Z for Zachariah Essay Example for Free

Z for Zachariah Essay Survival, basically refined is stated as the fact or state of continuing to live or exist, especially in difficult situations. â€Å"Never, never give in, in anything great or small, never give in† is a famous quote by Sir Winston Churchill that helps explain the real aspects of what it takes to survive. The following text will explain and explore ‘survival’ from various techniques and effects as well as compare and contrast the similarities and differences between ‘Z for Zachariah’, our class novel of a teenage girl living in a post-apocalyptic world and ‘Touching the Void’, a thrilling story of two adventurous climbers who’s journey takes a turn for the worse. ‘Z for Zachariah’ is based in a post –apocalyptic time period within the American Midwest. It is set out in a diary entry written by the protagonist, in this case a 16 year old girl named Ann Burden. It focuses on what she does just to stay alive during the daily events she encounters. Through the use of this 1st person perspective, you are able to believe that you are right there in the heart of the novel. Most importantly, it enables you to experience the life of Ann and what it is like to live in a time of struggle and despair. Other techniques such as flashbacks (a jump backwards in time to fill in details from the past) and symbolism (the use of an object or idea to represent something else by association) help re-instate the initial format of ‘survival’ portrayed in the novel. Touching the Void’ is a documentary based on the true story of two mountaineers climbing in the Peruvian Andes where one of the climbers falls and sustains a serious leg injury, making him unable to carry on. This leaves them with a serious conundrum of what to do to get out alive. Throughout the documentary a variety of techniques are portrayed to get the audience engaged. It is c onstantly reverting to the interviews of Joe Simpson and Simon Yates to concede a much better engaging atmosphere for the audience. Also, through the use of re-enactment it provides the feeling of you actually being there and witnessing the events that happen throughout the film. ‘Survival’ is clearly shown in its true form in this documentary. Both of these particular texts have differences and similarities between them. Whereas ‘Z for Zachariah’ is written with many themes in mind such as ‘good and evil’, ‘hope and despair’ and ‘life and death’ ‘Touching the Void’ is filmed with only one, this is ‘survival instinct ‘. This simply stated is what happens when you are put in a high pressure situation with no apparent way of etting out. Also, where the novel is a fiction story, the documentary happened in the ever present world in which we live, making it a more realistic and easier to understand the viewpoint. Although there are not many similarities shared between the two texts, there is one key feature they both possess and that is the aspect of ‘Lone Survival’. This is what makes these stories of survival what they are and why they are truly great. The ability to think when you are all by yourself is quite difficult, especially when the thought of death is in mind. However, in both of these texts the protagonists are able to regain their focus and carry on strong to finish alive at the end of the journey. Both ‘Z for Zachariah’ and ‘Touching the Void’ have uniquely different ideas of how survival can vary in different ways, as well as change the people involved in the event for the rest of their lives. These are great examples of survival at their best, and In the end Life is the struggle for survival, in which the strongest wins, and as Winston Churchill once said â€Å"Never, never give in, in anything great or small, never give in†.

Advantages And Disadvantages To Using Indexes Computer Science Essay

Advantages And Disadvantages To Using Indexes Computer Science Essay Put simply, database indexes help speed up retrieval of data. The other great benefit of indexes is that your server doesnt have to work as hard to get the data. They are much the same as book indexes, providing the database with quick jump points on where to find the full reference (or to find the database row). There are both advantages and disadvantages to using indexes,however. One disadvantage is they can take up quite a bit of space check a textbook or reference guide and youll see it takes quite a few pages to include those page references. Another disadvantage is using too many indexes can actually slow your database down. Thinking of a book again, imagine if every the, and or at was included in the index. That would stop the index being useful the index becomes as big as the text! On top of that, each time a page or database row is updated or removed, the reference or index also has to be updated. So indexes speed up finding data, but slow down inserting, updating or deleting data. Some fields are automatically indexed. A primary key or a field marked as unique for example an email address, a userid or a social security number are automatically indexed so the database can quickly check to make sure that youre not going to introduce bad data. So when should a database field be indexed? The general rule is anything that is used to limit the number of results youre trying to find. Its hard to generalise so well look at some specific but common examples. Note the database tables shown below are used as an example only and will not necessarily be the best setup for your particular needs. In a database table that looks like this: Note: The SQL code shown below works with both MySQL and PostgreSQL databases. CREATE TABLE subscribers ( subscriberid INT PRIMARY KEY, emailaddress VARCHAR(255), firstname VARCHAR(255), lastname VARCHAR(255) ); if we want to quickly find an email address, we create an index on the emailaddress field: CREATE INDEX subscriber_email ON subscribers(emailaddress); and any time we want to find an email address: SELECT firstname, lastname FROM subscribers WHERE emailaddress=[emailprotected]; it will be quite quick to find! Another reason for creating indexes is for tables that reference other tables. For example, in a CMS you might have a news table that looks something like this: CREATE TABLE newsitem ( newsid INT PRIMARY KEY, newstitle VARCHAR(255), newscontent TEXT, authorid INT, newsdate TIMESTAMP ); and another table for authors: CREATE TABLE authors ( authorid INT PRIMARY KEY, username VARCHAR(255), firstname VARCHAR(255), lastname VARCHAR(255) ); A query like this: SELECT newstitle, firstname, lastname FROM newsitem n, authors a WHERE n.authorid=a.authorid; will be take advantage of an index on the newsitem authorid: CREATE INDEX newsitem_authorid ON newsitem(authorid); This allows the database to very quickly match the records from the newsitem table to the authors table. In database terminology this is called a table join you should index any fields involved in a table join like this. Since the authorid in the authors table is a primary key, it is already indexed. The same goes for the newsid in the news table, so we dont need to look at those cases. On a side note, table aliases make things a lot easier to see whats happening. Using newsitem n and authors a means we dont have to write: SELECT newstitle, firstname, lastname FROM newsitem, authors WHERE newsitem.authorid=authors.authorid; for more complicated queries where more tables are referenced this can be extremely helpful and make things really easy to follow. In a more complicated example, a news item could exist in multiple categories, so in a design like this: CREATE TABLE newsitem ( newsid INT PRIMARY KEY, newstitle VARCHAR(255), newscontent TEXT, authorid INT, newsdate TIMESTAMP ); CREATE TABLE newsitem_categories ( newsid INT, categoryid INT ); CREATE TABLE categories ( categoryid INT PRIMARY KEY, categoryname VARCHAR(255) ); This query: SELECT n.newstitle, c.categoryname FROM categories c, newsitem_categories nc, newsitem n WHERE c.categoryid=nc.categoryid AND nc.newsid=n.newsid; will show all category names and newstitles for each category. To make this particular query fast we need to check we have an index on: newsitem newsid newsitem_categories newsid newsitem_categories categoryid categories categoryid Note: Because the newsitem newsid and the categories categoryid fields are primary keys, they already have indexes. We need to check there are indexes on the join table newsitem_categories This will do it: CREATE INDEX newscat_news ON newsitem_categories(newsid); CREATE INDEX newscat_cats ON newsitem_categories(categoryid); We could create an index like this: CREATE INDEX news_cats ON newsitem_categories(newsid, categoryid); However, doing this limits some ways the index can be used. A query against the table that uses both newsid and categoryid will be able to use this index. A query against the table that only gets the newsid will be able to use the index. A query against that table that only gets the categoryid will not be able to use the index. For a table like this: CREATE TABLE example ( a int, b int, c int ); With this index: CREATE INDEX example_index ON example(a,b,c); It will be used when you check against a. It will be used when you check against a and b. It will be used when you check against a, b and c. It will not be used if you check against b and c, or if you only check b or you only check c. It will be used when you check against a and c but only for the a column it wont be used to check the c column as well. A query against a OR b like this: SELECT a,b,c FROM example where a=1 OR b=2; Will only be able to use the index to check the a column as well it wont be able to use it to check the b column. Multi-column indexes have quite specific uses, so check their use carefully. Now that weve seen when we should use indexes, lets look at when we shouldnt use them. They can actually slow down your database (some databases may actually choose to ignore the index if theres no reason to use it). A table like this: CREATE TABLE news ( newsid INT PRIMARY KEY, newstitle VARCHAR(255), newscontent TEXT, active CHAR(1), featured CHAR(1), newsdate TIMESTAMP ); looks pretty standard. The active field tells us whether the news item is active and ready to be viewed on the site. So should we should create an index on this field for a query like this? SELECT newsid, newstitle FROM news WHERE active=1; No, we shouldnt. If most of your content is live, this index will take up extra space and slow the query down because almost all of the fields match this criteria. Imagine 500 news items in the database with 495 being active. Its quicker to eliminate the ones that arent active than it is to list all of the active ones (if you do have an index on the active field, some databases will choose to ignore it anyway because it will slow the query down). The featured field tells us whether the news item should feature on the front page. S hould we index this field? Yes. Most of our content is not featured, so an index on the featured column will be quite useful. Other examples of when to index a field include if youre going to order by it in a query. To get the most recent news items, we do a query like this: SELECT newtitle, newscontent FROM news ORDER BY newsdate DESC; Creating an index on newsdate will allow the database to quickly sort the results so it can fetch the items in the right order. Indexing can be a bit tricky to get right, however there are tools available for each database to help you work out if its working as it should. Well there you have it my introduction to database indexes. Hopefully youve learned something from this article and can apply what youve learned to your own databases. This entry was posted in Programming. Bookmark the permalink. 22 Responses to Introduction to Database Indexes Jim says: February 17, 2006 at 7:13 am I think you need to be a bit more the reader knows absolutly nothing when describing the table joins. You lost me for a bit there. Perhaps a better step by step hand holding example would be better. [ Editors note: Sure thing. Ill see what I can come up with for next month! If youre desperate for information and cant wait drop me a line chris at interspire dot com and Ill explain it further ] Reply khani says: May 14, 2006 at 3:55 pm Good effort chris, You ve described Indexes in a simple way. Reply VRS says: May 24, 2006 at 1:32 pm Good article.Do include some explanation on clustered and non clustered indexes. Reply Vivek says: July 13, 2006 at 3:25 am Good article. Helped a lot in understading the basics of indexing. Thanks Reply Unknown says: October 11, 2006 at 8:43 pm Good article man. I really appretiate your effort. Reply Ayaz says: November 14, 2006 at 9:22 am Good article to understand indexes for a beginner. Reply Debiz says: November 27, 2006 at 5:21 pm Very well written and simply explained for those looking for a basic overview Reply Nand says: December 14, 2006 at 11:46 am Good article, felt like walking over the bridge on a gorge. Can u pl. explain drawbacks of using index also. [ Chris note The main drawback is that every insert, update or delete has to change the index as well. If you have a lot of indexes, that adds a lot of overhead to the operation. ] Reply Myo says: December 19, 2006 at 11:56 pm Very easy to understand and gives examples with different situations to demonstrate when and where we should use indexes and why. Thanks man! Reply John Lowe says: March 14, 2007 at 2:57 am A quick a useful reminder to what idexes are all about, thanks. Reply Shravanti says: June 26, 2007 at 3:11 am Good Introduction to Indexes. It would also be valuable to have information on how do indexes work on OLAP side of a Data Warehouse. Reply Harsha says: August 13, 2007 at 11:21 pm crisp tutorial.. good work Reply krish says: September 24, 2007 at 2:44 am Really very nice explanation Reply Alagesan says: October 10, 2007 at 11:33 pm This is a great article to learn indexing for beginners I really appreciate your efforts and good will in explaining them in words here.Thanks! Reply Heather says: October 12, 2007 at 8:23 am This was a great explanation of indexes for me I am self-taught when it comes to databases so the language in this tutorial was very easy for me to understand. Also, you used great examples to help explain your information. THANKS! Reply Jess Duckin says: October 28, 2007 at 4:58 am The explaination on the usage of indexing is very helpful Reply Mayur says: October 29, 2007 at 1:56 pm Thank you very much, a really informative tutorialfor me it was a 100% match to what I was looking for. Thanks Reply satish soni says: January 11, 2008 at 7:17 am Great article on indexes even oracle has not provided that much knowledge about indexes Reply Shweta says: January 11, 2008 at 4:25 pm Good. Just the overview i needed. Reply Hemant Jirange says: January 17, 2008 at 3:39 am Great articlethis is very simple to understand whole disadvantages about index Reply ramesh says: January 18, 2008 at 2:26 am impossible.even wikipedi couldnt match your tutorial on this topicthank uuuuuuuuuuuuuuu very much Reply Ravi says: September 12, 2008 at 5:57 am thanks Chris, was an easy read for a database novice. I look forward to seeing the next chapter Reply Leave a Reply Name (required) Mail (will not be published) (required) Website Home | Email Marketing | Shopping Cart | Knowledge Management Software | Content Management Software | Ecommerce Software | Sell Products Online | Our Guarantee | Privacy Policy Copyright 1999-2010 Interspire Pty. Ltd. ACN: 107 422 631

Data Mining or Knowledge Discovery

Data Mining or Knowledge Discovery SYNOPSIS INTRODUCTION Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining or knowledge discovery, is the computed assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of data. Data sets of very high dimensionality, such as microarray data, pose great challenges on efficient processing to most existing data mining algorithms. Data management in high dimensional spaces presents complications, such as the degradation of query processing performance, a phenomenon also known as the curse of dimensionality. Dimension Reduction (DR) tackles this problem, by conveniently embedding data from high dimensional to lower dimensional spaces. The dimensional reduction approach gives an optimal solution for the analysis of these high dimensional data. The reduction process is the action of diminishing the variable count to few categories. The reduced variables are new defined variables which are the combinations of either linear or non-linear combinations of variables. The reduction of variables to a clear dimension or categorization is extracted from the unusual dimensions, spaces, classes and variables. Dimensionality reduction is considered as a powerful approach for thinning the high dimensional data. Traditional statistical approaches partly calls off due to the increase in the number of observations mainly due to the increase in the number of variables correlated with each observation. Dimensionality reduction is the transformation of High Dimensional Data (HDD) into a meaningful representation of reduced dimensionality. Principal Pattern Analysis (PPA) is developed which encapsulates feature extraction and feature categorization. Multi-level Mahalanobis-based Dimensionality Reduction (MMDR), which is able to reduce the number of dimensions while keeping the precision high and able to effectively handle large datasets. The goal of this research is to discover the protein fold by considering both the sequential information and the 3D folding of the structural information. In addition, the proposed approach diminishes the error rate, significant rise in the throughput, reduction in missing of items and finally the patterns are classified. THESIS CONTRIBUTIONS AND ORGANIZATION One aspect of the dimensionality reduction requires more studies to find out how the evaluations are performed. Researchers find to finish the evaluation with a sufficient understanding of the reduction techniques so that they can make a decision to use its suitability of the context. The main contribution of the work presented in this research is to diminish the high dimensional data into the optimized category variables also called reduced variables. Some optimization algorithms have been used with the dimensionality reduction technique in order to get the optimized result in the mining process. The optimization algorithm diminishes the noise (any data that has been received, stored or changed in such a manner that it cannot be read or used by the program) in the datasets and the dimensionality reduction diminishes the large data sets to the definable data and after that if the clustering process is applied, the clustering or any mining results will yield the efficient results. The organization of the thesis is as follows: Chapter 2 presents literature review on the dimensionality reduction and protein folding as application of the research. At the end all the reduction technology has been analyzed and discussed. Chapter 3 presents the dimensionality reduction with PCA. In this chapter some hypothesis has been proved and the experimental results has been given for the different dataset and compared with the existing approach. Chapter 4 presents the study of the Principal Pattern Analysis (PPA). It presents the investigation of the PPA with other dimensionality reduction phase. So by the experimental result the obtained PPA shows better performance with other optimization algorithms. Chapter 5 presents the study of PPA with Genetic Algorithm (GA). In this chapter, the procedure for protein folding in GA optimization has been given and the experimental result shows the accuracy and error rate with the datasets. Chapter 6 presents the results and discussion of the proposed methodology. The Experimental results shows that PPA-GA gives better performance compared than the existing approaches. Chapter 7 concludes our research work with the limitation which the analysis has been made from our research and explained about the extension of our research so that how it could be taken to the next level of research. RELATED WORKS (Jiang, et al. 2003) proposed a novel hybrid algorithm combining Genetic Algorithm (GA). It is crucial to know the molecular basis of life for advances in biomedical and agricultural research. Proteins are a diverse class of biomolecules consisting of chains of amino acids by peptide bonds that perform vital functions in all living things. (Zhang, et al. 2007) published a paper about semi supervised dimensionality reduction. Dimensionality reduction is among the keys in mining high dimensional data. In this work, a simple but efficient algorithm called SSDR (Semi Supervised Dimensionality Reduction) was proposed, which can simultaneously preserve the structure of original high dimensional data. (Geng, et al. 2005) proposed a supervised nonlinear dimensionality reduction for visualization and classification. Dimensionality reduction can be performed by keeping only the most important dimensions, i.e. the ones that hold the most useful information for the task at hand, or by projecting the original data into a lower dimensional space that is most expressive for the task. (Verleysen and Franà §ois 2005) recommended a paper about the curse of dimensionality in data mining and time series prediction. The difficulty in analyzing high dimensional data results from the conjunction of two effects. Working with high dimensional data means working with data that are embedded in high dimensional spaces. Principal Component Analysis (PCA) is the most traditional tool used for dimension reduction. PCA projects data on a lower dimensional space, choosing axes keeping the maximum of the data initial variance. (Abdi and Williams 2010) proposed a paper about Principal Component Analysis (PCA). PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. The goal of PCA are to, Extract the most important information from the data table. Compress the size of the data set by keeping only this important information. Simplify the description of the data set. Analyze the structure of the observations and the variables. In order to achieve these goals, PCA computes new variables called PCA which are obtained as linear combinations of the original variables. (Zou, et al. 2006) proposed a paper about the sparse Principal Component Analysis (PCA). PCA is widely used in data processing and dimensionality reduction. High dimensional spaces show surprising, counter intuitive geometrical properties that have a large influence on the performances of data analysis tools. (Freitas 2003) proposed a survey of evolutionary algorithms of data mining and knowledge discovery. The use of GAs for attribute selection seems natural. The main reason is that the major source of difficulty in attribute selection is attribute interaction. Then, a simple GA, using conventional crossover and mutation operators, can be used to evolve the population of candidate solutions towards a good attribute subset. Dimension reduction, as the name suggests, is an algorithmic technique for reducing the dimensionality of data. The common approaches to dimensionality reduction fall into two main classes. (Chatpatanasiri and Kijsirikul 2010) proposed a unified semi supervised dimensionality reduction framework for manifold learning. The goal of dimensionality reduction is to diminish complexity of input data while some desired intrinsic information of the data is preserved. (Liu, et al. 2009) proposed a paper about feature selection with dynamic mutual information. Feature selection plays an important role in data mining and pattern recognition, especially for large scale data. Since data mining is capable of identifying new, potential and useful information from datasets, it has been widely used in many areas, such as decision support, pattern recognition and financial forecasts. Feature selection is the process of choosing a subset of the original feature spaces according to discrimination capability to improve the quality of data. Feature reduction refers to the study of methods for reducing the number of dimensions describing data. Its general purpose is to employ fewer features to represent data and reduce computational cost, without deteriorating discriminative capability. (Upadhyay, et al. 2013) proposed a paper about the comparative analysis of various data stream procedures and various dimension reduction techniques. In this research, various data stream mining techniques and dimension reduction techniques have been evaluated on the basis of their usage, application parameters and working mechanism. (Shlens 2005) proposed a tutorial on Principal Component Analysis (PCA). PCA has been called one of the most valuable results from applied linear algebra. The goal of PCA is to compute the most meaningful basis to re-express a noisy data set. (Hoque, et al. 2009) proposed an extended HP model for protein structure prediction. This paper proposed a detailed investigation of a lattice-based HP (Hydrophobic – Hydrophilic) model for ab initio Protein Structure Prediction (PSP). (Borgwardt, et al. 2005) recommended a paper about protein function prediction via graph kernels. Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence. Simulating the molecular and atomic mechanisms that define the function of a protein is beyond the current knowledge of biochemistry and the capacity of available computational power. (Cutello, et al. 2007) suggested an immune algorithm for Protein Structure Prediction (PSP) on lattice models. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. (Yamada, et al. 2011) proposed a paper about computationally sufficient dimension reduction via squared-loss mutual information. The purpose of Sufficient Dimension Reduction (SDR) is to find a low dimensional expression of input features that is sufficient for predicting output values. (Yamada, et al. 2011) proposed a sufficient component analysis for SDR. In this research, they proposed a novel distribution free SDR method called Sufficient Component Analysis (SCA), which is computationally more efficient than existing methods. (Chen and Lin 2012) proposed a paper about feature aware Label Space Dimension Reduction (LSDR) for multi-label classification. LSDR is an efficient and effective paradigm for multi-label classification with many classes. (Brahma 2012) suggested a study of algorithms for dimensionality reduction. Dimensionality reduction refers to the problems associated with multivariate data analysis as the dimensionality increases. There are huge mathematical challenges has to be encountered with high dimensional datasets. (Zhang, et al. 2013) proposed a framework to inject the information of strong views into weak ones. Many real applications involve more than one modal of data and abundant data with multiple views are at hand. Traditional dimensionality reduction methods can be classified into supervised or unsupervised, depending on whether the label information is used or not. (Danubianu and Pentiuc 2013) proposed a paper about data dimensionality reduction framework for data mining. The high dimensionality of data can cause also data overload, and make some data mining algorithms non applicable. Data mining involves the application of algorithms able to detect patterns or rules with a specific means from large amounts of data, and represents one step in knowledge discovery in database process. OBJECTIVES AND SCOPE OBJECTIVES Generallydimension reduction is the process of reduction of concentrated random variable where it can be divided into feature selection and feature extraction. The dimension of the data depends on the number of variables that are measured on each investigation. While scrutinizing the statistical records data accumulated in an exceptional speed, so dimensionality reduction is an adequate approach for diluting the data. While working with this reduced representation, tasks such as clustering or classification can often yield more accurate and readily illustratable results, further the computational costs may also be greatly diminished. A different algorithm called Principal Pattern Analysis (PPA) is presented in this research. Hereby the desire of dimension reduction is enclosed. The description of a diminished set of features. For a count of learning algorithms, the training and classification times increase precisely with the number of features. Noisy or inappropriate features can have the same influence on the classification as predictive features, so they will impact negatively on accuracy. SCOPE The scope of this research is to present an ensemble approach for dimensionality reduction along with pattern classification. Dimensionality reduction is the process of reduction the high dimensional data i.e., having the large features in the datasets which contain the complicated data. The usage of this dimensionality reduction process yields many useful and effective results over the process in mining. The former used many techniques to overcome this dimensionality reduction problem but they are having certain drawbacks to it. The dimensional reduction technique enriches the execution time and yields the optimized result for the high dimensional data. So, the analysis states that before going for any clustering process, it is suggested for a dimensional reduction process of the high dimensional datasets. As in the case of dimensionality reduction, there are chances of missing the instruction. So the approach which is used to diminish the dimensions should be more corresponding to the whole datasets. RESEARCH METHODOLOGY The scope of this research is to present an ensemble approach for dimensionality reduction along with the pattern classification. Problems on analyzing High Dimensional Data are, Curse of dimensionality Some important factors are missed Result is not accurate Result is having noise. In order to mine the surplus data besides estimating gold nugget (decisions) from data involves several data mining techniques. Generally the dimension reduction is the process of reduction of concentrated random variables where it can be divided into feature selection and feature extraction. PRINCIPAL PATTERN ANALYSIS The Principal Component Analysis decides the weightage of the respective dimension of a database. It is required to reduce the dimension of the data (having less features) in order to improve the efficiency and accuracy of data analysis. Traditional statistical methods partly calls off due to the increase in the number of observations, but mainly because of the increase in number of variables associated with each observation. As a consequence an ideal technique called Principal Pattern Analysis (PPA) is developed which encapsulates feature extraction and feature categorization. Initially it applies Principal Component Analysis (PCA) to extract Eigen vectors similarly to prove pattern categorization theorem the corresponding patterns are segregated. The major difference between the PCA and PPA is the construction of the covariance matric. PPA algorithm for the dimensionality reduction along with the pattern classification has been introduced. The step by step procedure has been given as follows: Compute the column vectors such that each column is with M rows. Locate the column vectors into single matrix X of which each column has M x N dimensions. The empirical mean EX is computed for M x N dimensional matrix. Subsequently the correlation matric Cx is computed for M x N matrix. Consequently the Eigen values and Eigen vectors are calculated for X. By interrupting the estimated results, the PPA algorithm persists by proving the Pattern Analysis theorem. FEATURE EXTRACTION Feature extraction is an exception form of dimensionality reduction. It is needed when the input data for an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation set of features. By the way of explanation transforming the input data into the set of features is called feature