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| 1 | A comparative study for the location and scale parameters of the Weibull distribu- tion with given shape parameter 显示文摘 | Yeliz Mert Kantar Birdal Senolu | 2008 | Computers & Geo- sciences2008，34，： | 1 |
| 2 | Trade Latin American: Where are Journal of Environment and policy and pollution in the pollution Haven显示文摘 | Birdall N Wheller D | 1993 | Development1993，，2： | 1 |
| 3 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | | 0，，01： | 1 |
| 4 | Effects of anisometropia on binocularity 显示文摘 | Toma S Birdal E | 2001 | Pediatr Ophthalmol Strabismus2001，38，1： | 1 |
| 5 | Effect of resin-based materials on fracture resistance of endndontically treated thin-walled teeth 显示文摘 | Balkaya MC Birdal IS | 2013 | J Prosthet Dent2013，109，5： | 1 |
| 6 | Effects of anisometropia on binocularity 显示文摘 | Tomac S Birdal E | 2001 | J Pediatric Ophthalmol Strabismus2001，38，： | 1 |
| 7 | Effect of resin-based materials on frac- ture resistance of endodontically treated thin-walled teeth 显示文摘 | Balkaya MC Birdal IS | 2013 | J Prosthet Dent2013，109，5： | 1 |
| 8 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | Pediatr Ophthalmol Strabismus2001，38，1： | 1 |
| 9 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | J Pediatr Ophthalmol Strabismus2001，38，1： | 1 |
| 10 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | J Pediatr Ophthalmol Strabismus2001，38，1： | 1 |
| 11 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | J Pediatr Ophthalmol Strabismus2001，38，1： | 1 |
| 12 | Effects of anisometrop ia on binocularity显示文摘 | Tomac S Birdal E | | 0，，01： | 1 |
| 13 | Effects of anisometropia on binocularity 显示文摘 | Tomac S Birdal E | 2001 | Ophthalmol Strabismus2001，38，6： | 1 |
| 14 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | J Pediatr Ophthahnol Strabismus2001，38，1： | 1 |
| 15 | Effects of anisometropia on binocularity显示文摘 | Tomac S Birdal E | 2001 | J Pediatr Ophthalmol Strabismus2001，38，6： | 1 |
| 16 | 3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features显示文摘Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification. | Ramiz Gorkem Birdal Ahmet Sertbas | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 17 | The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction显示文摘Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction. | Ramiz Gorkem Birdal | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
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