Automatic Video Surveillance System AI & IOT Project

Surveillance is an integral part of security. The main objective of the Video Surveillance system IoT project is to build an effective system that can be used across different domains and technologies. The system is used to Detect Human intervention and breach in personal or commercial property of the user in real-time using AI and IoT.

It helps users to secure their property with the help of advanced artificial intelligence. The resulting system is fast and accurate, thus helping users with more secure surveillance systems.

For the most part, the job entails looking out for something undesirable to happen. The application is to have a system that provides real-time monitoring and alert security when a human is detected in a user’s property in their absence.


The main objective is to build an effective Video Surveillance System that can be used across different domains and technologies. The system is used to detect people trying to breach security in the personal or commercial property of the user in real time and send a message along with a short video clip to the user. 


Currently, the existing Rocker Bogie Suspension Systems Project or surveillance robot for defense Surveillance systems can keep video recordings of homes, offices, banks, and so on. But that is useful only after an incident or robbery happens. No Real-time Updates are provided when there is a breach in real-time. 

Just imagine, You’re at your home and someone breaks security and stole money or goods from your office or property. Or consider you’re out of town for some days and there is a robbery at your home. So after you come back home or someone identifies it after some time and they will update you about the breach at your place.

You can take any action after a breach has been done, not at the time it is being done. That issue will be resolved in our system with real-time monitoring and updates.


The Video Surveillance System can be implemented in any residential, Industrial, or commercial property. The system recommends detecting any human intervention on the user’s property and sends a notification along with a threshold of a 10-second video clip immediately as soon as it detects humans.


This Video Surveillance System project aims to develop an advanced Surveillance system that can keep on monitoring homes, offices, banks, and so on. With the help of this, you can find out if anyone breaches your security in your absence. We have to simply integrate our system into users existing surveillance systems. 

Module specification: 

  1. Raspberry pi
  2. Camera
  3. Server
  4. SNS
  5. S3  

Need Of Modules:

  • Raspberry pi as a Client to send frames to the server.
  • Camera to capture live video streams.
  • Server for processing frames and detecting humans.
  • SNS sends a multimedia message to a user when someone tries to breach security.
  • S3 to store a short video clip of the breach and send it to the user.

Non-Functional requirement.


When AI is taking care of your property then customers can relax and not have to worry about their security.


 The system should provide a reliable environment for both the client and the server.


The system is designed for a secure environment and ease of use.


Implementation of the system with pi, night vision camera, python, machine learning, and AI.


The whole system is expected to be delivered in four months of time with a weekly evaluation by the project guide.

Limitations of the System:-

False Positives

Due to different light variant conditions and camera resolutions, sometimes the system detects humans as false when there is none but that can be neglected if there is a human and the system doesn’t detect it then there should be a problem.

Limited Processing Power

As we are using a microcontroller to send feeds to the server, it cannot handle multiple feeds at once and will be slower as the device increases.

Download Automatic Video Surveillance Management System Project Python Code, Documentation & report, Paper Presentation PPT

Audio Classification On Cat’s And Dog’s Python Project

Our Audio Classification project illustrates a straightforward audio classification model supported by deep learning. we tend to address the matter of classifying the sort of sound-supported short audio signals and their generated spectrograms, from classifying dog’s audio to cat’s audio throughout model training. So as to satisfy this challenge, we tend to use a model-supported Convolutional Neural Network (CNN). The audio was processed with Mel-frequency Cepstral Coefficients (MFCC) into what is unremarkably called Mel spectrograms, and hence, was reworked into a picture. Our final CNN model achieved 89% accuracy on the testing dataset.

