Pt2520 Unit 6 Data Mining Project

Pt2520 Unit 6 Data Mining Project
STATS 415 Data Mining Project Insights into the prediction of the default payment through the
history of payments, the amount of previous payment and the amount of bill payment Zifan Li, Xi
Chen, and Yang Liu Data This dataset contains customer's default payments in Taiwan. This dataset
has 30000 observations and 24 features. The features are all real numbers. There is a binary variable,
default payment (Yes=1, No=0), as the response variable. The rest of the 23 features are explanatory
variables, including amount of the given credit (X1), history of past payment (X6–X11), amount of
bill statement (X12–X17), amount of previous payment (X18–X23), and some demographical data.
In predictions, we did not include some of the demographical variables ... Show more content on
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In contrast, QDA made mistakes on 49% of the observations, but has a relatively high accuracy
(81%) on people that defaulted on the next month's payment. In fact, other methods such as KNN,
logistic regression that achieved similar overall prediction accuracy also face the small problem of
low accuracy on people that defaulted. Undoubtedly, the overall prediction accuracy is very
important for the banks when making predictions. However, we might consider which type of
mistake we would like to avoid more. For the banks, not issuing loans to people who are actually
going to repay would only cause a small loss on interests. However, issuing loans to people who are
going to default would cause a big loss on the unpaid loans. Thus, it is reasonable to believe that
accurate selection of people who are going to default is of higher priority to the banks. In light of
this belief, QDA is also worth considering to the banks. A byproduct during the classification is the
importance table produced by the random forest, which is shown
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NT1210 Unit 1 Assignment 1
3.9 Data Processing and analysis.
The unit of analysis was the individual mobile phone service subscriber. Data were analyzed in two
major stages, that is, through descriptive statistics and specific tests of hypothesis. Cross tabulation
was used to analyze demographic variability of the respondents. Descriptive statistics including
measures of central tendencies, measures of dispersion, frequencies and percentages were calculated
to examine the respondents' characteristics. These statistics showed the basic characteristics of
research variables. The second stage of analysis focused on testing specific hypotheses of this study.
To analyze qualitative data, content analysis was used following the suggestion by SPSS BOSS
(2015). As noted by ... Show more content on Helpwriting.net ...
As noted by Zikmund, Babbin, Carr & Griffin (2013), the importance of editing, coding and
entering the data is to ensure that errors are checked. Saunders et al. (2009), on the other hand, note
that no matter how carefully data is coded and entered, there will always be some errors which need
to be checked and corrected before the data is analyzed. After this initial processing, the data was
analyzed using Statistical Package for Social Science (SPSS) version 20. In the second step, to get a
feel of the data, descriptive statistics was used. As explained by Saunders et al. (2009), descriptive
statistics enable the researcher to numerically describe and compare the variables under study. This
includes response rate; measures of central tendency, notably the mean; measures of dispersion,
particularly the standard deviation for Likert scale variables in the questionnaire (Magutu, 2013;
Gathenya, 2012). Descriptive statistics covered all response variables and the demographic
characteristics of respondents, which were analyzed using cross tabulation. As explained by Magutu
(2013, pp 84 ), 'descriptive statistics provide the basic features of the data collected on the variables
and provide the impetus for conducting further analyses on the

Article Critique: An Efficient Method of Classifying...
The article I chose to critique is "An Efficient Method for Classifying Perfectionists" (Rice, K &
Ashby, J, 2007). This article is very informative and detailed in its description of the study of
classification of perfectionists. Intro The introduction is lengthy but covers all needed aspects of the
study. The stated purpose is clear. It states at the end of the first paragraph that the study is looking
for an easy method for counselors or school staff to identify perfectionism within its student body
and further identify one's that may need intervention due to maladaptive perfectionism which can
lead to increased mental and physical issues. Rice and Ashby (2007) provide an extensive literature
review. This review covers most previous ... Show more content on Helpwriting.net ...
They used a variety of other tests along with the APS–R to find these cut off scores and validate
their data (dependent variables). These included: Frost's Multidimensional Perfectionism Scale
(FMPS), Multidimensional Perfectionism Scale (MPS), the Center for Epidemiological Studies –
Depression Scale (CES–D), Satisfaction with Life Scale (SWLS), students grade point average
(GPA), and self reported perfectionism rating. I believe the authors provided a strong theoretical
perspective and that their literature review backed the need for this type of research and their
hypothesis. Method Rice and Ashby use descriptive and correlational research methods for this
study. They compiled a sufficient number of subjects and who had completed multiple surveys and
tests and correlated the data. This data was quantitative and qualitative in measure. They used tests
with ordinal data to obtain raw scores for their correlation. They also provided cross sectional data
evaluation by using data from multiple other testing tools. I believe that the research design was
adequate for their needs. I do believe that there could be threats to their validity due to the fact that
the authors themselves did not collect the data; they used data

The Physics Of Infrared Radiation
A vibration can result in an absorption peak of infrared radiation, only if there is a change in the
dipole moment of the molecule. The larger this change, the more intense will be the absorption
band. Furthermore, the electron polarity vectors in the covalent bond should not cancel out i.e the
covalent bonds must be asymmetric. Asymmetrical bonds carry a net polarity vector and show
absorption peaks; bonds that are centered along the plane of symmetry cancel out their polarity
vectors upon stretching and there is no net change in dipole moment. Such molecules are known to
be infrared inactive. An infrared spectrum is the plot of absorbance (or % transmission) against the
wavenumber (cm–1) (Rehman et al., 2012). Wavenumber is defined as number of waves in a length
of one centimeter and is a commonly used unit for representing spectral regions. Wavenumber is
linear with energy and is expressed with the following equation.Significant parts of the spectrum are
the regions where the molecule absorbs the infrared energy. These are shown as characteristic
absorption peaks. A molecule can only absorb radiation when the incoming infrared radiation is of
the same frequency as one of the fundamental modes of vibration of the molecule (Stuart and
Barbara, 2004). The modes of vibration usually encountered include bending, stretching, wagging
and out of plane vibrations. These molecular vibrations result in changes in dipole moments of the
molecular groups and the resulting vibrating

Advantages Of Multimodal Biometric System
Study of Biometric, Multimodal Biometric Systems Dhanashri J. Ghate Department Of Computer
Engineering GHRCEM, Pune University, India Abstract: Now a days Security of computer science
and information technology is an important issue.Authentication and identification are related to the
security.the traditional methods for confirming persons identity involve use of ATM,Pins,Password
which can be lost or stolen.So it is needed to have a system which provide security and overcome
the limitations of traditional methods.A biometric is the automated method of identifying the
persons identity.Also biometric of the individual can not be hacked easily like password and ...
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Advantages of multimodal biometric system: 1. Non–universality: Multimodal biometric system
solves the problem of non universality which is occoured in unimodal biometric systems.For
example,if the persons cut finger prevents him from successfully enrolling into a fingerprint
system,then the availability of another biometric trait,say face,can be used in the inclusion in the
biometric system. 2. Indexing Large–scale Biometric database: Multimodal biometric systems can
make easy filtering of large scale biometric databases. 3. Spoof Attacks: It becomes increasingly
difficult for an impostor to spoof multiple biometric traits of a legitimately enrolled individual. 4.
Noise in Sensed Data: Multimodal biometric system considers the problem of noisy data.During
preprocessing of image,filteration of image is carried out.and the noise from image is removed out.
5. Fault tolerence: A multimodal biometric system may also be viewed as a fault tolerant system
which continues to operate even when certain biometric sources become unreliable due to sensor or
software malfunction, or deliberate user manipulation. VII. Applications of Multimodal Biometric
System: Most of the biometric applications are related to security.also,used in
commertial,forensic,government and public

