scholarly journals An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Li Xiong ◽  
Huiqi Li ◽  
Liang Xu
Keyword(s):  
Standard Deviation ◽  
Decision Tree ◽  
Cataract Surgery ◽  
Experimental Results ◽  
Retinal Images ◽  
Vitreous Opacity ◽  
Local Standard ◽  
Kappa Value ◽  
Automatic Grading ◽  
Universal Measure

Cataract is one of the leading causes of blindness in the world’s population. A method to evaluate blurriness for cataract diagnosis in retinal images with vitreous opacity is proposed in this paper. Three types of features are extracted, which include pixel number of visible structures, mean contrast between vessels and background, and local standard deviation. To avoid the wrong detection of vitreous opacity as retinal structures, a morphological method is proposed to detect and remove such lesions from retinal visible structure segmentation. Based on the extracted features, a decision tree is trained to classify retinal images into five grades of blurriness. The proposed approach was tested using 1355 clinical retinal images, and the accuracies of two-class classification and five-grade grading compared with that of manual grading are 92.8% and 81.1%, respectively. The kappa value between automatic grading and manual grading is 0.74 in five-grade grading, in which both variance and P value are less than 0.001. Experimental results show that the grading difference between automatic grading and manual grading is all within 1 grade, which is much improvement compared with that of other available methods. The proposed grading method provides a universal measure of cataract severity and can facilitate the decision of cataract surgery.

scholarly journals A Digital Improvement—Trimming a Digital Temperature Sensor with EEPROM Reprogrammable Fuses

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1700
Author(s):  
Anca Mihaela Vasile (Dragan) ◽  
Alina Negut ◽  
Adrian Tache ◽  
Gheorghe Brezeanu
Keyword(s):  
Standard Deviation ◽  
Power Supply ◽  
Temperature Sensor ◽  
Experimental Results ◽  
Temperature Error ◽  
Thermal Sensor ◽  
Memory Cells ◽  
Temperature Range ◽  
Read Only Memory ◽  
Digital Temperature Sensor

An EEPROM (electrically erasable programmable read-only memory) reprogrammable fuse for trimming a digital temperature sensor is designed in a 0.18-µm CMOS EEPROM. The fuse uses EEPROM memory cells, which allow multiple programming cycles by modifying the stored data on the digital trim codes applied to the thermal sensor. By reprogramming the fuse, the temperature sensor can be adjusted with an increased trim variation in order to achieve higher accuracy. Experimental results for the trimmed digital sensor showed a +1.5/−1.0 ℃ inaccuracy in the temperature range of −20 to 125 ℃ for 25 trimmed DTS samples at 1.8 V by one-point calibration. Furthermore, an average mean of 0.40 ℃ and a standard deviation of 0.70 ℃ temperature error were obtained in the same temperature range for power supply voltages from 1.7 to 1.9 V. Thus, the digital sensor exhibits similar performances for the entire power supply range of 1.7 to 3.6 V.

scholarly journals An image dehazing algorithm based on double priors constraint

2018 ◽  
Vol 189 ◽  
pp. 04009
Author(s):  
Kun Liu ◽  
Shiping Wang ◽  
Linyuan He ◽  
Duyan Bi ◽  
Shan Gao
Keyword(s):  
Standard Deviation ◽  
Mean Value ◽  
Experimental Results ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Color Distortion ◽  
Local Media ◽  
Dark Channel

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

scholarly journals Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multiple Classifiers ◽  
Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

scholarly journals EVALUATING EFFECTIVENESS OF ENSEMBLE CLASSIFIERS WHEN DETECTING FUZZERS ATTACKS ON THE UNSW-NB15 DATASET

2020 ◽  
Vol 36 (2) ◽  
pp. 173-185
Author(s):  
Hoang Ngoc Thanh ◽  
Tran Van Lang
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Cyber Security ◽  
Experimental Results ◽  
Ensemble Classifiers ◽  
Research Results ◽  
Ensemble Techniques ◽  
F Measure ◽  
Classification Quality

The UNSW-NB15 dataset was created by the Australian Cyber Security Centre in 2015 by using the IXIA tool to extract normal behaviors and modern attacks, it includes normal data and 9 types of attacks with 49 features. Previous research results show that the detection of Fuzzers attacks in this dataset gives the lowest classification quality. This paper analyzes and evaluates the performance of using known ensemble techniques such as Bagging, AdaBoost, Stacking, Decorate, Random Forest and Voting to detect FUZZERS attacks on UNSW-NB15 dataset to create models. The experimental results show that the AdaBoost technique with the component classifiers using decision tree for the best classification quality with F-Measure is 96.76% compared to 94.16%, which is the best result obtained by using single classifiers and 96.36% by using the Random Forest technique.

A novel astrophysics-based framework for prediction of binding affinity of glucose binder

2020 ◽  
Vol 34 (31) ◽  
pp. 2050346
Author(s):  
Rajesh Kondabala ◽  
Vijay Kumar ◽  
Amjad Ali ◽  
Manjit Kaur
Keyword(s):  
Decision Tree ◽  
Binding Affinity ◽  
Cross Validation ◽  
Learning Strategy ◽  
Experimental Results ◽  
The Other ◽  
Computational Time ◽  
Glucose Binding ◽  
Regression Algorithms

In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.

A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS

2012 ◽  
Vol 11 (02) ◽  
pp. 1250011 ◽  
Author(s):  
ZAHRA NIKDEL ◽  
HAMID BEIGY
Keyword(s):  
Decision Tree ◽  
Genetic Programming ◽  
Learning Algorithm ◽  
Fitness Function ◽  
Learning Algorithms ◽  
Hybrid Learning ◽  
Experimental Results ◽  
Programming Algorithm ◽  
Hybrid Learning Algorithm ◽  
Misclassification Costs

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

scholarly journals Objective assessment for characterising the flatness of garment sewing stitches

2013 ◽  
Vol 13 (4) ◽  
pp. 110-117
Author(s):  
Jiang Hongxia ◽  
Liu Jihong ◽  
Chai Zhilei ◽  
Wang Chunxia ◽  
Zhang Mingxia
Keyword(s):  
Standard Deviation ◽  
Empirical Mode Decomposition ◽  
Objective Assessment ◽  
Window Size ◽  
Experimental Results ◽  
Classification Method ◽  
Average Area ◽  
Mode Decomposition ◽  
Two Parameters

Abstract In this paper, a novel classification method of assessing garment sewing stitch based on amended bi-dimensional empirical mode decomposition (ABEMD) has been introduced. Two parameters that characterise garment sewing stitch, average area and standard deviation, have been defined based on the grey value of pixels. Experimental results showed that when the window size is 512×128 pixels with regard to average area, the threshold can be decided as 6.00, 5.50, 5.30 and 4.00 for five different grades , respectively. Meanwhile, with regard to standard deviation, the threshold can be decided as 48.00, 40.00, 30.00 and 20.00, respectively. It is demonstrated that the parameters are effective in discriminating sewing stitch images in terms of the grades when used as inputs for the ABEMD. The performance of the algorithm on different garment status is significantly reliable.

Count Distribution and Precision in X-Ray Fluorescence Analysis

1959 ◽  
Vol 3 ◽  
pp. 95-107 ◽  
Author(s):  
Kurt F.J. Heinrich
Keyword(s):  
Standard Deviation ◽  
Poisson Distribution ◽  
Fluorescence Analysis ◽  
Experimental Results ◽  
X Ray ◽  
Statistical Fluctuations ◽  
Count Distribution ◽  
Analytical Result ◽  
Instrument Reliability

AbstractThe statistical fluctuations of photon counts arediscussedas a factor limiting the precision of the analytical result. Assuming a Poisson distribution, the theoretical standard deviation of the result can be calculated. While this prediction does not consider causes of variation other than the count statistics, it is useful in developing methods and checking instrument reliability. Practical examples using experimental results are given.

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

2017 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Norhashimah Mohd Saad ◽  
Nor Nabilah Syazana Abdul Rahma ◽  
Abdul Rahim Abdullah ◽  
Mohd Juzaila Abd Latif
Keyword(s):  
Standard Deviation ◽  
Product Quality ◽  
Quality Inspection ◽  
Rule Based ◽  
Low Contrast ◽  
Contrast Gain ◽  
Local Standard ◽  
Local Standard Deviation ◽  
Rule Based Classifier ◽  
Ace Algorithm

This paper presents shape analysis using Local Standard Deviation (LSD) technique to detect shape defect of the bottle for product quality inspection. The proposed analysis framework includes segmentation, feature extraction, and classification. The shape of the bottle was segmented using LSD technique in order to obtain higher enhancement at the low contrast area and low enhancement at the high contrast area. <span lang="EN-MY">The contrast gain that was applied in Adaptive Contrast Enhancement (ACE) algorithm, was presented inversely proportional to LSD in order to detect and eliminate background noise at the bottle edge. After the segmentation process, the parameters of the bottle shape such as height, width, area, and extent were extracted and applied in classification stage. The rule-based classifier was used to classify the shape of the bottle either good or defect. The offline experimental results exhibit superior segmentation on performance with 100% accuracy for 100 sample images. This shows that the LSD could be an effective technique to monitor the product quality.</span>

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