Category: Python projects

Welcome to the Python Projects Section.

Find the below some projects we have developed using Python.

  1. Google Finance currency calculator API.
  2. using aprori on website to get relevent data
  3. Game like tictactoe,2048,
  4. Note/content sharing platform for college students
  5. book information parsing tool developed for opencart
  6. AI Crooswords
  7. An algorithm for steganography of multiple heterogeneous files on a single video carrier.
  8. college-administration
Posted on

Vehicle Registration for College Parking Android App & Web Application Project

Here Students can download the source code of Vehicle Registration for CBIT College Parking Android App & Web Application Python Sqlite Project. Complete Project Report, paper Presentation & output Video guide also available to download.

Problem Statement

The Vehicle Registration for College Parking project will consist of 2 main User Interfaces, the mobile application for students to sign up, log in to the app, add vehicles (up to 2 per person) and apply for a parking sticker, delete vehicles and apply for a new CBIT college parking sticker with fine.

The second User Interface is the admin functionalities which will be in a web application developed with python coding and MySQL database. The admin will be able to log in, receive all the applications for vehicle registration, verify and approve, receive fine payments physically, update payment status in the application and issue parking stickers and dismiss any vehicle registration (in case of misbehavior). It will also consist of a statistics page where registration and payment statistics will be displayed based on the date given.

Vehicle Registration for College Parking Use Case Diagram

Activity Diagram for Mobile App:

Activity Diagram for Web App:

DATABASE

The database for the Vehicle Registration for College Parking project was created in MySQL Workbench. 3 tables have been created.

User table

This table will be holding all the credentials of the users and admins. Every user id will be unique.
This data comes in handy when an admin/user tries to log in, if those credentials are authentic then they will be redirected to the dashboard.

Registration table

Holds the data of all the registered vehicles. Every registration id is unique for easy search. Necessary information about the person and vehicle is stored. Registration status can be dismissed, deleted, approved, or pending. The registration date is stored for statistics.

Payments table

Holds the data about the payments made physically. There are 3 criteria for payments – adding back deleted, dismissing vehicle, and for newsticker. Payment status gets updated to ‘paid’ once done physically. The payment date for statistics.

Implementation

Mobile Application (User)

Welcome page

Web Application Home Page:

Download the complete Vehicle Registration for CBIT College Parking Android App & Web Application Python Project Source Code, Project Report, PPT, and Output Video File.

Posted on

Online Healthcare System Python & SQLite Django Framework Web Application Project

ABOUT THE PROJECT

This Online Healthcare System project is a web application that was developed using Python & SQLite database with Django Framework 2.1.5. This Project provides students/patients quick online access to Health Care Centres and Medical Pharmacy shops. The students can get online prescriptions from various doctors, request an ambulance in emergency times, and get information about the medicines available in the Medical Pharmacy Shop.

PROBLEM IDENTIFIED

  • Need for assistance and monitoring of Healthcare in Hostel life as the number of students in the college area will rapidly increase in coming years so management will become difficult to handle.
  • Making the Healthcare process fully automated and online can increase the efficiency of the Health Centre as well as reduce the need for paperwork

THE SOLUTION

  • Online Healthcare System for College Health Centre connecting Students and Residents with Doctors.
  • Online prescriptions availability information.
  • Notifying users of suggestions from time to time.

Project Features

  • User Profile and Information Website Page
  • Login and Registration Website Page
  • Prescription History And Website Page
  • Health Centre Contact Information
  • Prescription and Doctor Specifications
  • Emergency Website Page
  • (Small Scale Medicine Pharmacy Shop)

The technology :

➢ Django Framework 2.1.5 – Python 3.6.8
➢ SQLite
➢ HTML | CSS | Bootstrap
➢ Javascript

Why healthcare?

● Healthcare cannot be neglected and should be given utmost importance by students.
● This Health Care System Python project might be helpful in case of emergency as well as the daily medical needs of the patient.

System Design Document:

Online Healthcare System Activity Diagram

Online Healthcare System Class Diagram

Online Healthcare System Development View Diagram

Online Healthcare System Physical View Diagram

Online Healthcare System Use Case Diagram

The output Screens:

Here students can easily access the complete Online Healthcare System project code, report & Output Video of the project.

For more information regarding the project development please visit the developer’s Online Healthcare System page on GitHub.

Posted on

Predictive Analytics for Retail Banking Machine Learning Python Project

Retail banking

Typical mass-market banking is used by local clients of local branches of large commercial banks. The services offered include checking and savings accounts, mortgages, personal loans, debit/credit cards. Attention is paid to the customer.

