A Critical Assessment of the Agricultural Subsidies of the United States of America (US) and the European Union (EU)

A Critical Assessment of the Agricultural Subsidies of the United States of America (US) and the European Union (EU) Introduction Subsidies are deployed as a means of boosting production, by giving financial grants from one party to another in order to increase production beyond the market equilibrium. From an economist’s perspective, funding has been adopted in different contexts to give varying implications and significance.Advertising We will write a custom essay sample on A Critical Assessment of the Agricultural Subsidies of the United States of America (US) and the European Union (EU) specifically for you for only $16.05 $11/page Learn More The Organisation for Economic Co-operation and Development (OECD) defines subsidies as â€Å"measures that keep prices for consumers below market levels, or measures that keep prices for producers above market levels† (OECD 2006, p.3). Many economists argue that the term subsidy can be applied to mean all forms of payments made directly by a government to various producers. In light of these explanations, this paper considers subsides as including direct means of regulating the equilibrium between consumption and supply of products and services in the market, among them being cash grants coupled with provisions of interest-free loans from the government. Low-interests loans, tax wavering write-offs of depreciation charged on assets, rent rebates and insurance are also considered among the alternative forms of subsidy. The main purpose of this paper is to conduct a comparative analysis of the similarities and differences between the US and the EU agricultural subsidies. The basis for this research is founded in existing literature that relate to economic theories on taxes and subsidies. In addition, to expand on the discussion involving the similarities and differences in agriculture subsidies within the US and EU, as well as their implications on economy, a literature review of economics of subsidies and taxes will be considered first.Advertising Looking for essay on social sciences? Le t's see if we can help you! Get your first paper with 15% OFF Learn More The paper further presents the differences between the US and the EU agricultural policies, concerning the legislation on agricultural subsidies and the critical reception towards these policies. This section is followed by a discussion on the similarities between the US and the EU’s agricultural policies in the context of benefits to farmers and influences on income. Lastly, the effects of agricultural subsidies on famers, from the dimension of family budgets and taxes are given substantial attention. Economic Theory on Subsidies and Taxes To understand the economic theory on subsidies and taxes, one needs to be aware of the function of subsidy and tax accords within the area of global trade pact. One will get to understand why the government upholds such accords as well as identifying the most preferred way of handling the subsidies within the global trading schemes. Therefore, studies pertaining to the economic impact of subsidies have provoked mixed reactions from economists. For instance, Krishna and Panagariya (2009) argue that, â€Å"subsidies are a form of protectionism or trade barrier by making domestic goods and services artificially competitive against imports† (OECD, 2006, p.236). Such protectionism impairs the ability of consumers to consider and select imported goods and services, in their pursuit of alternative goods that are produced outside a nation’s boundaries through unethical or illegal channels (Anderson 2005, p.165). In the economic sense, subsidies are unethical because they distort the market and impose higher economic costs. In addition to this case, there are different types of subsides that have been outlined. Amegashie identifies employment subsidies, production subsidies and export subsidies as three that are granted by governmental or non-governmental organisations (Amegashie 2006, p.8). Export subsidies take the form of financial support that is offered by a government to exported products and services, in an effort to improve a nation’s balance of payments (Amegashie 2006, p.8). From an agricultural product context, exporting subsidies is significant in nations whose GDP is mainly derived this particular source. However, the impact of export subsidies varies.Advertising We will write a custom essay sample on A Critical Assessment of the Agricultural Subsidies of the United States of America (US) and the European Union (EU) specifically for you for only $16.05 $11/page Learn More For instance, the Court of Auditors (2003) argues that, as evidenced by the case of the EU, export subsidies may result inartificially low prices of subsidised products. Furthermore, fluctuations in milk production costs may also occur, as shown in the graph below. Fig 1: Milk products production cost fluctuations in the EU in comparison to other parts of the world Source: Weers and Hemme (2012, p.13) Production subsidies have a key role to play when it comes to product yields. Production subsidies â€Å"encourage suppliers to increase the output of particular products by partially offsetting the production costs or losses† (Krishna Panagariya, 2009, p.237). Thus, the chief aim is to create a means of expansion for the production of certain products at much lower prices than the market forces would make possible (Mankiw, 1997). In doing so, governments also offer their support to consumers of the produced products. In an agricultural