Zobrazeno 1 - 10 of 23 pro vyhledávání: '"artificial neural networks classification."'
1
Akademický článek
Feature Selection and Radial Basis Function Network for Parkinson Disease Classification
Autor: Ashraf Osman Ibrahim, Walaa Akif Hussien, Ayat Mohammoud Yagoop, Mohd Arfian Ismail
Publikováno v: Kurdistan Journal of Applied Research, Vol 2, Iss 3, Pp 167-171 (2017)
Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Park
Externí odkaz: https://doaj.org/article/9d1a3820bd4f4d69987370cc592ca286
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2
Akademický článek
Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition
Autor: Jide Julius Popoola, Rex Van Olst
Publikováno v: Journal of Engineering Science and Technology Review, Vol 8, Iss 4, Pp 135-144 (2015)
This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pa
Externí odkaz: https://doaj.org/article/70485a35376443e682f350d64ffe2646
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3
Dissertation/ Thesis
Comparative Choice Analysis using Artificial Intelligence and Discrete Choice Models in A Transport Context
Autor: Sehmisch, Sebastian
Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel choice modeling issues and therefore constitutes a promising, competitive alternative towards conventional discrete choice models like the Logit approa
Externí odkaz: https://tud.qucosa.de/id/qucosa%3A76704
https://tud.qucosa.de/api/qucosa%3A76704/attachment/ATT-0/
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4
FİNANSAL BAŞARISIZLIK TAHMİNİNDE MAKİNE ÖĞRENMESİ YÖNTEMLERİNİN SINIFLANDIRMA PERFORMANSININ KARŞILAŞTIRILMASI: BORSA İSTANBUL ÖRNEĞİ
Autor: AKSOY, Barış, BOZTOSUN, Derviş
Publikováno v: Volume: 14, Issue: 1 56-86
Hitit Sosyal Bilimler Dergisi
This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=tubitakulakb::f17a52094f18ee5956318f3b59970238
https://dergipark.org.tr/tr/pub/hititsbd/issue/63056/880658
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6
Akademický článek
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7
Akademický článek
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8
Feature Selection and Radial Basis Function Network for Parkinson Disease Classification
Autor: Ismail Mohd Arfian, Ashraf Osman Ibrahim, Walaa Akif Hussien, Ayat Mohammoud Yagoop
Publikováno v: Kurdistan Journal of Applied Research, Vol 2, Iss 3, Pp 167-171 (2017)
Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Park
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cdeb0cf0f1df025774349f8fd9a9673
https://doi.org/10.24017/science.2017.3.121
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9
Akademický článek
Tree cover change detection through Artificial Neural Network classification using Landsat TM and ETM+ images (case study: Golestan Province, Iran)
Autor: Abdorrasool Salman Mahini, Jahangir Feghhi, Azadeh Nadali, Borhan Riazi
Publikováno v: تحقیقات جنگل و صنوبر ایران, Vol 16, Iss 3, Pp 505-495 (2008)
The aim of this research was to detect tree cover changes through Artificial Neural Network classification and post-classification comparison methods using landsat TM and ETM+ images in Golestan province, north of Iran with area of 20437.74 ha. Land
Externí odkaz: https://doaj.org/article/8fcdbabd082c4b15ad07ec7d1d1376be
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10
Rotation-Invariant Texture Analysis and Classification by Artificial Neural Networks and Wavelet Transform
Autor: HAŞİLOĞLU, Abdulsamet
Publikováno v: Volume: 25, Issue: 5 405-413
Turkish Journal of Engineering and Environmental Sciences
A large number of approaches for texture analysis have been suggested for the purpose of texture classification. Recently, wavelet frames were proposed for texture features extraction. In this study, non-subsampled wavelet frame transform was used fo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=tubitakulakb::fa9f9a04b1b1a44d9652835389321d55
https://dergipark.org.tr/tr/pub/tbtkengineering/issue/12179/145306
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