Linear Discriminant Analysis
Facial acknowledgment programming is a PC construct program that utilizations focuses in light of
still pictures and video pictures on facial components to distinguish a man. It was produced in th
1960s, and is the main semi–robotized framework for facial acknowledgment that required the
chairman to find facial elements on photos before it computed separations and proportions to a
typical reference indicate that was looked at reference information. (FBI.gov., n.d.) The product
works off two methodologies; geometric and photometric; geometric is based off of elements and
photometric depends on perspective. Out of the diffrent calculations that were produced, the three,
Principal Components Analysis( PCA), Linear Discriminant Analysis (LDA), ... Show more content
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After doing look into, I found out about the diverse focuses and trademark coordinates that are
utilized for the match, however I imagine that in any case too numerous things can turn out badly.
There are individuals on the planet that looks a like, and imagine a scenario in which somebody has
highlights like I have and are so comparative; we could conceivably be matches, however off–base.
The requirement for a higher exactness reasons for extraordinary sympathy toward me, and it could
bring about the pure to be observed blameworthy or the liable to be sans set. With more research
perhaps this would be useful in cases like terrorism, youngster snatching, and different cases, yet for
right now, DNA and video is by all accounts the best confirmation. I realize that these logical
confirmation can in some cases be questioned and does not mean a man is blameworthy, but rather
the exactness rate is higher. The idea driving facial acknowledgment programming is exceptionally
intriguing and has potential for enhancing security, controlling visitors, and recognizing guilty
parties on camera, it simply need a higher exactness rate and alter the

The Importance Of Image Analysis
Mohammed El–Helly et al. [8] proposed an approach for integrating image analysis technique into
diagnostic expert system. A diagnostic model was used to manage cucumber crop. According to this
approach, an expert system finds out the diseases of user observation. In order to diagnose a disorder
from a leaf image, five image processing phases are used: image acquisition, enhancement, and
segmentation, feature extraction and classification. Images were captured using a high resolution
color camera and auto focus illumination light. Firstly they transformed the defected RGB image to
the HSI color space then analyzed the histogram intensity channel then increased the contrast of the
image. Fuzzy C Means (FCM) segmentation is used in this ... Show more content on
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Algorithms for selecting useful texture features were developed by using stepwise discriminant
analysis. They developed four models i.e. HSI_39, HSI_15, HS_10 and I_11. Classification was
done by minimum distance classifier. The model using 15 selected HSI texture features got the best
classification accuracy (95.6%), which recommended that it would be best to use a reduced hue,
saturation and intensity texture feature set to differentiate orange diseases. The HSI_15 model, I_11
and HSI_39 models achieved classification accuracy 95.6%, 81.11% and 95.6% respectively.
Bauer et al. [10] developed algorithms for the automatic classification of leaf diseases based on
stereo and high resolution multispectral images. Leaves of sugar beet were used in this method.
Sugar beet leaves might be infected by several diseases. In controlled light laboratory environment,
They collected stereo images of single sugar beet leaves by RGB and multispectral cameras. The
leaves were either healthy or infected from diseases such as rusts, powdery mildew and Cercospora
leaf spot. They generated 3–D models of the leaves to fuse information from the two cameras.
Classification is done by k–nearest neighbour and an adaptive Bayes method. The classification
accuracy achieved were 91% for Cercospora leaf spots and 86% for rust disease.
Weizheng et al. [11] developed an accurate and fast new method based

5530 Ch11
Chapter 11: 4, 7, 8, 10, 11, 12, 14, 15, 18, 20, 21, 22, 23, 24, 26, 27
Chapter Eleven
Credit Risk: Individual Loan Risk
Chapter Outline
Introduction
Credit Quality Problems
Types of Loans
Commercial and Industrial Loans Real Estate Loans Individual (Consumer) Loans Other Loans
Calculating the Return on a Loan
The Contractually Promised Return on a Loan The Expected Return on a Loan
Retail versus Wholesale Credit Decisions
Retail Wholesale
Measurement of Credit Risk
Default Risk Models Qualitative Models Quantitative Models
Summary
Appendix 11A: Credit Analysis (www.mhhe.com/saunders7e)
Appendix 11B: ... Show more content on Helpwriting.net ...
5. What are the primary characteristics of residential mortgage loans? Why does the ratio of
adjustable–rate mortgages to fixed–rate mortgages in the economy vary over an interest rate cycle?
When would the ratio be highest?

Residential mortgage contracts differ in size, the ratio of the loan amount to the value of the
property, the maturity of the loan, the rate of interest of the loan, and whether the interest rate is
fixed or adjustable. In addition, mortgage agreements differ in the amount of fees, commissions,
discounts, and points that are paid by the borrower.
The ratio of adjustable–rate mortgages to fixed–rate mortgages is lowest when interest rates are low
because borrowers prefer to lock in the low market rates for long periods of time. When rates are
high, adjustable–rate mortgages allow borrowers the potential to realize relief from high interest
rates in the future when rates decline.
6. What are the two major classes of consumer loans at U.S. banks? How do revolving loans differ
from nonrevolving loans?
Consumer loans can be classified as either nonrevolving or revolving loans. Automobile loans and
fixed–term personal loans usually have a maturity date at which time the loan is expected to have a
zero balance, and thus they are considered to be nonrevolving loans. Revolving loans usually
involve credit card debt, or similar lines of credit, and as a result the balance will

The Data Mining Of Finance
Data Mining in Finance 1. Introduction Data mining is used to uncover hidden knowledge and
patterns from a large amount of data. In finance, there is enormous data which generates during
business operations and trading activities. Extracting valuable data from them manually might be
unable or spend a lot of time. As a result, data mining plays an importance role of discovering data
that can be used to increase profits and evaluate risks of strategic planning and investment. The data
mining methods in finance are derived from statistics, machine learning, and visualization. The most
commonly used methods are Decision Trees, Neural Networks, Genetic Algorithms, and Rough Set
Analysis (Hajizadeh, et al., 2010). Due to prediction and ... Show more content on Helpwriting.net
...
Prior forecasting method is the prediction based on the growth rate of fundamental factors such as
earning–per–share, book value, and invested capital. Another common method for forecasting is
time series analysis. The traditional analysis uses regression models to discover market trends.
However, financial time series are complex, noisy, and nonlinear pattern. As a result, later
techniques have applied artificial intelligences; the most popular technique is neural networks
(Zhang & Zhou, 2004). According to Refenes's experiment (1994), the back–propagation networks,
a type of neural network, predicts more accurately than regression models on the same datasets.
Other techniques are ARMA and ARIMA model, genetic algorithm, and random walk. Pai and Lin
(2005) introduces the A hybrid model ARIMA and support vector machine which give better result
than single ARIMA. Edward Tsang et al. (1998) proposes EDDIE, a tool which constructs a genetic
decision tree to evaluate whether a stock price increases in a specified time. Jar–Long and Shu–Hui
(2006) proposes a two–layer bias decision tree with simple technical indicators. Typically, the input
variables of prediction models are daily price, volume, rate of change, and technical indicators, for
example, moving average (MA), relative strength index (RSI), and volatility (Vol). 2.2 Foreign
exchange market Foreign exchange market opens 24 hours a day, 7 days a week. It is the

