PREDICTION OF FOREST FIRE USING NEURAL NETWORKS WITH BACKPROPAGATION LEARNING AND EXREME LEARNING MACHINE APPROACH USING METEOROLOGICAL AND WEATHER INDEX VARIABLES

2021 ◽  
Vol 14 (2) ◽  
pp. 118-124
Author(s):  
Dedi Rosadi ◽  
Deasy Arisanty ◽  
Dina Agustina
Keyword(s):  
Neural Networks ◽  
Forest Fire ◽  
Human Life ◽  
Hybrid Approach ◽  
Support Vector ◽  
Weather Index ◽  
Classification Approach ◽  
Fuzzy C Means Clustering ◽  
Learning Machine ◽  
Backpropagation Learning

Forest fire is one of important catastrophic events and have great impact on environment, infrastructure and human life. In this study, we discuss the method for prediction of the size of the forest fire using the hybrid approach between Fuzzy-C-Means clustering (FCM) and Neural Networks (NN) classification with backpropagation learning and extreme learning machine approach. For comparison purpose, we consider a similar hybrid approach, i.e., FCM with the classical Support Vector Machine (SVM) classification approach. In the empirical study, we apply the considered methods using several meteorological and Forest Weather Index (FWI) variables. We found that the best approach will be obtained using hybrid FCM-SVM for data training, where the best performance obtains for hybrid FCM-NN-backpropagation for data testing.

PERFORMANCE EVALUATION OF THREE PATTERN CLASSIFICATION TECHNIQUES USED FOR WATER QUALITY MONITORING

2012 ◽  
Vol 11 (02) ◽  
pp. 1250013 ◽  
Author(s):  
M. BOUAMAR ◽  
M. LADJAL
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Human Life ◽  
Recognition Rate ◽  
Real Data ◽  
Water Quality Monitoring ◽  
Support Vector ◽  
Rbf Neural Networks ◽  
Training Time ◽  
Classification Tool

Water quality is one of the major concerns of countries around the world. Monitoring of water quality is becoming more and more interesting because of its effects on human life. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Many techniques were developed in order to improve this process attending to rigorous follow-ups of the water quality. In this paper, we present a comparative study of the performance of three techniques resulting from the field of the artificial intelligence namely: Artificial Neural Networks (ANN), RBF Neural Networks (RBF-NN), and Support Vector Machines (SVM). Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. In order to evaluate their performances regarding the recognition rate, training time, and robustness, a simulation using generated and real data is carried out. To validate their functionalities, an application performed on real data is presented. Applied as a classification tool, the technique selected should ensure, within a multisensor monitoring system, a direct and quasi permanent control of water quality.

scholarly journals Nonlinear models for recognition of concentration of mixture

2021 ◽  
Vol 47 ◽  
Author(s):  
Feliksas Ivanauskas ◽  
Robertas Paulauskas ◽  
Pranas Vaitkus
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Nonlinear Models ◽  
Support Vector ◽  
Artificial Neural ◽  
Learning Machine

In this paper extreme learning machine and support vector regression are used for biosensors response to mixtures of compounds classification. The results are compared with the results obtained using artificial neural networks and others.

Condensed Nearest Neighbor Decision Rule Input Weight Sequential Feed-Forward Neural Networks

2014 ◽  
Vol 574 ◽  
pp. 712-717 ◽  
Author(s):  
Shu Xia Lu ◽  
Yang Fan Zhou ◽  
Bin Liu
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Feed Forward ◽  
Optimization Constraints ◽  
Feed Forward Neural Networks ◽  
The Difference ◽  
Learning Machine

This paper proposes a new approach is referred to as condensed nearest neighbor decision rule (CNN) input weight sequential feed-forward neural networks (CIW-SFFNS). In this paper, it is firstly shown that the difference of optimization constraints between the extreme learning machine (ELM) and constrained-optimization-based extreme learning machine. For the second time, this paper proposes a method that using CNN to select the hidden-layer weights from example. Moreover, we compare error minimized extreme learning machines (EM-ELM), support vector sequential feed-forward neural networks (SV-SFFNS) and CIW-SFFNS from two aspects:test accuracy and the number of hidden nodes. We present the result of an experimental study on 10 classification sets. The CIW-SFFNS algorithm has a statistically significant improvement in generalization performance than EM-ELM and SV-SFFNS.

scholarly journals A Hybrid EEG-Based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks (WCNN) and Support Vector Machine

