scholarly journals Study of Image Classification Accuracy with Fourier Ptychography

2021 ◽  
Vol 11 (10) ◽  
pp. 4500
Author(s):  
Hongbo Zhang ◽  
Yaping Zhang ◽  
Lin Wang ◽  
Zhijuan Hu ◽  
Wenjing Zhou ◽  
...  
Keyword(s):  
Linear Regression ◽  
Linear Relationship ◽  
Image Classification ◽  
Classification Accuracy ◽  
Numerical Aperture ◽  
Visual Appearance ◽  
Customized Training ◽  
Appearance Quality ◽  
The Difference ◽  
Strong Linear Relationship

In this research, the accuracy of image classification with Fourier Ptychography Microscopy (FPM) has been systematically investigated. Multiple linear regression shows a strong linear relationship between the results of image classification accuracy and image visual appearance quality based on PSNR and SSIM with multiple training datasets including MINST, Fashion MNIST, Cifar, Caltech 101, and customized training datasets. It is, therefore, feasible to predict the image classification accuracy only based on PSNR and SSIM. It is also found that the image classification accuracy of FPM reconstructed with higher resolution images is significantly different from using the lower resolution images under the lower numerical aperture (NA) condition. The difference is yet less pronounced under the higher NA condition.

Comparing usage between dynamic and static e-reference collections

2015 ◽  
Vol 34 (3) ◽  
pp. 78-88 ◽  
Author(s):  
Alain R Lamothe
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Relationship ◽  
Reference Data ◽  
Linear Regression Analysis ◽  
Content Type ◽  
Dynamic Content ◽  
Reference Collection ◽  
Simple Count ◽  
Strong Linear Relationship

Purpose – The purpose of this article was to present the results of a quantitative analysis that compared usage levels between an e-reference collection that has experienced continual updated content and growth and an e-reference collection that has not experienced any recent changes. The aim of the study was to determine quantitatively if e-reference collections with dynamic content experience greater levels of usage compared to e-reference collections that are static in both size and content. Design/methodology/approach – E-reference data were separated into a dynamic collection and a static collection. Usage for e-reference belonging to the dynamic collection was compared to usage of e-reference belonging to the static collection. The number of e-reference was obtained by simple count. Additional statistics tracked include the number of viewings. A linear regression analysis was used to determine the strength of the linear relationship between collection size and usage. Findings – Results indicate that e-reference collections that continue to grow in both size and content also continue to experience year-to-year increases in usage. E-reference collections that remain static in size and content experienced a decline in usage. A linear regression analysis indicates the existence of an extremely strong linear relationship between dynamic content and usage. A weaker linear relationship was calculated for static content. Originality/value – To this author’s knowledge, this research is the first to systematically and quantitatively compare usage levels between e-reference titles from growing collections to collections that have not had any new titles added recently.

Comparing usage between a Dynamic and a Static e-monograph Collection

2015 ◽  
Vol 34 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Alain R Lamothe
Keyword(s):  
Regression Analysis ◽  
Quantitative Analysis ◽  
Linear Regression ◽  
Linear Relationship ◽  
Design Methodology ◽  
Linear Regression Analysis ◽  
Content Type ◽  
Dynamic Content ◽  
Simple Count ◽  
Strong Linear Relationship

Purpose – The purpose of this paper is to present the results from a quantitative analysis comparing usage levels between an e-monograph collection that has experienced continual growth and an e-monograph collection that has not experienced any recent growth whatsoever. The aim of the study was to determine quantitatively if e-monograph collections with dynamic content experience greater levels of usage compared to e-monograph collections that are static in both size and content. Design/methodology/approach – E-monograph data were separated into a Dynamic and a Static Collection. Usage for e-monographs belonging to the Dynamic Collection was compared to usage of e-monographs belonging to the Static Collection. The number of e-monographs was obtained by simple count. Additional statistics tracked include the number of viewings. A linear regression analysis was used to determine the strength of the linear relationship between collection size and usage. Findings – Results indicate that e-monograph collections that continue to grow in both size and content also continue to experience year-to-year increases in usage, whereas e-monograph collections that remain static in size and content experience a decline in usage. A linear regression analysis indicates the existence of a very strong linear relationship that exists between Dynamic Collection size and usage. A weaker linear relationship was calculated for Static Collection size and usage. Originality/value – This research is one of very few studies systematically and quantitatively comparing usage levels between e-monographs from growing collections to collections that have not had any new titles added recently.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Feasibility study of assessing the Preclinical Alzheimer Cognitive Composite (PACC) score via videoconferencing

2021 ◽  
Author(s):  
Giulia Seghezzo ◽  
Yvonne Van Hoecke ◽  
Laura James ◽  
Donna Davoren ◽  
Elizabeth Williamson ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Clinical Practice ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Cognitive Tests ◽  
Strong Positive Correlation ◽  
Pearson Correlation Coefficient ◽  
Face To Face ◽  
The Difference

Abstract Background The Preclinical Alzheimer Cognitive Composite (PACC) is a composite score which can detect the first signs of cognitive impairment, which can be of importance for research and clinical practice. It is designed to be administered in person; however, in-person assessments are costly, and are difficult during the current COVID-19 pandemic. Objective To assess the feasibility of performing the PACC assessment with videoconferencing, and to compare the validity of this remote PACC with the in-person PACC obtained previously. Methods Participants from the HEalth and Ageing Data IN the Game of football (HEADING) Study who had already undergone an in-person assessment were re-contacted and re-assessed remotely. The correlation between the two PACC scores was estimated. The difference between the two PACC scores was calculated and used in multiple linear regression to assess which variables were associated with a difference in PACC scores. Findings Of the 43 participants who were invited to this external study, 28 were re-assessed. The median duration in days between the in-person and the remote assessments was 236.5 days (7.9 months) (IQR 62.5). There was a strong positive correlation between the two assessments for the PACC score, with a Pearson correlation coefficient of 0·82 (95% CI 0·66, 0·98). The multiple linear regression found that the only predictor of the PACC difference was the time between assessments. Interpretation This study provides evidence on the feasibility of performing cognitive tests online, with the PACC tests being successfully administered through videoconferencing. This is relevant, especially during times when face-to-face assessments cannot be performed.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

First report of redbacked cutworm damage to cow cockle [Vaccaria hispanica (Mill.) Rauschert], a potential new crop for western Canada

2011 ◽  
Vol 91 (2) ◽  
pp. 425-428 ◽  
Author(s):  
Christian Willenborg ◽  
Lloyd Dosdall
Keyword(s):  
Linear Relationship ◽  
Seed Yield ◽  
Seed Weight ◽  
Insect Pest ◽  
Western Canada ◽  
First Report ◽  
Euxoa Ochrogaster ◽  
Redbacked Cutworm ◽  
New Crop ◽  
Strong Linear Relationship

Willenborg, C. J. and Dosdall, L. M. 2011. First report of redbacked cutworm damage to cow cockle [ Vaccaria hispanica(Mill.) Rauschert], a potential new crop for western Canada. Can. J. Plant Sci. 91: 425–428. We report the effects of redbacked cutworm Euxoa ochrogaster (Guenée) on cow cockle [Vaccaria hispanica (Mill.) Rauschert] plant height, seed yield, and 1000-seed weight (TSW). Euxoa ochrogaster damage to plots varied considerably among genotypes, with some genotypes exhibiting <10% damage and others >45%. Seed yield also varied significantly among genotypes and exhibited a strong linear relationship with the extent of E. ochrogaster damage. This is the first known report of any insect pest feeding on cow cockle. Results suggest that E. ochrogaster has the potential to cause significant losses in cow cockle crops.

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