scholarly journals The Main Component of Smart Tourism: A Principal Component Analysis Approach

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 295-307
Author(s):  
Ruci Meiyanti ◽  
Dana Indra Sensuse ◽  
Yudho Giri Sucahyo
Keyword(s):  
Principal Component Analysis ◽  
Information And Communication Technologies ◽  
Principal Component ◽  
Component Analysis ◽  
Successful Implementation ◽  
Information And Communication ◽  
Main Components ◽  
Main Component ◽  
Development And Management ◽  
Smart Tourism

Introduction. The establishment of a smart tourism model is indispensable for the effectiveness of tourism development and management supported by advances in information and communication technology. The successful implementation of smart tourism is determined by the smart tourism model. Determination of the main components has a significant role in building an effective model. The purpose of this study, to determine the main components forming a smart tourism model using Principal Component Analysis (PCA). PCA is used to compress variables by reducing the number of dimensions, without losing much information. Method This research method is a quantitative method using SPSS software version 22. Result. The result obtained 9 main components, namely stakeholders, infrastructure, goals, resources, activities, challenges, innovation in various applications, strategies, and use of information and communication technologies. Conclusion. Those main components are expected to construct a smart tourism model with components that are comprehensive, interrelated, and adaptable.

scholarly journals Chemical Composition and Variability of Leaf and Berry oils from Corsican Juniperus macrocarpa

2018 ◽  
Vol 13 (2) ◽  
pp. 1934578X1801300
Author(s):  
Joséphine Ottavioli ◽  
Ange Bighelli ◽  
Joseph Casanova ◽  
Félix Tomi
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Chemical Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Germacrene D ◽  
Leaf Oil ◽  
Main Components ◽  
Main Component ◽  
C Nmr

The chemical composition of five leaf oil samples and eighteen berry oil samples from Corsican Juniperus macrocarpa have been investigated by GC(RI), GC-MS and 13C NMR. The composition of berry oils was dominated by monoterpene hydrocarbons with α-pinene (56.4-78.9%) as main component followed by myrcene (2.2-11.9%). Germacrene D (4.5-103%) was the major sesquiterpene. The contents of the main components of leaf oils varied drastically from sample to sample: α-pinene (28.7-76.4%), δ3-carene (up to 17.3%), β-phellandrene (up to 12.3%), manoyl oxide (up to 8.1%). The occurrence of the unusual ( Z)-pentadec-6-en-2-one (0.1-1.2%) should be pointed out. Statistical analysis (Principal Component Analysis and k- means partition) suggested a unique group with atypical samples.

scholarly journals Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama

2020 ◽  
Vol 1 (1) ◽  
pp. 40
Author(s):  
Samsul Arifin ◽  
Anna Islamiyati ◽  
Raupong Raupong
Keyword(s):  
Principal Component Analysis ◽  
Predictor Variable ◽  
Principal Component ◽  
Component Analysis ◽  
Research Data ◽  
Predictor Variables ◽  
Analysis Method ◽  
Main Components ◽  
Main Component ◽  
New Variables

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.

scholarly journals Principal component analysis implementation for brainwave signal reduction based on cognitive activity

2017 ◽  
Vol 3 (3) ◽  
pp. 125 ◽  
Author(s):  
Ahmad Azhari ◽  
Murein Miksa Mardhia
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Task ◽  
Cognitive Activity ◽  
Electrical Potential ◽  
Principal Component ◽  
Component Analysis ◽  
Wave Form ◽  
Brain Response ◽  
Cumulative Rate ◽  
Main Component

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.

scholarly journals Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Principal Component Analysis ◽  
Optic Disc ◽  
Retinal Image ◽  
Principal Component ◽  
Component Analysis ◽  
Retinal Images ◽  
Image Area ◽  
Main Components ◽  
Vessel Tree ◽  
Optic Discs

A retinal image has blood vessels, optic disc, fovea, and so forth as the main components of an image. Segmentation of these components has been investigated extensively. Principal component analysis (PCA) is one of the techniques that have been applied to segment the optic disc, but only a limited work has been reported. To our knowledge, fovea segmentation problem has not been reported in the literature using PCA. In this paper, we are presenting the segmentation of optic disc and fovea using PCA. The PCA was trained on optic discs and foveae using ten retinal images and then applied on seventy retinal images with a success rate of 97% in case of optic discs and 94.3% in case of fovea. Conventional algorithms feed one patch at a time from a test retinal image, and the next patch separated by one pixel part is fed. This process is continued till the full image area is covered. This is time consuming. We are suggesting techniques to cut down the processing time with the help of binary vessel tree of a given test image. Results are presented to validate our idea.

The Principal Component Analysis of Coastal Areas

2014 ◽  
Vol 513-517 ◽  
pp. 3703-3706
Author(s):  
Qiu Ju Wang ◽  
Da Shen Xue
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Coastal Areas ◽  
Principal Component Analysis Analysis ◽  
Consumer Prices ◽  
The Government ◽  
Main Component ◽  
The Impact

In order to development the economic of China's coastal areas better, the paper mainly discusses the coastal areas of China's consumer price factors, the main use of software SPSS, using statistical analysis, principal component analysis, analysis that the impact is the main component of consumer prices and reached the level of consumer prices in coastal areas, which is conducive to the Government to take appropriate measures to faster and better development of the region's economy.

scholarly journals Analysis of the physical-chemical and sensorial properties of Maria type cookies

