scholarly journals An Approach for Detecting Fruit Quality with RBF-SVM Classification Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 1052-1058
Author(s):  
Sonia Chaudhary
Keyword(s):  
Fruit Quality ◽  
Detection System ◽  
Region Of Interest ◽  
Classification Model ◽  
Quality Analysis ◽  
Classification Rate ◽  
Svm Classification ◽  
Performance Factors ◽  
Quality Detection ◽  
Analysis System

For the regular development of rural area especially for the agriculture domain, automation is very important. Currently fruit's quality plays an important factor in their sales and production. Detecting the quality of fruits by using manual methods are not recommended because of the reasons that it cause delay and the results are also not upto the mark. Therefore, machine learning and computer vision is gaining much interest from current researchers to develop fruit quality detection systems. This paper contributes to provide an effective and advanced Orange fruit quality detection system. the proposed scheme is focused on giving an Fuzzy C-Mean based region of interest extracting scheme along with RBF-SVM classification model to improve the classification rate in comparison to existing approaches. The proposed scheme is simulated in MATLAB software and results are evaluated in terms of various performance factors such as Accuracy, Sensitivity, Specificity, Precision, Recall and F-Score. Finally a comparison of the proposed scheme is given that show an improvement of approximately 18% with respect to various state of art techniques. this strengthen the recommendation of proposed scheme for future fruit quality analysis system.

Study about Recognition of Digital Meter Dial Reading Based on SVM

2014 ◽  
Vol 615 ◽  
pp. 194-197
Author(s):  
Zhen Yuan Tu ◽  
Fang Hua Ning ◽  
Wu Jia Yu
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Feature Points ◽  
Svm Classification ◽  
Performance Factors ◽  
Model Combining ◽  
Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

scholarly journals Utility of the optical quality analysis system for decision-making in Nd: YAG laser posterior capsulotomy in patients with light posterior capsule opacity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Lu ◽  
Weijie Zhu ◽  
Yu Fan ◽  
Dong Shi ◽  
Liwei Ma
Keyword(s):  
Area Under The Curve ◽  
Quality Parameters ◽  
Optical Quality ◽  
Quality Analysis ◽  
Posterior Capsule ◽  
Clinical Trial Registry ◽  
Posterior Capsulotomy ◽  
Significant Difference ◽  
Analysis System ◽  
Scattering Index

Abstract Background A prospective cohort study was performed to evaluate whether the Optical Quality Analysis System (OQAS) can serve as a valuable additional indicator for appropriate posterior capsulotomy referral. Methods One hundred and five eyes from 96 patients undergoing capsulotomy were divided into precapsulotomy logMAR CDVA ≤0.1 group and logMAR CDVA > 0.1 group. CDVA, and the Visual Function 14 index (VF-14) score were estimated before and 1 month after capsulotomy. The objective scattering index (OSI) value was measured by using the OQAS. Posterior capsule opacification (PCO) severity was assessed with Evaluation of PCO 2000 (EPCO 2000) software. Results In logMAR CDVA > 0.1 group, the correlations of OSI, logMAR CDVA, EPCO score and VF-14 score were very strong preoperatively. In logMAR CDVA ≤0.1 group, preoperatively, OSI was correlated with logMAR CDVA (r = 0.451), EPCO score (r = 0.789), and VF-14 score (r = 0.852). LogMAR CDVA has weak correlation with VF-14 score (r = − 0.384) and EPCO score (r = 0.566). VF-14 score was correlated with EPCO score (r = − 0.669). In the logMAR CDVA ≤0.1 group, there was no significant difference in logMAR CDVA between precapsulotomy and postcapsulotomy (P > 0.05). In the two groups, all the other optical quality parameters were significantly improved after capsulotomy (P < 0.05). In logMAR CDVA > 0.1 group, the area under the curve of the ROC of the OSI was 0.996 (P = 0.000). In logMAR CDVA ≤0.1 group, the area under the curve of the ROC of the OSI was 0.943 (P = 0.000). Conclusions The OSI was useful for evaluating of PCO and prediction of beneficial capsulotomy. Especially for patients with slight PCO and better visual acuity, OSI is more valuable than CDVA and completely objective examination. Trial registration The study protocol was registered at the Chinese Clinical Trial Registry. Register: ChiCTR1800018842 (Registered Date: October 13th, 2018).

scholarly journals Multispectral Assessment of Sweet Pepper (Capsicum annuum L.) Fruit Quality Affected by Calcite Nanoparticles

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 832
Author(s):  
Monika Vidak ◽  
Boris Lazarević ◽  
Marko Petek ◽  
Jerko Gunjača ◽  
Zlatko Šatović ◽  
...  
Keyword(s):  
Convex Hull ◽  
Capsicum Annuum ◽  
Fruit Quality ◽  
Sweet Pepper ◽  
Quality Analysis ◽  
Morphological Properties ◽  
Agricultural Practice ◽  
Vegetable Crops ◽  
Physiological Maturity ◽  
Capsicum Annuum L

