Fibonacci polynomial based multilayer perceptron neural network for classification of medical data

2021 ◽  
Author(s):  
Asia Maqsood ◽  
Umer Iqbal ◽  
Ijaz Ali Shoukat ◽  
Zohaib Latif ◽  
Afshan Kanwal
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Medical Data ◽  
Fibonacci Polynomial

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

PCA–Based Human Posture Classification

2012 ◽  
Author(s):  
Nooritawati Md Tahir ◽  
Aini Hussain ◽  
Salina Abdul Samad ◽  
Hafizah Husain
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Principal Component ◽  
Optimal Choice ◽  
Correct Classification ◽  
Human Posture ◽  
Posture Classification

Kertas kerja ini membentangkan suatu mekanisme untuk pengelasan susuk tubuh manusia berdasarkan kombinasi pelbagai jelmaan ruang eigen yang dinamakan sebagai eigenposture dan Multilayer Perceptron (MLP) sebagai pengelas. Penjelmaan komponen utama telah digunakan untuk menyari sifat pada bayang-bayang bentuk badan manusia. Gabungan sarian sifat ini digunakan untuk pengelasan susuk tubuh manusia sebagai berdiri atau sebaliknya berasaskan bentuk badan yang diperoleh selepas proses peruasan. Uji kaji telah dijalankan dengan mengubah bilangan vektor eigen yang dijadikan perwakilan untuk tujuan pengelasan. Keputusan yang diperoleh menunjukkan gabungan eigenposture kedua dan keempat memberi keputusan pengelasan bentuk badan manusia yang agak baik iaitu 98% dan boleh dijadikan sebagai pilihan optimal masukan bagi tujuan pengelasan menggunakan bilangan input minima. Kata kunci: Analisa komponen utama, vektor eigen, pengelasan, rangkaian neural tiruan, susuk tubuh manusia This paper outlines a mechanism for human body posture classification based on various combination of eigenspace transform, which we named as eigenposture, and using Multilayer Perceptron (MLP) as classifier. We apply principal component transformation to extract the features from human shape silhouettes. Combinations of the extracted features were used to classify the posture of standing and non-standing based on the human shape obtained from the segmentation process. We experiment by using various combinations of eigenvectors as input representations for classification purpose. Results showed that the second and fourth eigenpostures combination gives reasonably good result with 98% correct classification of human posture and can be adopted as the optimal choice of input for classification using a minimal combination. Key words: Principal component analysis (PCA), eigenvectors, classification, artificial neural network, human posture

Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based Multilayer Perceptron Neural Network

2018 ◽  
Vol 34 (3) ◽  
pp. 1669-1677 ◽  
Author(s):  
Ankit Narang ◽  
Bhumika Batra ◽  
Arpit Ahuja ◽  
Jyoti Yadav ◽  
Nikhil Pachauri
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

scholarly journals Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts

2006 ◽  
Vol 3 (1) ◽  
pp. 201-227 ◽  
Author(s):  
N. Lauzon ◽  
F. Anctil ◽  
C. W. Baxter
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Network Models ◽  
Rainfall Runoff ◽  
Neural Network Models ◽  
Kohonen Neural Network ◽  
Streamflow Forecasts ◽  
Rainfall Runoff Model

Abstract. This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the classification of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this classification. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed classification method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography). The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-à-vis the classification of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.

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