scholarly journals Zheng Classification with Missing Feature Values Using Local-Validity Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Yan Wang ◽  
Lizhuang Ma
Keyword(s):  
Liver Cirrhosis ◽  
Clinical Practice ◽  
Chinese Medicine ◽  
Classification Accuracy ◽  
Classification Performance ◽  
New Approach ◽  
Feature Values ◽  
Public Datasets ◽  
The Given ◽  
Missing Feature

Zheng classification is a very important step in the diagnosis of traditional Chinese medicine (TCM). In clinical practice of TCM, feature values are often missing and incomplete cases. The performance of Zheng classification is strictly related to rates of missing feature values. Based on the pattern of the missing feature values, a new approach named local-validity is proposed to classify zheng classification with missing feature values. Firstly, the maximum submatrix for the given dataset is constructed and local-validity method finds subsets of cases for which all of the feature values are available. To reduce the computational scale and improve the classification accuracy, the method clusters subsets with similar patterns to form local-validity subsets. Finally, the proposed method trains a classifier for each local-validity subset and combines the outputs of individual classifiers to diagnose zheng classification. The proposed method is applied to the real liver cirrhosis dataset and three public datasets. Experimental results show that classification performance of local-validity method is superior to the widely used methods under missing feature values.

scholarly journals Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4723
Author(s):  
Patrícia Bota ◽  
Chen Wang ◽  
Ana Fred ◽  
Hugo Silva
Keyword(s):  
Emotion Recognition ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Electrodermal Activity ◽  
Decision Fusion ◽  
Classification Performance ◽  
Physiological Data ◽  
Systematic Analysis ◽  
Public Datasets

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

ПАТОЛОГИЯ ЛИЧНОСТИ В ТЕОРЕТИЧЕСКИХ ПОСТРОЕНИЯХ ПРЕДСТАВИТЕЛЕЙ ОТЕЧЕСТВЕННОЙ МЕТАФИЗИЧЕСКОЙ ТРАДИЦИИ В ПСИХОЛОГИИ

1970 ◽  
Vol 7 (1) ◽  
pp. 208-215
Author(s):  
Александр Бреусенко-Кузнецов
Keyword(s):  
Clinical Practice ◽  
Subject Matter ◽  
Clinical Psychology ◽  
History Of Psychology ◽  
History Of ◽  
Metaphysical Tradition ◽  
The Given

Статья посвящена проблеме восстановления искусственно прерванной метафизической традиции в отечественной персонологии. Данная проблема принадлежит областям истории психологии и психологии личности, но имеет выходы и в предметные области многих других психологических наук, в частности – клинической психологии. Указана важность соотнесения персонологических концептуализаций учёных-метафизиков с клинической практикой в процессе их опытной верификации. Проведена реконструкция и анализ взглядов на психопатологию и психотерапию представителей метафизической традиции в отечественной психологии личности. Согласно данным взглядам, суть патологии личности – в её уклонении от своего назначения, от подлинного бытия ради неподлинных, онтологически неоправданных форм жизнедеятельности. The article is devoted to the problem of restoration of artificialy interrupted metaphysical tradition in domestic personology. The given problem belongs to the areas of history of psychology and psychology of personality, but provides outcomes in subject matter of many other psychological sciences, in clinical psychology in particular. Importance of correlation between personological conceptualizations of scientists-metaphysicists and clinical practice in the process of their skilled verification is pointed out. The reconstruction and analysis of views at psychopathology and psychotherapy by representatives of metaphysical tradition in domestic psychology of personality have been made. According to the mentioned views, the essence of pathology of personality is in its evasion from the purpose, from original life for the sake of not original, ontologically unjustified forms of ability to live.

Phytochemistry, Pharmacological Activities, Toxicity and Clinical Application of Momordica cochinchinensis

2019 ◽  
Vol 25 (6) ◽  
pp. 715-728 ◽  
Author(s):  
Hai-Yue Lan ◽  
Bin Zhao ◽  
Yu-Li Shen ◽  
Xiao-Qin Li ◽  
Su-Juan Wang ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Southeast Asia ◽  
Clinical Application ◽  
Chemical Constituents ◽  
Folk Medicine ◽  
Pharmacological Activities ◽  
Pharmacological Studies ◽  
Momordica Cochinchinensis

Momordica cochinchinensis (Lour.) Spreng (M. cochinchinensis) is a deciduous vine that grows in Southeast Asia. It is known as gac in Vietnam and as Red Melon in English. Gac is reputed to be extremely benificial for health and has been widely used as food and folk medicine in Southeast Asia. In China, the seed of M. cochinchinensis (Chinese name: Mu biezi) is used as traditional Chinese medicine (TCM) for the treatment of various diseases. More than 60 chemical constituents have been isolated from M. cochinchinensis. Modern pharmacological studies and clinical practice demonstrate that some chemical constituents of M. cochinchinensis possess wide pharmacological activities, such as anti-tumor, anti-oxidation, anti-inflammatory, etc. This paper reviews the phytochemistry, pharmacological activities, toxicity, and clinical application of M. cochinchinensis, aiming to bring new insights into further research and application of this ancient herb.

Aliskiren, a Direct Renin Inhibitor, in Clinical Practice: A New Approach in the Treatment of Hypertension

2010 ◽  
Vol 8 (3) ◽  
pp. 344-362 ◽  
Author(s):  
Elisavet Moutzouri ◽  
Matilda Florentin ◽  
Moses S. Elisaf ◽  
Dimitri P. Mikhailidis ◽  
Evangelos N. Liberopoulos
Keyword(s):  
Clinical Practice ◽  
Renin Inhibitor ◽  
New Approach ◽  
Direct Renin Inhibitor ◽  
Treatment Of Hypertension

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

2021 ◽  
Author(s):  
Sebastian Nowak ◽  
Narine Mesropyan ◽  
Anton Faron ◽  
Wolfgang Block ◽  
Martin Reuter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Liver Cirrhosis ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Automatic Segmentation ◽  
Analysis Data ◽  
Caudate Lobe ◽  
Natural Image ◽  
Liver Segmentation

Abstract Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. Results Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). Conclusion This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. Key Points • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

A Unified Approach to H∞ Control of Singular Markovian Jump Systems

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Guoliang Wang ◽  
Hongyi Li
Keyword(s):  
Transition Probability ◽  
Markovian Jump Systems ◽  
Linear Matrix ◽  
Markovian Jump ◽  
New Approach ◽  
Control Approach ◽  
Singular Markovian Jump Systems ◽  
Jump Systems ◽  
Bounded Uncertainties ◽  
The Given

This paper considers the H∞ control problem for a class of singular Markovian jump systems (SMJSs), where the jumping signal is not always available. The main contribution of this paper introduces a new approach to a mode-independent (MI) H∞ controller by exploiting the nonfragile method. Based on the given method, a unified control approach establishing a direct connection between mode-dependent (MD) and mode-independent controllers is presented, where both existence conditions are given in terms of linear matrix inequalities. Moreover, another three cases of transition probability rate matrix (TRPM) with elementwise bounded uncertainties, being partially unknown and to be designed are analyzed, respectively. Numerical examples are used to demonstrate the effectiveness of the proposed methods.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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