context, production subsides may also be provided by offering technical support and financial assistance to help create new agricultural firms and processing plants, possibly on a regional basis. Moreover, employment subsides and incentives, such as social security benefits, are sometimes offered by governments to boost employment levels in certain regions and for certain industries (Szymanski Valletti, 2005). They may also con duct research into new areas of development that would lead to more absorption of unemployed persons.Advertising Looking for essay on social sciences? Let's see if we can help you! Get your first paper with 15% OFF Learn More Although subsidies may be seen as a plausible way of regulating and controlling the markets, they attract controversies over their overall impacts on the performance of a nation’s economy. Subsidies influence competitive equilibrium, and from the fundamental principles of supply/demand relationships, particularly in cases where the demand for a given product goes beyond the supply, prices normally fall. Consequently, reduction of goods supplied to levels below the equilibrium quantity results in price hikes (Covey et al., 2007, P.41). Supposing that the bazaar for any product operates flawlessly â€Å"at the competitive equilibrium, the overall effect of subsidies is to increase the supply of goods and services to levels above the equilibrium quantity† (Kym Will 2011, P.1303). This move leads to an increase of costs beyond corresponding gains of the subsidy. That is, the amount of increase is proportionate to the size of the injected subsidy and hence a â€Å"market failure or inefficiency† (Jerome et al. 2006, p.16). For this reason, some economists claim that subsidies are undesirable in a competitive market. This claim is particularly significant in cases where foreign competition is desired. In support of this assertion, Westcott and Young (2004) argue that, instead of lowering the prices of goods and services, subsides make goods produced within a nation, cheaper in comparison to those produced in the foreign nations; hence incredibly reducing foreign competition. In the context of agricultural goods, giving subsidies(especially in the developing nations) implies that such nations are barred from engaging in the international trade in a more competitive manner, since they receive substantially lower prices on products traded on the global market. In economic theory, offering subsidies in the form of tax wavering is considered as an attempt of protectionism (Wyatt Ashok, 2010, p.1927). In such scenarios, market distortion occurs, acc ompanied by social welfare reductions. On the grounds of economic logic, World Bank policies advocate the total removal of subsidies offered by the developing nations, even though it has no mandate to enforce this removal (Westcott Young, 2004, p.11). The impact includes a reduction in revenue generated by producers operating in foreign nations, which can cause tension between the European Union and the United States, and the nations in the developing world that are known to be offering economic stimuli in the form of subsidies. The developing nations protect their local industries against the influx of foreign products. Such foreign products cost less in the developing nations’ markets due to the economies of scale in the production processes and techniques for production used in the developed world’s industries. In summary, subsidies can provide a short-term solution to an industry. However, on a long-term basis, they end up being unethical, often evolving into nega tive effects (Organisation for Economic Co-operation and Development 2007, p. 26) Comparison of the US and the EU Agricultural Policies The subject of subsidies and tax reduction for agricultural products is approached from different directions, yet in similar ways for both the EU and the US. Subsidising the agricultural sector is done with the ultimate objective of boosting aggregate demand or investment in the agricultural sector (Becker 2002). In accordance to the theory of economics of subsidies, such an attempt causes the cost of production for agricultural products to be lowered for both the US and the EU. Consequently, making products become cheaper than importing them. In both the US and the EU, it is appreciated that the removal of subsidies has the impact of dampening economic activities in the agricultural sector. This blow is dependent to certain issues associated with the agricultural sector budget balance. Appreciating the role of subsidies and taxes in shaping the agr icultural sector in the US and the EU warrants consideration of the differences and similarities between their different approaches to agricultural subsidies. Differences between the US and the EU agricultural Policies Legislation on Agricultural Subsidies The US and the EU have put policies in place that encourage their farmers to produce certain agricultural products at much lower costs, in comparison to the global costs of such products (Alston 2008). Although there have been less legislative efforts to encourage the production of various products in the EU, the governing bodies have implemented policies that ensure farmers receive subsidies for producing certain commodities such as dairy products (Alston 2008). In similar fashion, the US has created a process to facilitate subsidy offers for farmers that produce various products, mainly cotton, through legislation such as the 2010 Farm Bill (Babcock 2007. Subsidising US cotton results in low global market prices of the crop, whe rein levels of making cotton produced elsewhere