scholarly journals ANALYSIS OF COLOR AND SPECTRAL CHARACTERISTICS OF HEN EGG YOLKS FROM DIFFERENT MANUFACTURERS

2019 ◽  
Vol 7 (2) ◽  
pp. 103-122
Author(s):  
Mariya Georgieva-Nikolova ◽  
Atanas Genchev ◽  
Zlatin Zlatev
Keyword(s):  
Spectral Characteristics ◽  
Visible Spectrum ◽  
Egg Yolk ◽  
Classification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Processing ◽  
General Classification ◽  
Egg Yolks ◽  
Hen Egg

In the present work an analysis of the separability of hen egg yolks from different manufacturers is made using image and spectral processing and analysis techniques. Apparent properties of three types of egg yolks were determined and a comparative analysis of these properties was made. Discriminant and SVM (Support vector machines) classifiers were used for classification. A general classification error with lower values ​​is obtained with the b (Lab) color component. In the studies of the spectral characteristics of egg yolks from different manufacturers, the highest accuracy of separation of the target areas is obtained with the kernel SVM classifier combined with the kernel variant of the principal components. When using this classifier, general classification errors of up to 1% were obtained. The results confirm the hitherto known research in this area because the major part of the chicken egg yolk properties studied are visible properties that can be analyzed in the visible spectrum of the reflected light.

scholarly journals Convolutional Neural Network for Offline Signature Verification via Multiple Classifiers

2021 ◽  
Author(s):  
Fadi Mohammad Alsuhimat ◽  
Fatma Susilawati Mohamad
Keyword(s):  
Signature Verification ◽  
Classification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Digital Environment ◽  
K Nearest Neighbors ◽  
Offline Signature Verification ◽  
Error Classification ◽  
Run Time ◽  
Computerized System

The signature process is one of the most significant processes used by organizations to preserve the security of information and protect it from unwanted penetration or access. As organizations and individuals move into the digital environment, there is an essential need for a computerized system able to distinguish between genuine and forged signatures in order to protect people's authorization and decide what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.

scholarly journals Spectral Characteristics of Citrus Black Spot Disease

2016 ◽  
Vol 26 (3) ◽  
pp. 254-260 ◽  
Author(s):  
Alireza Pourreza ◽  
Won Suk Lee ◽  
Mark A. Ritenour ◽  
Pamela Roberts
Keyword(s):  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Black Spot ◽  
Spectral Signature ◽  
Support Vector ◽  
Svm Classifier ◽  
Wave Band ◽  
Citrus Black Spot ◽  
Phyllosticta Citricarpa ◽  
Significant Yield

Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa (synonym Guignardia citricarpa). CBS causes fruit lesions and significant yield loss in all citrus (Citrus) species. The most distinguishing CBS symptom is called hard spot, which is a circular lesion with gray tissue at the center surrounded by a black margin. The spectral characteristic of CBS lesions was investigated and compared with the spectral signature of healthy fruit tissue to determine the best distinguishing wave band. Healthy and CBS-affected samples presented similar reflectance below 500 nm and above 900 nm. However, healthy samples reflected more light between 500 and 900 nm, especially within the visible band. Also, spectral reflectance of the same symptomatic lesion was acquired six times over a 2-month period to determine the variation of symptom’s spectral signatures over time after being harvested. A two-sample t test was employed to compare each pair of consecutive repetitions. The results showed that the spectral signature of the CBS lesion did not change significantly over 2 months. The wavelengths between 587 and 589 nm were identified as the distinguishing band to develop a monochrome vision–based sensor for CBS diagnosis. A support vector machine (SVM) classifier was trained using the spectral reflectance data at the selected bands to identify CBS-affected samples in each repetition. The overall CBS detection accuracies varied between 93.3% and 94.6%.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals PSVIII-2 Antibody yield for polyclonal egg yolk antibody (IgY) production in laying hens

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 252-253
Author(s):  
Cherrie Nolden ◽  
Abbey Grisham ◽  
Dan Schaefer ◽  
Matt Akins ◽  
Mark Cook
Keyword(s):  
Repeated Measures ◽  
Laying Hens ◽  
Egg Yolk ◽  
Interleukin 10 ◽  
Cpg Odn ◽  
Slow Decline ◽  
Egg Yolks ◽  
Egg Yolk Antibody ◽  
To Receive ◽  
Phosphate Buffered Saline

Abstract Antibody production in egg yolks of immunized laying hens is an alternative to conventional mammalian production. Antibody yield peak and duration have not been described for immunoglobulin Y technology using Freund’s incomplete adjuvant (FIA) and C-phosphate-guanosine oligodeoxynucleotides (CpG-ODN) without the inclusion of Freund’s complete adjuvant for enhancing the immune response to an interleukin-10 (IL-10) peptide. This study sought to describe the antibody titer production for an 8 amino acid sequence from the surface of the bovine IL-10 protein (VMPQAENG) as the antigen emulsified with CpG-ODN and FIA in phosphate buffered saline (PBS). 60 hens were assigned to receive the complete vaccine (Peptide), 20 received the vaccine without the IL-10 peptide (Control), and 8 received a PBS injection (Blank). Hens were immunized with 0.25 mL in 4 locations, each breast and each thigh on days 1, 15 and 29. The complete vaccine delivered 0.6 mg IL-10 peptide, 8 µg CpG-ODN, and 0.33 mL FIA per hen on each vaccination day. Eggs were collected regularly until 175 days after the first immunization and the anti IL-10 peptide activities of the yolk were determined by ELISA. Egg titers by treatment were analyzed with a repeated measures ANOVA in SAS. The supplementation of FIA with CpG-ODN produced high titers, of over 100 µg of antibody per mL of yolk (µg Ab/mL yolk), around day 33 through day 76, with a slow decline through day 175 when average titers remained above 40 µg Ab/mL yolk. Peptide egg titers were significantly higher than Blank or Control titers from day 31 though day 175 (P &lt; 0.0001). Titers recovered from Marcq et al. (2015) with similar methods were 1.5 to 7 times lower than these results over the same number of days.

scholarly journals The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 739
Author(s):  
Alessandro Bevilacqua ◽  
Margherita Mottola ◽  
Fabio Ferroni ◽  
Alice Rossi ◽  
Giampaolo Gavelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
B Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Primary Role ◽  
High B Value ◽  
Wilcoxon Rank Sum Test ◽  
Apparent Diffusion ◽  
Clinically Significant

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals Modelling of Amplitude Modulated Vocal Fry Glottal Area Waveforms Using an Analysis-by-Synthesis Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 1990
Author(s):  
Vinod Devaraj ◽  
Philipp Aichinger
Keyword(s):  
Voice Quality ◽  
Low Frequency ◽  
Voice Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Power Spectral ◽  
Analysis By Synthesis ◽  
Simple Support ◽  
Frequency Modulations ◽  
Synthesis Approach

The characterization of voice quality is important for the diagnosis of a voice disorder. Vocal fry is a voice quality which is traditionally characterized by a low frequency and a long closed phase of the glottis. However, we also observed amplitude modulated vocal fry glottal area waveforms (GAWs) without long closed phases (positive group) which we modelled using an analysis-by-synthesis approach. Natural and synthetic GAWs are modelled. The negative group consists of euphonic, i.e., normophonic GAWs. The analysis-by-synthesis approach fits two modelled GAWs for each of the input GAW. One modelled GAW is modulated to replicate the amplitude and frequency modulations of the input GAW and the other modelled GAW is unmodulated. The modelling errors of the two modelled GAWs are determined to classify the GAWs into the positive and the negative groups using a simple support vector machine (SVM) classifier with a linear kernel. The modelling errors of all vocal fry GAWs obtained using the modulating model are smaller than the modelling errors obtained using the unmodulated model. Using the two modelling errors as predictors for classification, no false positives or false negatives are obtained. To further distinguish the subtypes of amplitude modulated vocal fry GAWs, the entropy of the modulator’s power spectral density and the modulator-to-carrier frequency ratio are obtained.

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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