Essay On Multivariate Analysis
MBA in Multivariate Data Analysis–I MBA Multivariate Data Analysis–I MBA ( Multivariate Data
Analysis–I ) MODULE – I Multivariate Data Analysis–I MODULE I Overview of Multivariate
Statistics Module Description The goal of studying this module is to outline the concept of
multivariate analysis. This module identifies the specific terms and techniques included in
multivariate. It also explains the key concepts of multivariate analysis. It also discusses the
multivariate techniques based on variables. By the end of this module, students will be able to know
the multivariate data analysis, statistical terms, and concepts. They will be able to explain the
multivariate techniques. Chapter 1.1 Introductions to Multivariate Analysis Multivariate Data
Analysis–I Multivariate Data Analysis–I Chapter Table of Contents Chapter 1.1 Introduction to
Multivariate Analysis Aim 1 Instructional Objectives 1 Learning Outcomes 1 1.1.1. Introduction 3
1.1.2. What is Multivariate Analysis? 3 1.1.3. Multivariate Analysis in statistical terms 3 1.1.4. Basic
concepts of Multivariate Analysis 5 1.1.5. Multivariate Techniques 5 (a) Classification 5 (b) Types 5
Self–assessment Questions 11 Summary 22 Terminal Questions 23 Answer Keys 23 Activity 25
Bibliography 26 e–References 26 External Resources 27 ... Show more content on Helpwriting.net
...
This basically models reality where every circumstance, item, or choice includes more than a
solitary variable. The data age has brought about masses of information in each field.
Notwithstanding the quantum of information accessible, the capacity to acquire a reasonable picture
of what is happening and settle on canny choices is a test. At the point when accessible data is put
away in database tables containing lines andcomponents, Multivariate Analysis can be utilized to
process the data in a meaningful

Gender Recognition And Android Development
Gender Recognition and
Android Development
Summer Internship Report
TBI Online, Noida
Prakhar Singh
IV Year, ECW
Acknowledgement
The internship I had with TBI Online was a great chance for me to work with and learn from a
professional environment. I am very grateful that I was provided with this opportunity. I consider
myself lucky for having a chance to meet so many wonderful people and professionals who
mentored me throughout my internship period.
I am using this opportunity to express my deepest gratitude and special thanks to my mentor Rohit
Sharma (CTO), who took out his valuable time to help me out and guide me, allowing me to carry
out my project, and for giving me necessary guidance
I would also like to express my deepest sense of gratitude to Mr Vipul (Lead Developer) for his
careful and precious guidance which were extremely valuable to me, and his helping in getting me
acquainted with the company infrastructure.
I perceive this opportunity as a big milestone in my career development. I will strive to use gained
skills and knowledge in the best possible way, and I will continue to work on their improvement, in
order to attain desired career objectives.
Sincerely
Prakhar Singh
Table of Contents
Abstract
Company Profile
About TBI Online

Technologies
Products
Interactions
Objective
Literature Review
Technical Details (Work Done)
Gender Recognition
Android Code Review
Results
Timeline
Conclusion
Future Work
References

Example Of Probabilistic Approach
Probabilistic Approach
In addition to univariate analysis and discriminant analysis, researchers also tried to explore
probabilistic prediction of corporate bankruptcy. Zavgren (1985) opined that the models which
generate a probability of failure are more useful than those that produce a dichotomous classification
as with multiple discriminant analysis.
Option to Default Methodology
Merton (1974) applied the option pricing method developed by Black and Scholes (1973) to the
valuation of a leveraged firm and relates the risk of default to the capital structure of the company.
According to this model, "The firm's equity can be seen as a European call option on the firm's
assets with a strike price equal to the book value of the firm's liabilities. The option like property of
the firm's equity follows from the absolute priority rule with respect to which the shareholders can
be seen as residual claimants with limited liability. This limited liability gives the shareholders the
right but not the obligation to pay off the debt holders and to take over the remaining assets of the
firm."
Logistic Regression
Martin (1977) used the logit model for bank failure prediction. Later, Ohlson (1980) developed O –
Score model to predict failure for companies using conditional ... Show more content on
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ID3 uses entropy to measure the values of each attribute. After that it derives rules through a
repetitive decomposition process that minimizes the overall entropy. Messier and Hansen (1988)
used ID3 to derive prediction rules from loan default and corporate bankruptcy cases. The loan
default training sample contained 32 firms with 16 in each group (default or non–default). In the
corporate bankruptcy case, the training sample contained 8 bankrupt and 15 non–bankrupt firms.
For the holdout samples, the rules derived by ID3 correctly classified the bankrupt/non–bankrupt
firms with perfect accuracy and 87.5% accuracy for the loan

Children 's Manifest Anxiety Scale
Abstract
The Revised Children's Manifest Anxiety Scale is one of the most widely used self–report measures
of anxiety in youth. It is used to diagnose overall anxiety in youth and also to characterize the nature
of that anxiety. The purpose of revising the Children's Manifest Anxiety Scale (CMAS) was to
shorten the administration time, increase the clarity of the items, and reduce the reading level.
Reliability and validity data appear to be adequate, though the internal consistency estimates for
some of the subscales of the RCMAS are rather low. While self–report measures, such as the
RCMAS, appear to be good at demonstrating convergent validity, they often struggle with
demonstrating discriminant validity. Further reliability and validity data is analyzed, and strengths
and weaknesses of the measure are discussed. Review of the Revised Children's Manifest Anxiety
Scale
The study of child and adolescent anxiety and depression has become an increasing concern over the
past quarter of a century. With this increasing concern comes a need to establish psychometrically
sound measures specifically geared toward a youth population. One of the most widely used self–
report measures of anxiety in youth is the Revised Children's Manifest Anxiety Scale (RCMAS:
Reynolds & Richmond, 1979; Dadds, Perrin, & Yule, 1997). The history of the RCMAS can be
traced back to 1951 to Taylor's Manifest Anxiety Scale (MAS), which was created based on items
from the Minnesota Multiphasic Personality

Zero Base Training Report
Other studies have processed EEG data by using different classification methods in the zero base
training report, different classification parameters in order to rate the effectiveness of electrode
relevancy. In my experiment I used linear discriminant analysis and focused on the 02 electrode in
order to classifier event–related potentials from the eeg data. In this project the experiment model
was the most effective way to test this hypothesis and create the system with the limited resources.
But their is always better ways to preprocess the data and optimize the filters. The experiment only
gave the difference in data result and event related potential (ERP) differences. If a system that
looked at these parameters and looked at more parameters ... Show more content on Helpwriting.net
...
More specifically, we sought to use brain activity measurements of ERPs to reconstruct images. A
recent study used functional magnetic resonance imaging (fMRI) to measure brain activity in visual
cortex as a person looked at several hours of movies. We then used these data to develop
computational models that could predict the pattern of brain activity that would be elicited by any
arbitrary movies (i.e., movies that were not in the initial set used to build the model). We used EEG
data to measure brain activity during stimulus. Then created a system for preprocessing the data to
fine tune the data to recognise image stimulus ERPs.
In the future of this study we will be able to create an intuitive way of image reconstruction. As you
move through the world or you look at pictures, a dynamic, ever–changing pattern of activity is
evoked in the brain. The goal of the mind image identification system was to create a more fine
tuned way to read and related image evoked data. To do this, we create encoding models that
describe how images are transformed into brain activity, and then we use those models to decode
brain activity and reconstruct the