is unsustainable in the market. Hence, many nations are opposed to the legislation provided in the Farm Bill 2002. For instance, Brazil challenged the US subsidies for cotton farmers at the World Trade Organisation (WTO), stating that this distorted the cotton market at an international level. They further argued that it encouraged an increase in income to large-scale farmers at the advantage of the small-scale and poor farmers in the developing world. This case lasted from 2002 to 2008, when it was concluded with Brazil being the victor. In support of this argument, Riedl (2008, p.316) believes that policies encouraging subsidising of the agricultural products in both the EU and the US products have an opposite impact. However, despite the opposition to the continued indirect funding of the EU and the US farmers through subsidies, Summer (2013) proposes that it is imperative to stop offering direct incentives for production, because s uch a strategy for boosting production makes farmers in the US and the EU compete unfairly with others across the globe. Critical Reception of Agricultural Policies From the above arguments, subsidies on agricultural products, especially by major global giant producers like the EU and the US, are not received in good faith within the international arena. In relation to this, LaBorde (2013) postulates, â€Å"a series of weather-related shocks in 2012- including severe droughts in Central Asia, Eastern Europe, and the United States- contributed to global food prices remaining high for a fifth consecutive year† (LaBorde 2013, Para.5). Although the EU and the US do provide subsidies to different agricultural products, they have refused to heed to these calls. Rather, they have opted to increase subsidies on agricultural sectors domestically. Such strategies have long-term implications on the worldwide food systems, coupled with impairing food securities in the developing nations (Babcock, 2007: Alston, 2008). Similarities between the US and the EU Agricultural Policies Benefits to Farmers According to Westcott and Young (2004), one of the major similarities between subsidies of the agricultural sectors in both the US and the EU is that subsidising has resulted in lowering the costs of production. In fact, â€Å"in 2005, the US government gave farmers agricultural subsidies amounting to$14 billion and in the European Union, dairy farmers received subsidies amounting to $47 billion† (Westcott Young 2004). This equates to more than the sum earned by every person (on average) in the developing nations. This explicitly implies that the US and the EU farmers were able to produce their products much cheaper than those outside of their countries because of government subsidies. Consequently, based on research by Drabenstott (2008), and the U.S. Department of Agriculture, Economic Research Service’s (2007), foreign traders found it difficult to introd uce their products into these two markets, since the price of the subsidised farm products were lower than those they could sell their products at, even for them to break even. Although such subsidies resulted in stimulation of, and therefore, over-production of subsidised agricultural sector products, farmers in the US and EU benefited in that they were able to place their surplus products in the market in higher quantities. This case meant that the consumption of the products also increased. Influences on Income In 2002, cotton farmers in America earned much of their revenues from the country’s administration through subsidies brought into force under provisions of the Farm Bill (Morgan, Gaul, Cohen 2009). These subsides also stimulated overproduction of cotton in 2002. Much of this excess production was sold to the global markets at much lower prices well below the break-even points of cotton farmers in the developing world. Similarly, in the EU, in 2004,  £3.30 was spe nt in the exportation of sugar worth £1(Oxfam International 2004, p.39). Although, the subsidies had a positive impact on the incomes earned by EU farmers, global market distortions occurred. Subsidies for agricultural products in the US and the EU have an overall impact of protecting revenue losses that are likely to be encountered by EU and the US farmers. However, in the US for instance, farm subsidies favour large-scale farmers over small-scale ones, with non-farm families’ incomes being negatively impacted (see Fig 2). This situation is inappropriate, especially considering that most of the farm families are located in rural areas where the cost of living is lower. Fig 2: Differences between incomes for large farms and small farms in the US Source: (Becker 2002) Effects of Agricultural Subsidies on Famers Family Budgets Farm policies that are realised through subsidies are meant to provide relief on farmers’ household budgets. Unfortunately, they produce opposi te effects, as Becker (2002) states they, â€Å"harm family farmers by excluding them from most subsidies, encouraging the merging of family farms, and raising land values to levels that prevent young people from entering farming† (p.17). This suggests that agricultural subsidies fail to provide relief to the struggling farmers, especially those who are new to farming, because overproduction results in low prices of agricultural products. In turn, the net effect on the capacity to fund family budgets is minimal. Tax Farm subsidies have the overall impact of increasing the cost of living through increased taxes. As the economy for both the US and the EU continues to boom, congress also increases the subsidies offered to farms. Wyatt and Ashok (2010) support this