A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran

2018 ◽  
Vol 10 (3) ◽  
pp. 229-256 ◽  
Author(s):  
Bashir Shokouh Saljoughi ◽  
Ardesir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Comparative Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Mineral Potential Mapping ◽  
Mineral Potential ◽  
Potential Mapping ◽  
Artificial Neural Network Ann

Perbandingan Metode Support Vector Machine (SVM) dan Artificial Neural Network (ANN) pada Klasifikasi Gizi Balita

2021 ◽  
Vol 2021 (1) ◽  
pp. 1036-1043
Author(s):  
Harifa Hananti ◽  
Kartika Sari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kasus kekurangan gizi atau gizi buruk pada balita menyebar hampir di seluruh provinsi yang ada di Indonesia. Provinsi Sulawesi Barat merupakan salah satu provinsi yang memiliki nilai persentase kekurangan gizi pada balita, sehingga dari faktor-faktor yang mempengaruhi gizi balita sangat penting untuk dilakukan dalam pengklasifikasian. Data yang digunakan adalah data dari Puskesmas Salissingan pada Tahun 2018. Penelitian ini bertujuan untuk melakukan pengklasifikasian dan mendapatkan metode terbaik pada gizi balita (gizi baik & gizi kurang) di Puskesmas Salissingan Sulawesi Barat dengan metode support vector machine (SVM) dan artificial neural network (ANN). Metode klasifikasi yang terbaik dalam melihat ukuran ketepatan klasifikasi adalah metode SVM dan ANN. Dari hasil analisis diperoleh ukuran ketepatan klasifikasi pada metode ANN (accuracy=94,82%, precision=51.00%, recall=51.09%, dan AUC=0.910), sedangkan pada metode SVM (accuracy=94,46%, precision=46.08%, recall=50.59%, dan AUC=0.900) dan dari hasil ukuran tersebut diperoleh bahwa metode yang terbaik dalam pengklasifikasian gizi balita di Puskesmas Salissingan Sulawesi Barat adalah ANN.

scholarly journals Probabilistic estimation of software development effort techniques using machine learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1085
Author(s):  
Dr P. Vidya Sagar ◽  
Dr Nageswara Rao Moparthi ◽  
Venkata Naresh Mandhala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Support Vector ◽  
Development Effort ◽  
Probabilistic Estimation ◽  
Software Development Effort ◽  
Artificial Neural Network Ann ◽  
High Level

Precisely assessing programming exertion is likely the greatest test confronting for programming engineers. Assessments done at the prop-osition arrange has high level of incorrectness, where prerequisites for the degree are not characterized to the most reduced subtle elements, but rather as the venture advances and necessities are explained, exactness and certainty on appraise increments. It is vital to pick the correct programming exertion estimation systems for the forecast of programming exertion. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been utilized on guarantee dataset for forecast of programming exertion in this article.  

scholarly journals Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Thabo Michael Bafitlhile ◽  
Zhijia Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Optimization Method ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Climatic System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Semi Arid

The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.

scholarly journals Comparison of Machine Learning Methods for the Detection of Focal Cortical Dysplasia Lesions: Decision Tree, Support Vector Machine and Artificial Neural Network

2021 ◽  
Author(s):  
Zohreh Ganji ◽  
Seyed Amir Zamanpour ◽  
Hoda Zare
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Focal Cortical Dysplasia ◽  
Normal Subjects ◽  
Cortical Dysplasia ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Background: Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem.Methods: MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated.Results: Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 96.7%, 100% and 98.6%, respectively; It was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN.Conclusion: Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

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