scholarly journals Paradox of otolith shape indices: routine but overestimated use

2021 ◽  
Author(s):  
Víctor M. Tuset ◽  
José Luis Otero-Ferrer ◽  
Carolina Siliprandi ◽  
Amalia Manjabacas ◽  
Pere Martí-Puig ◽  
...  
Keyword(s):  
Performance Measures ◽  
Kappa Statistic ◽  
Support Vector ◽  
Contour Analysis ◽  
Otolith Shape ◽  
First Case ◽  
Shape Indices ◽  
Identification Of Species ◽  
And Performance

The identification of fish species using otolith shape has been common in many fields of the marine science. Different analytical processes can be applied for the morphological discrimination, but reviewing the literature we have found conceptual and statistical limitations in the use of shape indices and wavelets (contour analysis), being specially worrying in the first case due to their widespread routine use. In the present study, 42 species were classified using otolith shape indices and wavelets and applying traditional and machine learning classifiers and performance measures (accuracy, Cohen’s kappa statistic, sensitivity and precision). Our results were conclusive, wavelets were a more adequate option for the classification of species than shape indices, independently of classifiers and performance measures considered. The artificial neural network and support vector machine provided the highest values for all performance measures using wavelets. In all cases, the measures of sensitivity and precision pointed out a higher confusion between some otolith patterns using shape indices. Therefore, we strongly discourage the routine use of shape indices for the identification of species.

scholarly journals Detection and Classification of R-Peak Using Naïve Bayes Classifier

2018 ◽  
Vol 7 (3.27) ◽  
pp. 397 ◽  
Author(s):  
S Celin ◽  
K Vasanth
Keyword(s):  
Feature Extraction ◽  
Performance Measures ◽  
Heart Diseases ◽  
Processing Technique ◽  
Challenging Problem ◽  
Bayes Classifier ◽  
Extraction Techniques ◽  
And Performance ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and performance measures are presented in the pre-processing technique. And also the classification of ECG, analysis of input beat selection and the output of classifiers are also discussed in this paper.  

scholarly journals DIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS

2014 ◽  
pp. 24-31
Author(s):  
Peter C. Hung ◽  
Seán F. McLoone ◽  
Ronan Farrell
Keyword(s):  
Performance Measures ◽  
High Frequency ◽  
Low Noise ◽  
Support Vector ◽  
Functional Testing ◽  
Svm Classification ◽  
Direct Support ◽  
Noise Amplifier ◽  
High Frequency Performance

The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip. One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter. An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP classifiers marginally outperforming SVMs.

scholarly journals Comparative Analysis of Machine Learning Algorithms for Multi-Syndrome Classification of Neurodegenerative Syndromes

2021 ◽  
Author(s):  
Leonie Lampe ◽  
Sebastian Niehaus ◽  
Hans-Jürgen Huppertz ◽  
Alberto Merola ◽  
Janis Reinelt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Performance Measures ◽  
Careful Consideration ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods

Abstract Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for predictions of structured neuroimaging data, yet nobody compared them in this context.Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.Design, Setting, and Participants Atlas-based volumetry was performed on multi-centric T1weighted MRI data from 940 subjects, i.e. 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.Interventions n.a.Main Outcomes and Measures Cohen’s Kappa, Accuracy, and F1-score to assess model performance.Results Over all, the neural network produced both the best performance measures as well as the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with wide-spread and rather weak atrophy.Conclusions and Relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.Trial Registration n.a.

scholarly journals Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

2019 ◽  
Vol 3 (1) ◽  
pp. 58
Author(s):  
Yefta Christian
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Kappa Statistic ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Results ◽  
Online Shoppers ◽  
Online Stores

<p class="8AbstrakBahasaIndonesia"><span>The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumers’ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppers’ intentions can be done by using several algorithms, such as Naïve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppers’ intentions.</span></p>

scholarly journals Evaluation of Phonetic System for Speech Recognition on Smartphone

2019 ◽  
Vol 8 (10) ◽  
pp. 3354-3359
Keyword(s):  
Performance Evaluation ◽  
Speech Recognition ◽  
Language Processing ◽  
English Language ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Recording Equipment ◽  
Classification Techniques ◽  
And Performance

This paper presents detailed study and performance evaluation of phonetic system by comparing it with various classification techniques of automatic speech recognition such as Neural Network, Hidden Markov Model, Support Vector Machine and Gaussian Mixture Model. In the phonetic system, recognized speech is processed by using language processing i.e. matching phonemes and hence generates more correct output text. The accuracy of speech recognition of ASR classifier and phonetic system is evaluated on day to day human to machine communications, using high-quality recording equipment, while the results for enhancement of existing systems is done on everyday android phones, and evaluated for normal conversations in Hindi and English language. Classifier is used to classify the fragmented phonemes or words after the fragmentation of the speech signal. Different classification techniques are implemented and comparing accuracy of speech recognition of different classifier. It is seen that GMM is better at the classification of signal data, outcomes of performance evaluation shows that GMM outperforms the other three classifiers in terms of accuracy by more than 20%. This result is compared with implemented phonetic system which shows that ASR accuracy, using phonetic system is better than GMM. We observed 6% improvement in ASR accuracy with phonetic system.

scholarly journals Identificación de stocks de Engraulis ringens en Chile utilizando morfometría de otolitos

2019 ◽  
Vol 54 (1) ◽  
pp. 144
Author(s):  
Cecilia Machuca ◽  
Francisco Cerna ◽  
Lizandro Muñoz
Keyword(s):  
Discriminant Analysis ◽  
Shape Analysis ◽  
Fourier Coefficients ◽  
Canonical Discriminant Analysis ◽  
Contour Analysis ◽  
Otolith Shape ◽  
Elliptical Fourier Analysis ◽  
Shape Indices ◽  
Engraulis Ringens ◽  
Fourier Harmonics

