A New Flaw Identification Model of Ultrasonic Signals Based on EMD and Possibility SVM

Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shafts. A novel automatic identification model of defects is presented. Empirical Model decomposition (EMD) is applied to feature extraction of ultrasonic signals, and possibility Support Vector Machine (SVM) with Dempster-Shafer (DS) theory to perform the identification task. Meanwhile, comparative study on convergent velocity and classified effect is done among SVM and artificial neural network (ANN) with DS models. To validate the method, some experiments are performed and the results show the proposed system has very high identification performance for large shafts and the possibility SVM processes better classification performance and spreading potential than ANN with DS model under the small study sample condition.

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Automatic identification of bird targets with radar via patterns produced by wing flapping

Journal of The Royal Society Interface ◽

10.1098/rsif.2007.1349 ◽

2008 ◽

Vol 5 (26) ◽

pp. 1041-1053 ◽

Cited By ~ 54

Author(s):

Serge Zaugg ◽

Gilbert Saporta ◽

Emiel van Loon ◽

Heiko Schmaljohann ◽

Felix Liechti

Keyword(s):

Bird Migration ◽

Wind Farms ◽

Classification Performance ◽

Future Research ◽

Support Vector ◽

Automatic Identification ◽

Radar Meteorology ◽

Ground Clutter ◽

Environmental Impact Assessments ◽

Radar Signals

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

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Automatic identification of NBOMe illicit psychoactive substances based on combined molecular descriptors

MATEC Web of Conferences ◽

10.1051/matecconf/202134205008 ◽

2021 ◽

Vol 342 ◽

pp. 05008

Author(s):

Adelina Ion ◽

Mirela Praisler ◽

Catalina Mercedes Burlacu ◽

Nicolae Catalin Stanica

Keyword(s):

Parent Compound ◽

Classification Performance ◽

Molecular Structures ◽

New Psychoactive Substances ◽

Automatic Identification ◽

Psychoactive Substances ◽

Law Enforcement Agencies ◽

Synthetic Derivative ◽

Input Variables ◽

Very High

During the last decade, a growing prevalence of new psychoactive substances (NPS) has been noticed by the law enforcement agencies. Although NPS have no medical use due to their very high toxicity, they are often sold on the black market. NBOMe defines a group of toxic amphetamines that has as parent compound 25I-NBOMe, a synthetic derivative of 2C-I (2,5-dimethoxy-4-iodophenetylamine). In this paper, we are presenting a series of Artificial Neural Networks (ANNs) designed to identify the NBOMe class membership based on a mixture of topological and 3D-MoRSE descriptors. For this purpose, the molecular structures of 160 compounds representing NBOMe compounds, narcotics, sympathomimetic amines, potent analgesics, as well as their main precursors have been first optimized. Then a molecular database was formed by computing a large number of topological and 3D-MoRSE descriptors that characterize these structures. This database was used as input for building an ANN system designed to recognize NBOMes. The relevance of the input variables on its classification performance has been assessed and new systems have been built by using different combinations of selected topological and 3D-MoRSE descriptors. The best performing system has been found by comparing various classification efficiency criteria.

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Classification by a stacking model using CNN features for COVID-19 infection diagnosis

Journal of X-Ray Science and Technology ◽

10.3233/xst-211031 ◽

2021 ◽

pp. 1-16

Author(s):

Yavuz Selim Taspinar ◽

Ilkay Cinar ◽

Murat Koklu

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Support Vector ◽

Clinical Environment ◽

X Ray ◽

Left Behind ◽

Study Results ◽

Chest X Ray ◽

Ann Models ◽

Artificial Neural Network Ann

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3,486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

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A machine learning model of microscopic agglutination test for diagnosis of leptospirosis

PLoS ONE ◽

10.1371/journal.pone.0259907 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259907

Author(s):

Yuji Oyamada ◽

Ryo Ozuru ◽

Toshiyuki Masuzawa ◽

Satoshi Miyahara ◽

Yasuhiko Nikaido ◽

...

Keyword(s):

Machine Learning ◽

Dark Field ◽

Classification Performance ◽

Agglutination Test ◽

Microscopic Agglutination Test ◽

Support Vector ◽

Automatic Identification ◽

Identification Algorithm ◽

Machine Learning Model ◽

Negative Images

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospires, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision-making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine agglutination within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.

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A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis

10.1101/2020.12.08.410712 ◽

2020 ◽

Author(s):

Yuji Oyamada ◽

Ryo Ozuru ◽

Toshiyuki Masuzawa ◽

Satoshi Miyahara ◽

Yasuhiko Nikaido ◽

...

Keyword(s):

Machine Learning ◽

Dark Field ◽

Classification Performance ◽

Agglutination Test ◽

Microscopic Agglutination Test ◽

Support Vector ◽

Automatic Identification ◽

Identification Algorithm ◽

Negative Images ◽

Antibody Titers

AbstractLeptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospiras, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine aggregation within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira-infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images, that it gave the further possibility of making a much suitable algorithm by adding more indices (e.g., antibody titers and bacterial counts) in the future.

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Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Scientifica ◽

10.1155/2021/8810279 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Aicha Moumni ◽

Abderrahman Lahrouni

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Support Vector ◽

Spatiotemporal Resolution ◽

Semiarid Area ◽

Crop Type ◽

Irrigated Perimeter ◽

High Resolution Satellite Imagery ◽

Artificial Neural Network Ann ◽

Combined Images

The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

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Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

International Journal of Computational Physics Series ◽

10.29167/a1i1p120-130 ◽

2018 ◽

Vol 1 (1) ◽

pp. 120-130 ◽

Cited By ~ 1

Author(s):

Chunxiang Qian ◽

Wence Kang ◽

Hao Ling ◽

Hua Dong ◽

Chengyao Liang ◽

...

Keyword(s):

Support Vector Machine ◽

Environmental Factors ◽

Cross Validation ◽

Concrete Strength ◽

Simulation Method ◽

Support Vector ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Influence Degree ◽

Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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Binary Spectrum Feature for Improved Classiﬁer Performance

10.36227/techrxiv.12993122 ◽

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>

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Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽

10.3390/diagnostics11030574 ◽

2021 ◽

Vol 11 (3) ◽

pp. 574

Author(s):

Gennaro Tartarisco ◽

Giovanni Cicceri ◽

Davide Di Pietro ◽

Elisa Leonardi ◽

Stefania Aiello ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Behavioral Science ◽

Autistic Traits ◽

Classification Performance ◽

Recursive Feature Elimination ◽

Diagnostic Tools ◽

Support Vector ◽

K Nearest Neighbors ◽

Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Applied Sciences ◽

10.3390/app11094055 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4055

Author(s):

Mahdi S. Alajmi ◽

Abdullah M. Almeshal

Keyword(s):

Gaussian Process ◽

Cutting Force ◽

Predictive Accuracy ◽

Gaussian Process Regression ◽

Machining Process ◽

Support Vector ◽

Process Data ◽

Cutting Force Prediction ◽

Artificial Neural Network Ann ◽

Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

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