Improved Edge Detection Algorithm Based on Decision Tree

The edge detection is the important role in the image disposal. Traditional methods had some limitations more or less in practical applications such as pseudo-edge or need setting parameters by manual.Now, proposed a method can solve these problems in this paper. The histogram of gradient effective features was selected to composite the feature space, and during the process of classifier training, combined with AdaBoost and decision tree algorithm to improve the classification accuracy. Finally, the application of the algorithm proposed to image of Lena edge detection and comparative experimental show that the algorithm has better self-adaptability and good edge can be detected through this new algorithm.

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Relative Estimate of Revised Cardiovascular Risk Combining Traditional and Non-traditional Image-based CV Markers: A Kerala Based Study

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200218125539 ◽

2020 ◽

Vol 16 (9) ◽

pp. 1131-1153 ◽

Cited By ~ 1

Author(s):

Paulin Paul ◽

Noel George ◽

B. Priestly Shan

Keyword(s):

Cardiovascular Risk ◽

Decision Tree ◽

Classification Accuracy ◽

Multinomial Logistic Regression ◽

Hypertension Treatment ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

The Mean ◽

Risk Categories ◽

Traditional Image

Background: Non-traditional image markers can improve the traditional cardiovascular risk estimation, is untested in Kerala based participants. Objective: To identify the relationship between the ‘Modified CV risk’ categories with traditional and non-traditional image-based risk markers. The correlation and improvement in reclassification, achieved by pooling atherosclerotic non-traditional markers with Intermediate (≥7.5% and <20%) and High (≥20%) 10-year participants is evaluated. Methods: The cross-sectional study with 594 participants has the ultrasound measurements recorded from the medical archives of clinical locations at Ernakulum district, Kerala. With carotid Intima-Media Thickness (cIMT) measurement, the Plaque (cP) complexity was computed using selected plaque characteristics to compute the carotid Total Plaque Risk Score (cTPRS) for superior risk tagging. Statistical analysis was done using RStudio, the classification accuracy was verified using the decision tree algorithm. Results: The mean age of the participants was (58.14±10.05) years. The mean cIMT was (0.956±0.302) mm, with 65.6% plaque incidence. With 94.90% variability around its mean, the Multinomial Logistic Regression model identifies cIMT and cTPRS, age, diabetics, Familial Hypercholesterolemia (FH), Hypertension treatment, the presence of Rheumatoid Arthritis (RA), Chronic Kidney Disease (CKD) as significant (p<0.05). cIMT and cP were found significant for ‘Intermediate High’, ‘High’ and ‘Very High’ ‘Modified CV risk’ categories. However, age, diabetes, gender and use of hypertension treatment are significant for the ‘Intermediate’ ‘Modified CV risk’ category. The overall performance of the MLR model was 80.5%. The classification accuracy verified using the decision tree algorithm has 78.7% accuracy. Conclusion: The use of atherosclerotic markers shows a significant correlation suitable for a nextlevel reclassification of the traditional CV risk.

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Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

Wireless Communications and Mobile Computing ◽

10.1155/2021/4632722 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhu Gu ◽

Chaohu He

Keyword(s):

Decision Tree ◽

Mobile Computing ◽

Classification Accuracy ◽

Decision Tree Algorithm ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Data Set ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Fitness Industry

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.

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Gravitation-Based Edge Detection for Hyperspectral Images

10.20944/preprints201705.0142.v1 ◽

2017 ◽

Author(s):

Genyun Sun ◽

Aizhu Zhang ◽

Jinchang Ren ◽

Jingsheng Ma ◽

Peng Wang ◽

...

Keyword(s):

Edge Detection ◽

Feature Space ◽

Detection Algorithm ◽

Hyperspectral Images ◽

Detection Methods ◽

Gravitational Potential Energy ◽

Neighboring Pixel ◽

Key Issues ◽

Edge Detection Method ◽

Edge Features

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

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Decision Tree Generation Algorithm without Pruning

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.441.731 ◽

2013 ◽

Vol 441 ◽

pp. 731-737

Author(s):

Jing Gao

Keyword(s):

Decision Tree ◽

Rough Set ◽

Classification Accuracy ◽

Negative Impact ◽

Decision Tree Algorithm ◽

Generation Algorithm ◽

Tree Algorithm ◽

Tree Generation ◽

Negative Effect

On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying and predicting the coming data. To settle these problems, the restrained factor is introduced in this paper. For expanding nodes in generating decision tree algorithm, besides traditional terminating condition, an additional terminating condition is involved when the restrained factor of sample is higher than a given threshold, then the node will not be expanded any more. Thus, the problem of much more meticulous partition is avoided. Furthermore, the size of decision tree generated with restrained factor involved will not seem too large to be understood.

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