The main problems in this sector are:

Which product is right to recommend to the customer?
What is the best time to sell a product?
What is the most effective channel to communicate with the customer?

PROBLEM STATEMENT

The data in this regard are related to the direct marketing campaigns of the banking institution. Marketing campaigns are based on phone calls. Often multiple contacts with a customer were required to reach the product (term bank deposit) whether to subscribe (‘yes’) or unsubscribe (‘no’). The goal is to know in advance whether the customer will sign up for a term deposit.

ABOUT THE INFORMATION

This is a classic marketing bank data package uploaded to the UCI machine learning repository. The data set provides you with information about the financial institution’s marketing campaign, which you need to analyze to find ways to look for future strategies to improve the bank’s future marketing campaigns.

These are the columns in the data set:

Age: age of the client (quantitative)
Job: Client’s profession – (categorical) (administrator, worker, entrepreneur, housekeeper, manager, retiree, self-employed, service provider, student, technician, unemployed, unknown)
Marital: Client’s marital status – (categorical) (divorced, married, single, unknown, note: divorced means divorced or widowed)
Education: Client’s level of knowledge – (categorical)
By default: Indicates whether the customer has a debt – (categorical) (no, yes)
Balance: average annual balance, in euros (quantitative).
Housing: Does the client have a home loan? – (categorical) (no, yes)
Loan: Is the client a personal loan? – (categorical) (no, yes)
Contact: Contact type – (categorical) (unknown, mobile, phone)
Date: The date of the last contact with the customer.
Month: Last customer contact month – (categorical) (January-December)

CONCLUSION

In the real world, most classification problems are two-sided. Also, data sets have almost no meaning. In this post, we cover strategies to combat unbalanced data sets with missing values. We will also explore different ways to build packages within the sklearn. Here are some exceptions:

Accuracies compared

  • K-nearest Neighbour: 75.3%
  • Logistic Regression: 80.9%
  • Decision Tree: 78.2%
  • Random Forest Classifier: 78%
  • Support vector Machine: 53%


Sometimes we may be willing to give up some model improvements if this estimate is much more complex than the percentage change in metric improvement.
When building ensemble models, try to use as many different good models as possible to minimize the correlation between basic students. We could improve the model of our concentrated ensemble by adding a dense neural network and other types of basic learners, as well as adding more layers to the concentrated model.
Easy Ensemble generally works better than other re-modeling methods.

Download the complete Predictive Analytics for Retail Banking Machine Learning Python Project Source Code, Report, PPT.

For more details about the project visit this page

 

Posted on

Liver Patient Analysis Machine Learning Project

Project description

In India, delays in diagnosing diseases are a major problem due to a lack of medical professionals. The typical scenario, which is mainly in rural and slightly urban areas:

1. A patient who sees a doctor with certain symptoms.
2. The doctor will perform some tests, such as blood and urine tests, depending on the symptoms.
3. The patient undergoes the above tests in the analytical laboratory.
4. The patient takes the reports back to the hospital, where they are examined and diagnosed.

The goal of this project is to reduce some of the delays caused by unnecessary detours between the hospital and the pathology laboratory. Historically, work has been done to detect the onset of heart disease, such as Parkinson’s, and machine learning algorithms have been developed to predict liver disease in patients based on a variety of characteristics.

Problem statement

The problem report is officially defined as follows:

“Considering the data set containing the various attributes of 584 Indian patients, use the functions in the data set and determine a controlled classification algorithm to determine whether a person is suffering from liver disease. This data set contains 416 liver recordings and 167 non-liver recordings. collected in northeastern Andhra Pradesh. This data set contains records of 441 male patients and 142 female patients. Each patient over the age of 89 is “90” years old.

Strategy

This seems to be a classic example of controlled learning. We are given a fixed number of functions for each data point, and our goal is to teach different controlled learning algorithms based on this data, so that when a new data point appears, our best-performing classifier can be used. information point as a positive or negative example. Detailed information on the number and types of algorithms used for training is contained in the “Algorithms and Techniques” section of the “Analysis” section.

Conclusion

Initially, the data set was studied and prepared for inclusion in the classifiers. This was achieved by removing some rows containing zero values, modifying some columns indicating the skewness, and using appropriate conversion techniques (a hot coding) to make the labels more useful for classification purposes. The performance indicators for which the models will be evaluated have been resolved. The data set was then divided into a reading and testing package.

First, a simple predictive and base model (“Logistic Regression”) was developed in the data set to determine the value of the base accuracy. The biggest challenge in implementing this project was in two areas: defining learning algorithms and selecting the appropriate parameters for precise configuration. Initially, making a decision on 3 or 4 methods out of the many choices available at sklearn was very tedious.

