Classification of tea specimens using novel hybrid artificial intelligence methods

Pławiak, Paweł; Maziarz, Wojciech

doi:10.1016/j.snb.2013.10.065

Cited by 38 publications

(21 citation statements)

References 22 publications

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“…As examples, work has been done towards arrhythmia detection [68], mass lesion detection [69], risk factors of metabolic syndrome [70], ovarian cancer diagnosis [71]. Hybrid models found in applications and problems in other fields, such as [72][73][74][75].…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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“…MLP utilises a supervised learning technique called backpropagation for training. Moreover, the connections of an MLP are always directed forward, hence they are also called feed-forward networks [28,29]. The connections define a relationship between the input and output variables of the network.…”

Section: Multilayer Percepton Architecturementioning

confidence: 99%

“…The automatic determination of the number of neurons and the number of hidden layers that optimise the resolution of the problem at hand has been debated [30]. Indeed, it is not possible to demonstrate that using architectures in which connections from one layer are removed or added to layers that are not immediately subsequent will produce better results [29]. Therefore, there is no method or rule that determines the optimal number of hidden layers and/or neurons to solve a given problem.…”

Section: Multilayer Percepton Architecturementioning

confidence: 99%

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Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

et al. 2018

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Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.

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“…Artificial neural networks are dynamic and self-adapting systems that resemble the process of human learning and are capable of machine learning and the pattern recognition. Different types of neural networks were used for data processing in electronic nose systems and metal oxide gas sensor arrays [14][15][16][17][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Hydrogen Detection With a Gas Sensor Array – Processing and Recognition of Dynamic Responses Using Neural Networks

Gwiżdż

Brudnik

Zakrzewska

2015

Metrology and Measurement Systems

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An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.

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Classification of tea specimens using novel hybrid artificial intelligence methods

Cited by 38 publications

References 22 publications

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

Hydrogen Detection With a Gas Sensor Array – Processing and Recognition of Dynamic Responses Using Neural Networks

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