Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: emotional neural network

Sortuj według:

Ogranicz wyniki do:

Blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network

Temeng Victor Amoako, Ziggah Yao Yevenyo, Arthur Clement Kweku

Journal of Sustainable Mining

2021

Vol. 20, iss. 1

28--38

Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.

A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network

Shahid Afzal Hussain, Singh M. P.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1568--1585

Coronary artery disease (CAD) can cause serious conditions such as severe heart attack, heart failure, and angina in patients with cardiovascular problems. These conditions may be prevented by knowing the important symptoms and diagnosing the disease in the early stage. For diagnosing CAD, clinicians often use angiography, however, it is an invasive procedure that incurs high costs and causes severe side effects. Therefore, the other alternatives such as data mining and machine learning techniques have been applied extensively. Accordingly, the paper proposes a recent development of a highly accurate machine learning model emotional neural networks (EmNNs) which is hybridized with conventional particle swarm optimization (PSO) technique for the diagnosis of CAD. To enhance the performance of the proposed model, the paper employs four different feature selection methods, namely Fisher, Relief-F, Minimum Redundancy Maximum Relevance, and Weight by SVM, on Z-Alizadeh sani dataset. The EmNNs, with addition to the conventional weights and biases, uses emotional parameters to enhance the learning ability of the network. Further, the efficiency of the proposed model is compared with the PSO based adaptive neuro-fuzzy inference system (PSO-ANFIS). The proposed model is found better than the PSO-ANFIS model. The obtained highest average values of accuracy, precision, sensitivity, specificity, and F1-score over all the 10-fold cross-validation are 88.34%, 92.37%, 91.85%, 78.98%, and 92.12% respectively which is competitive to the known approaches in the literature. The F1-score obtained by the proposed model over Z-Alizadeh sani dataset is second best among the existing works.