Algorithms and Techniques used to develop this Liver Patient Analysis Machine Learning Project are

1. Random Forest Classifier:
2. Gaussian Naive Bayes Classifier
3. Logistic Regression:

Download the complete Liver Patient Analysis Machine Learning Project Python source code, project report, PPT Presentation.

For more details about the project visit this page

Posted on

Student Grade Analysis & Prediction Machine Learning Project

The main objective of this Python project is to Analysis & Prediction the final grade of Portuguese high school students. This is Machine Learning Project and The algorithms used to implement this project are Linear Regression, ElasticNet Regression, Random Forest, Extra Trees, SVM, Gradient Boosted, Baseline.

Problem statement

The problem statement can be defined as follows: Considering the data set containing the attributes of 396 Portuguese students, the available functions of the data set were used and classification algorithms were defined to determine whether the student performed well in the final qualifying exam.

Description of the data set

These data reflect the performance of secondary school students in two Portuguese schools. Information attributes (student grades, demographic, social, and school-related characteristics) were collected using school reports and questionnaires. There are two sets of indicators for two different subjects: mathematics (math) and Portuguese (por). [Cortez and Silva, 2008], two data sets were modeled under binary / five-level classification and regression tasks. Important Note: The target attribute G3 has a strong correlation with the G2 and G1 attributes.

Methodology

As universities are prestigious universities, it is a matter of great concern that students stay at these universities. It was found that the majority of students dropped out of university in the first year due to a lack of adequate support for undergraduate courses. For this reason, the first year of a bachelor’s degree is called the “make or break” year. Without finding any support for mastering the course and its complexity, you can lower the student’s motivation and cause him to drop out of the course.

There is a great need to develop an appropriate solution to help students stay in higher education. Early grade forecasting is one of the solutions aimed at monitoring the progress of students in the undergraduate courses of the university and leads to the improvement of the learning process of students on the basis of projected grades.

The use of machine learning with the extraction of educational information improves the learning process of students. Various models can be developed to estimate a student’s grades in registered courses, which will provide valuable information to make it easier for students to stay in those courses. This information can be used for the early identification of students at risk, based on which the system can recommend teachers to pay special attention to those students. This information will also help predict the grades of students in different courses to better monitor their performance in a way that will improve student retention in universities.

Using different packages, such as cuffs, seaborn, and matplotlib, to analyze the data set to predict the final price (G3), to display the data graphically or graphically along with various attributes.

Download the Complete Student Grade Analysis & Prediction Machine Learning Python Project Source code, Report.

For more details regarding the project – Click here

Posted on

Nearby Hotels Python Web Application

This is a simple web application that can be used to search hotels. This application will help in searching nearby hotels and also give weather reports of that place such that they can know the weather details priorly to near that hotel in order to travel. A currency converter is included with which everyone can view hotel prices in their desired currency. Additionally, discount coupons are included, which will help user to access the cheapest hotels nearby.

API’s to be used:

Google maps:

The google maps API fetches the data from google e.g. here Maps API is a server that returns information about a place, an establishment, a geographical location, or a prominent point of interest using an HTTP request. Methods are available for place search, details, and autocomplete queries.

The place API returns in JSON format.

Currency converter:

Planning to use online currency converter API web services. This API allows covering the currency to whatever the users needed.

This API returns data in XML format.

Weather API:

This API forecast the weather near the hotels, which uses information from the Rapid API: this API has all the information like temperature, humidity, wind, etc..

This API returns data in JSON format.

Discount coupon:

Planning develops discount coupon API, which gives what best hotels are available nearby with the best discounts possible.

This API returns data in XML format.

Technologies used:

  • JavaScript
  • HTML
  • CSS
  • Nodejs
  • Python
Posted on

Car Parking System Python Project

The python program should make a car park where it’s a 8×9 grid using [square brackets], the parking space should be filled with colours, and letters to signify its availability and which type of car is parked. For example bikes, trucks, cars, sports cars… etc.

Objectives of the Project:

  • Here the car parking needs to be a code which will make a car parking area.
  • There should be red and green light to indicate if it’s vacant or occupied.
  • [square brackets] should be used to show each parking spot
  • Use different letters to show what kind of vehicle is in the spot. For example car, truck, bike, cycle..etc.

