
  177. Using Data Mining Techniques to Predict Student Performance to Support Decision
  178. Making in University Admission Systems

Latest CSE Python Projects on ML & AI – 2022

  1. An automated system to limit COVID-19 using facial mask detection in a smart city network
  2. Analysis and Prediction of COVID-19 using Time Series Forecasting
  3. Analysis and prediction of occupational accidents
  4. Analysis of Women’s Safety in Indian Cities Using Twitter Data
  5. Artist Recommendation System using Collaborative Filtering
  6. A-Secure-Searchable-Encryption-Framework-for-Privacy-Critical-Cloud-Storage-Services
  7. Authorship Identification Using Text Mining
  8. Automated word prediction in Telugu language using a Statistical approach
  9. Biometric Steganography Using Mid Position Value Technique
  10. Classification of COVID-19 Using Chest X-ray
  11. Covid-19 future forecasting
  12. Covid19 Social Distance Monitoring System Using YOLO
  13. Credit Card Fraud Detection
  14. Detection and Classification of Fruit Diseases
  15. Diabetic Retinopathy Detection
  16. Early prediction of Diabetes Mellitus using intensive care data to improve clinical decisions
  17. E-Certificates Issue Services Using Blockchain
  18. Face to Emoji using OpenCV and haar cascade classifier
  19. Facial Emotion Recognition using ML Algorithms
  20. Fake Job Recruitment Detection
  21. Fusion Approach to Infrared and Visible Images
  22. High-value customers identification for an E-Commerce company
  23. Image Caption And Speech Generation Using LSTM and GTTS API
  24. Image Deblurring
  25. IoT based Attendance System using Blockchain
  26. Malicious Application Detection Using Machine Learning
  27. Money Laundering Detection Using Machine Learning Methods
  28. Music Genre Classification using ML algorithms
  29. Object Detection and Alert System for Visually Impaired People
  30. Object detection and localization
  31. Offline Signature Forgery Detection
  32. People count on Surveillance Video
  33. Politeness Transfer A Tag and Generate Approach
  34. Prediction Analysis Using Support Vector Machine In Cardiovascular Ailments
  35. Product Recommendation System Using ML Technique
  36. Rice Crop Disease Detection
  37. Sign Language Translator for Speech Impaired
  38. Skin Lesion Classification
  39. StellarStudent Social Web Application for Colleges
  40. Survival of Heart Failure Prediction Using Feature Scaling
  41. Text Summarization Method
  42. Text to Image Generator using GAN
  43. Vessel detection from spaceborne images
  44. HR Analysis of Employee Attrition & Performance
  45. Analysis of Forest Fire Area Prediction
  46. Prediction of Chronic Kidney Disease Using Machine Learning
  47. Predicting the Resale Price of a Car
  48. Prediction Of Diabetes Mellitus
  49. Prediction of Exact Niche using Bank Data
  50. Prediction of Insurance Claims using Health Analysis
  51. Sales Prophesy in Business using ML
  52. Prediction Of Taxi Fare Using Exploratory Analysis
  53. Prediction of customer churn in the telecom industry
  54. Estimation Of Wine Quality Using Chemical Analysis Data
  55. The Simpsons Character Recognition
  56. IMDB Movie Review Analysis Using Bidirectional LSTM
  57. Pattern Recognition of IRIS Flower based on Artificial Intelligence
  58. Recommendation System
  59. Zomato Review System
  60. Drug Review Prediction
  61. Web Traffic Prediction
  62. Building a Model for MNIST Dataset using Convolutional
  63. Toxic Comment Detection
  64. Sonar Prediction
  65. Accuracy analysis using Fashion MNIST dataset
  66. Survival Analysis Of Diabetes
  67. Wine Quality Prediction
  68. Bank Churn Model
  69. Survival Analysis (Human Breast Cancer Prediction)
  70. Black Friday Sales Data Analysis Prediction
  71. Bank Marketing
  72. Breast Cancer Classification
  73. Company Turnover Predictor
  74. Pneumonia Prediction
  75. Employee Attrition Prediction
  76. Intelligent Album Creator
  77. Predicting The Readmission To Hospitals For Diabetic Patients
  78. Communication Through Gesture
  79. Twitter Sentiment Analysis
  80. Smart Security System Using Image Recognition
  81. Text Generation
  82. Crop Health Assistant using Artificial intelligence
  83. Air Quality Prediction
  84. Suspicious activity Detector
  85. Watch Bot
  86. Pneumonia Detection Using CNN
  87. Sentiment analysis of Twitter comments
  88. Motor Health Prediction
  89. Real or fake face Detection
  90. predicting simpson Characteristics
  91. The Sorting Hat
  92. Analysis of Airline reviews using NLP
  93. Heart Disease Prediction
  94. Cereal Analysis
  95. Fertilizer Prediction
  96. Turbine power prediction
  97. Kidney Disease Analysis
  98. Analysis of Accidents in 2017
  99. A Machine Learning Approach to Predict Crime Rate Analysis
  100. Advertising Based on Usage
  101. Life Expectancy
  102. Abalone Age Prediction
  103. Exploring the Bitcoin Cryptocurrency
  104. Predicting High Potential Employees and Employees at Risk
  105. Insurance Purchase Prediction
  106. MNIST Classification using CNN
  107. Vehicle resale value Prediction
  108. University Admission Prediction
  109. Automatic Challan Generation
  110. Smart investment Prediction
  111. Smart Security using Artificial Intelligence
  112. Bike Buyer Prediction
  113. Best Crop Prediction
  114. Taxi Fare Prediction
  115. Resale Values of Predicting Cars
  116. Power Consumption Prediction
  117. Health Insurance Prediction
  118. Health Monitoring System
  119. Crime Rate Prediction
  120. Assert Failure Prediction
  121. Adult Census Income Prediction Using Random Forest
  122. Smart Predictors
  123. Nutrition Analysis Using Image Classification
  124. Liver Patient Analysis
  125. Flood Prediction
  126. Customer Recommendation System
  127. Crop Protection Using Deep Learning Techniques
  128. Communication Using Gestures
  129. Car Performance Prediction
  130. Avalanche Prediction
  131. 3D Printer Material Prediction
  132. Income Prediction Using Random Forest
  133. Term Deposit Subscription Prediction
  134. Advertisement popularity Prediction
  135. Google Review of Places Certificates
  136. Blood Cell Image Prediction
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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 the student’s information will store in a  video file with the student’s name and hall ticket no. The video will have a user with a hall ticket no and name on each face. If the admin finds any unknown user tag on the 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 performed 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.
  • The time taken for prediction and processing is less and prediction is 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 them 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

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Movie Character Recognition From Video And Images Project

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 trains each image and saves the model into 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 a video or image.

Problem statement:

Classification of characters 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 at any time from images or 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:

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

Advantages:

  • The time taken for prediction and processing is less and prediction is 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

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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

Posted on

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

Posted on

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