|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | An optimum feature extraction method for texture classification 显示文摘 | Avci Engin Sengur Abdulkadir Hanbay Davut | 2009 | Expert Systems with Applications2009，36，： | 1 |
| 2 | Performance prediction of a ground-coupled heat pump system using artificial neural networks 显示文摘 | HikmetEsen Mustafa Inalli Abdulkadir Sengur | 2008 | Expert Systems with Applications2008，35，4： | 1 |
| 3 | An expert system based on principal component analysis,artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases显示文摘 | Abdulkadir Sengur | 2007 | Computers in Biology and Medicine2007，，： | 1 |
| 4 | Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing 显示文摘 | Hikmet Esen Mustafa Inalli Abdulkadir Sengur | 2008 | International Journal of Thermal Sciences2008，47，4： | 1 |
| 5 | Artificial neural networks and adaptive neuro-fuzzy assessments for groundcoupled heat pump system 显示文摘 | Hikmet Esen Mustafa Inalli Abdulkadir Sengur | 2008 | Energy and Buildings2008，40，6： | 1 |
| 6 | Wavelet packet neural networks for texture classification显示文摘 | Sengur A Turkoglu I Ince M C | 2007 | Expert Systems with Applications2007，32，2： | 1 |
| 7 | Color texture classification using wavelet transform and neural network ensembles 显示文摘 | Sengur A | 2009 | The Arabian Journal for Science and Engineering2009，34，2： | 1 |
| 8 | Mul~iclass Least-squares Support Vector Machines for Analog Modulation Classification显示文摘 | Sengur A | 2009 | Expert Systems with Applications2009，36，3： | 1 |
| 9 | An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals显示文摘This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram(EEG) data in an automatic way. This study introduces an optimum allocation based sampling(OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes(e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods(decision table,support vector machine(SVM), k-nearest neighbor(k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD(i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals. | Siuly Siuly Varun Bajaj Abdulkadir Sengur Yanchun Zhang | 2019 | International Journal of Automation and computing2019，16，6： | 1 |
| 10 | Performance prediction of a ground - coupled heat pump system using artificial neural networks 显示文摘 | Hikmet Esen Mustafa Inalli Abdulkadir Sengur | 2008 | Expert Systems with Applications2008，35，4： | 1 |
| 11 | Forecasting of a ground - coupled heat pump per- formance using neural networks with statistical data weighting pre - processing 显示文摘 | Hikmet Esen Mustafa Inalli Abdulkadir Sengur | 2008 | International Journal of Thermal Sciences2008，47，40： | 1 |
| 12 | Color texture image segmentation based on neutrosophic set and wavelet transformation 显示文摘 | Sengur A Guo Y H | 2011 | Computer Vision and Image Understanding2011，115，8： | 1 |
| 13 | Artificial neural networks and adaptive neuro - fuzzy assessments for ground- coupled heat pump system 显示文摘 | Hikmet Esen Mustafa Inalli Abdulkadir Sengur | 2008 | Energy and Buildings2008，40，6： | 1 |
| 14 | Color Texture Image Segmentation Based on Neutrosophic Set and Wavelet Transformation 显示文摘 | SENGUR A GUO Y | 2011 | Computer Vision and Image Understanding2011，115，8： | 1 |
| 15 | Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures显示文摘In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised.Silica fume was used at concentrations of 0%,5%,10%,and 20%.Cube specimens(100 mm×100 mm×100 mm)were prepared for testing the compressive strength and ultrasonic pulse velocity.They were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 d.After curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.Two well-known deep learning approaches,i.e.,stacked autoencoders and long short-term memory(LSTM)networks,were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures.The forecasting experiments were carried out using MATLAB deep learning and neural network tools,respectively.Various statistical measures were used to validate the prediction performances of both the approaches.This study found that the LSTM network achieved better results than the stacked autoencoders.In addition,this study found that deep learning,which has a very good prediction ability with little experimental data,was a convenient method for civil engineering. | Harun TANYILDIZI Abdulkadir SENGUR Yaman AKBULUT Murat SAHtNa | 2020 | Frontiers of Structural and Civil Engineering2020，14，6： | 1 |
| 16 | Modelling of a new solar air heater through least-squares support vector machines显示文摘 | Hikmet Esen Filiz Ozgen Mehmet Esen Abdulkadir Sengur | 2009 | Expert Systems With Applications2009，，7： | 1 |
| 17 | Modelling of a new solar air heater through least-squares support vector machines显示文摘 | Esen H Ozgen F Esena M Sengur A | 2009 | Expert Systems With Applications2009，36，7： | 1 |
| 18 | Multiclass least-squares support vector machines for analog modulation classification显示文摘 | Sengur A | 2009 | Expert Systems with Applications2009，36，3： | 1 |
| 19 | Multiclass least-squares support vector machines for analog modulation classification显示文摘 | Sengur A | 2009 | Expert Systems with Applications2009，36，3： | 1 |
| 20 | Multiclass least-squares support vector machines for analog modulation classification显示文摘 | Sengur Abdulkadir | 2009 | Expert Systems withApplications(S0957-4174)2009，36，3： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员