2021 ◽  
pp. 1-29
Author(s):  
Sara Bagherzadeh ◽  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Support Vector ◽  
Emotional States ◽  
Eeg Signals ◽  
Color Representation ◽  
Time Frequency ◽  
Average Accuracy

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) is proposed to improve recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to time-frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19 and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, subject-independent Leave-One-Subject-Out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results show that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increases the average accuracy, precision and recall about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Combining CNN and MSVM increased recognition of emotion from EEG signal and results were comparable to state-of-the-art studies.

scholarly journals Improving Convolutional Neural Networks’ Accuracy in Noisy Environments Using k-Nearest Neighbors

2018 ◽  
Vol 8 (11) ◽  
pp. 2086 ◽  
Author(s):  
Antonio-Javier Gallego ◽  
Antonio Pertusa ◽  
Jorge Calvo-Zaragoza
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Neural Codes ◽  
K Nearest Neighbors ◽  
Network Topologies ◽  
Common Technique

We present a hybrid approach to improve the accuracy of Convolutional Neural Networks (CNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (kNN) algorithm for inference. Although this is a common technique in transfer learning, we apply it to the same domain for which the network was trained. Previous works show that neural codes (neuron activations of the last hidden layers) can benefit from the inclusion of classifiers such as support vector machines or random forests. In this work, our proposed hybrid CNN + kNN architecture is evaluated using several image datasets, network topologies and label noise levels. The results show significant accuracy improvements in the inference stage with respect to the standard CNN with noisy labels, especially with relatively large datasets such as CIFAR100. We also verify that applying the ℓ 2 norm on neural codes is statistically beneficial for this approach.

scholarly journals Forest Fire Prediction in Northern Sumatera using Support Vector Machine Based on the Fire Weather Index

2020 ◽  
Author(s):  
Darwis Robinson Manalu ◽  
Muhammad Zarlis ◽  
Herman Mawengkang ◽  
Opim Salim Sitompul
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Meteorological Data ◽  
Burned Area ◽  
Support Vector ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Data Mining Approach ◽  
Prevention Activity

Forest fires are a major environmental issue, creating economical and ecological damage while dangering human lives. The investigation and survey for forest fire had been done in Aek Godang, Northern Sumatera, Indonesia. There is 26 hotspot in 2017 close to Aek Godang, North Sumatera, Indonesia. In this study, we use a data mining approach to train and test the data of forest fire and the Fire Weather Index (FWI) from meteorological data. The aim of this study to predict the burned area and identify the forest fire in Aek Godang areas, North Sumatera. The result of this study indicated that Fire fighting and prevention activity may be one reason for the observed lack of correlation. The fact that this dataset exists indicates that there is already some effort going into fire prevention.

A Novel Deformation Prediction Model for Mine Slope Surface Using Meteorological Factors Based on Kernel Extreme Learning Machine

2014 ◽  
Vol 12 ◽  
pp. 67-81 ◽  
Author(s):  
Sun Wen Du ◽  
Jin Zhang ◽  
Zeng Bing Deng ◽  
Jing Tao Li
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Influence Factor ◽  
Open Pit ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Slope Surface ◽  
Deformation Prediction ◽  
Mine Slope ◽  
Learning Machine

Extreme learning machine (ELM), as an emergent technique for training feed-forward neural networks, has shown good performance on various learning domains. This work evaluates the effectiveness of a new Gaussian kernel function-based extreme learning machine (KELM) algorithm for the deformation prediction of mine slope surface utilizing various kinds of meteorological influence factor data including the temperature, atmospheric pressure, cumulative rainfall, relative humidity and refractive index of the mining slope. The KELM model was applied to the deformation of Anjialing diggings, which is an open-pit mine of the China Coal PingShuo Group Co., Ltd. in China. The prediction performance on real data suggests that the proposed KELM model can effectively express the non-linear relationship between the mine open-pit slope surface deformation and various kinds of meteorological influence factors. The prediction results are compared with the most widely used algorithms – Support vector machine (SVM) and back-propagation neural networks (BP NN) in terms of the ease of use ( for example, the number of user-defined parameters), regression accuracy and computation cost. The comparison shows that the new algorithm is similar to SVM and BP NN but more accurate, and has notable lower computational cost and stronger generalization ability.

Induction machine stator short-circuit fault detection using support vector machine

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saddam Bensaoucha ◽  
Youcef Brik ◽  
Sandrine Moreau ◽  
Sid Ahmed Bessedik ◽  
Aissa Ameur
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Short Circuit ◽  
Induction Machine ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Three Phase ◽  
Learning Machine ◽  
Short Circuit Fault

Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

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