Face Recognition Of Java Environment
FACE RECOGNITION IN JAVA ENVIRONMENT ABSTRACT: In today's world, face recognition
has its application in many fields incorporating:Automotive sector, image enhancing, robotics,
gaming & manufacturing. It is an exciting field with hurdles. Such as limited hardware, poor
visualisation or quality & connectivity. This paper demonstrates a face recognition system in JAVA
environment. The aim is to have high recognition rate. Key words:Face recognition, Open CV, JAVA
environment. I. Introduction:Image processing is a field that deals with manipulation of image with
intent to carry out to enhance image and to extract some useful information from it. It usually deals
with treating images at 2D signals and applying signal processing methods to them. It can be
generally defined as a 3 step process starting by importing the image. Continuing with its analysis
and ending with either an alter image or an output. The application of in processing can be classified
into five groups. The 5 groups are shown in fig 2 II. Face Recognition Techniques: This section is
about different techniques that are used in recognizing the face, detecting the face and tracking the
video object in general. It also briefly describes the algorithms available in the market for the above.
Face detection is a computer technology that determines the locations and sizes of human faces in
digital images. Face tracking is an extension of face–detection when applied to a video image. Sno.
Name

Education Consulting Services By Excel
Data science, as an illustrious application in Statistics, is my direction in career. The four–year study
in applied math motivates me to take the challenge of pursuing a master 's degree in statistics. In
addition, my aspiration to become a professional data analyst also gives the graduate study an
absolute necessity. To trace my determination in data science, my intern as an assistant consultant in
Wisedge Education is an inspiration. There, I constructed my very first data report on evaluating
education–consulting services by Excel. The sample was prospective Chinese undergraduates of
colleges and universities in the U.S in the previous five years. The scatter plot on acceptance rates
and ranking of universities enrolled showed an ... Show more content on Helpwriting.net ...
For methodology, it was fortunate that I got the opportunity to join the Directed Reading Program
on statistical methods, ranking soccer teams in cooperation with Dr. Steve. Because outcomes
involving multiple categories occur more often, we wanted to compare and improve existing ranking
methods, and made them suitable for more classifications. For example, primarily, there was only
one classification: scores. To advance, currently, classification of yardages would also be considered
when ranking. After collecting unstructured data online, we used least squares to get the squared
matrix for Massey 's Method but found it failed to cover multiple categories simultaneously. After
research and comparison of other methods, we found that Markov Method succeeded because the
weights can be assigned to eigenvectors of normalized stochastic matrices. Since we have found the
desired method, we reached the agreement that the comparison between the two methods was
consequential here. The results by using the two methods of ranking the same set of scores were
different. Therefore, on the seminar, I concluded that Markov methods outperformed Massey's
Method in the research, but further research suggests that Massey's Methods have a better
performance under some circumstances. It is the charisma of data science that analysts should adjust
measures to local situations. During the research, MATLAB dominated to produce outcomes.
However, it functions more on mathematical calculation,

Examples Of Meta-Analysis In Criminal Justice
Introduction The quantitative and qualitative approaches have been proven to have been struggling
for power to criminal justice and criminological research. These strategies are only devices to help
social researchers comprehend their general surroundings. The debate concerning which approach is
extraordinary has turned out to be genuine in its outcomes as the quantitative methodology has
increased high ground in the control. The problem that has been found is that the new quantitative
methods are moving fast than the evolution of an arrange curricula. New procedures can be
produced and spread all through the order far quicker than the managerial maze of college
educational modules changes can be explored, In simple terms, this basically ... Show more content
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Meta–Analysis is a measurable strategy for consolidating the discoveries from the independent
studies and it is regularly used to survey the clinical adequacy of human services mediations; it does
this by joining information from two or more randomized control trials. Meta–Analysis are turning
out to be increasingly normal in the criminal justice and criminological literate. There are
advantages and they have been criticized too. People do not find meta–analysis useful all the time.
There are most important issues that the meta–analysis have been faced with in the recent years. The
first one is that " The conditions under which meta–analysis are, and are not, most useful" the
second one is that "the dilemma of whether or not to include unpublished work in the sample of
studies to be analyzed" and the last one is that "the choice of bivariate versus multivariate effect size
estimates to be synthesized". The goal is to take these issues out of what has ostensibly been level
headed discussions about specialized conventionality and rather to place them into a more extensive
examination connection inside of criminal equity and

Forensic And Age Determination Of Blood Stains
Forensic approaches to age determination of blood stains Among the many pursuits of forensic
scientists, one of the foremost attempts is that of establishing time of death of a victim by use of
whatever evidence is available. Even though some calculations for estimations are claimed to be
available to a few branches of the forensics community, such as medical examiner determination by
use of internal temperature of the body, or the onset of rigor and livor mortis, or that of the
entomologist 's estimations based on the pupal and larval stages of different carrion insects, it may
be that the body is in such an advanced state of decay that these calculations cannot give an accurate
calculation, or, even worse, that a body may be absent. In light of such possibilities, it may however
be possible to use other types of evidence to establish time of death, time of deposition, and render a
partial, if not full reconstruction of events on the scene based on the age and order of deposition of
other evidence. Bloodstains represent one such potential piece of evidence that, aside from having
the possibility of providing a link to a person by DNA, may allow investigators to determine the
time of deposition, and perhaps aid in the reconstruction of the purported events. Attempts to
establish the age of bloodstains have been made since early 1900s (Bremmer et al., 2012), but no
early technique could really be established as a universal and accepted method. One issue that most
techniques

Analysis Of Omnitel Pronto Italia's Greatest Strength
Omnitel Pronto Italia's greatest strength was its customer service. With sales below expectations,
Omnitel Pronto Italia performed extensive market research to identify the needs of its customer
segments.
Through analysis, a need for a new pricing strategy was evident. From the research, it was
determined that consumers viewed the monthly usage fee as a tax and deeply resented it, they did
not want to pay an activation fee and only wanted to pay for services used. Through the competitor
analysis, it was found that Telecom Italia Mobile (TIM) had a strong distribution channel and was
primarily directed to upper–class income individuals who used cellphones as a status symbol. As
they were essentially a monopoly, their marketing costs were low. From a customer perspective,
consumers were impulsive and wanted different rates for local calls, long distance and international
calls. With interview with over 5000 current and potential customers, it was also determined that
there was low brand loyalty, high consideration scores on activation cost and disliked the monthly
fee.
Therefore, Omnitel Pronto Italia details a decision case where the company is looking for a strategy
that will differentiate the brand, build market share, and maximize profits all while avoiding a price
war with its leading competitor TIM.
This case details the viability of the new plan called LIBERO. For the chosen strategy, it had to meet
three requirements:
By eliminating the monthly fee, it will not