assertion by claiming, â€Å"After averaging less than $14 billion per year during the 1990s, annual farm subsidies have topped $25 billion in the current decade since the passage of the 2002 farm bill, the most expen sive farm bill in American history† (Wyatt Ashok 2010, p.1931). This argument implies that all spending by the federal governments has to be funded by taxes levied from citizens. In the US, Morgan, Gaul, and Cohen (2009) put the costs of farm subsidies at $216 per household in the form of yearly taxes, with an additional extra charging of $104 per household as escalated food prices. Conclusion The central purpose for enacting farm subsidy policies is centred on the need for alleviating poverty among rural farmers, and provision of food security by encouraging over production. Noting this purpose, the paper argues that scrutiny of the impact towards agricultural subsidies from an economic perspective, fails to contend with this aim. This is because such policies result to disadvantaging small-scale farmers outside the EU and the US, since the subsidies result in over production. The repercussion is to lower the prices of products in the global market, with the result that farm ers’ operations in other nations without the subsidies never break even. Within the US and the EU, subsidies create an imbalance of income between families that own farms and those that do not. Agricultural subsidies make families that own farms to earn higher incomes compared to those that do not own farms, yet families owning farms live in the rural areas where the cost of living is low. Hence, overall, farm subsidies have negative impacts on a nation’s economy, even though farmers (especially large-scale farmers) benefit incredibly from higher incomes. The underlying issue however, is that this is achieved at the expense of the smaller farmers operating in the same global market, who do not enjoy farm subsidies. References Alston, J 2008, Lessons from Agricultural Policy Reform in Other Countries: The 2007 Farm Bill and Beyond, American Enterprise Institute, New York. Amegashie, A 2006, ‘The Economics of Subsidies’, Crossroads, vol. 6 no.2, pp. 7-15. An derson, J 2005, ‘Tariff Index Theory’, Review of International Economics, vol. 3 no. 2, pp. 156-173. Babcock, B 2007, Money for Nothing: Acreage and Price Impacts of U.S. Commodity Policy for Corn, Soybeans, Wheat, Cotton, and Rice in American Enterprise Institute, The 2007 Farm Bill and Beyond, AEI Press, Washington, D.C. Becker, E 2002, ‘Land Rich in Subsidies and Poor in Much Else’, The New York Times, January 22, pp.17-18. Chapman, D, Foskett, K, Clarke, M 2006, ‘How Savvy Growers Can Double, or Triple, Subsidy Dollars’, The Atlanta Journal-Constitution, vol. 2 no.1, pp. 121-127. Court of Auditors 2003, Special Report no 9/2003 concerning the system for setting the rates of subsidy on exports of agricultural products (export refunds), together with the Commission’s replies, Court of Auditors, London. Covey, T et al. 2007, Agriculture Income and Finance Outlook, U.S. Department of Agriculture, Economic Research Service, New York. Dra benstott, M 2008, ‘Do Farm Payments Promote Rural Economic Growth? Federal Reserve Bank of Kansas City, Centre for the Study of Rural America’, The Main Street Economist, vol. 8 no. 1, pp. 57-61. Jerome, M, Stam, D, Milkove, L, George, B 2006, Indicators of Financial Stress in Agriculture Reported by Agri ­cultural Banks, 1982-99 AIS-74, U.S. Department of Agriculture, Economic Research Service. Krishna, P Panagariya, A 2009, ‘A Unification of Second Best Results in International Trade’, Journal of International Economics, vol. 52 no. 2, pp. 235-257. Kym, A Will, M 2011, ‘Agricultural Trade Reform and the Doha Development Agenda’, The World Economy, vol. 28 no. 9, pp. 1301–1327. LaBorde, D 2013, The hidden cost of US and EU farm subsidies, ifpri.org/blog/hidden-costs-us-and-eu-farm-subsidies Mankiw, N 1997, Principles of economics, Harcourt Brace, Fort Worth. Morgan, D, Gaul, G, Cohen, S 2009, ‘Farm Program Pays $1.3 Billion to People Who Dont Farm’, The Washington Post, vol. 5 no. 2, pp. 99-103. Organisation for Economic Co-operation and Development 2006, Agricultural Policies in OECD Countries: At a Glance, OECD Publishing, Paris. Organisation for Economic Co-Operation and Development 2007, Subsidy Reform and Sustainable Development, OECD, Paris. Oxfam International 2004, ‘A Sweeter Future? The potential for EU sugar reform to contribute to poverty reduction in southern Africa’, Oxfam Briefing Paper No. 70. November 2004, pp. 39-40. Riedl, B 2008, ‘How Farm Subsidies Harm Taxpayers, Consumers, and Farmers, European’, Journal of Economics, vol. 3 no. 2, pp. 315-321. Summer, D 2013, Effects of Farm Subsidies for the Rich on Poor Farmers, North Western University, California. Szymanski, S Valletti, T 2005, ‘Incentive Effects of Second Prises’, European Journal of Political Economy, vol. 2 no. 1, pp. 467-481. U.S. Department of Agriculture, Economic Research Service, 2007, Food Expenditures by Families and Individuals as a Share of Disposable Personal Income data, Economic Research Service, U.S. Department of Agriculture. Weers, A Hemme, T 2012, Global Review-The Supply of Milk and Dairy Products, Wilhelmitorwall, IFCN Dairy Network. Westcott, P Young, E 2004, U.S. Farm Program Benefits: Links to Planting Decisions and Agricultural Markets, U.S. Department of Agriculture, New York. Wyatt , T Ashok, M 2010, ‘Farm Household Income and Transfer Efficiency: An Evaluation of United States Farm Program Payments’, American Journal of Agricultural Economics, vol. 91 no. 5, pp. 1926–1937.