Anchovy (Engraulis ringens) population units were analyzed in three zones off the coast of Chile: 1: Arica-Iquique, 2: Coquimbo and 3: Talcahuano-Valdivia from samples obtaineds during the 2012 spawning season. We used 50 left sagittae otoliths from each zone to perform a morphometric analysis, which included basic measurements, shape indexes and contour analysis (elliptical Fourier analysis). A MANOVA and Tukey multiple comparison analyses, applied on basic measures and shape indexes showed significant differences between zone 3 and zones 1 and 2. A classification by Canonical Discriminant analysis of elliptical Fourier harmonics, indicated significant differences among zones. It is concluded, therefore, that otolith shape analysis could be used to discriminate population units of Engraulis ringens. Better results were achieved using elliptic Fourier coefficients than using only shape indices.

scholarly journals Denoising and SAR Image Classification with K-SVD Algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 36 ◽  
Author(s):  
D V.R Mohan ◽  
I Rambabu ◽  
B Harish
Keyword(s):  
Support Vector ◽  
Gray Level ◽  
Infrared Sensors ◽  
Sar Images ◽  
Feature Spaces ◽  
Vector Machines ◽  
And Performance ◽  
Occurrence Matrix ◽  
Value Decomposition

Synthetic Aperture Radar (SAR) is not only having the characteristic of obtaining images during all-day, all-weather, but also provides object information which is distinctive from visible and infrared sensors. but, SAR images have more speckles noise and fewer bands. This paper propose a method for denoising, feature extraction and classification of SAR images. Initially the image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. Then the Gray Level Histogram (GLH) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of features. Secondly, the extracted feature vectors from the first step were combined using the correlation analysis to decrease the dimensionality of the feature spaces. Thirdly, Classification of SAR images was done in Sparse Representations Classification (SRC) and Support Vector Machines (SVMs). The results indicate that the performance of the introduce SAR classification method is good. The above mentioned classifications techniques are enhanced and performance parameters are computed using MATLAB 2014a software.  

A MACHINE LEARNING APPROACH BASED ON SVM FOR CLASSIFICATION OF LIVER DISEASES

2020 ◽  
Vol 32 (03) ◽  
pp. 2050018
Author(s):  
Mohammad Fathi ◽  
Mohammadreza Nemati ◽  
Seyed Mohsen Mohammadi ◽  
Reza Abbasi-Kesbi
Keyword(s):  
Cross Validation ◽  
The Body ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Partition ◽  
Linear Quadratic ◽  
Machine Learning Approach ◽  
And Performance ◽  
Fold Cross Validation

The liver is an organ in the body that plays an important role in the production and secretion of the bile. Recently, the number of liver patients are increasing because of the inhalation of harmful gases, the consumption of contaminated foods, herbs, and narcotics. Today, classification algorithms are widely used in diverse medical applications. In this paper, the classification of the liver, and non-liver patients is performed based on a support vector machine (SVM) on two datasets. To this end, the dataset is normalized and then sorted based on a proposed algorithm. After that, the feature selection is performed in order to remove the outliers and missing data. Then, 10-fold cross-validation is used for the data partition. In the end, the classification models of Linear, Quadratic and Gaussian SVM are defined and performance evaluation of the proposed method is investigated by calculation of F1-score, accuracy, and sensitivity. The results show that ILPD data have maximum accuracy, sensitivity, and F1-score of 90.9%, 89.2%, and 94%, respectively, so that a minimum improvement of 17.9% is obtained in accuracy than previous works. Additionally, the highest accuracy, sensitivity, and F1-score of BUPA data is 92.2%, 89%, and 94.3%, separately.

scholarly journals Analisis Sentimen Mahasiswa untuk Perbaikan Desain Afektif Ruang Kelas Jurusan Teknik Industri, Universitas Trisakti

2021 ◽  
Vol 10 (1) ◽  
pp. 27-34
Author(s):  
Anik Nur Habyba ◽  
Novia Rahmawati ◽  
Triwulandari SD
Keyword(s):  
Student Performance ◽  
Engineering Students ◽  
Support Vector ◽  
Classroom Design ◽  
Linear Kernel ◽  
Performance Achievement ◽  
Design Elements ◽  
And Performance ◽  
Positive Label

Improving the affective classroom design is essential to maximize student performance and learning achievement. The comfort and performance achievement of Trisakti University Industrial Engineering students are influenced by the affective design of classrooms. This study aimed to use sentiment analysis in the classification of students’ perceptions of the affective classroom design. Student sentiment classification is done using a Support Vector Machine (SVM). The questionnaire analysis results also showed perceptions about the subjects that were considered the most difficult (statistics). The classrooms had a positive sentiment: FGTSC, a sample for the next stage of classroom design formulation. The results show what impressions and things the students consider in choosing the FGTSC class. Some examples of the dominant kansei word are “comfortable,” this shows that students really care about the comfort of a classroom in the learning process. The word kansei for the design concept was collected from students' perceptions of the “positive” label. Design elements that need to be improved include equipment that is less comfortable to use, less lighting, walls with graffiti and uncomfortable seating. The classification results using three SVM types Kernel linear, radial and polynomial obtained linear have the best accuracy value (76%). These results indicate that the classification of student sentiment has the maximum results with SVM linear kernel (dot) type. This method will be used in classifying student sentiment on the results of improving classroom design.  

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