Tag Archives: Principal Component Analysis

Categorization And Translation Operating System’s Assistance in Explication of Different Bangladeshi Accents (Published)

National language of Bangladesh is Bengali and it’s also the official language used frequently. Our paper’s focal point was to categorize and differentiate West Bangla language or Bangladeshi Bangla accent in a Bengali sentence. We first amassed text from literature files. Then converted text sentence data to numeric data by using TF-IDF. After PCA application by MATLAB, final data set was being obtained. Our strategy for future will assist in developing an automatic software that detects if a sentence has been written in West Bangla or Bangladeshi Bangla and then it will do translation from one to another form. Differences between both Bangladeshi accents is already so minimum that only native speaker can identify them distinctively. There was no data available previously for this study. This work denoted that as if languages seems to be same but are unique and different in their own way and depicts the identity of two geographically separated regions. The major output of this work paid heed on identification of the form of language frequently used today. Many other studies could be conducted, based on the results of our study, on the effects of Sanskrit and Foreign literature

Keywords: Bangladeshi Bangla, Inverse Data Frequency, Linear SVM, Principal Component Analysis, Python, Term Frequency, West Bangla

Characterisation of Wheat (Triticum Aestivum L.) Cultivars Grown In Lesotho by Morphological Markers (Published)

Wheat is one of the major cereal crops grown in Lesotho, ranking third after maize and sorghum. Cultivars of wheat are imported from South Africa without characterization.  The study was therefore conducted with the following objectives; (1) to distinguish wheat cultivars grown by farmers, (2) to estimate genetic distance among wheat cultivars and (3) to identify the characters that have high discriminatory power. Complete Randomized Block Design with ten treatments and three replications were applied. Data were collected using Descriptor and analysed using GENSTAT software to perform cluster and principal component analysis. The first three principal components constituted 84.572% of the total variation. First principal component variation accounted for 55.738%, while second principal component contributed 15.737% and third principal component constituted 12.858%. Characters responsible for variation in the first component were spikelets, spike height and tillers. Separation among second component was brought about by plant height, reproductive tillers and seeds per spike. Variation in component three was due to glume hairiness, seed size and plant height. Cluster analysis formed two groups, A and B, and one outlier. Group A comprised of Gariep, Koonap, Elands and Senqu while Group B consisted SST374, SST356, PAN3195, PAN3379 and TugelaDN. Group C was an outlier containing Matlabas. The findings showed that the cultivars were different from each other and as such genetic variation exists that broaden the spectrum of germplasm, from which farmers can make a wider choice.

Keywords: Cluster analysis, Lesotho, Principal Component Analysis, Wheat, morphological characters

Genetic diversity of common bean (Phaseolus vulgaris L.) introduced for adaptation in Lesotho (Published)

Common beans are introduced in Lesotho from CIAT-Malawi annually to evaluate them for adaptation and other characters of economic importance. They are not being characterized for identity, therefore the study was conducted at National University of Lesotho located in the Maseru District of Lesotho with specific objectives of (1) estimating genetic distances among the common bean genotypes using morphological features and (2) identifying morphological characteristics that contributed to discrimination of these cultivars. Randomized Complete Block Design was applied with four replications. Twenty cultivars of common beans from CIAT-Malawi were used as treatments. Data were collected using descriptor of common beans compiled by International Board of Genetic Resources Unit. Data generated were subjected to cluster analysis and principal component analysis using Genstat recover (2015). Results of cluster analysis revealed four groups, of which two consisted of five cultivars, another had four and the last one only two cultivars. Besides, there were three outliers. The results of principal component analysis showed the total variation accounted for by both principal component 1 and 2 was 35.95% with each constituting 18.62 and 17.33 %, respectively. The characters responsible for variation from the first principal component analysis were seed shape, colour of flowers, colour of wings, seed-coat pattern and pod beak orientation. The characters influencing separation along the second principal component were number of locules per pod, number of seeds per pod, leaflet length, days to flowering and pod colour. It can be deduced that the cultivars broad in to Lesotho is diverse broadening the genetic base of the existing common bean genotypes

Keywords: Cluster analysis, Common bean, Lesotho, Principal Component Analysis

An Investigation on Crime Rate in Southeastern Nigeria (Published)

The modern world is everything but a safe place. This sad but indisputable fact has been proven to be true by several research. Crime rate is souring in this part of the continent and there exists many determinant to this anti-societal behavior amongst the people. In this paper, those variables that are crime prone in Southeastern Nigeria over a 10 year study period were determined using Principal Component Analysis (PCA); a Multivariate Statistical Technique that is use to reduce the dimensionality of a large number of interrelated crime variables while retaining as much of the information as possible. Data were collected on seven crime variables, from the data bank of National Bureau of Statistics (NBS). Moderate correlations exists between sizeable number of crimes, two principal components was extracted using the scree plot, explaining 86.4% of the total variation in the dataset. The highest and most committed crime in the study region are Armed Robbery, Murder and Grievous Harm and Wounding

Keywords: Eigenvalues, Principal Component Analysis, crime rate, scree plot, southeastern nigeria

Principal Component Analysis of Customer Satisfaction and Repeat Purchase Behaviour in the Mobile Telephony Market in Ghana (Published)

