다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

Flora of Turkey and the East Aegean Islands.

Trends in extreme learning machines

Texture features for image classification and retrieval.

Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy

White blood cells detection and classification based on regional convolutional neural networks.

The performance of a solar collector system using sodium carbonate decahydrate (Na"2CO"3.10H"2O) as Phase Change Material (PCM) was experimentally investigated during March and collector efficiency was compared with those of convectional system including no PCM. We also made a series of predictions by using three different soft computing techniques as Artificial Neural Networks (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). It was found that the solar collector system with PCM is more effective than convectional systems. Soft computing techniques can be used to model of a solar collector with PCM. Furthermore, analysis of soft computing showed that SVM technique gives the best results than that of ANFIS and ANN.

Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques

In this paper, an expert speech recognition system is presented. This paper especially deals with the combination of feature extraction and classification for real speech signals. A Wavelet packet adaptive network based fuzzy inference system (WPANFIS) model is developed in this study. WPANFIS consists of two layers: wavelet packet and adaptive network based fuzzy inference system. The wavelet packet layer is used for adaptive feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The performance of the developed system is evaluated by using noisy speech signals. Test results showing the effectiveness of the proposed speech recognition system are presented in the paper. The rate of correct classification is about 92% for the sample speech signals.

Speech recognition using a wavelet packet adaptive network based fuzzy inference system

The support vector machines is a new technique for many pattern recognition areas. The digital modulation classification is one of these pattern recognition areas. In SVM training, the kernels, kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for SVM training. In this study, a hybrid of genetic algorithm-support vector machines (HGASVM) approach is presented in digital modulation classification area for increasing the support vector machines (SVM) classification accuracy. This HGASVM approach proposed in this paper selects of the optimal kernel function type, kernel function parameter, most appropriate wavelet filter type for problem, wavelet entropy parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The classification accuracy of this HGASVM approach is tried by using real digital modulation dataset and compared with the SVMs, which has kernel function type, kernel function parameter, wavelet filter type, wavelet entropy parameter, and C parameter are randomly selected. Here, discrete wavelet transform (DWT) and adaptive wavelet entropy are used in feature extraction stage of this HGASVM approach. The digital modulation types used in this study are ASK-2, ASK-4, ASK-8, FSK-2, FSK-4, FSK-8, PSK-2, PSK-4, and PSK-8. The experimental studies conducted in this study show that the classification accuracy of this HGASVM approach is more superior than SVM, which has constant parameters.

Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM

Engin Avci

Papers

Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy

White blood cells detection and classification based on regional convolutional neural networks.

Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques

Speech recognition using a wavelet packet adaptive network based fuzzy inference system

Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM