object detection and voice alert
Deep Learning Projects , Python Projects

Object detection and voice alert for visually impaired

Object detection with YOLO-V2 and voice alert for visually impaired is a Python project implemented with YOLO pre-trained model. This uses webcamera, it can used either externally or through laptop camera for demonstration. The system generates the voice alert and movement in direction such as ‘Right’ or ‘Left’ is also specified on the output. The object detected for the demonstration purpose on the given demo video is Person and Mobile phone. Whenever the objects moves, it gives voice alert to user.

How Object detection can be implemented with YOLO?

YOLO is the pre-trained model, which can detect more than 9000 classes of objects. The pre-trained weight files and class name files are loaded to the project directory. Opencv is the library used for YOLO object detection. More information of YOLO object detection can be viewed at https://opencv-tutorial.readthedocs.io/en/latest/yolo/yolo.html

How to generate Voice alert for Object detection project?

Voice alert can be integrated to our application through Python code. GTTS (Google Text to Speech) is the library used generally for conversion of detected object as text value to Speech data. This is very fast in conversion and more reliable to use.

How to get Architecture diagram for Object detection project?

The following figure shows the architecture diagram of the object detection project. This diagram shows all modules implemented on this work. Input for the project is Video stream converted to image frames for object detection detection. Output of the project is detected object is shown with bounding boxes and voice output.

Object detection and voice alert for visually impaired

Request Us Project Abstract and PPT Call 9600095046

Demo Video of Object Detection project

Related Object detection project are as follows

Luggage detection and approval in Airports using CBIR -Content based Image retrieval

Lane detection using CNN algorithm with Vehicle detection

speech emotion detection using d
Deep Learning Projects , Python Projects

Speech Emotion detection using Deep learning

Top 8 Human Speech Emotion detection or recognition using deep learning algorithm

Top 8 Human Speech emotion detection is identified in this project. It useful to understand the human emotions when they deliver through speech. Emotion of 8 categories namely Calm, Happy, Sad, Angry, Fearful, Disgust, Surprised and Neutral. These emotions are considered with female and male audio “.wav” wave files are considered as dataset. The speech emotion detection dataset represents as follows

File name example 03-01-01-01-01-01-02

Audio-only (03)
Speech (01)
Neutral (01)
Normal intensity (01)
Statement “dogs” (02)
1st Repetition (01)
12th Actor (02)
Female, as the actor ID number is even

The dataset is trained with Convolutional Neural Networks (CNN) algorithm. More specifically, CNN1 is used for training and validation. The implementation includes live detection of emotion from audio file, some audio we tested from Youtube. There are 8 emotion is classified in this project. Speech emotion detection is more useful to identify the person’s emotion without seeing face. The source code is neatly written in Python for speech emotion recognition from audio files. The following image represents the result of emotion on real time detection from audio input.

TestResult

You can Check the demo video below with complete implementation

Speech Emotion detection using Deep learning | ieee project demo

Human emotions are of two types, speech and face emotion. Here we studied Speech emotion. Human face emotions are also discussed in our previous post, Check the link

a particle swarm optimized featu
Deep Learning Projects , Python Projects

A Particle Swarm Optimized Feature selection and Learning for Fault Classification in Web-Apps

Web attacks are considered to be serious attack as the users of website may not get proper services if web application got an attack. The proposed work aim to classify web attacks as malicious and normal. There are 2 datasets used in proposed work are

  1. HTTP dataset CSIS 2010
  2. FWAF dataset created by FSECURITY

Deep neural network DNN algorithm is used for this study.

The efficient feature selection algorithm applied in this proposed work is PSO (Particle Swarm Optimization). The hybrid model of web attack detection is implemented.

The model outperformed with highest accuracy of 99.5% using FWAF dataset

The dataset is classified as binary classification model using DNN algorithm and source code is available in Python. Keras and Tensorflow are used for deploying and building a deep learning model for web attack detection. The following output image shows the accuracy of the DNN algorithm.

FWAFTrainigAccuracy

Further, the working model of the proposed work is available in following demo

A Particle Swarm Optimized Feature selection and Learning for Fault Classification in Web-Apps

Such kind of intrusion detection and attack detection and classification is available in the following link and provided more information

Intrusion detection and classification

Air-pollution prediction through feature selection and deep learning
Deep Learning Projects , Python Projects

Air pollution prediction through feature selection and deep learning

Air pollution detection

Air pollution detection and monitoring is one the important area as these days air pollution is increasing through various parameters such as vehicles, industries etc. Air pollution prediction is made easier through artificial intelligence. This proposed work used feature selection algorithm for choosing important attributes from dataset.

The dataset from Central Air pollution board is used for this study. It has many attributes and the labels are considered as multi class classification as given below

  1. Residential
  2. Industrial
  3. RIRUO
  4. Sensitive

Regression algorithm and Neural Network algorithm is used for air pollution detection of above class type. Logistic regression gives best accuracy of around 63% for air pollution prediction.