Source Code:

import colorama

from termcolor import colored

options_message = """
Choose:
1. To park a vehicle
2. To remove a vehicle from parking
3. Show parking layout
4. Exit
"""


class Vehicle:

    def _init_(self, v_type, v_number):
        self.v_type = v_type
        self.v_number = v_number
        self.vehicle_types = {1: 'c', 2: 'b', 3: 't'}

    def _str_(self):
        return self.vehicle_types[self.v_type]


class Slot:

    def _init_(self):
        self.vehicle = None

    @property
    def is_empty(self):
        return self.vehicle is None


class Parking:

    def _init_(self, rows, columns):
        self.rows = rows
        self.columns = columns
        self.slots = self._get_slots(rows, columns)

    def start(self):
        while True:
            try:
                print(options_message)

                option = input("Enter your choice: ")

                if option == '1':
                    self._park_vehicle()

                if option == '2':
                    self._remove_vehicle()

                if option == '3':
                    self.show_layout()

                if option == '4':
                    break

            except ValueError as e:
                print(colored(f"An error occurred: {e}. Try again.", "red"))

        print(colored("Thanks for using our parking assistance system", "green"))

    def _park_vehicle(self):
        vehicle_type = self._get_safe_int("Available vehicle types: 1. Car\t2. Bike\t3. Truck.\nEnter your choice: ")

        if vehicle_type not in [1, 2, 3]:
            raise ValueError("Invalid vehicle type specified")

        vehicle_number = input("Enter vehicle name plate: ")
        if not vehicle_number:
            raise ValueError("Vehicle name plate cannot be empty.")
        vehicle = Vehicle(vehicle_type, vehicle_number)

        print('\n')
        print(colored(f"Slots available: {self._get_slot_count()}\n", "yellow"))
        self.show_layout()
        print('\n')

        col = self._get_safe_int("Enter the column where you want to park the vehicle: ")
        if col <= 0 or col > self.columns:
            raise ValueError("Invalid row or column number specified")

        row = self._get_safe_int("Enter the row where you want to park the vehicle: ")
        if row <= 0 or row > self.rows:
            raise ValueError("Invalid row number specified")

        slot = self.slots[row-1][col-1]
        if not slot.is_empty:
            raise ValueError("Slot is not empty. Please choose an empty slot.")

        slot.vehicle = vehicle

    def _remove_vehicle(self):
        vehicle_number = input("Enter the vehicle number that needs to be removed from parking slot: ")
        if not vehicle_number:
            raise ValueError("Vehicle number is required.")

        for row in self.slots:
            for slot in row:
                if slot.vehicle and slot.vehicle.v_number.lower() == vehicle_number.lower():
                    vehicle: Vehicle = slot.vehicle
                    slot.vehicle = None
                    print(colored(f"Vehicle with number '{vehicle.v_number}' removed from parking", "green"))
                    return
        else:
            raise ValueError("Vehicle not found.")

    def show_layout(self):
        col_info = [f'<{col}>' for col in range(1, self.columns + 1)]
        print(colored(f"|{''.join(col_info)}|columns", "yellow"))

        self._print_border(text="rows")

        for i, row in enumerate(self.slots, 1):
            string_to_printed = "|"
            for j, col in enumerate(row, 1):
                string_to_printed += colored(f"[{col.vehicle if col.vehicle else ' '}]",
                                             "red" if col.vehicle else "green")
            string_to_printed += colored(f"|<{i}>", "cyan")
            print(string_to_printed)

        self._print_border()

    def _print_border(self, text=""):
        print(colored(f"|{'-' * self.columns * 3}|{colored(text, 'cyan')}", "blue"))

    def _get_slot_count(self):
        count = 0
        for row in self.slots:
            for slot in row:
                if slot.is_empty:
                    count += 1
        return count

    @staticmethod
    def _get_slots(rows, columns):
        slots = []
        for row in range(0, rows):
            col_slot = []
            for col in range(0, columns):
                col_slot.append(Slot())
            slots.append(col_slot)
        return slots

    @staticmethod
    def _get_safe_int(message):
        try:
            val = int(input(message))
            return val
        except ValueError:
            raise ValueError("Value should be an integer only")


def main():
    try:
        print(colored("Welcome to the parking assistance system.", "green"))
        print(colored("First let's setup the parking system", "yellow"))
        rows = int(input("Enter the number of rows: "))
        columns = int(input("Enter the number of columns: "))

        print("Initializing parking")
        parking = Parking(rows, columns)
        parking.start()

    except ValueError:
        print("Rows and columns should be integers only.")

    except Exception as e:
        print(colored(f"An error occurred: {e}", "red"))


if _name_ == '_main_':
    colorama.init()  # To enable color visible in command prompt
    main()
Posted on

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
Posted on

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

Posted on

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