Project Overview :

The input to our model, in this project, is cats and associated dogs recording audio go in WAV kind. It lies below the supervised machine learning class. Thus, a dataset is also present as well as a target class. Hence, the intention here is to classify if the given input wav file is that of a cat or dog. Each of the dog and cat sounds is incredibly distinguished like in their pitch and frequency level since completely different| sounds have different sample rates. By default, Librosa mixes all audio to mono and resamples them to 22050 cycles/second at load time. For music and audio analysis, Librosa is associated ASCII text file python package. The info and the sampling rate are provided by Librosa. Audio or sound is in its raw kind, and the data provided should be pre-processed to extract significant and meaningful features so we implemented an algorithm i.e., MFCC (Mel Frequency Cepstral Coefficients) rule. Then, when audio extraction is done, the information is fed and the dataset is split into training and test set. So, after the preprocessing, a Convolutional Neural Network model is designed using tensor flow. For every code and model building, Keras API was used to implement Google colab.


Machine learning can be used in image processing, understanding speech, and musical instruments, speech-to-text, environmental sound classification, and many more. And as for our project, we implemented a class of speech processing i.e, audio classification. Converting sound waves into audio and spectrograms which is a visual representation of frequencies with the help of function provided by machine learning.

There are many techniques to classify images as many different in-built neural networks under CNN are already there, especially if it is related to images. And it’s straightforward to extract options from pictures as a result of pictures already being available in the shape of numbers, because the formation of a picture may be an assortment of pixels, and pixels area units within the sort of numbers. When we have data as text, we use the sequential encoder and decoder-based techniques to find features. But if it is to sound recognition or audio it is more difficult compared to text because it is based on frequency and time. Therefore a proper model is to be made to extract the frequency and pitch of that audio so as to make it easier to later recognize it.

Flow Chart:

Preliminaries and Background 

Related work

Machine learning: Image classification of cats and dogs – Before a decade, in computer notion, many problems had been saturating in accordance with their precision. However, the accuracy of those troubles significantly stepped forward with the boom of deep gaining knowledge of strategies. The majority of the problems that arise from image class is that it is defined as predicting the distinct categories a photo can belong to. Hence, for the supplied enter/ photograph detection with the aim of accomplishing high precision, a state-of-the-art approach is incorporated, i.e., a convolutional neural network turned into the build for the photo category mission of puppies and cats. A dataset become given from Kaggle comprising a total of 25000 pix of each dog and cat.

Machine learning: Audio classification of different bird species – Here, the methodology and results of using deep learning to assist in the classification of birds by their sounds are presented. As birds indicate the health of an ecosystem, hence this topic is of high importance. Random Forest Classification and custom-made six CNN models from the literature were performed on a dataset of ten birds that were composed of The highest accuracy was achieved at around 65% by the Random Forest and at about 58% for the CNN model.

conclusion and future work 

In this report, we first briefly explained the overview of this project and showed some referred project work already established. Then, we precisely illustrated our task, including the learning task and the performance task. After that, we explained the approach we are heading toward in order to classify the datasets. The approach/model we used is a neural network which is an implementation of the deep network which is a trainable model by which we were able to classify the dog’s and cat’s audio. The highest accuracy we got was 89.6%.

  1. In the future, we will try to implement the different high-level models in order to achieve much higher
  2. We’ll build a system that can directly intake a live raw

Intelligent Customer Help Desk with Smart Document Understanding



We will be designing an application that leverages multiple Watson Airservices (Discovery, Assistant, Cloud function, and Node Red). By the end of the project, we’ll learn best practices of combining Watson services, and how they can be used to build interactive information retrieval systems with

Discovery + Assistant.

  • Project Requirements: Python, IBM Cloud, IBM Watson
  • Functional Requirements: IBM Cloud
  • Technical Requirements: AI, ML, WATSON AI, PYTHON
  • Software Requirements: Watson assistant, Watson

Scope of Work

  • Create a customer care dialog skill in Watson Assistant
  • Use Smart Document Understanding to build an enhanced Watson Discovery collection
  • Create an IBM Cloud Functions web action that allows Watson Assistant to post queries to Watson Discovery

Proposed solution

For the above problem, we are able to put a virtual agent in the chatbot so it can understand the queries that are posted by customers. The virtual agent should train from some insight records-based company background so it can answer queries supported by the merchandise or associated with the company. In other words, some styles of manual will be accustomed to training the bot using AI. Here I’m using Watson Discovery as a tool for implementing AI and getting trained by the owner’s manual.


