Non-invasive measure of heat stress in sheep using machine learning techniques and infrared thermography

2021 ◽  
pp. 106592
Author(s):  
A. Joy ◽  
S. Taheri ◽  
F.R. Dunshea ◽  
B.J. Leury ◽  
K. DiGiacomo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heat Stress ◽  
Infrared Thermography ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

Non-invasive Deception Detection in Videos Using Machine Learning Techniques

2021 ◽  
Author(s):  
Siam Islam ◽  
Popin Saha ◽  
Touhidul Chowdhury ◽  
Asif Sorowar ◽  
Raqeebir Rab
Keyword(s):  
Machine Learning ◽  
Deception Detection ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

scholarly journals Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Attique Ur Rehman ◽  
Tek Tjing Lie ◽  
Brice Vallès ◽  
Shafiqur Rahman Tito
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Water Heating ◽  
Energy Monitoring ◽  
Non Invasive ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Load Monitoring

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

scholarly journals Predicting dairy cattle heat stress using machine learning techniques

2021 ◽  
Vol 104 (1) ◽  
pp. 501-524
Author(s):  
C.A. Becker ◽  
A. Aghalari ◽  
M. Marufuzzaman ◽  
A.E. Stone
Keyword(s):  
Machine Learning ◽  
Heat Stress ◽  
Dairy Cattle ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1160 ◽  
Author(s):  
Monika Simjanoska ◽  
Martin Gjoreski ◽  
Matjaž Gams ◽  
Ana Madevska Bogdanova
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Techniques ◽  
Invasive Blood Pressure ◽  
Non Invasive ◽  
Learning Techniques ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Maryamsadat Shokrekhodaei ◽  
David P. Cistola ◽  
Robert C. Roberts ◽  
Stella Quinones
Keyword(s):  
Machine Learning ◽  
Optical Sensor ◽  
Glucose Monitoring ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

scholarly journals Non-Invasive Arterial Pressure Estimating With the Cardiac Monitor CardioQvark

2017 ◽  
Vol 12 (2) ◽  
pp. 536-546 ◽  
Author(s):  
О.В. Сенько ◽  
O.V. Senko
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Arterial Pressure ◽  
Systolic Blood Pressure ◽  
General Purpose ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Cardiac Monitor ◽  
Learning Techniques ◽  
Invasive Arterial Pressure

The outcome of the research on possibility to non-invasively estimate systolic blood pressure is presented. The estimating was performed by applying machine learning techniques to the data acquired with the cardiac monitor CardioQvark. The developed in Russia cardiac monitor represents a portable device capable of registering synchronous electrocardiogram and photoplethysmogram. The presented results confirm the possibility of constructing algorithms capable of estimating systolic blood pressure of individual patients. Also the possibility to construct general purpose algorithms, i.e. algorithms capable of estimating blood pressure of any patient without additional setup, was confirmed.

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