scholarly journals A Survey on Fatigue Detection of Workers Using Machine Learning

2020 ◽  
Vol 11 (3) ◽  
pp. 1-8
Author(s):  
Nisha Yadav ◽  
Kakoli Banerjee ◽  
Vikram Bali
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Human Performance ◽  
Learning Algorithm ◽  
Facial Features ◽  
Machine Learning Algorithm ◽  
Computer Screen ◽  
Health Risk Factor ◽  
Fatigue Detection

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

scholarly journals Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0241696
Author(s):  
Xubo Leng ◽  
Margot Wohl ◽  
Kenichi Ishii ◽  
Pavan Nayak ◽  
Kenta Asahina
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Confidence Levels ◽  
Training Images ◽  
Neuroscience Research ◽  
Automated Quantification

Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. Such steps include the design of the algorithm for machine learning, the methods used for animal tracking, the choice of training images, and the benchmarking of classification outcomes. However, how these design choices contribute to the interpretation of automated behavioral classifications has not been extensively characterized. Here, we quantify the effects of experimenter choices on the outputs of automated classifiers of Drosophila social behaviors. Drosophila behaviors contain a considerable degree of variability, which was reflected in the confidence levels associated with both human and computer classifications. We found that a diversity of sex combinations and tracking features was important for robust performance of the automated classifiers. In particular, features concerning the relative position of flies contained useful information for training a machine-learning algorithm. These observations shed light on the importance of human influence on tracking algorithms, the selection of training images, and the quality of annotated sample images used to benchmark the performance of a classifier (the ‘ground truth’). Evaluation of these factors is necessary for researchers to accurately interpret behavioral data quantified by a machine-learning algorithm and to further improve automated classifications.

Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm

2021 ◽  
pp. 21-30
Author(s):  
S. Magesh ◽  
V. R. Niveditha ◽  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
P. S. Rajakumar
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Comparative Study ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals NDVI Classification using Supervised Learning Method

2020 ◽  
Vol 9 (3) ◽  
pp. 721-728
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Land Utilization ◽  
Land Quality ◽  
Novel Approach

With the blessings of Science and Technology, as the death rate is getting decreased, population is getting increased. With that, the utilization of Land is also getting increased for urbanization for which the quality of Land is degrading day by day and also the climates as well as vegetations are getting affected. To keep the Land quality at its best possible, the study on Land cover images, which are acquired from satellites based on time series, spatial and colour, are required to understand how the Land can be used further in future. Using NDVI (Normalized Difference Vegetation Index) and Machine Learning algorithms (either supervised or unsupervised), now it is possible to classify areas and predict about Land utilization in future years. Our proposed study is to enhance the acquired images with better Vegetation Index which will segment and classify the data in more efficient way and by feeding these data to the Machine Learning algorithm model, higher accuracy will be achieved. Hence, a novel approach with proper model, Machine Learning algorithm and greater accuracy is always acceptable

scholarly journals Wisconsin quality of life machine learning algorithm for predicting quality of life in kidney stone patients

2020 ◽  
Vol 19 ◽  
pp. e1195-e1196
Author(s):  
D-D. Nguyen ◽  
J.W. Luo ◽  
J.R.Z. Lim ◽  
K.B. Scotland ◽  
S.K. Bechis ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Kidney Stone ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos

2020 ◽  
Author(s):  
Kailas Vodrahalli ◽  
Roxana Daneshjou ◽  
Roberto A Novoa ◽  
Albert Chiou ◽  
Justin M Ko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

MP43-14 WISCONSIN QUALITY OF LIFE MACHINE LEARNING ALGORITHM FOR PREDICTING QUALITY OF LIFE IN KIDNEY STONE PATIENTS

2020 ◽  
Vol 203 ◽  
pp. e652
Author(s):  
Jack W. Luo* ◽  
David-Dan Nguyen ◽  
Jonathan RZ. Lim ◽  
Kymora B. Scotland ◽  
Seth K. Bechis ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Kidney Stone ◽  
Learning Algorithm ◽  
Machine Learning Algorithm
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