Block/Flow Diagram

Hardware / Software Designing

  1. Create IBM Cloud services
  2. Configure Watson Discovery
  3. Create IBM Cloud Functions action
  4. Configure Watson Assistant
  5. Create flow and configure the node
  6. Deploy and run Node-Red app


Create IBM Cloud services

Create the following services:

  • Watson Discovery
  • Watson Assistant
  • IBM cloud function
  • Node-Red


  • Companies can deploy chatbots to rectify simple and general human queries.
  • Reduces manpower
  • Cost efficient
  • No need to divert calls to customer agents and customer agents can look at other


  • Sometimes chatbots can mislead customers
  • Giving the same answer for different sentiments.
  • Sometimes cannot connect to customer sentiments and intentions


  • It can deploy in popular social media applications like Facebook, slack, and telegram.
  • A chatbot can deploy any website to clarify basic doubts of viewer


By doing the above procedure and all we successfully created an Intelligent help desk smart chatbot using Watson assistant, Watson discovery,

Node-RED and cloud functions.


We can include Watson studio text-to-speech and speech-to-text services to access the chatbot hands-free. This is one of the future scopes of this project.

Body Fitness Prediction using Random Forest Classifier Project

Purpose of the Project

To avoid several health issues, we should monitor our body fitness by using various fitness prediction gadgets like smartwatches, oximeters, B-P machines, etc. we can monitor our B-P, calories burnt, bone weight, etc. the devices work with smart device technology to exchange data via Bluetooth communication protocol. Here, in this project, we import the data which consists of (date, step count, mood, calories burned, hours of sleep, bool of activity, and weight in kg) and split the dataset into the testing set and training set. We are using a random forest classifier in this project.

Existing problem

Body fitness prediction play’s a key role in leading a healthy life. Fitness is a state of health and well-being, more specifically the ability to perform daily activities body fitness is generally achieved through proper nutrition and physical exercise, and rest. By this, we are losing our body fitness and it leads to various chronic issues

Proposed solution

Importing Dataset

Exploratory Data Analysis ]: df. shape

Here, in this project, we import the data which consists of (date, step count, mood, calories burned, hours of sleep, bool of activity, and weight in kg) and split the dataset into the testing set and training set. We are using a random forest classifier in this project.



We will use the body fitness prediction dataset which was retrieved from

  • Check if there are associations between physical activity (by counting steps), caloric expenditure, body weight hours of sleep, and the feeling of feeling active and/or inactive.
  • Compare caloric expenditure between the categories of mood and self-perceived activity (active and inactive)
  • Compare the hours of sleep between the categories of mood and self-perceived activity (active and inactive)
  • Compare body weight between categories of self-perceived activity (active and inactive)
  • Database The database has 96 observations, and 7 columns. Its quantitative variables are “number of steps” (step_count), “caloric expenditure” (calories_burned), “hours of sleep” (hours_of_sleep and “body weight” (weight_kg). And qualitative variables “dates” (date), “mood” “(mood), self-perceived activity” active or inactive “(bool_of_active). The variable” humor “was assigned the value” 300 “to mean” Happy “, the value” 200 “for” Neutral “and” 100 “for” sad “and for the variable” self-perceived activity
  • Contingency tables of categorical variables will be exposed.
  • A correlation matrix between variables will be presented
  • Bar charts and violins to demonstrate the distribution of quantitative variables by categories
  • Scatter plot for analysis of the possible linear relationship between two variables


Output result


Random Forest Classifier


Correlation Plot


Body fitness prediction Output


There are so many different kinds of applications used to predict the fitness of Human beings today.


Splitting the data:

We use sklearn. ensemble module train_test_split which is used for the training and testing part.

Dependent and Independent variables:

Independent variables contain a list of variables on which the bool of activity is dependent.

The dependent variable is the variable that is dependent on the other variable’s values.

Independent variables are mood,step_count, calories burned, hours of sleep,weightkg.

The dependent variables are bool_of_active.


We use Random Forest Classifier for predicting Body Fitness Prediction. Because it gives an accurate prediction.