Menstrual Cycle
CHAPTER ONE
INTRODUCTION
1.0 BACKGROUND OF THE STUDY
Menstrual cycle is a process experienced by every female at one point in their life cycle. In the
olden days a young lady experiences her first menstruation in her late teens or early twenties. In
recent times, however, some begin their menstrual cycle as early as nine years. The age at menarche
(the first time a girl or a young woman menstruates) is widely considered as an important landmark
in sexual maturity. However it varies widely between the same people. These variations has been
attributed to a number of possible causes such as; advancement in technology, health care delivery,
weather and climate change, food, daily activities, socio–economic activities, lifestyle and many ...
Show more content on Helpwriting.net ...
Despite variability, most normal cycles range from 21 to 45 days, even in the first gynaecologic
year, although short cycles of fewer than 20 days and long cycles of more than 45 days may occur.
Because long cycles most often occur in the first 3 years post menarche, the overall trend is toward
shorter and more regular cycles with increasing age. By the third year after menarche, 60% to 80%
of menstrual cycles are 21 to 34 days long, as is typical of adults. An individual's normal cycle
length is established around the sixth gynaecologic year, at a chronologic age of approximately 19
or 20 years.
Two large studies, one cataloguing 275947 cycles in 2702 females and another reporting on 31645
cycles in 656 females, support the observation that menstrual cycles in girls and adolescents
typically range from 21 to approximately 45 days, even in the first gynaecologic year. In the first
gynaecologic year, the fifth percentile for cycle length is 23 days and the 95th percentile is 90 days.
By the fourth gynaecologic year, fewer females are having cycles that exceed 45 days, but
anovulation (the state of not ovulating because of a medical condition, suppression by drugs or
menopause) is still significant for some, with the 95th percentile for cycle length at 50 days. By the
seventh gynaecologic year, cycles are shorter and less variable, with the fifth percentile for cycle
length at 27 days and the 95th percentile at only 38 days. Thus, during the early years after

Increasing Student Retention : A Predictive Model Of...
Increasing Student Retention: A Predictive Model of Undergraduate Degree Non–Completion
Abstract
This study seeks to develop a predictive model of college student dropout, using aggregate high
school variables and individual postsecondary achievement variables to predict non–graduating
students' academic year of departure. After performing multiple linear regression and discriminant
function analysis, the research found that a cohort of students admitted in the fall 2007 semester
from several universities could be assigned an academic year of departure using data readily
available by the end of a student's third academic term. The university can use this model to predict
student departure and improve the effectiveness of student retention efforts by focusing on targeted
times when at–risk students are predicted to drop out.
Introduction
While many academic, psychological, and institutional variables influencing undergraduate student
dropout have been studied, these factors have generally only been examined using models that treat
student dropout as a binary, dependent variable. One of the shortcomings of using logistic regression
in the study of undergraduate dropout is that it restricts the study's ability to infer when a given
student is likely to drop out. In contrast, the present study considered undergraduate dropout as
occurring over a set of intervals, in this case academic terms, and sought to identify those crucial
times when students are considering departure

Concrete Gravity Dams
Table of Contents Table of Contents 1 1. Introduction 2 2. Literature Review 2 3. Classification
Techniques in Machine Learning 3 3.1 K–nearest Neighbor 3 3.2 Support Vector Machine 4 3.3
Naïve Bayes Classifier 5 References 8 Introduction Dams are important structures to supply water
for irrigation or drinking, to control flood, and to generate electricity. The safety analysis of concrete
gravity dams in seismic regions is significant due to the high potential of life and economic losses if
these buildings fail. Many of existing dams were built using outdated analysis methods and lack of
knowledge (Bernier 2016). In the design of many existing dams, dam–water–foundation interactions
affecting the earthquake response were not ... Show more content on Helpwriting.net ...
Another study is carried out by Gaspar et al. in order to investigate the effects of uncertainties of
dam properties. A thermos–mechanical model was developed to define the behavior. Classification
Techniques in Machine Learning The classification of the data is the problem to observe new
numbers of discrete categories. The aim is to learn a model making accurate predictions on new
observations based on a set of data points. For example, for the observations set, x1, ..., xn, the
corresponding categories, y1, ..., yn, where yi ∈{–1, +1}. Here, yi=–1 refers to category of failure
region, and yi=+1 refers to safe region. A new observation, x value, is assigned to one of these
categories. The three popular classification techniques are explained: (1) K–nearest neighbor
(KNN), (2) support vector machine (SVM), and (3) naïve Bayes classifier (NBC). The given
algorithms here include both deterministic and probabilistic classification approaches. 3.1 K–nearest
Neighbor In the machine learning, one of the simplest method for classification is the K–nearest
neighbor algorithm. Given a new observation x∈R, K training observations from the rows of the
Xtm closest in distance to x are found. After that, using the mainstream division among these K
nearest observations from the training set, x is classified. Consequently, the performance of KNN
algorithm depends on the choice of K and the algorithm is sensitive to local structure of

The Impact Of Credit Scoring And How Best It Can Be...
In this chapter, i aim to present a background on the changes in credit scoring and how best it can be
implemented within the nancial sector, also highlighting past researches done on the issues with
credit scoring and show the approach used in achieving their results. 2.2 Credit The term credit can
be dated back as far as when human languages and friendships began. In the past people borrowed
cowries (a form of money in certain areas of the world) from friends to take care of personal issues
with the intention of paying back as soon as their crops were harvested and sold during the market
days. As civilisation progressed,credit grants became more important to individuals, Small and large
organisations to fund businesses and living ... Show more content on Helpwriting.net ...
The history of consumer credit scoring can be dated back to half a century ago [3]. However, history
records the same approach was used to identify groups in a population even before. In 1936 Fisher
came up with the rst approach a method that identi ed various groups in a population. He was keen
to nd out the di erences between two varieties of iris through the measurement of the physical
sizes of plants, thus ascertaining the di erence in the origins of skulls using their physical
measurement. In 1941, Durand discovered that the same credit scoring technique could be used to
distinguish between good and bad loans. Though his research project was for the National Bureau of
Economic Research, it wasn 't used for any predictive purpose. Due to the positive e ect of credit
scoring in credit cards, banks in the 1980s started using credit scoring in personal , home and small
business loans, a method which is still utilised till date [1]. Credit unworthiness has always been a
problem encountered by the nancial sectors as they are faced with lapses regarding the credit they
o er to individuals and corporate bodies. The credit obtained from nancial sectors is sometimes
not repaid. To minimise the risk of unpaid credit, nancial sectors have devised a number of
techniques to mitigate this risk. One of such techniques discussed in the latter part of this report, is
credit scoring. Credit

Impact Of Perceptual Mapping Of Star Hotel
Perceptual Mapping of Star Hotels in Lonavala on SERQUAL Attributes
ABSTRACT
The rationale of this research was to present observed information about service quality in Star
Hotel Industry. The paper enunciates the strong attributes of SERQUAL in hotel industry and ranks
the hotels on strong and weaker aspects. There are many identified and unidentified factors that
compose the perceptions, however discrimination between the vital elements of perception is
important from hotelier's point of view .Perceptual mapping technique is applied to investigate the
essential aspects of customer perception about services provided by hotels in and around tourist city
of Lonavala. This study has successfully identified the important factors that need to be given more
attention as it largely influences the customer perception.
The Names of the Hotels are deliberately changed to preserve the confidentiality.
KEYWORDS: Service Quality, Perceptual Mapping, Customer perception, Satisfaction. A.
INTRODUCTION
Various studies in the field of hospitality & marketing have examined different components which
constitute the customer ... Show more content on Helpwriting.net ...
The hotel has firm commitment and processes towards customer services, employees in the hotel are
responding easily and are keen to resolve customer complaints. The hotel employees try to build
confidence in providing services. Interestingly here we observe that UD has moderate focus on
Reliability, Responsiveness and Assurance, while less emphasize on tangibility and empathy. This
differentiates the customer segment. However the scores indicate that these attributes have little
significance in SERQUAL model. DK and UD are quite similar in mapping of SERQUAL but in
comparison the segments vary in income groups, at DK the segment shall be from higher income
groups while in UD middle income groups. KP has focus to build loyal customers through
customized

