Görsel Uyaranlara İlişkin Manyetoensefalografi Sinyallerinin Genelleştirilmiş Regresyon Sinir Ağı ile Sınıflandırılması

Objective: The aim of this study is to classify the magnetoencephalography (MEG) signals with artificial neural network to solve brain activity. Methods: The Generalized Regression Neural Network (GRNN) was used to classify MEG signals. The features of the signals were extracted by the Riemannian approach and the accuracy of the GRNN was calculated by the 10-fold cross validation technique. Results: In the study, MEG data recorded from 306 channels belonging to 7 male subjects and 9 female subjects were used. Approximately 588 stimuli were shown to each individual, so the entire data set is composed of 9414 stimuli. Mean specificity, mean sensitivity and mean classification accuracy were obtained 75.43%, 82.57% and 79%, respectively. The classification accuracies obtained by this study and other studies for same MEG dataset were presented comparatively. Conclusion: GRNN is thought to be a successful alternative to existing methods for classifying MEG signals

EEG Based Emotion Prediction with Neural Network Models

The term "emotion" refers to an individual\u27s response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent

Hybrid Classification Model for Emotion Prediction from EEG Signals: A Comparative Study

This paper introduces a novel hybrid algorithm for emotion classification based on electroencephalogram (EEG) signals. The proposed hybrid model consists of two layers: the first layer includes three parallel adaptive neuro-fuzzy inference systems (ANFIS), and the second layer called the adaptive network comprises various models such as radial basis function neural network (RBFNN), probabilistic neural network (PNN), and ANFIS. It is examined that the feature distribution graphs of the dataset, which includes three emotion classes: positive, negative, and neutral, and selected the most appropriate features for classification. The three parallel ANFIS structures were trained using the selected features as input vectors, and the outputs of these models were combined to obtain a new feature vector. This feature vector was then used as the input to the adaptive network, which produced the output of emotion prediction. In addition, it is evaluated the accuracy of the network trained using only the first features of the dataset. The hybrid structure was designed to enhance the system's performance, and the best accuracy result of 96.51% was achieved using the ANFIS-ANFIS model. Overall, this study provides a promising approach for emotion classification based on EEG signals.&nbsp

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

The tongue is one of the few organs with high mobility in the case of severe spinal cord injuries. However, most tongue-machine interfaces (TMIs) require the patient to wear obtrusive and unhygienic devices in and around the mouth. This paper aims to develop a TMI based on the glossokinetic potentials (GKPs), i.e. the electrical signals generated by the tongue when it touches the buccal walls. Ten patients were recruited for this research. The GKP patterns were classified by convolutional neural network (CNN) and support vector machine (SVM). It was observed that the CNN outperformed the SVM in individual and average scores for both raw and preprocessed datasets, reaching an accuracy of 97 similar to 99%. The CNN-based GKP processing method makes it easy to build a natural, appealing and robust TMI for the paralyzed. Being the first attempt to process GKPs with the CNN, our research offers an alternative to the traditional brain-computer interfaces (BCIs), which suffers from the instability and low signal-to-noise ratio (SNR) of electroencephalography (EEG)