We have analyzed the Body fitness prediction Data and used Machine Learning to Predict the fitness of a human being. We have used a Random Forest classifier and its variations, to make predictions and compared their performance. xgboost regressor has the lowest RMSE and is a good choice for this problem.

Intelligent Customer Help Desk Python and Node-Red Project

Project Summary:

In this Intelligent Customer Help Desk project, we need to create a chatbot application that can answer the question(s) that falls outside the scope of the pre-determined question set.

This can be done using a chatbot that will use the intelligent document understanding feature of Watson Discovery. 

Project Requirements:

IBM Cloud, IBM Watson, Python, Node-Red.

Project Scope:

In this Python and Node-Red Project, we need to create a website first using HTML code. Next, we should create a chatbot with help of IBM Watson Assistant and Watson discovery.

Using Node-Red we need to build a web application that integrates all services and deploys the same on the IBM cloud.

This project will answer all queries of the user and if any question falls outside the scope of the predetermined question set then this project will use the Smart Document Understanding feature of Watson Discovery to train it on what text in the owner’s manual is important and what is not.

This will improve the answers returned from the queries.

AI-Powered News Articles Search Web Application using IBM Cloud and Slack Bot


The purpose of this News Articles Search project is to develop a web application that fulfills our need to find the obvious and recent news articles and update them regularly. After the discovery service is integrated with Slack Workspace, it gives a bot as an intermediate to search news with a keyword. In addition, the web application also analysis the sentimental present in the news article and extracts keywords and concepts to make it an attractive and understandable format for the user to understand what is important and what is not.

Literature Survey

Existing Problem

News Article applications that are currently used are confusing the users, with multiple functions and an overflow of design, these applications still do not fulfill the demand of the news users and often get results from the past days, weeks, and months, which confuses the users only. Also, there is no way in these apps to know what the approximate feeling of the audience is regarding the article or news topic, which makes it less interactive and very low number of users.

Proposed Solution

Discovery service available in the IBM cloud, creating a web app to get the latest and obvious news results fast and user friendly. When integrated with Red Node Flow, the IBM Discovery Service can create a simple, engaging, organized user interface that provides users with relevant news articles as Discovery Service continuously crawls the web for the latest news to provide. By adding emotional analysis, we make the user interface more interactive, easier to understand, and attain more users.

Project Tasks

1. Creating and deploying the Watson discovery news app locally.
2. Integrating Slack-bot with Watson Discovery.
3. Creating node-red user Interface.
4. Integrating node-red UI with Watson Discovery.

Flow Chart:

Flow Chart

Experimental Investigation

First, we use the discovery service to configure and query adding our collection. A red node application is created in which the discovery is integrated and a simple flow of 5 nodes is created to enter the news topic and the results show related news. Slack then integrates with Watson’s discovery service so that news articles can be searched on more than one platform, and finally, sentiment analysis is performed on the data/news articles being searched.

Advantages and Disadvantages

1. The News Articles Search web application provides interactive sentiment analysis.
2. It can be accessed through more than one platform which is slack.
3. It collects and delivers the most recent data.
4. It does not have additional features like storing news history.
5. It does not provide a stand-alone app but rather uses a web application.


1. This News Articles Search web application can be used by any user in need of accurate and fast results.
2. Can be used by firms and organizations.
3. Can be used in the stock market to make predictions.

Bot on slack

Bot on slack


This News Articles Search project gives some basic working knowledge of the Watson Discovery Service and showed you how to use Discovery along with JavaScript and Node.js to build your own news mining web application. It also gives insight into real-world applications of AI and helps us understand Slack better.

Future Scope

1. The IBM Cloud and Slack Bot web application can be integrated with the cloud and made into a mobile app to use on it on-the-go.
2. Additional sentiments can be added to the UI.
3. Related and trending news topics can be shown to the user.


Development of Speech Recognition AI Project with Python


Working on the Speech Recognition Python Project. Design and Development of Speech Recognition AI Project with Python Source code, report, and ppt using NLP, PLP, and Deep Neural Networks.