A Research Study On Ethics
Ethics, deriving from the Greek ethos meaning character (Jennings, 2010), are the "moral principles
that govern a person's behaviour or the conducting of an activity" (Oxford Dictionary, 2015b) and
establish the foundation of present–day research (Cropley, 2008; Flick, 2006). They are the "norms
of conduct that distinguish between acceptable and unacceptable behaviour" (Resnik, 2011) and
establish the values essential for the collaborative work between the researcher and the research
subject (Jennings, 2010; Veal, 2006). The research at hand is therefore in accordance with the ethical
principles as specified by Jennings (2010) and Veal (2006). This includes the free choice of
participation in the research, the confidential usage of sensitive data received from respondents and
the avoidance of harm to participants (Jennings, 2010). Besides, before the completion of the
questionnaire all potential participants were informed about the purpose of the research and how the
data will be used. All research subjects participated in the questionnaire survey on a voluntary basis
and gave their consent for the usage of their data provided.
4.6 Analytical Methods
Data was analysed with SPSS Statistics 20 and Mplus Software Version 7.11 by performing
descriptive and inferential statistics. In order to get an impression of the characteristics of the sample
mean, standard deviation, Skewness and Kurtosis were determined. Descriptive analysis was
conducted for all demographic information

What Criteria Could You Differentiate Among Multiple...
Questions
1. How would you differentiate among multiple discriminant analysis, regression analysis, logistic
regression analysis, and analysis of variance?
The main difference is in the number of independent and dependent variables and the way in which
these variables are measured. Note the following definitions:
In Multiple discriminant analysis (MDA), the predictor or independent variables are metric and the
single criterion or dependent variable is non metric . In Regression Analysis , both the multiple
independent variables and the single dependent variable are metric. In Analysis of Variance
(ANOVA) , the single independent variable is non metric and the multiple dependent variables are
metric.
2. What criteria could you use in ... Show more content on Helpwriting.net ...
The result is an upward bias in statistical significance that must be identified during the analysis and
interpretation.
4. How would you determine the optimum cutting score?
For equal group sizes, the optimum cutting score is defined by ZA + ZB
ZCE = ––––––––––
N
ZCE is the critical cutting score value for equal size groups, ZA is the centroid for group A and ZB
is the centroid for Group B and N is the total sample size.
For unequal group sizes, the optimum cutting score is defined by NAZA + NBZB
ZCU = ––––––––––––
NA + NB
ZCU = is the critical cutting score value for unequal size groups, NA is the sample size for group A
and NB is the sample size for Group B
5. How would you determine whether the classification accuracy of the discriminant function is
sufficiently high relative to chance classification?
There has to be some chance criterion that should be established. This is generally a fairly straight–
forward function of the classifications that is used in the model and of the sample size. The authors

then suggest the criterion that the classification accuracy or the hit ratio must be at least twenty five
percent greater than by chance. Other test can be to use a test of proportions to examine for the
significance between the obtained hit–ratio proportion and the chance criterion proportion .
6. How does a two–group discriminant analysis differ from a three–group

V. Particle Swarm Optimization ( Pso )
V. Particle Swarm optimization (PSO): It is a swarm–based intelligence algorithm influenced by the
social behavior of animals cherishes a flock of birds finding a food supply or a school of fish
protecting themselves from a predator. A particle in PSO is analogous to a bird or fish flying through
a search (problem) area. The movement of every particle is coordinated by a rate that has each
magnitude and direction. Every particle position at any instance of your time is influenced by its
best position and also the position of the most effective particle in an exceedingly drawback area.
The performance of a particle is measured by a fitness worth that is drawback specific. The PSO
rule is analogous to different biological process algorithms. In PSO, the population is that the range
of particles in a drawback area. Particles square measure initialized arbitrarily. Each particle can
have a fitness worth, which is able to be evaluated by a fitness perform to be optimized in every
generation. Each particle is aware of its best position pbest and also the best position so far among
the whole cluster of particles gbest. The pbest of a particle is that the best result (fitness value) to
date reached by the particle, whereas gbest is that the best particle in terms of fitness in a whole
population. Algorithm 2 PSO algorithm: 1. Set particle dimension as equal to the size of ready tasks
in {ti) € T 2. Initialize particles position randomly from PC = 1,....,j and velocity vi, randomly.

Profiling of MicroRNA Expression in Myocardial Samples
Purpose of the study:
This study was aimed to perform genome–wide profiling of microRNA (miRNA) expression in
myocardial samples of patients belonging to different diagnostic groups (dilated cardiomyopathy
(DCM), ischemic cardiomyopathy (ICM), aortic stenosis (AS), and nonfailing controls).
Background:
Heart failure (HF) is one of the main causes of morbidity and mortality worldwide, with dilated
cardiomyopathy being the most common type (1,2). It has been estimated that around 5.1 million
American adults have HF and its prevalence was projected to increase 46% from 2012 to 2030 (2).
For this reason, huge efforts were devoted to identify the underlying pathophysiological and
molecular aspects of HF aiming to develop effective diagnostic and therapeutic strategies to improve
the prognosis of the disease (3,4).
Heart failure patients have many pathological changes in their cardiomyocytes' gene expression that
impair cardiomyocytes survival and contraction resulting in cardiac hypertrophy and failure (5,6).
MicroRNAs – small noncoding RNAs with approximately 22 nucleotides in length – are negative
regulators of gene expression at post–transcriptional level (7). They are implicated in the
pathogenesis and progression of various pathological conditions including cardiovascular diseases,
diabetes mellitus, hypertension, and cancer (4). Interestingly, single microRNA can target various
genes, and an individual gene can be controlled by multiple microRNAs. So far, over 700

Detection Using Principle Component Analysis And Case...
Splice site detection using principle component analysis and case based reasoning with support
vector machine
Srabanti Maji*1 and Haripada Bhunia2
1 Computer Science Department
Sri Guru Harkrishan College of Management and Technology, Raipur, Bahadurgarh;
Dist: Patiala,Punjab, India
2 Department of Chemical Engineering
Thapar University, Patiala–147004, India
*Address Correspondence to this author at
Dr. Srabanti Maji
Computer Science Department,
Sri Guru Harkrishan College of Management and Technology, Raipur, Bahadurgarh;
District: Patiala, Punjab, India
E–mail address: srabantiindia@gmail.com, srabanti9@gmail.com
Tel: +91–9356006454
ABSTRACT
Identification of coding region from genomic DNA sequence is the foremost step ... Show more
content on Helpwriting.net ...
feature selection; and the final stage, in which a support vector machine (SVM) with Polynomial
kernel is used for final classification. In comparison with other methods, the proposed SpliceCombo
model outperforms other prediction models as the prediction accuracies are 97.25% sensitivity,
97.46% Specificity for donor splice site and 96.51% Sensitivity, 94.48% Specificity for acceptor
splice site prediction.
Keywords: Gene Identification, Splicing Site, Principal Component Analysis (PCA); Cased Based
Reasoning (CBR); Support Vector Machine(SVM)
*Correspondence to Srabanti Maji,
E–mail address: srabantiindia@gmail.com, srabanti9@gmail.com

Tel: +91–9356006454
Splice site detection using principle component analysis and case based reasoning with support
vector machine
1. INTRODUCTION
Research in the genome sequencing technology have been creating an enormous amount of genomic
sequencing data as its main objective is gene identification. In the eukaryotes, the prediction of a
coding region depends upon the exon–intron structures recognition. Whereas its very challenging to
predict exon intron structure in sequence due to its complexity of structure and vast length. Research
analysis on the human genome have nearly 20,000–25,000 protein–coding genes [1]. Still, there are
nearly 100,000 genes in human genome. Which indicates a huge number of genes are still
unidentified [2,3]. Most of the computational techniques