Kimyasal sensör dzilerinde yapay sinir ağları ve bulanık mantık uygulamaları: Gazların sınıflandırılması ve gaz konsantrasyonlarının belirlenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kimyasal Sensör Dizilerinde Yapay Sinir Ağlan ve. Bulanık Mantık Uygulamaları : Gazların Sınıflandırılması ve Gaz Konsantrasyonlarının Belirlenmesi Anahtar kelimeler Gaz sensörleri, Gaz Konsantrsyonu Saptama, Miktarsal sınıflandırma, Yapay Sinir Ağlan, Geri Yayıİım, Bulanık mantık. Bu çalışmada Yapay Sinir Ağlan (YSA) ve Bulanık Mantık(BM) tabanlı algortimalar kullanılarak Gaz konsantrasyonunun saptanmasına ve Gaz kanşımlanmn sınıflandırılması amaçlanmıştır. Bu amaç ile sensör olarak Kuartz Kristal Mikrobalans (QCM) ve Interdijital Transduser (DDT) tip sensörler kullanılmıştır. Gaz konsantrasyon değerlerini ve gaz kanşımlannı kontrol etmek ve sensör sinyal datalarmı ölçmek için, IEEE 448 kartım kullanan bilgisayar konrollü bir ölçüm ve otomasyon sistemi kurulmuştur. Ük aşama olarak YSA ve Bulanık Mantık algoritmalan kullanılarak CC14, CHC13, Toluen ve Metanol gazlarının konsantrasyonlan saptanmaya çalışılmıştır. Bulanık Mantık algoritması kullanılan çalışmada sensörlerin sadece kararlı hal cevaplan kullanılmıştır. YSA Algoritması kullanılarak yapılan konsantrasyon saptama çalışmasında sensörlerin cevap süreleri olarak adlandırılan "sürenin içinde kalan geçici hal cevaplan da kullanılarak tahmin süresinin kısaltılmasına çalışılmıştır. Çalışmanın ikinci aşamasında YSA ve YSA-BM algoritmalan kullanılarak ikili CC14 - Toluen, CCI4 - Metanol ve Metanol - Toluen gaz kanşımlanmn miktarsal sınıflandırması üzerinde çalışılmıştır. Bu amaç ile ilk olarak iki katmanlı ileri beslemeli YSA yapısı kullanılmıştır. Elde edilen miktarsal sınıflandırma sonuçlarının iyileştirimesi için ikinci adım olarak BM karar algoritmali paralel YSA yapısı kullanılmıştır. Böylece oldukça sınırlı bir sayıdaki sensör tepkisiyle bile oldukça iyi sonuçlar elde edilmesi sağlanmıştır. Çalışmalarda gerek YSA ve gerek bulanık mantık ile yapılan Gaz konsantrasyonu tespitlerinde ve bulanık mantık karar mekanizman Paralel YSA yapısı ile yapılan miktarsal sınıflandrrmada oldukça iyi sonuçlar elde edilmiş ve YSA ve bulanık mantık yapılarının gaz kanşımlanmn miktarsal sınıflandırılması ve konsantrasyon saptamada uygun araçlar olduğu görülmüştür.Artificial Neural Networks and Fussy Logic Applications in the Chemical sensor Arrays : Classification of Gases and Determination of Gas Concentrations Keywords: Gas sensors, Gas Concentration Detection, Quantitative Classification, Neural Network, Back propagation. Fuzzy Logic. In this study, Artificial Neural Network (ANN) and Fuzzy Logic based algorithms are used in the determination of the gas concentrations and quantitative classification of the gas mixtures. For this purpose Quartz Crystal Microbalance (QCM) and Interdigital Transducer (IDT) type sensors were used. For control of the gas concentration values and gas mixtures, a computer controlled measurement and automation system with IEEE 448 card was set up. As a first step, an Artificial Neural Network (ANN) and Fuzzy Logic based algorithms were used to determination of the concentrations of the CCL», CHC13, Methanol and Toluene gases. In the study made by using Fuzzy Logic algorithms, steady state response of the sensors were used. In the study made by using ANN algorithms, transient response of the sensors were also used So the prediction time was decreased. In the second step ANN and ANN-Fuzzy Logic based algorithms were used for quantitative classification of the binary CCI4 - Toluen, CCU - Metanol and Metanol - Toluen gas mixtures. Firstly two layer feed forward ANN structure were used. Secondly, for improving the result of quantitative classification parallel ANN structure with fuzzy logic decision algorithm were used By using this parallel ANN structure, very good results were obtained even with a limited number of sensors. In the gas concentration determination studies made by both ANN and Fuzzy Logic and in the quantitative classification studies of the binary gas mixtures made by parallel ANN structure with fuzzy logic decision algorithm, quite good result were obtained Consequently, appropriateness of the ANN and Fuzzy Logic for the quantitative classification and the gas concentration determination were seen XV

A neural network implemented microcontroller system for quantitative classification of hazardous organic gases in the ambient air

