Detection of Attacks Using Multilayer Perceptron Algorithm

2022 ◽  
pp. 943-950
Author(s):  
S. Dilipkumar ◽  
M. Durairaj
Keyword(s):  
Multilayer Perceptron ◽  
Perceptron Algorithm

Functional Testing on Heart Disease Detection Using Multilayer Perceptron Technique

2020 ◽  
Vol 17 (9) ◽  
pp. 3915-3920
Author(s):  
E. Naresh ◽  
Madhuri D. Naik ◽  
M. Niranjanamurthy ◽  
Sahana P. Shankar
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Heart Diseases ◽  
Health Data ◽  
Functional Testing ◽  
Prediction System ◽  
Perceptron Algorithm ◽  
Artificial Neural

The proposed work shows how important and useful testing for any machine learning application and need to make better models such that bugs and errors are minimized. The proposed work considers patient’s health data which can be used for decision making or prediction using various calculation, in this work, developing a heart condition prediction system mainly concentrating on artificial neural network, which uses the multilayer perceptron algorithm for the execution. Our dataset consists of vital information about the patient such as age, gender, blood pressure, ECG measures, and stroke history. This labeled dataset predicts the probability of a patient to have heart diseases or not. To fulfill the proposed work, created a GUI to get the information about a new patient. Once the development of the model finished, complete functionality testing was applied. The report of the testing helped in identifying the bugs and errors which on rectification by the developer helped in increasing the accuracy of the artificial neural network.

scholarly journals Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

2021 ◽  
Vol 0 (0) ◽  
pp. 14-27
Author(s):  
Omid Hazbeh ◽  
Mehdi Ahmadi Alvar ◽  
Saeed Khezerloo-ye Aghdam ◽  
Hamzeh Ghorbani ◽  
Nima Mohamadian ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Volume Factor ◽  
Hybrid Computing ◽  
Perceptron Algorithm ◽  
Oil Formation Volume Factor ◽  
Oil Formation ◽  
Computing Models

scholarly journals Remote sensing tree classification with a multilayer perceptron

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6101 ◽  
Author(s):  
G Rex Sumsion ◽  
Michael S. Bradshaw ◽  
Kimball T. Hill ◽  
Lucas D.G. Pinto ◽  
Stephen R. Piccolo
Keyword(s):  
Multilayer Perceptron ◽  
Tree Species ◽  
Scientific Progress ◽  
Training Data ◽  
Support Vector ◽  
Species Classification ◽  
Perceptron Algorithm ◽  
Average Accuracy ◽  
Vector Machines ◽  
Lidar Observations

To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8–93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species.

scholarly journals Recommender System for Topic Articles Based on Forum Trending using Multilayer Perceptron

2020 ◽  
Vol 8 (6) ◽  
pp. 2478-2486
Keyword(s):  
Human Resources ◽  
Recommender System ◽  
Multilayer Perceptron ◽  
Topic Modeling ◽  
Family Health ◽  
Editorial Team ◽  
Limited Time ◽  
Discussion Forums ◽  
Perceptron Algorithm ◽  
Over Time

SehatQ is a portal and application that helps manage personal and family health. One of SehatQ's services is providing information and directories in the form of articles. To improve relations with web visitors, SehatQ also provides services in the form of discussion forums. The forum actually contains a variety of topics and changes very quickly over time, so to identify a topic from a collection of forums is very difficult and time-consuming if done manually by humans. But unfortunately the SehatQ editorial team has limited time and human resources in sorting out information sourced from the SehatQ forum to draw conclusions as a topic in the article. This research will offer a solution in analyzing Topic modeling using text mining with the Multilayer perceptron algorithm to provide trending information on the topics most frequently discussed at the forum at a certain time.

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