Level Set Segmentation Paper
T.F.Chen [9] segmentation is the process of portioning the images, where we need to find the
particular portion, there are several methods segmentation such as active contour, etc. segmentation
can be done both manually and automatically. Here the new technique of segmentation known as
level sets segmentation are described, the level set segmentation reduces the problems of finding the
curves which is enclose with respect to the region of interest. The implementation of this involves
the normal speed and vector field, entropy condition etc. The implementation results produced was
two different curves, which can be splitted. M.M Dersouky and T.E Taha [10] this paper a computer
aided diagnosis system was proposed to provide a comprehensive ... Show more content on
Helpwriting.net ...
[14] uses the T2 weighted images for extracting the ROI for the diagnosis purpose where the ROI
here is concentrated on the temporal region and intracranial region of the brain as ROIs and in [16]
the proposed methods are used for extracting the brain regions by considering the T1 Weighted
images so used to find , the seed point which are located on the brain tissues and then to perform the
region growing.[17] here the feature extraction are done by applying the new method for the feature
extraction for the identification of the affected regions and then the diagnosing the cognitive
disorders foe the T1 weighted images. The method used in [14] are balloon model which gives the
contour triangle approximated to a shape of the temporal lobe regions by using only three points,
AAM method are used for the statistical shape and texture model which can search for an object.
The third step was the temporal lobe region, which was included with in the intracranial region. The
proposed method in [16] are as follows first is analyzing distributions of brain tissues, which
includes brain tissues, CSF, scalp and marrow. The second method was applying the threshold
method for removing non–brain tissues, where to find the upper and the lower bounds and the last
method was to find seed point and performing region growing. The methods of [17] are as follows,
sparse logistic regression, feature dimension reduction for efficient classification etc. The temporal
region extraction and intracranial region extraction [14] was found to be 80.4 and 98 percent. The
experimental results [17] was of 87.5% classification rate and other parameters are nearly equal to
the accuracy

Advantages And Disadvantages Of Eye And Face Recognition
Sudeep Sarkar et.al.[10] Researchers have suggested that the ear may have advantages over the face
for biometric recognition. Our previous experiments with ear and face recognition, using the
standard principal component analysis approach, showed lower recognition performance using ear
images. We report results of similar experiments on larger data sets that are more rigorously
controlled for relative quality of face and ear images. We find that recognition performance is not
significantly different between the face and the ear.
Haitao Zhao, Pong Chi Yuen says face recognition has been an active research area in the computer–
vision and pattern–recognition societies [11] in the last two decades. Since the original input–image
space has a very high dimension, a dimensionality–reduction technique is usually employed before
classification takes place. Principal component analysis (PCA) is one of the most popular
representation methods for face recognition. It does not only reduce the image dimension, but also
provides a compact feature for representing a face image. In 1997, PCA was also employed for
dimension reduction for linear discriminant . PCA is ... Show more content on Helpwriting.net ...
The images forming the training set (database) are projected onto the major eigenvectors and the
projection values are computed. In the recognition stage the projection value of the input image is
also found and the distance from the known projection values is calculated to identify who the
individual is.Neural Network Based Face Recognition procedure is followed for forming the
eigenvectors as in the Eigenface approach, which are then fed into the Neural Network Unit to train
it on those vectors and the knowledge gained from the training phase is subsequently used for
recognizing new input images. The training and recognition phases can be implemented using
several neural network models and

Neural Networks : An Important Component Of Determining...
Neural Networks in Finance
2600 Words
By Maria L. Vicente
University of Hawaiʻi at Hilo
QBA 362
Fall 2016
Introduction
Predictions are an important component of determining the financial progress of a business.
Business decisions rely on forecasting techniques to predict things such as price movements or
overall success in markets. In the attempt to forecast market predictions, it must be assumed that
future occurrences may be partly based on present and past data (Abu–Mostafa, Yaser S 1996).
Further assumptions must be made to conclude that there is a predictable pattern in past data. There
is evidence for both the idea that financial market forecasting is futile due to the unpredictable
nature of finance, as well as for the idea that financial markets are predictable to an extent. The
consequences of financial decision–making imply an inherent need for the use of forecasting tools in
making predictions about future occurrences. The issue resides in the fact that there is an abundance
of data and information that must be organized and interpreted. A number of techniques may be used
to manage present and past data in order to create a forecast prediction, though with more research
and trials, neural networks have been shown to be superior in performance.
Traditional Techniques Neural networks provide an alternative solution to the traditionally used
statistical methods of forecasting. Traditional method models include variances of linear

Why Is Positive Accounting Standards?
1. Introduction:
This paper is a contribution made my Ross L. Watts and Jerold L. Zimmerman, titled as "Towards a
Positive Theory of Determination of Accounting Standards" published by American Accounting
Association. It explores the factors that have been influencing management's attitudes in lobbying
on accounting standards. It describes an attempt made by two in evolution and development of the
Positive Accounting Theory by reasoning, factors like taxes, regulations, management compensation
plans, book keeping cots, etc. The results concerned with the theory are consistent.
2. Summary of the Article
To begin with it is important to understand why Positive accounting theory is incorporated by a
Company. Commonly know as PAT, it ... Show more content on Helpwriting.net ...
This theory has a precondition, which focuses on understanding the management incentives.
The paper begins with introduction of the factors affecting management wealth which are listed as;
i)Taxes, ii) Political costs, iii) Information Production Costs, and iv) Management Compensation
Plans. Moving on, it gives description about various assumption that an organisation needs to
consider before they start following the Positive Accounting Theory approach. Two main
assumption the research paper is based on are:
Individual act to maximise their own utility. (Article) This assumption is also used by Gordon
(1964) in an early attempt to derive positive accounting theory. Gordon model and variants of it,
were also tested know as the "smooth" literature.
Management utility is a positive function of the expected compensation in future periods and
decreases the firms reported earning.
The Study 1978 corroborated the hypothesis that management is a determinant role in accounting
standard and concretely pointed that firm size is the most consequential factor will influence
managerial deportment, which has a paramount implicative insinuation for setting accounting
standard. Furthermore, this conclusion of this paper convinced the feasibility of positive theory in
accounting research which as a revolution in accounting research area. On the other hand, Study
1998 additionally gave us a paramount conclusion which

Data Science Statement Of Purpose
Statement of Purpose Data science, as an illustrious application in Statistics, is my direction in
career. The four–year study in applied math motivates me to take the challenge of pursuing a
master's degree in statistics. In addition, my aspiration to become a professional data analyst also
gives the graduate study an absolute necessity. To trace my determination in data science, my intern
as an assistant consultant in Wisedge Education is an inspiration. There, I constructed the very first
data report on evaluating education–consulting services by Excel. The sample was prospective
Chinese undergraduates of colleges and universities in the U.S in previous five years. The
scatterplot on acceptance rates and ranking of universities enrolled showed an interesting
phenomenon that they had a negative correlation. It was unusual because ranking ought to be a
dominant factor in clients' decision on selection of schools. As the industry was developing, the two
variables were expected to have positive correlation. To figure out what was happening, I ... Show
more content on Helpwriting.net ...
The reputation of Columbia guarantees the statistics graduate program to be the most promising one
in the U.S. The curriculum is comprehensive in required methodology, such as inference and
regression, useful in implementation, like classes in Python and R, and flexible enough with
voracious electives in Finance, Math, and etc. Moreover, the LinkedIn services catch my eyes
because it plays an important role in networking when finding a job. The capstone project is another
plus. I look forward to sparkles happened in the real life application of machine learning in GR5242.
Additionally, New York is a landmark of business and job placement of the program parallels with
the advancement in location. Depending on the unique platform with abundant opportunities on
profession, students in Columbia are able to win more

