scholarly journals Agro-morphological diversity of plantain accessions from different part of the world

2020 ◽  
Vol 14 (4) ◽  
pp. 1308-1321
Author(s):  
Amien Isaac Amoutchi ◽  
Oulo N’nan-Alla ◽  
Deless Edmond Fulgence Thiemele
Keyword(s):  
Principal Component Analysis ◽  
Genetic Improvement ◽  
Principal Component ◽  
Morphological Diversity ◽  
Black Sigatoka ◽  
Fruit Texture ◽  
Breeding Objective ◽  
The World ◽  
Qualitative Variables ◽  
Improvement Programme

The objective of this study was to characterize the agro-morphological diversity of plantain accessions. 18 quantitative variables and 20 qualitative variables were measured. The results of the analysis of the qualitative variables revealed important traits such as black Sigatoka resistance of FHIA 21, Pita 3, M53, Calculta 4 and Banskii accessions and the firm fruit texture of Galeo, Kokor, French sombre and Corne 1 accessions. A Principal Component Analysis (PCA) performed with the quantitative variables separated the 9 accessions into 4 groups with particular and important characteristics which can be exploited differently in genetic improvement programme according to the breeding objective. From these results, it appears clearly that the objective is achieved.Keywords: Sigatoka, qualitative variables, quantitative variables, genetic improvement.

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

Evaluation of the morphological diversity of tea roses (Rosa x odorata) based on phenotypic traits

2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Morphological Traits ◽  
Significant Positive Correlation ◽  
Flower Colour ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Germplasm Resources ◽  
Trait Correlation

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.

scholarly journals What Do Global Metrics Tell Us about the World?

2019 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Global Indicators ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global indicators that measure different economic, political and social attributes of countries in the world. This paper seeks to answer two questions. First, what is the degree of overlap between these different measures? Are they, in fact, measuring the same underlying dimension? To answer this question, we employ a principal component analysis (PCA) to 15 indices across 145 countries. The results demonstrate that there is one underlying dimension that combines economic development and social progress with state stability. Second, how do countries score on this dimension? The results of the PCA allow us to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as rich, poor and middle countries. A five-group classification provided a more nuanced categorization described as: The very rich, free and stable; affluent and free; upper middle; lower middle; poor and not free.

scholarly journals Harnessing the Predictive Power of Lower-division Statistics of Cricketers to Predict Their Rates of Success at the International Level

2021 ◽  
Vol 9 (12) ◽  
pp. 489-499
Author(s):  
Vishal C V
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Dimensionality Reduction ◽  
Predictive Power ◽  
Principal Component ◽  
International Level ◽  
Sports Analytics ◽  
The World ◽  
Lower Division ◽  
Different Parts

Abstract: Statistics has always been an integral part of the sporting world. Selectors pick players based on numerous factors such as averages, strike-rates, runs scored or goals scored. Teams have exclusive ‘talent hunters’, who spend weeks, if not months, trying to uncover talent from different parts of the world. With the rise of this new niche field called Sports Analytics, teams can now perform player evaluations on tons of data that is available. This paper aims to examine the factors that truly indicate the capacity of cricket players to perform at the top-most level – international cricket. Though this research has been carried out on cricket data, it is hoped that similar methods can be used to hunt for true talent in other sports! Keywords: Cricket Analytics, Random Forest, Principal Component Analysis, Dimensionality Reduction.

Variation in rice landraces adapted to the lowlands and hills in Nepal

2007 ◽  
Vol 5 (03) ◽  
pp. 120-127 ◽  
Author(s):  
R. C. Sharma ◽  
N. K. Chaudhary ◽  
B. Ojha ◽  
L. Yadav ◽  
M. P. Pandey ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Genetic Improvement ◽  
Agronomic Traits ◽  
Diversity Index ◽  
Principal Component ◽  
Field Studies ◽  
Rice Landraces ◽  
Improved Cultivars ◽  
Index Of Diversity

The landraces of rice (Oryza sativaL.) possess wide diversity, which needs to be properly characterized for their use in genetic improvement. Replicated field studies were conducted in 1998, 1999 and 2000 at two sites in Nepal to determine diversity in 183 landraces of rice adapted to the lowlands and the hills in Nepal. Fourteen improved genotypes were also used for comparison. Thirteen agronomic traits were investigated. Shannon–Weaver diversity index (H) and Simpson's index of diversity (D) were estimated to determine the level of genetic richness among the landraces. The landraces differed significantly for all traits. Except for plant height and maturity, at least one of the landraces compared well with the performance of improved cultivars. A principal component analysis separated the lowland- and hill-adapted landraces into two broad groups.

scholarly journals Factors determining the operational self-sufficiency of microfinance institutions

