Comparison of Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in Predicting Green Pellet Characteristics of Manganese Concentrate

2016 ◽  
pp. 311-320
Author(s):  
Mohammad Nadeem ◽  
Haider Banka ◽  
R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Green Pellet ◽  
Manganese Concentrate

scholarly journals Feature Selection using Hybrid Dragonfly Algorithm in a Heart Disease Predication System

2019 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Disease ◽  
Mutual Information ◽  
Multilayer Perceptron ◽  
Time Algorithm ◽  
Support Vector ◽  
Ordinary People ◽  
Fatal Disease ◽  
Dragonfly Algorithm

The heart disease considers as one of the fatal disease in many countries. The main reason is due to the approved methods of diagnostic are not available to the ordinary people. Many studies have been done to handle this case with the use of both methods of soft computing and machine learning. In this study, a hybrid binary dragonfly algorithm and mutual information proposed for feature selection, support vector machine and multilayer perceptron employed for classification. The Statlog dataset used for experiments. Out of a total of 270 instances of patient data, 216 employees for the purpose of practicing, 54 of them used for the purpose of examining. Maximum classification accuracy of 94.44% achieved with support vector machine and 92.59% with multilayer perceptron on features selected with binary dragonfly algorithm, whereas with features obtained from mutual information combined with binary dragonfly (MI_BDA) algorithm support vector machine and multilayer perceptron attained an accuracy of 96.29%. The time algorithm takes reduced from 15.4 with binary dragonfly algorithm to 6.95 seconds with MI_BDA.

scholarly journals Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

2021 ◽  
Vol 17 (14) ◽  
pp. 90-102
Author(s):  
Fadwa Abakarim ◽  
Abdenbi Abenaou
Keyword(s):  
Support Vector Machine ◽  
Multilayer Perceptron ◽  
Recognition System ◽  
The Other ◽  
Support Vector ◽  
Transform Method ◽  
Average Variance ◽  
Voice Pathology Detection ◽  
Orthogonal Transform ◽  
Skewness And Kurtosis

In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.

Recognition of human emotion with spectral features using multi layer-perceptron

2020 ◽  
Vol 24 (3) ◽  
pp. 227-233
Author(s):  
A. Pramod Reddy ◽  
Vijayarajan V
Keyword(s):  
Support Vector Machine ◽  
Spectral Density ◽  
Emotion Recognition ◽  
Power Spectral Density ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Spectral Features ◽  
Emotional States ◽  
Multi Layer Perceptron ◽  
Power Spectral

For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

scholarly journals Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

2019 ◽  
Vol 11 (13) ◽  
pp. 3586 ◽  
Author(s):  
Oyeniyi Akeem Alimi ◽  
Khmaies Ouahada ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Power System ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Hybrid Algorithm ◽  
Test System ◽  
Support Vector ◽  
Detection Accuracy ◽  
Bus System

In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

scholarly journals PEMILIHAN MODEL PREDIKSI INDEKS HARGA SAHAM YANG DIKEMBANGKAN BERDASARKAN ALGORITMA SUPPORT VECTOR MACHINE(SVM) ATAU MULTILAYER PERCEPTRON(MLP) STUDI KASUS : SAHAM PT TELEKOMUNIKASI INDONESIA TBK

2019 ◽  
Vol 5 (1) ◽  
pp. 29-35
Author(s):  
Rusma Insan Nurachim
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Multilayer Perceptron ◽  
Support Vector ◽  
Rescaled Range ◽  
Vector Machines

Dalam memprediksi suatu kondisi harga saham,beberapa model analisa teknik telah dipakai dandikembangkan. Salah satunya dengan model datamining. Data mining merupakan salah satu cabangilmu komputer yang mencakup database, kecerdasanbuatan (artificial intelligence), statistik dan sebagainya.Penelitian ini melakukan analisis teknikal, yaitu diawalidengan mencari sifat multifraktal pada return sahamobjek penelitian dengan analisis rescaled range (untukmendapatkan eksponen hurst) untuk mengetahui apakahdata return tersebut bersifat acak atau terdapatpengulangan trend. Berikutnya akan dilakukan prediksiterhadap return saham tersebut dengan metode SVM(Support Vector Machines) dan MLP (MultilayerPerceptron) untuk kemudian akan dilakukan komparasimetode mana yang memiliki kesalahan lebih kecildalam memprediksi indeks harga saham.

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