Air Pollution Detection

The following link shows you the implementation process and methodology used. Source code used is Python and dataset downloaded from Central pollution board of India.

Air-pollution prediction through feature selection and deep learning | ieee project demo

More Machine Learning projects are available, some listed below

Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Learning

Human Action Recognition Using Deep Learning
Deep Learning Projects , Python Projects

Human Action Recognition Using Deep Learning

Human Action Recognition using Deep learning

Human Action Recognition using Deep learning is implemented with CNN algorithm. This achieves good accuracy for GUI single input Video file.

What is Human Action Recognition?

Human actions are simply hand weaving, clapping, boxing, jogging, running, walking etc. Human action recognition refers to automated detection of any of these activities from human. This is helpful for monitoring human through surveillance camera.

Human Action recognition PPT

Human action recognition is python project and PPT contains Abstract, Introduction, Existing study, Literature survey, UML diagram.

Human activity detection is one of the challenging job, however, artificial intelligence made it easier. This detection is more useful in surveillance and monitoring purposes. The dataset with 6 activities is considered for training, which includes boxing, hand waving, hand clapping, jogging, running, walking.

Human action recognition project source code download

Human action recognition project source code download is available with highest accuracy on action detection. Deep learning algorithm Convolutional Neural Network (CNN) is used for activity classification project. The accuracy of the model is as high as 50%. Source code execution needs keras and tensorflow environment. The demo of the activity recognition is can be seen below. For Human action recognition PPT, architecture diagram, project report are available. Contact us

Human Action Recognition Using Deep Learning | ieee project demo

More works on human monitoring is available are

Helmet wear detection using artificial intelligence

Human face emotion detection by deep learning

Driver drowsiness detection by deep learning model

Face detection and attendance monitoring through artificial intelligence

Human Speech emotion detection by deep learning

College Admission Through ML and AI
Deep Learning Projects , Machine Learning Projects , Python Projects

College Admission Through ML and AI

This is a web application in Python Flask. This application enable users to make use of Artificial Intelligence to get the students details in admission portal. Face recognition is used to capture students face and train them. For further student update data, face recognition used to retrieve data from database.

Check demo under following link

College Admission Process through ML and AI | ieee project demo
Skin Disease Detection using deep learning
Deep Learning Projects , Python Projects

Skin Disease Detection using deep learning

Skin disease / lesion detection through artificial intelligence is the growing area of study. In this work, Skin disease is identified by deep learning algorithm. Convolutional Neural Network (CNN) was used to train.

This work has implemented with 3 different pre-trained algorithms models and compared their performance.

  1. Alexnet
  2. VGG-16
  3. ResNet-18

Below figure shows the UML diagram of Skin disease detection project using CNN algorithm

Uml Skin disease

The performance shows that Alexnet and ResNet was giving good accuracy more than 90%. The demo video of the project is given below.

Skin Disease detection using Deep learning | ieee project demo

As like skin disease various disease can be classified with deep learning, some of the are listed with corresponding links below

Liver Disease detection using Machine learning

Breast cancer prediction source code download

Diabetes detection through machine learning Project

Lung cancer detection and classification project in python

Fruit Quality Assessment using Artificial Intelligence
Deep Learning Projects , Python Projects

Fruit Quality Assessment using Artificial Intelligence

Artificial Intelligence in Agricultural and food packaging industry is growing nowadays. Fruit quality detection using machine learning is more easier for packaging business. As the demand for food increasing with growing population, fast detection of fruit quality is needed. The is handled binary classification “healthy” and “damaged”.

Image features are extracted from images of healthy and damaged fruit dataset are trained with Support Vector Machine (SVM) algorithm. Demo of the working model is show in below link. Source code of the project implementation in python

Fruit Quality Assessment using Artificial Intelligence | ieee project demo

Looking for similar project, don’t forget to check the

Fruit Stage Monitoring and Quality detection using Artificial intelligence

Plant Leaf Disease Detection using Deep learning algorithm
Deep Learning Projects , Python Projects

Plant Leaf Disease Detection using Deep learning algorithm

image
System Architecture for Plant Leaf Disease Detection

MODULES

The modules included in our implementation are as follows

  • Dataset collection
  • Data pre-processing
  • Training and prediction using Regression Models

DATASET COLLECTION

The dataset is downloaded from kaggle.com with two classes ‘healthy’ and ‘diseased’. The dataset contains plant leaf image with training set and test set folders.

The dataset variable names are described below

Variable nameAttribute Description
ClassBinary class ‘healthy’ and ‘diseased’
Training set364 images in diseased 388 images in healthy
Test set60 images in diseased 60 images in healthy

Project Demo Video

Plant Leaf Disease Detection through Deep Learning Algorithm