REDUCING SAMPLE SIZE REQUIREMENTS WHEN TESTING THE NORMALITY HYPOTHESIS BIOMETRIC DATA THROUGH A NEURAL NETWORK ASSOCIATION OF SEVEN STATISTICAL CRITERIA

Introduction. The aim of the work is to reduce the requirements to test sample size when testing the hypothesis of normality. Materials and Methods. A neural network generalization of three well-known statistical criteria is used: the chi-square criterion, the Anderson–Darling criterion in ordinary form, and the Anderson–Darling criterion in logarithmic form. Results. The neural network combining of the chi-square criterion and the Anderson–Darling criterion reduces the sample size requirements by about 40 %. Adding a third neuron that reproduces the logarithmic version of the Andersоn–Darling test leads to a small decrease in the probability of errors by 2 %. The article deals with single-layer and multilayer neural networks, summarizing many currently known statistical criteria. Discussion and Conclusion. An assumption has been made that an artificial neuron can be assigned to each of the known statistical criteria. It is necessary to change the attitude to the synthesis of new statistical criteria that previously prevailed in the 20th century. There is no current need for striving to create statistical criteria for high power. It is much more advantageous trying to ensure that the data of newly synthesized statistical criteria are low correlated with many of the criteria already created.

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Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network

Sustainability ◽

10.3390/su14020861 ◽

2022 ◽

Vol 14 (2) ◽

pp. 861

Author(s):

Han-Cheng Dan ◽

Hao-Fan Zeng ◽

Zhi-Heng Zhu ◽

Ge-Wen Bai ◽

Wei Cao

Keyword(s):

Neural Network ◽

Active Learning ◽

Convolutional Neural Network ◽

Sample Size ◽

Pavement Distress ◽

Image Labeling ◽

Gray Area ◽

Calculation Results ◽

Labeling Method ◽

And Training

Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural network, using active learning combined with reverse and correction labeling. According to the calculation results in this paper, the sample size required by the interactive labeling is about half of the traditional labeling method for the same recognition precision. Meanwhile, the accuracy of interactive labeling method based on the mean intersection over union (mean_IOU) index is 6% higher than that of the traditional method using the same sample size and training epochs. In addition, the accuracy analysis of the noise and boundary of the prediction results shows that this method eliminates 92% of the noise in the predictions (the proportion of noise is reduced from 13.85% to 1.06%), and the image definition is improved by 14.1% in terms of the boundary gray area ratio. The interactive labeling is considered as a significantly valuable approach, as it reduces the sample size in each epoch of active learning, greatly alleviates the demand for manpower, and improves learning efficiency and accuracy.

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Long-Short Term Neural Network Analysis of Center of Pressure of Gait

International Journal of Extreme Automation and Connectivity in Healthcare ◽

10.4018/ijeach.2020010102 ◽

2020 ◽

Vol 2 (1) ◽

pp. 15-34

Author(s):

Arshia Khan ◽

Janna Madden

Keyword(s):

Neural Network ◽

Vascular Dementia ◽

Center Of Pressure ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

Short Term ◽

Biometric Data ◽

Gait Patterns ◽

Walking Gait ◽

Early Predictors

Detection of vascular dementia in early stages of cognitive impairment is difficult to do in a clinical setting since the earliest changes are often discrete and physiological in nature. One major aspect of this is gait patterns. This project utilizes force-sensing platforms, motion capture, and EMG sensors to unobtrusively collect biometric data from an individual's walking gait patterns. The data parameters gathered were center of pressure, gait phase and end of unloading/toe-ff events. By quantifying and analyzing machine learning algorithms, specifically deep learning time-series based models, onset patterns of vascular dementia are explored with an overarching goal of creating a system that will assist in understanding and diagnosing cases of vascular dementia. The proposed system provides a tool for which gait can be analyzed and compared over a long period of time and opens opportunity to increased personalization in health monitoring and disease diagnosis and provides an avenue to increase patient-centricity of medical care. Since gait is one of the early predictors of vascular dementia, we developed a long short-term neural network to predict the gait variations from which we can predict the onset of vascular dementia.

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