La variabilité morphologique des taxons marocains du genre Ornithogalum sous-genre Heliocharmos : une étude biométrique

1988 ◽  
Vol 66 (11) ◽  
pp. 2178-2186 ◽  
Author(s):  
J. Moret ◽  
H. Couderc ◽  
R. Gorenflot ◽  
J. M. Hubac
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Key Words ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Reproduction Biology ◽  
Group Form

Taxa of the subgenus Heliocharmos Baker belonging to the genus Ornithogalum L. (Liliaceae) have been studied in Morocco from a biometric standpoint. It appears, from multidimensional analyses (principal component analysis and discriminant analysis), that it is not possible to recognize the existence of all the taxa reported by former authors. Two groups are morphologically distinct. The first corresponds to O. kochii Parl, and the second to O. algeriense Jord. and Fourr. The latter group includes all individuals that, according to Maire's Flora, can be related to O. comosum L., O. tenuifolium Guss., and O. umbellatum L. ssp. orthophyllum Maire and Weiller. Maire's separation of this group into three species seems artificial and the determination of the taxa seems incorrect. However, the populations of this group form two sets, one littoral and the other inland. These results, which are in agreement with data on karyology and reproduction biology, make it possible to revise this subgenus in Morocco. Three taxa are recognized: O. algeriense Jord. and Fourr. (=O. umbellatum L. ssp. orthophyllum Maire and Weiller) ssp. algeriense Moret ssp. nov., O. algeriense Jord. and Fourr. ssp. atlanticum Moret ssp. nov., and O. kochii Parl. A key to the various taxa and a map showing their distribution in Morocco are submitted. Key words: Ornithogalum, Heliocharmos, Liliaceae, Maroc, biosystematics, biometry. [Journal translation]

Determination of Agarwood oil’s significant chemical compounds using principal component analysis

2020 ◽  
Author(s):  
Mohamad Hushnie Haron ◽  
Mohd Nasir Taib ◽  
Nurlaila Ismail ◽  
Nor Azah Mohd Ali ◽  
Saiful Nizam Tajuddin
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Chemical Compounds ◽  
Component Analysis ◽  
Significant Chemical

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

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