Probabilistic Neural Network MATLAB

Probabilistic Neural Network MATLAB where Our experts can provide you with creative topics and exceptional support. Drop us all your reasech details we will help you to the fullest. To execute a PNN (Probabilistic Neural Network) in MATLAB, we offer detailed and simple procedures that are accompanied by suitable instance and probable project concepts for assisting you throughout this process:

Measures to Design a Probabilistic Neural Network in MATLAB

1. Data Creation:

• The dataset must be imported and preprocessed.
• Then, we have to classify the data into testing and training sets.

2. PNN Model:

• Encompassing the summation layer, input layer, pattern layer and output layer, the PNN framework ought to be specified.

3. Training the PNN:

• By using a training dataset, we must train the network.

4. Examining the PNN:

• To assess the functionality, make use of a test dataset to examine the network.

Sample Code for PNN in MATLAB

% Load and prepare the dataset

load fisheriris

data = meas;

labels = species;

% Encode labels as numeric values

labelNum = grp2idx(labels);

% Split data into training and testing sets

[trainInd,~,testInd] = dividerand(size(data,1),0.7,0,0.3);

trainData = data(trainInd,:);

trainLabels = labelNum(trainInd);

testData = data(testInd,:);

testLabels = labelNum(testInd);

% Define and train the PNN

spread = 0.1; % Spread parameter for the PNN

net = newpnn(trainData’, ind2vec(trainLabels’), spread);

% Test the PNN

predictedLabels = vec2ind(sim(net, testData’));

% Evaluate performance

accuracy = sum(predictedLabels == testLabels’) / length(testLabels);

disp([‘Accuracy: ‘, num2str(accuracy * 100), ‘%’])

% Confusion matrix

confMat = confusionmat(testLabels, predictedLabels);

disp(‘Confusion Matrix:’)

disp(confMat)

Description

1. Data Creation:

• Here, the Fisher Iris dataset is loaded. It is efficiently classified into training and testing sets.
• It uses grp2idx to encrypt labels as numeric values.

2. PNN Model:

• For the PNN model, the spread parameter is clearly specified. In the pattern layer, the width of the Gaussian functions is efficiently managed by the spread parameter.
• With the labels and training data, it implements newpnn and develops PNN.

3. Training the PNN:

• At the time of generation with newpnn, the PNN is trained inherently.

4. Examining the PNN

• On the test data, it deploys sim to test the trained network.
• It clearly estimates the authenticity of the network.
• A confusion matrix is visualized.

Important 50 probabilistic neural network Projects

The term PNN stands for Probabilistic Neural Network that can be used for solving the complex problems in pattern recognition. A collection of 50 intriguing and capable topics on PNN is proposed by us:

1. Customer Segmentation in Marketing Using PNN
2. Weather Forecasting Using Probabilistic Neural Networks
3. Classification of Medical Data Using PNN
4. Network Intrusion Detection Using PNN
5. Stock Market Prediction with PNN
6. Image Recognition with Probabilistic Neural Networks
7. Fault Diagnosis in Engineering Systems Using PNN
8. Speech Recognition Using Probabilistic Neural Networks
9. Land Cover Classification Using Satellite Images and PNN
10. Traffic Flow Prediction with Probabilistic Neural Networks
11. Sentiment Analysis of Text Data with PNN
12. Handwriting Recognition Using PNN
13. Early Detection of Plant Diseases Using PNN
14. Renewable Energy Production Forecasting Using PNN
15. Energy Consumption Forecasting Using PNN
16. Detection of Manufacturing Defects Using Probabilistic Neural Networks
17. Personal Finance Management and Prediction Using PNN
18. Recommendation System for E-commerce Using PNN
19. Fraud Detection in Telecommunications Using PNN
20. Financial Time Series Forecasting Using PNN
21. Real-time Emotion Recognition Using PNN
22. Predicting Customer Lifetime Value Using PNN
23. Disease Outbreak Prediction with Probabilistic Neural Networks
24. Credit Scoring Using Probabilistic Neural Networks
25. Energy Load Forecasting Using PNN
26. Oil and Gas Reservoir Characterization Using PNN
27. Financial Risk Assessment Using Probabilistic Neural Networks
28. Pattern Recognition in Genomic Data with PNN
29. Biometric Authentication Using PNN
30. Quality Control in Manufacturing Using PNN
31. Sports Outcome Prediction Using PNN
32. Detection of Phishing Websites Using Probabilistic Neural Networks
33. Drug Discovery and Design with Probabilistic Neural Networks
34. Text Classification for Spam Detection Using PNN
35. Satellite Image Classification for Environmental Monitoring Using PNN
36. Human Activity Recognition Using Wearable Sensors and PNN
37. Prediction of Academic Performance Using PNN
38. Optical Character Recognition (OCR) with Probabilistic Neural Networks
39. Image Segmentation for Medical Imaging Using PNN
40. Detection of Credit Card Fraud Using Probabilistic Neural Networks
41. Classifying EEG Signals for Brain-Computer Interfaces Using PNN
42. Automated Essay Scoring Using Probabilistic Neural Networks
43. Predictive Maintenance in Industrial Systems Using PNN
44. Wildlife Species Identification Using PNN
45. Air Quality Monitoring and Prediction Using PNN
46. Crop Yield Prediction Using PNN
47. Personalized Recommendation Systems Using PNN
48. Anomaly Detection in Time Series Data with PNN
49. Real-time Object Detection Using PNN
50. Customer Churn Prediction Using PNN

Generally, MATLAB is used for designing novel algorithms, solving mathematical and numerical computational problems. By using MATLAB, here we offer crucial measures to execute a PNN in an efficient manner.