The purpose of the current study was to establish the relationship mobile telephony customers in Ghana’s repeat purchase behaviour and satisfaction. In order to achieve the aim of the study, principal component analysis was made to establish the principal components factors of customer repeat purchase behaviour, dimensions of customer satisfaction, and the relationship between repeat purchase and satisfaction. A sample of three thousand (3,000) mobile phone users was selected from the three major cities in Ghana of Accra, Kumasi and Takoradi for the survey, using Likert scale questions. The findings revealed that three variables: call rate, service reliability, meeting customer’s expectation were the key underlying dimensions determining customer satisfaction of mobile telephone users in Ghana. Again it was found that change of mobile network is a permanent feature of mobile users in Ghana which might not necessarily reflect dissatisfaction. The results further indicated that customer satisfaction of mobile telephony users in Ghana is reflected by high or very high commitment to repeat purchase. Finally, the study showed that mobile users repeat purchase behaviour is influenced primarily by reference group influence rather than customer satisfaction. Thus no relationship was found between customer repeat purchase and satisfaction of mobile users in Ghana.

Keywords: Customer Satisfaction, Principal Component Analysis, Purchase Intentions, Repeats Purchase

A Novel Method Of Average Filtering For Removing Noise And Face Recognition (Published)

Face recognition is new and difficult which requires great effort and determination due to the Wide variety of faces, complexity of noises and image backgrounds. In this paper, we propose an Average Filtering based novel method for face recognition in cluttered and noisy images. It is imperative that computational researchers know of the key findings from experimental studies of face recognition by human. These findings provide insights into the nature of starting symbol to begin that the human visual system relies upon for achieving its great deal of performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we are presenting what we believe are various basic results, with implications for the computational design systems. The aim of our proposed work of average filtering based method for face recognition is to improve the recognition accuracy. We use AT&T face database and experiments on it are performed to demonstrate the effectiveness of the proposed method.

Keywords: Average Filter, Eigenfaces, Face Recognition, Feature Extraction, Fisherfaces., Laplacianfaces, Linear Discriminant Analysis, Principal Component Analysis, Smooth Mean Filter

An Investigation on Crime Rate in Southeastern Nigeria (Published)

The modern world is everything but a safe place. This sad but indisputable fact has been proven to be true by several research. Crime rate is souring in this part of the continent and there exists many determinant to this anti-societal behavior amongst the people. In this paper, those variables that are crime prone in Southeastern Nigeria over a 10 year study period were determined using Principal Component Analysis (PCA); a Multivariate Statistical Technique that is use to reduce the dimensionality of a large number of interrelated crime variables while retaining as much of the information as possible. Data were collected on seven crime variables, from the data bank of National Bureau of Statistics (NBS). Moderate correlations exists between sizeable number of crimes, two principal components was extracted using the scree plot, explaining 86.4% of the total variation in the dataset. The highest and most committed crime in the study region are Armed Robbery, Murder and Grievous Harm and Wounding.

Keywords: Eigenvalues, Principal Component Analysis, crime rate, scree plot, southeastern nigeria

A STATISTICAL MODEL OF PRICES OF ESSENTIAL COMMODITIES IN GHANA (Published)

The purpose of this study is to report an “index” that can be used as a measure of the standard of living of Ghanaians. To accomplish this objective, secondary data on prices of some selected commodities compiled by the Price Statistics section of the Ghana Statistical Service (G.S.S.) was used to conduct the study. The data covers the period from 2008 to 2013 and it was collected by month and for each Region (nine in all). The data was analyzed using Principal Component Analysis, a multivariate data analysis tool. At the end of the analysis, nine (9) indices were reported, one for each Region. These indices allowed for comparative study of the cost of living for the six years for all the Regions. The cost of living for instance, was highest in Eastern Region and Lowest in Ashanti Region for the period 2008; for 2009, it was highest in Eastern Region and lowest in Central Region; for 2010, it was highest in Volta Region and lowest in the Ashanti Region; for 2011, it was highest in Central Region and lowest in Ashanti Region; for 2012, it was highest in Central Region and lowest in Ashanti Region and lastly, for 2013, it was highest in Central Region and lowest in Ashanti Region

Keywords: Commodities, Highest and Lowest, Index, Price Section of the Ghana Statistical Service, Principal Component Analysis, Region, Standard of Living

A Principal Component Analysis based assessment of the factors influencing online shopping in Mauritius: Binary Regression Modelling (Review Completed - Accepted)

In our modern world, the intensive use of internet has imposed new lifestyles and encouraged new behaviour amongst many across the globe. With the development in Internet technologies, the emergence of online shopping has altered the way businesses operate. While many of them have embraced this platform to present their offerings, many customers on the other hand, are finding it more cost-effective and convenient to carry out their transactions online. Therefore, the present study was undertaken to refine our understanding on consumers’ attitudes, perception and behaviour towards online shopping in a Mauritian context.  Data was collected among 22250 respondents in Mauritius whereby a questionnaire was administered through personal interviews with the aim of achieving a higher response rate. Principle Component Analysis (PCA) was performed to reveal the underlying factors influencing people’s perceptions and attitudes towards online shopping and the results uncovered that ‘online shopping conveniences’, ‘security and product risk’, ‘complexity and waiting time’ and ‘enjoyment and pleasure’ were major contributors to overall attitudes towards online shopping attributes. The binary regression model was also fitted   and factors such as marital status and internet at home were the significant factors to contribute towards online shopping

Keywords: Binary Logistic, Online Shopping, Principal Component Analysis, Security