Comparison of Fisher's linear discriminant to multilayer perceptron networks in the classification of vapors using sensor array data

2006 ◽  
Vol 115 (2) ◽  
pp. 647-655 ◽  
Author(s):  
Matteo Pardo ◽  
Brian C. Sisk ◽  
Giorgio Sberveglieri ◽  
Nathan S. Lewis
Keyword(s):  
Multilayer Perceptron ◽  
Sensor Array ◽  
Array Data ◽  
Linear Discriminant ◽  
Multilayer Perceptron Networks ◽  
Fisher’S Linear Discriminant

scholarly journals Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura Gagliano ◽  
Elie Bou Assi ◽  
Dang K. Nguyen ◽  
Mohamad Sawan
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Focal Epilepsy ◽  
Single Layer ◽  
Epileptic Seizures ◽  
Novel Approach ◽  
Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

scholarly journals Identification of Wheat Varieties with a Parallel-Plate Capacitance Sensor Using Fisher’s Linear Discriminant Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
C. V. K. Kandala ◽  
K. N. Govindarajan ◽  
N. Puppala ◽  
V. Settaluri ◽  
R. S. Reddy
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Parallel Plate ◽  
Wheat Variety ◽  
Capacitance Sensor ◽  
Wheat Varieties ◽  
Linear Discriminant ◽  
Variety Classification ◽  
Fisher’S Linear Discriminant

Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle (θ), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD models.Zandθof a parallel-plate capacitance system, holding the wheat samples, were measured using an impedance meter, and theCvalue was computed. The best model developed classified the wheat varieties, with accuracy of 95.4%, over the six wheat varieties tested. This method is simple, rapid, and nondestructive and would be useful for the breeders and the peanut industry.

scholarly journals Discrimination and Classification of Poultry Feeds Data

2013 ◽  
Vol 2 (5) ◽  
pp. 37-44
Author(s):  
Olosunde A.A ◽  
Soyinka A.T
Keyword(s):  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Discriminant Function ◽  
Copper Salt ◽  
Analysis Method ◽  
Linear Discriminant ◽  
Discriminant Analysis Method ◽  
Organic Copper ◽  
Fisher’S Linear Discriminant

This study is aimed at employing discriminant analysis method and classification for the purpose of achieving the assessment of a discriminant function through which we can discover the reasons of the actual difference between two groups of eggs of which the chicken were fed with different combination of feeds. Fisher’s Linear Discriminant Function (LDA) was used as a tool for the Statistical analysis. It was estimated on the basis of a sample of 96 chickens, which were classified into two groups of 48 chickens each. One group was fed with in-organic copper salt combination while the second group with organic copper salt combination. Some important attributes are measured from the eggs produced from these two groups; such as egg’s size(g) and cholesterol level(mg).The results obtained assert the efficiency of the discriminant function which we obtained and the possibility of its use for the purpose of discriminating and classifying the eggs of unknown feeds into corresponding group in future.

Optimization of Electronic Nose Sensor Array and its Application in the Classification of Vinegar

2010 ◽  
Vol 121-122 ◽  
pp. 27-32 ◽  
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Xuan Zhou
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Gas Sensors ◽  
Sensor Array ◽  
Principal Component ◽  
Optimization Method ◽  
Linear Discriminant ◽  
Gas Sensor Array ◽  
The Given

Five kinds of vinegars were measured by a gas sensor array composed of six TGS gas sensors. The sensor array should be optimized by the minimal Wilks statistic value, then, the four best sensor array used to detect the type of vinegars were formed, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were applied to analyze the data of primary and optimized sensor array. The results indicated that optimization sensor array could be more adaptable to recognize the five kinds of vinegars. Thereby the given optimization method is effective.

scholarly journals A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound

2021 ◽  
Vol 11 (4) ◽  
pp. 1592
Author(s):  
Nemesio Fava Sopelsa Neto ◽  
Stéfano Frizzo Stefenon ◽  
Luiz Henrique Meyer ◽  
Rafael Bruns ◽  
Ademir Nied ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Distribution Systems ◽  
Ultrasonic Inspection ◽  
Test Machine ◽  
Electrical Networks ◽  
Electrical Transmission ◽  
Ceramic Insulators ◽  
Distribution Class ◽  
Multilayer Perceptron Networks

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

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