Speak– The assistant will speak the following introduction, the output, and the following things according to which good is given. It will use the laptop microphone to hear the input from the user and later recognize the voice said by the user and match the code words and if anything matches it will show the output.

Wish Me-The assistant will speak the Message included in the introduction even if it will wish the morning afternoon and even the evening depending upon the real-time based scenario. It will wish the morning from 04HH to 11HH 59MM. It will wish the afternoon from 12HH to 17HH 59MM. It will wish the evening from 18HH to 03HH 59MM.

Take Command– The assistant will take microphone(speech) input from the user and returns string output. It will be sub-divide into many different parts as described below. Listening-The assistant will open the microphone and try to hear what the user wants to convey to it.

Recognizing– The assistant will try to recognize the input spoken by the user and then check the code whether the word that is recognized by the assistant is there or not if the input matches it will show the output otherwise it will speak “Say that again please” this line which means to give the input again by the user. If the word is correctly recognized, it will follow the instructions assigned to it.

Wikipedia– If the word is recognized as “Wikipedia” it will search Wikipedia according to the input given by the user. E.g. if we say Narendra Modi Wikipedia so the assistant will speak “searching Wikipedia Narendra Modi” and then after it “According to Wikipedia…” and the details of that particular person. Youtube- If the word is recognized as “YouTube”, it will open the internet explorer and directly start opening the default web browser by the link “”.

Google– If the word is recognized as “Google”, it will open the internet explorer and directly start opening the Google by the link “”.

Train Information– If the word is recognized as “Train info”. It will fetch the detail from a CSV file and returns the detail of all the train and display them on the terminal. Stack Overflow- If the word is recognized as “Stack Over Flow” it will open the internet explorer and directly start opening the Stack Over Flow website by the link “”.

Play Music– If the word is recognized as “Play Music” it will search the .mp3 or .mp4 file in the default path of the device that is provided by the programmer in the programming. E.g. if we say Play Music so the assistant will search in the path like “D:\\Non Critical\\songs\\Favourite Songs2” and it will play that particular song. The Time- If the word is recognized as “The Time” it will check the real-time from the device and speak the same in terms of “HH:MM: SS”. E.g. if we say the time so the assistant will check the time and if the time is 08:14:21 P.M. it will speak “Sir, the time is 20HH:14MM:21SS”.

Open Code– If the word is recognized as “Open Code” it will search the .java or .py file in the default path of the device that is provided by the programmer in the programming. E.g. if we say Open Code so the assistant will search in the path like “C:\\Users\\XYZ\\AppData\\Local\\Programs\\” and it will open the code. Stop- If the word is recognized as “Stop” it will speak “Quitting sir thanks for your time” and the code terminates.


Speech Recognition Project Coding

Algorithms used in Speech Recognition

NLP (Natural Language Processing) & Tokenization
Deep Neural Networks
Discrimination training
WFST Frameworks etc;

The following must be installed-:

1. sudo pip install SpeechRecognition.
2. Sudo apt-get installs python-pyaudio python3-pyaudio or pip install pyaudio.
This is the most important module in your project as it provides the main functionality in our project to convert speech into text.

Future Scope

This specific area of AI ends up being productive in each specialized field. We have additionally actualized this to show how it is valuable in various fields as we have made a little undertaking to exhibit its use in various documented, for example, railroad, looking through feed and so on; Like PCs began to play chess better than human, speech recognition before long will be improved by PCs as well. Critically, that will include some significant information about nature in general and the human mind specifically. So speech recognition is a significant advance in our investigation of natural laws. Our venture can be utilized by railroads and another center point to show distinctive data utilizing speech recognition.

Used Car Price Prediction AI / Machine Learning Project using Python


Used Car price prediction using AI / Machine Learning techniques has picked researchers’ interest since it takes a significant amount of work and expertise on the part of the field expert. For a dependable and accurate forecast, a large number of unique attributes are analyzed. We employed 6 different machine learning approaches to develop a model for forecasting the price of used automobiles.