2021 ◽  
Vol 7 (2) ◽  
pp. 1-13
Author(s):  
Nejra Hadžiahmetović
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Social Goals ◽  
Microfinance Institutions ◽  
The Public ◽  
Independent Variables ◽  
Self Sufficiency ◽  
The World ◽  
Widespread Belief ◽  
Core Mission

Abstract The main aim of this paper is to explore the factors determining Microfinance institutions (MFIs) self-sufficiency. The data on selected variables for this research were obtained from the public MIX Market Database and cover the year of 2017. The empirical model is constructed with application of a Principal Component Analysis (PCA) and Logistic regression analysis. Sample is consisted of 342 MFIs from all around the world, with 21 independent variables grouped into eight factors/components, and OSS (operational self-sufficiency) as dependent variable. The obtained results suggest that higher revenue and MFIs profitability combined with decrease of credit risk lead to higher probability of MFI to be self-sufficient. These results also confirm widespread belief that MFIs will not be able to achieve their social goals without achieving sustainable profitability. In addition, results also confirm importance of MFIs core mission as with increase in outreach, probability of MFIs achieving self-sustainability also increases.

scholarly journals Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier

2020 ◽  
Vol 13 (2) ◽  
pp. 11
Author(s):  
Bekti Endar Susilowati ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Large Area ◽  
Robust Principal Component Analysis ◽  
Data Set ◽  
Silhouette Width ◽  
The World

Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster. 

scholarly journals Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020 ◽  
Author(s):  
Edy Irwansyah ◽  
Ebiet Salim Pratama ◽  
Margaretha Ohyver
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Principal Component Analysis ◽  
Data Reduction ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Techniques ◽  
The World ◽  
Using Data

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

scholarly journals Agro-Morphological Characterization of Kenyan Slender Leaf (Crotalaria brevidens and C. ochroleuca) Accessions

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Fridah A. Mwakha ◽  
Bernard M. Gichimu ◽  
Johnstone O. Neondo ◽  
Peter K. Kamau ◽  
Eddy O. Odari ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Quantitative Data ◽  
Genetic Improvement ◽  
Field Experiments ◽  
Block Design ◽  
Principal Component ◽  
Morphological Diversity ◽  
Group Method ◽  
Arithmetic Average ◽  
Qualitative And Quantitative

Slender leaf (Crotalaria spp) is among the indigenous and underutilized vegetables in Kenya whose production is limited to the Western and Coastal regions of the country. For a long time, this crop has been neglected in terms of research and genetic improvement. There is therefore scanty information on its morphological diversity and agronomic performance, hence the need for this study. Field experiments were carried out for two seasons in October to December 2018 and March to May 2019. The experiments were laid out in Randomized Complete Block Design with 29 accessions and replicated three times. Both qualitative and quantitative data were recorded from the accessions based on the Crotalaria descriptors. Quantitative data were subjected to analysis of variance using XLSTAT Version 2019, and accession means were separated using Student’s Newman Keuls test at 95% level of confidence. Both qualitative and quantitative data were subjected to multivariate cluster analysis, and a dendrogram was constructed using the unweighted pair-group method with arithmetic average. The principal component analysis was conducted to obtain information on the importance of the characters. Significant variation in agro-morphological traits was found within and between the two species. Cluster analysis grouped the accessions into seven major classes with a between-classes diversity of 75.13% and a within-classes diversity of 24.87%. This study sets the basis for genetic improvement of slender leaf in Kenya since the observed diversity can be exploited in selection for intraspecific and interspecific hybridization.

scholarly journals Unsupervised genome-wide cluster analysis: nucleotide sequences of the omicron variant of SARS-CoV-2 are similar to sequences from early 2020

2021 ◽  
Author(s):  
Georg Hahn ◽  
Sanghun Lee ◽  
Dmitry Prokopenko ◽  
Tanya Novak ◽  
Julian Hecker ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Entire Range ◽  
Principal Component ◽  
Jaccard Index ◽  
Genome Wide ◽  
A Genome ◽  
The World ◽  
Unsupervised Cluster Analysis

The GISAID database contains more than 100,000 SARS-CoV-2 genomes, including sequences of the recently discovered SARS-CoV-2 omicron variant and of prior SARS-CoV-2 strains that have been collected from patients around the world since the beginning of the pandemic. We applied unsupervised cluster analysis to the SARS-CoV-2 genomes, assessing their similarity at a genome-wide level based on the Jaccard index and principal component analysis. Our analysis results show that the omicron variant sequences are most similar to sequences that have been submitted early in the pandemic around January 2020. Furthermore, the omicron variants in GISAID are spread across the entire range of the first principal component, suggesting that the strain has been in circulation for some time. This observation supports a long-term infection hypothesis as the omicron strain origin.

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