Advantages Of Meta-Analysis In Criminal Justice
Introduction The quantitative and qualitative approaches have been proven to have been struggling
for power to criminal justice and criminological research. These strategies are only devices to help
social researchers comprehend their general surroundings. The debate concerning which approach is
extraordinary turned out to be genuine in its outcomes as the quantitative methodology has increased
high ground in the control. The problem that has been found is that the new quantitative methods are
moving faster than the evolution of an arrange curricula. New procedures can be produced and
spread all through the order far quicker than the managerial maze of college educational module
changes can be explored, In simple terms, this basically means ... Show more content on
Helpwriting.net ...
Social network is not is not new and it has derived out of three traditions. First which is that
subjective and social therapist of the 1930s, working under the gestalt worldview, inquired about the
structure of the gatherings and also the data stream among the individuals. The second one is the
Harvard anthropologists tried to consider and refine the premises of anthropologists, by
concentrating on interpersonal relations and sub groups inside of the informal community. And the
last one is that Researchers at Manchester University analyzed on tribal social orders utilizing these
studies to advance refine social hypothesis and the investigation of community

Speech Processing : Using Mel Frequency Cepstral...
Speaker reognition using Mel Frequency Cepstral Coefficients(MFCC)
Abstract Speech processing has emerged as one of the most important application area of digital
signal processing. Various fields for research in speech processing are speech recognition, speaker
recognition, speech analysis, speech synthesis, speech coding etc. The objective of automatic
speaker recognition is to extract, characterize the discriminant features and recognize the
information about speaker identity. In this paper we present a voice recognition system based on
Mel Frequency Cepstral Coefficients (MFCC) and vector Quantization (VQ). This technique has an
advantage that it creates fingerprint of human voice by exploiting human acoustic system and
cepstral analysis. MFCC is widely accepted as a baseline for voice recognition due to these unique
features.
KeywordsMFCC, Vector Quantization, Speaker recognition, Feature extraction, Fast Fourier
Transform
Introduction
Human speech is the most natural form of communication and conveys both meaning and identity.
The identity of a speaker can be determined from the information contained in the speech signal
through speaker identiﬁcation. Speaker identiﬁcation is concerned with identifying unknown
speakers from a database of speaker models previously enrolled in the system. Speaker (voice)
identification has varied applications ranging from opening doors to security systems.
Speech processing is widely divided into 5 different

In this paper we present an analysis of face recognition...
In this paper we present an analysis of face recognition system with a combination of Neural
networks withSub–space method of feature extraction. Here we are considering both single layer
such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector
quantization (LVQ). The analysis of these neural networks are done between feature vectors with
respect to the recognition performance of subspace methods, namely Principal Component Analysis
(PCA) and Fisher Linear Discriminant Analysis (FLDA).Subspace is a multiplex embedded in a
higher dimensional vector space and extracting important features from the damn dimensionality.
The experiments were performed using standard ORL, Yale and FERET database. From the ... Show
more content on Helpwriting.net ...
In section IV, Experimental results are discussed and analysis is briefed. Finally, Conclusions are
drawn.
II. PROPOSED METHOD
In this section an overview of different subspaces methods such as PCA and FLDA are describes in
detail.
A. Principal Component Analysis
PCA is a classical feature extraction and data representation technique also known as Karhunen–
Loeve Expansion [20, 21]. It is a linear method that projects the high–dimensional data onto a lower
dimensional space. It seeks a weight projection that best represents the data, which is called
principal components. Fig.1 Schematic illustration of PCA
Principal component analysis seeks a space of lower dimensionality, known as the principal
subspace and denoted by the magenta line, such that the orthogonal projection of the data points (red
dots) onto this subspace maximizes the variance of the projected points (green dots). An alternative
definition of PCA is based on minimizing the sum–of–squares of the projection errors, indicated by
the blue lines as described in figure 1.
PCA is described as let a face image be A(x, y) be a two–dimensional N by Narray. The training set
images are mapped onto a collection of vector points in this huge space, these vector points are
represented as subspace. These vector points are the eigen vectors which is obtained from the
covariance matrix which defines the subspace of face images.Let the

Conscientiousness: a Review
Conscientiousness: A review Introduction: A major contribution of our personality is an essential
trait known as Conscientiousness. It was first grouped in the Five Factor Model personality and the
circumplex model of interpersonal behavior 40 years ago by well known psychologists named Tupes
and Christal(1961) (McCrae and Costa,1985).Recent developments on the work were carried on by
several other psychologists like McCrae and Costa (1985a), Digman and Inouye (1986), Hogan
(1983), and Peabody and Goldberg(1989) (Paul D. Trapnell and Jerry S. Wiggins 1990).
Conscientiousness can be defined as "governing, persevering, unselfish behavior and impelling the
individual to duty as conceived by his (or her) culture. A Conscientious person ... Show more
content on Helpwriting.net ...
According (McCrae and colleagues ) the factors measured by the FFM are comprehensive that is
they account for almost all the common variance in personality trait scores, on the other hand NEO–
PI–R facets have been shown to have discriminant (McCrae & Costa, 1992) and specific heritability
(Jang, McCrae, Angleitner, Riemann, & Livesley, 1998) and to contribute incrementally to
predictive validity (Paunonen & Ashton, 2001) validity However, due to the popularity of the NEO–
PI–R after being translated in major languages it is used extensively in research by professionals
(Fruyt, McCrae, Szirmák, Nagy,2004).Two published studies examined correlation between FFPI
and NEO–PI–R (Costa, Yang, and McCrae (1998)." correlated FFPI factors(using an earlier
orthogonalization algorithm) with NEOPI–R factors in a sample of 116 middle–age men and
women, and found convergent correlations ranging from .32 (for autonomy with openness) to .71
(for extraversion). Hendriks et al. (1999b) correlated FFPI factors with NEOPI– R domains in
samples of 88 to 102 persons and reported correlations ranging from .40 for autonomy and openness
to .79 for extraversion. In both of these studies, the convergent correlations for the autonomy factor
are rather low; in

A Report On The Data
Based on the objectives of the experiment, it is important to describe the credit datasets, the
classi ers, the combination techniques of the classi ers, and lastly the software used in carrying
out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classi ers and their
combinations in the two class classi cation problem analysis, two real life datasets taken from the
University of California, Irvine(UCI) repository were used. These datasets are described below [28].
3.1.1 German Credit Datasets This is a nancial dataset which is made up of 1,000 instances. This
dataset records 700 cases of creditworthy applicants and 300 cases of applicants who are not credit–
worthy. It contains categorical and symbolic attributes. The German credit dataset is of two forms
[26] [29]. The original dataset german.data consists of 20 attributes of which,7 are numerical
and 13 categorical). The german.data–numeric an edited copy of the original dataset which
consist of 24 numeric attributes. The german.data–numeric which has 24 input variable basically
represents 19 attributes with 4 of these attributes changed to dummy variables. The 20 attributes in
the German.data or the 24 attributes in the german.data–numeric are some basic information
about applicants needed in creating a score card which will be used to predict if an applicant will
default or not. This information is seen in the table below. Attribute Information Attribute 1 Status of
existing

Pt2520 Unit 6 Data Mining Project

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Pt2520 Unit 6 Data Mining Project