Problem statement

With the Coronavirus sway on the lookout, we have seen a lot of changes in the vehicle market. Presently some vehicles are sought after subsequently making them exorbitant and some are not popular and consequently less expensive. With the adjustment of the market due to the Coronavirus 19 effect, people/sellers are facing issues with their past Car Price valuation AI/Machine Learning models. Along these lines, they are searching for new AI models from new information. Here we are building the new car price, valuation model.

The primary point of this Used Car Price Prediction AI / Machine Learning Project is to create a dataset with the help of web scraping and anticipate the cost of a trade-in vehicle given different elements.

The objective of the Project:

1. Data Collection: To scrape the data of at least 5000 used cars from various websites like Olx, cardekho, cars24, auto portal, cartrade, etc.
2. Model Building: To build a supervised machine learning model for forecasting the value of a vehicle based on multiple attributes.

Motivation Behind the Project:

There are a few major worldwide multinational participants in the automobile sector, as well as several merchants. By trade, international companies are mostly manufacturers, although the retail industry includes both new and used automobile dealers. The used automobile market has seen a huge increase in value, resulting in a bigger percentage of the entire market. In India, about 3.4 million automobiles are sold each year on the secondhand car market.

Collecting the data

We have scraped the data for over 5000 cars using Selenium script from 4 different websites from different locations around the country. The websites are as followed:
1. OLX
2. Cars24
3. CarDekho
4. Autoportal

There are 9 columns:

1.’Brand & Model’: It gives us the brand of the car along with its model name and      manufacturing year

2.’Varient’: It gives us a variety of particular car model

3.’Fuel Type’: It gives us the type of fuel used by the car

4.’Driven Kilometers’: It gives us the total distance in km covered by car

5.’Transmission’: It tells us whether the gear transmission is Manual or Automatic

6.’Owner’: It tells us the total number of owners cars had previously

7.’Location’: It gives us the location of the car

8.’Date of Posting Ad’: It tells us when the advertisement for selling that car was posted online

9.’Price (in ₹)’: It gives us the price of the car.

Here ‘Price (in ₹)’ is our target variable.

Reading the dataset

Now we read the dataset into Pandas and since the target column ‘Price’ is of integer data type, we will apply regression algorithms to it.

Data Cleaning

We check for null values and find that there are few in column ‘Variant’ and we will treat them with Mode.
Since all the features are categorical hence we need not check for outliers and skewness.
Exploratory data analysis
Firstly, we will plot the boxplot and distribution plot for the target variable. And find that few outliers need not be treated and the data is tightly distributed with an almost normalized distribution.

Bar graph

Since Brands, Varients, Driven Kilometers & locations have a wide range of values in them, we will not perform bivariate analysis for them as they will not give us any specific details. Now by plotting the graph of Fuel Type, Transmission, and Owner against Price, we conclude that a Car that uses Diesel has automatic Transmission, and Has only 1 owner is more likely to have a high price.

Model building

The models used in training and testing datasets are as followed:

Linear Regression
SGD Regressor
neighbors Regressor
Decision Tree Regressor
Random Forest Regressor
Only Decision Tree Regressor and Random Forest Regressor are performing well and giving an accuracy of 80.2 % and 87.7%, respectively.

Final model

The accuracy of Model ‘PriceCar’ (Random Forest Regressor) after applying Hyper Tuned Parameters is found to be 87.79% and the score is 0.98 which is quite good.


Here, we can see that all the predicted prices are either equal or nearly equal to the original prices of the car. Hence we conclude that our model ‘price car’ is working very well. And we shall save it for further use.

Limitations of this work and Scope for Future Work

As a part of future work, we aim at the variable choices over the algorithms that were used in the project. We could only explore two algorithms whereas many other algorithms exist and might be more accurate. More specifications will be added to a system or provide more accuracy in terms of price in the system i.e.
1) Horsepower
2) Battery power
3) Suspension
4) Cylinder
5) Torque

As we know technologies are improving day by day and there is also advancement in-car technology, so our next upgrade will include hybrid cars, electric cars, and Driverless cars.

Download Used Car Price Prediction AI / Machine Learning Project using Python. For more details about the project feel free to contact the developer at github

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