Teaching behavior evaluation model establishment and feature extraction

2016 ◽  
Author(s):  
Yinjia Shi ◽  
Jiwei Liu ◽  
Ye Zhang ◽  
Xin Zhao ◽  
Chaowen Yan
Keyword(s):  
Feature Extraction ◽  
Evaluation Model ◽  
Teaching Behavior

Research on Bone Age Automatic Judgment Algorithm Based on Deep Learning and Hand X-ray Image

2021 ◽  
Vol 11 (1) ◽  
pp. 156-161
Author(s):  
Xiang Shen ◽  
Feng Zhu ◽  
Zhi Sun ◽  
Shuli Zhao
Keyword(s):  
Feature Extraction ◽  
Evaluation Model ◽  
Image Data ◽  
Bone Age ◽  
Skeletal Age ◽  
Test Model ◽  
Automatic Assessment ◽  
Neural Network Learning ◽  
X Ray ◽  
Hand Bones

Objective: To learn the depth of paper using feature extraction, combined with critical areas of heat syndrome and related information, X-ray image of hand to analyze bone age children. Methods: The thesis of the X-ray image data preprocessing left hand, the use of depth of depth neural network learning methods, combined with clinical data skeletal age evaluation model to evaluate the effectiveness of the test model. Results: X-ray image of hand artificial feature extraction, combined SVM classification, automatic assessment of skeletal age. The method of automatic assessment of bone age SVM-based feature primarily artificial, SIFT features extracted image, LBP features, characteristics of GCLM, these features are combined, and then used to train the SVM, have some ability to automatically assess bone age assessment based on SVM. Conclusion: This topic X-ray image based on the hand bones, computer vision, machine learning to extract the relevant methods, pretreatment and segmentation of X-ray images of the hand bones, characterized by automatic assessment of bone age, lack the core image of the sample problem.

scholarly journals Plant Species Classification through New Feature Extraction Model-Velocity Clamping Based Intersecting Cortical Model

2019 ◽  
Vol 9 (1S) ◽  
pp. 22-26
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Evaluation Model ◽  
Research Area ◽  
Cortical Model ◽  
Species Classification ◽  
Geometric Moments ◽  
Active Research ◽  
New Feature ◽  
Extraction Model

Plant classification is an active research area. The purpose of our current work is to develop a suitable feature extraction model. This paper suggests a technique to extract the geometric invariants of leaf images using a new velocity clamping based particle swarm optimized intersecting Cortical Model (VCPSO-ICM). Earlier geometric moments were assessed by transforms, separate normalization was used and they were costly. Intersecting cortical model (ICM) is used to avoid the usage of separate normalization for moment invariants of leaf images. In this model, the image is directly processed, as there is no need for preprocessing images. Parameters used in the intersecting cortical model (ICM) are difficult to set for each image separately. This is solved by our model. Time sequences are extracted from each image based on new parameters. Finally, a neural network is preowned to segregate the species of leaf images. This new feature evaluation model is tested on leaf snap database and results are compared with traditional Pulse Coupled neural network (PCNN), simplified Intersecting Cortical Model (ICM).This model achieves a higher accuracy than the existing methods.

Method of Health Performance Evaluation and Fault Prognostics for Electronic Equipment

2012 ◽  
Vol 229-231 ◽  
pp. 1343-1346
Author(s):  
Fei Xin Lou ◽  
Xi Hong Chen ◽  
Yu Liang Xu ◽  
Ji Zhe Sun ◽  
Fa Wen Wu
Keyword(s):  
Feature Extraction ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Kernel Method ◽  
Evaluation Model ◽  
Performance Degradation ◽  
Electronic Equipment ◽  
Health Evaluation ◽  
Health Performance ◽  
Fault Prognostics

To deal with the health performance degradation of electronic equipment, a new health evaluation method based on improved manifold learning algorithm and HMM is proposed in this paper. Firstly, according to SNPP algorithm KSUNPP is proposed by introducing an uncorrelated constraint and kernel method, and the improved algorithm is used for feature extraction. Secondly, the health evaluation model of electronic equipment is constructed. Then, by calculating KL distance which can measure the fault degradation, the model can evaluate the health performance degradation. Finally, the proposed method is applied to health evaluation of electronic equipment which belongs to one type of missile, experiment results demonstrate that the method is effective.

Iris Feature Extraction Algorithm Valuation Based on the Fuzzy Clustering Evaluation Model

2014 ◽  
Vol 513-517 ◽  
pp. 555-558
Author(s):  
Jin Liu ◽  
Cang Ming Liu ◽  
Lei Zhao
Keyword(s):  
Feature Extraction ◽  
Fuzzy Clustering ◽  
Evaluation Model ◽  
Identification System ◽  
Evaluation Scheme ◽  
Clustering Model ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Iris Feature Extraction ◽  
Iris Identification

Iris Feature Extraction Algorithm Evaluation is an important link of evaluation on iris identification system. For the need of optimal feature extraction algorithm according to the identification, this thesis provides a evaluation scheme based on the fuzzy clustering model, and builds a relevant evaluation model, which proves to make a scientific evaluation on the algorithm in the phase of feature extraction.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Moving From Interprofessional Education Toward Interprofessional Practice: Bridging the Translation Gap

2019 ◽  
Vol 4 (5) ◽  
pp. 971-976
Author(s):  
Imran Musaji ◽  
Trisha Self ◽  
Karissa Marble-Flint ◽  
Ashwini Kanade
Keyword(s):  
Interprofessional Education ◽  
Interprofessional Collaboration ◽  
Evaluation Model ◽  
Implementation Strategies ◽  
Future Research ◽  
Successful Implementation ◽  
Interprofessional Practice ◽  
Translational Model ◽  
Key Stakeholders ◽  
Clear Identification

Purpose The purpose of this article was to propose the use of a translational model as a tool for identifying limitations of current interprofessional education (IPE) research. Translational models allow researchers to clearly define next-step research needed to translate IPE to interprofessional practice (IPP). Method Key principles, goals, and limitations of current IPE research are reviewed. A popular IPE evaluation model is examined through the lens of implementation research. The authors propose a new translational model that more clearly illustrates translational gaps that can be used to direct future research. Next steps for translating IPE to IPP are discussed. Conclusion Comprehensive reviews of the literature show that the implementation strategies adopted to date have fostered improved buy-in from key stakeholders, as evidenced by improved attitudes and perceptions toward interprofessional collaboration/practice. However, there is little evidence regarding successful implementation outcomes, such as changed clinician behaviors, changed organizational practices, or improved patient outcomes. The authors propose the use of an IPE to IPP translational model to facilitate clear identification of research gaps and to better identify future research targets.

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