extraction. It is expected that the feature set will extract the relevant information from the input data in order to perform the desired task using the reduced information of the full size input. ESSENTIAL STATISTICS MEASURES CORRELATION MATRIX A correlation matrix is used for pointing the simple correlation r, among all possible pairs of variables included in the analysis; also it is a lower triangle matrix. The diagonal elements are usually omitted. BARTLETT’S TEST OF SPHERICIY Bartlett’s test of Sphericity is a test statistic used to examine the hypothesis that the variables are uncorrelated in the population. In other words, the population correlation matric is an identity matrix; each variable correlates perfectly with itself but has no correlation with the other variables. KAISER MEYER OLKIN (KMO) KMO is a measure of sampling adequacy, which is an index. It is applied with the aim of examining the appropriateness of factor/Principal Component Analysis (PCA). High values indicate that factor analysis benefits and their value below 0.5 imply that factor suitable may not be suitable. 4.3.4MULTI-LEVEL MAHALANOBIS-BASED DIMENSIONALITY REDUCTION (MMDR) Multi-level Mahalanobis-based Dimensionality Reduction (MMDR), which is able to reduce the number of dimensions while keeping the precision high and able to effectively handle large datasets. MERITS OF PPA The advantages of PPA over PCA are, Important features are not missed. Error approximation rate is also very less. It can be applied to high dimensional dataset. Moreover, features are extracted successfully which also gives a pattern categorization. CRITERION BASED TWO DIMENSIOANL PROTEIN FOLDING USING EXTENDED GA Extensively, protein folding is the method by which a protein structure deduces its functional conformation. Proteins are folded and held bonded by several forms of molecular interactions. Those interactions include the thermodynamic constancy of the complex structure, hydrophobic interactions and the disulphide binders that are formed in proteins. Folding of protein is an intricate and abstruse mechanism. While solving protein folding prediction, the proposed work incorporates Extended Genetic Algorithm with Concealed Markov Model (CMM). The proposed approach incorporates multiple techniques to achieve the goal of protein folding. The steps are, Modified Bayesian Classification Concealed Markov Model (CMM) Criterion based optimization Extended Genetic Algorithm (EGA). 4.4.1MODIFIED BAYESIAN CLASSIFICATION Modified Bayesian classification method is used grouping of protein sequence into its related domains such as Myoglobin, T4-Lysozyme and H-RAS etc. In Bayesian classification, data is defined by the probability distribution. Probability is calculated that the data element ‘A’ is a member of classes C, where C = {C1, C2 †¦ CN}. (1) Where, Pc(A) is given as the density of the class C evaluated at each data element.

The Murder of James Byrd Jr. Essay -- Essays Papers

The Murder of James Byrd Jr. In June of 1998, a sadistic murder of a middle-aged black man from Jasper, Texas, rekindled memories of lynching practices from the blood stained American past. James Byrd, Jr., 49, was beaten savagely to the point of unconsciousness, chained to the back of a pickup truck by his neck, and dragged for miles over rural roads outside the town of Jasper. It is believed that Byrd survived through most of this experience, that is, until he was decapitated. Three white men, John William King, 23, Shawn Berry, 23, (both of whom had links to white supremacist groups) and Lawrence Brewer Jr., 31, were arrested. Brewer and King were sentenced to death for a racial hate crime that shocked the nation. Berry was sent to prison for life. In order to understand the reas...

Essay --

Rosie the Riveter is a cultural icon of the United States whom represented the women who worked in factories during World War II, many of whom produced military equipment and war supplies. These women sometimes took entirely new jobs replacing the male workers who were in the military. The symbol of feminism and women's economic power was often amplified through Rosie the Riveter. "Rosie the Riveter" was a popular phrase first used in 1942 in a song of the same name written by Redd Evans. Auto factories were converted to build airplanes, shipyards were expanded, and new factories were built, and all these facilities needed workers. While the men were busy fighting in war, women were dominant in assistance. Companies took the idea of hiring women seriously. Eventually, women were needed because companies were signing large, lucrative contracts with the government just as all the men were leaving for the service. The various elements or figures of Rosie was based on a group of women, m ost of whom were named Rose. Many of these women named "Rose" varied in class, ethnicity, geography, and background diversity. One specially, who's had the biggest impact of all Rosie's was Rose Will Monroe. Rose Will Monroe, the most influential "Rosie" at the time, represented women during World War II by working most of her time in a Michigan factory. Primarily, in December 1941, Pearl Harbor was bombed by the Japanese and was the time span with full integration of the United States. As a result, the U.S military proliferated male work force to accumulate ranks. America was in desperation for factory out out and military equipment increased. Many adversities agencies, one specifically J.Walter Thompson, assisted the United States government with c... ...n everywhere. The song truly hit how America was at the time. The pain verse of the song goes,"that little frail girl can do/more than a man can do." Long before she was a sensational Hollywood star, Marilyn "Rose" Monroe served as a "Rosie" at just the age of nineteen. Marilyn Monroe worked at a Radioplane Munition factory. A famous photographer named David Conover had a job of capturing pictures of women working on the workforce. Conover came across Marilyn Monroe and was captured by her beauty. Eventually as time escalated, Marilyn Monroe began modeling as a military work woman and soon her fame began to arise. Marilyn Monroe helped expose the need for women in the workfare at the time. In light of Rosie the Riveter, Rose Will Monroe, the most influential "Rosie" at the time, represented women during World War II by working most of her time in a Michigan factory.

Research paradigm Essay

The research paradigm considered by the researcher in regard to this work included the consideration of packaging in special occasions and which factors companies should focus during the period of social occasion in order to make their packaging their selling point. During the festival seasons market is flooded with various gift options. Due to the competition various organizations offer attractive schemes and offers to allure the consumers. Consumers due to various kind of motives of buying gift which can be personal, individual, altruism, cultural, reciprocal and other reason wants to buy attractive gifts for the people within his circle from family to the friends and relatives. The competitions companies face to attract these customers comes from various sectors of the industry or outside the industry. Now a days even service organization have become very competitive and services can also be offered as a gift. In such case company faces all kind of product, generic, industry specific and other kind of competitions. The packaging decisions are one of the important aspects of the marketing mix which can not be ignored in such a competitive environment where everything needs to be perfect. This paradigm has been utilized many times in the study of packaging in special occasions for all the group of respondents. I. i. a. Sample selection The data sampling was randomly managed utilizing stratified means with sixty five questionnaires completed by both male and female retail consumers. The percentages of female respondents were higher than that of male. The choice to use retail consumers alone in this research was made for three reasons. †¢ First, it was far simpler to have access to consumers from retail organization in regard to the researcher’s availability. †¢ Second, the focus itself is on understanding attitudes and perceptions for the packaging of gifts in special occasions and retail organization is a place attracting major customers to buy gifts. †¢ Third, the quantification of such information allows the researcher to gain a broader perspective on how respondents observe and realize the meanings of different components of packaging during special occasions and how it impacts their buying behaviour. I.i. b. Reliability and viability Reliability for the researcher was achieved in the assurance that only a specified group of men and women were utilized in regard to the research. The focus of the research has been on the consumers from retail organization. These consumers from retail organization were approachable. Data was collected in the presence of researcher. This gave the research a more focused view of the research goal. The validity was managed as a result of this focus and emphasised in the considerations involved in the data collection, variables and sampling methods. Privacy and confidentiality methods included assigning numeric and alphabetic coding to each responding questionnaire. This ensured anonymity in regard to the researcher and the subjects of the research process. I. i. c. Sample size Approximately 100 questionnaires have been distributed to collect the information. However in 35 questionnaires the information was not completed and due to that these has been withdrawn from the studies. 65 fully filled questionnaires have been utilized for the purpose of study. I. i. d. Questionnaire design The questionnaire design was simple. The questions included in the paper are related to the attributes of the packaging. The time taken to complete the questionnaire was less due to its simple nature. Most of the questions are simple circle question where respondent has to make a circle around the most appropriate and applicable option. I. i. e. Data analysis and findings Analysis of information in regard to research managed by the researcher must include complete and full understanding of the questionnaire. This understanding focuses in the use of the questionnaire created specifically for this process. It is the considerations realized within the questionnaires, no matter their simplicity, that will focus considerations in later chapters of this work. Within this section of Analysis and Findings there will be measurement of all responses in regard to the questionnaire. †¢ Analysis strategy Analysis strategy included a full series of statistical diagrams of all information collected including positive and negative responses, gender variations and marital status. This strategy provided the researcher with a wider spread for the conclusions that became evident in regard to the researcher’s focus. This information was broken down into specific charts for the benefit of visual context. The visual context provided insight in regard to perceptions of packaging and considerations by consumers from retail organization in regard to the impact of components on them. These perceptions and considerations provide the researcher with evidence to support the hypothesis made in that effective packaging decisions during the special occasions will support organizations to delight the consumer. †¢ Awareness The researcher held awareness of the potential for study in regard to packaging during special occasion through many methods. Those methods include observation, interaction and extensive research. The awareness of the media discussions of packaging amongst collegiate level men and women and the similar studies within this idea would in fact have influenced the choice made. The choice of analyzing how consumers from retail organization amongst this particular population would in fact consider their packaging options and knowledge allows for a singular perspective isolated from the more broadly painted view. The focus itself was on the consumers from retail organization and how they absorbed information available before deciding on packaging decisions. Overall, the respondents to the questionnaires provided insight in regard to how many individuals are learning more and more about packaging decisions. From the literature review it is evident that usually colour of the packaging which makes impact on sender or receiver of any gift. The questionnaire aims to measure the frequency to which people buy gifts for others and what are the factors they consider for the packaging. †¢ Understanding The understanding of this information gathered is proven in the statistics within the questionnaires that were completed and submitted. Each respondent have been explained the objective of study. Researcher has helped the respondents if s/he faces any difficulty in understanding any question. The research found that all respondents understood the material being requested and filled the questionnaires accordingly in timely fashion. The responses of questionnaires were filled in the database as the completed questionnaires had been received from the respondents. This information was then examined thoroughly for consistency and validity. The researcher now understands that there is a great deal of diversity in gift buying and packaging behaviour among the consumers from retail organization. Respondents can learn about better packaging options through their own personal research, their parents, friends or other family members. †¢ Findings The questionnaire included demographic details like of the respondents Demographic Profile †¢ Age Large section of the respondents (41. 5%) fall under the age group of 22-30 years old, followed by people within the age group of 41-50 years who are 20% of the total respondents. Approximately 15% of the total respondents fall under the age group of 18-21. Figure III. 1 Age of Respondents Ethnic background: 43% of the total respondents were from White community while 34% were Asian. Black other and Black African were 14% and 6% respectively of the total respondents. None of the respondent was from Hispanic or African background. Figure III. 2 Ethnic Origin of the Respondents Gender Majority of respondents are female with percentage of 66 while remaining 34% of the respondents are male. Figure III. 3 Gender Consumer behaviour. When respondents were asked the question â€Å"How frequently do you buy gift items? † twenty five out of sixty five responded that they buy gifts once in a month. Twenty two out of sixty five responded that they buy three to six times in a year. Nine respondents told that they buy less often gifts for anyone while the same number of respondents told that they buy gifts once in a week. No respondent told that s/he never buys any gift items. Figure III. 4 Frequency of buying Gift Items Components of Packaging on Special Occasion Colour:When consumers were asked what they feel about the statement â€Å"It feels good to receive a present in Colourful packaging on special occasions† approximately 48% of the respondent strongly agreed to the statement while 35% agreed to the statement. Remaining respondents felt they were neutral to the Colour of packaging. Figure III. 5 Colourful Packaging Respondents when asked to respond on the statement â€Å"On special occasions (like Christmas), packaging is more Colourful than normal. † Majority of them agreed to the statement. 37% of the total respondents strongly agreed to the statement while approximately 50% agreed to the statement. 10-% of the total respondents felt neutral about the statement while approximately 1% disagreed to the same. Figure III. 6 Packaging on Special Occasions Respondents were asked to respond on the shape and Colour attribute of a product. They were asked to respond on the statement â€Å"People are mostly attracted by different shapes and sizes in gift items. † 58% of the total respondents agreed to the statement while approximately 16% strongly agreed. 18% respondents were neutral to this statement while 4% disagreed. 1. 5% of respondents strongly disagreed to the statement. Figure III. 7: Colours and Shapes of Product. When respondents were asked whether or not they agree to the statement â€Å"Packaging styles vary for different occasions† 44% agreed to the statement while 30% strongly agreed. 18% were neutral to the statement and 4% strongly disagreed. Figure III. 8 Packaging Style in Different Occasions When consumers were asked about their own perception regarding shapes and Colours of the product by the statement â€Å"I am attracted to a product because of different Colours and shapes in packaging when I buy for a special occasion† approximately 40% of the total respondents agreed to the statement while 26% strongly agree to the same. 18% respondents were neutral to the same, 17% disagreed and 1. 5% strongly disagreed to the statement. Figure III. 9 Impact of Shape and Size of packaging in special occasions To the statement â€Å"Colourful and attractive packaging makes a good impression about the sender to the receiver. † Approximately 50% strongly agreed to the statement while 43% agreed to the same. 6% of the total respondents were neutral while 1. 5% strongly disagreed to it. Figure III. 10: Colourful and attractive packaging and impression about sender.

Consumer Behavior and Purchase Decision Making Process

Consumer behavior and purchase decision making process Every day we need to make a decissions – buy or not to buy anything. There are many things which helps us to make decissions like location, mood, advertisments and other. Consumer behavior is the mental and emotional processes and physical activities people engage in when they select, purchase, use, and dispose of products or services to satisfy particular needs and desires. A consumer goes through several stages before purchasing a product or service: 1. need; 2. information gathering/search; 3. evaluation of alternatives; . purchase of product/service; 5. post purchase evaluation. In my opinion, in this process there are two main steps – information gathering and evaluation of alternatives. These steps help to understand how much you need the product and how good it is. The buying process starts with comming needs. A need can be activated through internal or external stimuli. A need can also be aroused by an exter nal stimulus such as sight of a new thing in a shop while purchasing other things. Need is the most important factor which leads to buying of products and services.Need infact is the catalyst which triggers the buying decision of individuals. After need arousal, the consumer tries to solve it and gathers the sources and information about the product. Depending upon the intensity of need, it produces two states of individual. The first state is called heightened attention when the consumer becomes more receptive to the information regarding the item he needs. If a consumer needs to purchase a refrigerator, he will pay mere attention to fridge ads and the remarks made by friends and associates about fridges.If need is more intense, the individual enters a state of active information search and he tries to collect more information about the product, its key attributes, qualities of various brands and about the outlets where they are available. There are a lot of consumer information so urces like family, friends, advertisements, mass media, salesman. In my opinion, it is hard to arrange in the order wich one is the best source where get information about product. All these sources have pluses and minuses.If I prefer get information from family there is possibility that I do not get info about technical stuff. Or if I choose only salesman advice he can aggrandize about product advantages. That is why I recommend to take information as much as possible from every source. Having collected the information, the consumer clarify and evaluate the alternatives. There is, unfortunately no simple and single evaluation process used by all consumers or even by one consumer in all buying situations. Consumers can make choices based on their emotions and feelings.They elicit from memory their overall evaluations of products and choos the alternative for which they have the most positive feelings. One of the most current process of evaluation is to judge the product largely on a conscious and rational basis. Various considerations form the part of judgment such as product attributes, importance, weights, brand image, utility function for each attribute, and attitude and other. After evaluation of various alternatives, he takes the decision to buy or not to buy.Ofcourse, people can make choice on the spur of the moment, often without prior problem recognition. But I recommend going through all theese five steps focusing on information gathering and evaluation of alternatives. And always remember that the best impression about product you can get only after you try it. Izmantota literatura: http://www. managementstudyguide. com/consumer-decision-making-process. htm http://uwmktg301. blogspot. com/2010/01/evaluation-of-alternatives. html http://www. slideshare. net/Annie05/consumer-buying-behavior-and-decision-making-presentation