Sweet pepper (Capsicum annuum L.) is one of the most important vegetable crops in the world because of the nutritional value of its fruits and its economic importance. Calcium (Ca) improves the quality of sweet pepper fruits, and the application of calcite nanoparticles in agricultural practice has a positive effect on the morphological, physiological, and physicochemical properties of the whole plant. The objectives of this study were to investigate the effect of commercial calcite nanoparticles on yield, chemical, physical, morphological, and multispectral properties of sweet pepper fruits using a combination of conventional and novel image-based nondestructive methods of fruit quality analysis. In the field trial, two sweet pepper cultivars, i.e., Šorokšari and Kurtovska kapija, were treated with commercial calcite nanoparticles (at a concentration of 3% and 5%, calcite-based foliar fertilizer (positive control), and water (negative control) three times during vegetation). Sweet pepper fruits were harvested at the time of technological and physiological maturity. Significant differences were observed between pepper cultivars as well as between harvests times. In general, application of calcite nanoparticles reduced yield and increased fruit firmness. However, different effects of calcite nanoparticles were observed on almost all properties depending on the cultivar. In Šorokšari, calcite nanoparticles and calcite-based foliar fertilizers significantly increased N, P, K, Mg, Fe, Zn, Mn, and Cu at technological maturity, as well as P, Ca, Mg, Fe, Zn, Mn, Cu, and N at physiological maturity. However, in Kurtovska kapija, the treatments increased only Ca at technological maturity and only P at physiological maturity. The effect of treatments on fruit morphological properties was observed only at the second harvest. In Šorokšari, calcite nanoparticles (3% and 5%) increased the fruit length, minimal circle area, and minimal circle radius, and it decreased the fruit width and convex hull compared to the positive and negative controls, respectively. In Kurtovska kapija, calcite nanoparticles increased the fruit width and convex hull compared to the controls. At physiological maturity, lower anthocyanin and chlorophyll indices were found in Kurtovska kapija in both treatments with calcite nanoparticles, while in Šorokšari, the opposite effects were observed.

scholarly journals A survey on modern day trends in water level and quality analysis system

2021 ◽  
Vol 1964 (4) ◽  
pp. 042078
Author(s):  
N Revathi ◽  
P Sengottuvelan ◽  
J Thimmia Raja
Keyword(s):  
Water Level ◽  
Quality Analysis ◽  
Analysis System

scholarly journals A Vision-Based Social Distancing and Critical Density Detection System for COVID-19

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4608
Author(s):  
Dongfang Yang ◽  
Ekim Yurtsever ◽  
Vishnu Renganathan ◽  
Keith A. Redmill ◽  
Ümit Özgüner
Keyword(s):  
Visual Cues ◽  
Detection System ◽  
Critical Density ◽  
Region Of Interest ◽  
Spatial Awareness ◽  
Real Time System ◽  
Effective Measure ◽  
Social Distancing ◽  
Record Data ◽  
Real World Datasets

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

Micro Line Quality Measurement Based on Normally Scanned Digital Image

2012 ◽  
Vol 236-237 ◽  
pp. 839-843
Author(s):  
Xiao Meng Cui ◽  
Guang Xue Chen ◽  
Huan Mei Wang ◽  
Lin Lin Chen
Keyword(s):  
Low Cost ◽  
Quality Measurement ◽  
Digital Printing ◽  
Quality Analysis ◽  
Output Performance ◽  
Micron Size ◽  
Measurement Principle ◽  
Analysis System ◽  
Detection Instrument

In this study we present a new framework to assess line micro quality of multicolor prints adapted to a low-cost image quality analysis system based on common flatbed scanner. The contribution elements including quality metrics, measurement principle, applying methods and detection instrument, which were complemented and sorted in terms of ISO 13660 standard, were described and a case experiment was conducted to survey the output performance of both inkjet printer and xerographic printer, two most popular digital printing technologies. Their qualities were compared in details rendering angle such as width and straightness of line, raggedness and blurriness in line edges, as well as darkness and contrast in color. The results verify the efficiency and shortcomings of the framework. Meanwhile, the metric values let us take an investigation in the character of each digital printing technology in micron size.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408&ndash;1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

scholarly journals Robust whole slide image analysis for cervical cancer screening using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shenghua Cheng ◽  
Sibo Liu ◽  
Jingya Yu ◽  
Gong Rao ◽  
Yuwei Xiao ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
True Positive Rate ◽  
Classification Model ◽  
Computer Assisted ◽  
Whole Slide Image ◽  
Slide Image ◽  
Positive Rate ◽  
Analysis System

AbstractComputer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.

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