Human Presence Detection with Thermal Sensor using Multilayer Perceptron Algorithm

2021 ◽  
Author(s):  
Lassi Puurunen ◽  
Jatin Chaudhary ◽  
Rajeev Kanth ◽  
Jukka Heikkonen
Keyword(s):  
Multilayer Perceptron ◽  
Thermal Sensor ◽  
Human Presence ◽  
Perceptron Algorithm ◽  
Human Presence Detection

Smart Light Control Using Thermal Sensor As Human Presence Detection

2021 ◽  
Author(s):  
Mario Vicky Rafliana Roostandi ◽  
Timotius Austin Nathaniel ◽  
Dafin Qinthara ◽  
Siswanto S.T. Boby
Keyword(s):  
Thermal Sensor ◽  
Light Control ◽  
Human Presence ◽  
Human Presence Detection

Human presence detection and tracking for a concierge robot

2004 ◽  
Vol 37 (8) ◽  
pp. 986-991
Author(s):  
Iñaki Rañó ◽  
Bogdan Raducanu ◽  
Sriram Subramanian
Keyword(s):  
Human Presence ◽  
Detection And Tracking ◽  
Human Presence Detection

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.

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