2013 ◽  
Vol 6 (2) ◽  
pp. 269-280 ◽  
Author(s):  
Daniela Pereira ◽  
Paula M. R. Correia ◽  
Raquel P. F. Guiné
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Volume Density ◽  
Physical Parameters ◽  
Multivariate Statistical ◽  
Main Component

Abstract Given the importance of the cookies of type Maria worldwide, and considering the absence of any scientific study setting out their main features, it becomes important to identify the differentiating characteristics of several commercialized brands, in particular related to the chemical, physical and sensory characteristics. In this way, the aim of this work was to study and compare eight different brands of cookies of type Maria. The elemental chemical analysis (moisture, ash, protein, fat, fibre and carbohydrates contents), determination of physical parameters (volume, density, texture and colour) and sensory evaluation of studied cookies were performed. Multivariate statistical methods (Pearson correlation, principal component analysis and cluster analysis) were applied to estimating relationships in analysed data. The results for the elemental analysis showed that the samples were very similar in terms of some components, like for example ashes, while quite different in terms of other components, such as moisture and fat contents. With respect to texture and colour the samples showed, in general, some important differences. In terms of sensory evaluation, the sample C was the one that in most sensory tests gathered the preference of the panellists. The cluster analysis showed that the sample A was much different from the other samples. The results of principal component analysis showed that the main component explains 32.6 % of the total variance, and is strongly related to variables associated to colour.

scholarly journals Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

scholarly journals Detection of structural alterations in metal bodies: An approximation using Fourier transform and principal component analysis (PCA)

2020 ◽  
Vol 25 (2) ◽  
pp. 255-260
Author(s):  
Brayan Eduardo Tarazona ◽  
Camilo Leonardo Sandoval R ◽  
Carlos Gerardo Cárdenas Arias ◽  
Javier Gonzalo Ascanio V. ◽  
Jhon Jairo Valencia N.
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Standard Deviation ◽  
Mechanical Vibration ◽  
Principal Component ◽  
Component Analysis ◽  
Repeated Measurements ◽  
Statistical Parameters ◽  
Structural Alterations ◽  
Main Components

In this theme some advances have been developed, verified in the background, where attempts have been made to determine the existence of structural alterations such as perforations, defective welding and dents in metal structures; a pattern of mechanical vibration that allows to differentiate each alteration has not yet been clearly defined. In this work, the data taking was carried out taking into account the position of the sensors, two beams were added without alteration, in order to be able to interact with the five configurations, which were adopted for the experimental design.  To the tests of repeated measurements, in each configuration, analysis (ANOVA) was used for the validation of NULL hypotheses, and thus to determine the number of test to be treated. After having the defined matrices representing each configuration, in each anomaly, it is necessary to apply the principal component Analysis (PCA), to the data obtained by the calculation of the fast Fourier transform (FFT). And thus determine the number of components by means of three Criteria (Jollife, Kaiser and PVA), using a classification algorithm, which evaluates the percentage of classification vs lower standard deviation. In this analysis the descriptors were not calculated but the main components of each criterion were taken as a description tool.  The process of extraction of characteristics was fundamental to determine the proper configuration in each alteration (fissure, welded, perforated, deformed).  On the other hand, statistical parameters were calculated (average, standard deviation, variation factor, Euclidean distance) of each anomaly. Taking as descriptors. 

scholarly journals Assessment of Under Five Child Mortality in Tanzania Mainland Based on Principal Component Analysis

2019 ◽  
pp. 1-13
Author(s):  
Privatus Christopher
Keyword(s):  
Principal Component Analysis ◽  
Child Mortality ◽  
Vitamin A Deficiency ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Set ◽  
Under Five ◽  
Long Time ◽  
Main Components

Deaths of children younger than 5 years has been a global problem for long time. This study is focused on evaluating diseases that caused under five child mortality in Tanzania in 2013. Diseases that causes child mortality were collected from 25 regions and analysed for 42 disease variables. The data obtained were standardized and subjected to principal component analysis (PCA) to define the diseases responsible for the variability in child mortality. PCA produced seven significant main components that explain 73:40% of total variance of the original data set. The results reveal that Thyroid Diseases, Snake and Insect Bites, Vitamin A Deficiency /Xerophthalmia, Eye Infections, Schistosomiasis (SS), Intestinal Worms, Ear Infections, Haematological Diseases, Diabetes Mellitus, Ill Defined Symptoms no Diagnosis, Poisoning, Anaemia, HIV/AIDS, Burns, Rheumatic Fever, Bronchial Asthma, Peri-natal conditions and Urinary tract infection are most significant diseases in assessing under five child mortality in Tanzania mainland. This study suggest that PCA technique is useful tool for identification of important diseases that causes death of children less than five years.

Principal Component Analysis of Structure Model Indexes on Composite Friction Materials

2014 ◽  
Vol 1004-1005 ◽  
pp. 459-463
Author(s):  
Jian Hua Du ◽  
Shi Meng Xu ◽  
Run Bo Ma ◽  
Lei Gong
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
General Rule ◽  
Element Model ◽  
Component Analysis ◽  
Friction Material ◽  
Basic Element ◽  
Structure Model ◽  
Friction Materials ◽  
Main Components

For the copper-based composite friction material, the statistical models of the element structure were given in this paper to pick up the main components, that was, through the principal component analysis on basic element model of surface structure, the two kinks of main indicators or indexes are ascertained can be found from the original four indicators. Here, the main components were the area radio and long radius, the others among the four were short radius, inclination. Moreover, the indicator reduction criterion was put forward as a general rule by this paper.

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