In this study, a microcontroller-based gas mixture classification system is proposed to use real-time analyses of the trichloroethylene and acetone binary mixture. A Feed Forward Neural Network (FFNN) structure is performed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The phthalocyanine-coated Quartz Crystal Microbalance (QCM) type sensors were used as gas sensors. A calibrated Mass Flow Controller (MFC) was used to control the flow rates of carrier gas and trichloroethylene and acetone gas mixtures streams. The components in the binary mixture were quantified by applying the sensor responses from the QCMs sensor array as inputs to the FFNN. The microcontroller-based gas mixture classification system performs Neural Network (NN)-based estimation, the data acquisition and user interface tasks. This system can estimate the gas concentrations of trichloroethylene and acetone with the average errors of 0.08% and 0.97%, respectively

A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures

In this study, a comparative study was performed for the quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures using transient and steady state sensor responses. For this purpose, three neural network (NN) structures were used. The quartz crystal microbalance (QCM) type sensors were selected as gas sensors. One of the neural networks was used for quantitative identification using only steady state response. The other two neural networks were used for quantitative identification using both transient and steady state responses. One of them was a neural network with tapped time delays, and this NN used sensor frequency responses and past values of these responses. The other NN structure used sensor frequency responses and slope values of these sensors frequency responses to quantify the components in the binary mixture. Levenberg-Marquardt training algorithm was performed as the training method of the neural network structure. Quantitative analysis of trichloroethylene (TCE) and acetone was evaluated in terms of neural network structures and sensor responses. (c) 2006 Elsevier B.V. All rights reserved

A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems

In this study, the feed forward neural networks (FFNNs) were applied and an adaptive neuro-fuzzy inference system (ANFIS) was proposed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The quartz crystal microbalance (QCM) type sensors were used as gas sensors. The components in the binary mixture were quantified by applying the steady state sensor responses from the QCM sensor array as inputs to the FFNNs and ANFISs. The back propagation (BP) with momentum and adaptive learning rate algorithm, resilient BP (RP) algorithin, Fletcher-Reeves conjugate-gradient (CG) algorithm, Broyden, Fletcher, Goldfarb, and Shanno quasi-Newton (QN) algorithin, and Levenberg-Marquardt (LM) algorithm were performed as the training methods of the FFNNs. A hybrid training method, which was the combination of least-squares and back propagation algorithms, was used as the training method of the ANFISs. Quantitative analysis of trichloroethylene and acetone was evaluated in terms of training algorithms and methods. (c) 2005 Elsevier B.V. All rights reserved

A study on radial basis function neural network size reduction for quantitative identification of individual gas concentrations in their gas mixtures

In this study, the multilayer neural networks (MLNNs) with sigmoid hidden layers and radial basis function neural networks (RBFNNs) were compared for quantitative identification of individual gas concentrations in their gas mixtures (trichloroethylene and n-hexane), and a method to reduce the RBFNN size for quantitative analysis of gas mixtures was proposed. For this purpose, three MLNNs and three RBFNNs structures were applied. A data set consisted of the steady state sensor responses from the quartz crystal microbalance (QCM) type sensors was used for the training of the first MLNN and RBFNN. The other MLNNs and RBFNNs were trained using two different reduced training data. The components in the binary mixture were quantified applying the sensor responses from the QCM sensor array as inputs to the MLNN and radial basis neural networks. The performances of the neural networks were compared and discussed based on the experimental results. (c) 2007 Elsevier B.V. All rights reserved

LECTURE NOTES IN ARTIFICIAL INTELLIGENCE

The use of microcontroller in neural network realizations is cheaper. than those specific neural chips. In this study, an intelligent gas concentration estimation system is described. A neural network (NN) structure with tapped time delays was used for the concentration estimation of CCl4 gas from the trend of the transient sensor responses. After training of the NN, the updated weights and biases were applied to the embedded neural network implemented on the 8051 microcontroller. The microcontroller based gas concentration estimation system performs NN based concentration estimation, the data acquisition and user interface tasks. This system can estimate the gas concentrations of CCl4 with an average error of 1.5 %, before the sensor response time. The results show that the appropriateness of the system is observed