Writing a Critical Essay

Writing a Critical Essay Writing a Critical Essay While writing a critical essay, your purpose is to convey in your own words the sense of what the text is saying. Explain how the text creates its meaning. Open your critical literary essay with a thesis statement which demonstrates your point of view. The body of your critical essay is a presentation or defense of your interpretation. While writing critical essay, you should present your understanding of the text. In the conclusion of the critical essay, you sum up your findings, restate your thesis statement and arguments briefly. If you wish, you may extend the significance of the reading - comment on cultural or moral or technical significances of the topic and techniques of the text. You may also start writing critical essay in other way. For example, note what the main difficulties are to an interpretation of the novel. It is important to give the reader a sense of how you are proceeding in the critical essay and why: Critical Essay Help While writing a critical essay outline, you should describe the message that author wanted to bring, the significance of the book. Describe what the book is about, enumerate the major parts of the book in their order and relation, and define the problem that the author is trying to solve. Try to find the most important sentences in the book and use them to support your argumentation. Determine the author' solutions of the problem. Writing a critical essay means that you begin to argue with the author and express your point of view. Choose a specific topic that you want to explore further in your critical essay. The goal of the critical essay writing is to persuade the reader to accept your point of view. Include a thesis statement describing the subject you have chosen and why it interests you. You must have a clearly defined argumentative thesis. Write the thesis statement somewhere in the first paragraph, preferable it should be the last sentence.Read the books that you feel will be relevant to your chosen topic. Write your ideas as a summary form to create the basis of critical essay writing. Along with this summary, make an outline for you critical essay. Each idea should be started with new paragraph; each paragraph should have its topic sentence. The reader should see the development of your analysis by looking at the beginning of each paragraph. At the end, when you think critical essay is ready, proofread and correct all mistakes. Custom Critical Essay Writing Service If critical essay writing is a challenging assignment for you, do not panic! We are available at any time of day and night to help you with any step of critical essay writing. is experienced in custom writing and we know how great critical essay should be written! Popular posts: Research Paper Conclusion Persuasive Research Paper Paper Research Help Writing a Research Paper Free Research Paper

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Comparison On Classification Techniques Using Weka Computer Science Essay

Comparison On Classification Techniques Using Weka Computer Science Essay Computers have brought tremendous improvement in technologies especially the speed of computer and reduced data storage cost which lead to create huge volumes of data. Data itself has no value, unless data changed to information to become useful. In past two decade the data mining was invented to generate knowledge from database. Presently bioinformatics field created many databases, accumulated in speed and numeric or character data is no longer restricted. Data Base Management Systems allows the integration of the various high dimensional multimedia data under the same umbrella in different areas of bioinformatics. WEKA includes several machine learning algorithms for data mining. Weka contains general purpose environment tools for data pre-processing, regression, classification, association rules, clustering, feature selection and visualization. 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Introduction Computers have brought tremendous improvement in technologies especially the speed of computer and data storage cost which lead to create huge volumes of data. Data itself has no value, unless data can be changed to information to become useful. In past two decade the data mining was invented to generate knowledge from database. Data Mining is the method of finding the patterns, associations or correlations among data to present in a useful format or useful information or knowledge[1]. The advancement of the healthcare database management systems creates a huge number of data bases. Creating knowledge discovery methodology and management of the large amounts of heterogeneous data has become a major priority of research. Data mining is still a good area of scientific study and remains a promising and rich field for research. Data mining making sense of large amounts of unsupervised data in some domain[2]. Data mining techniques Data mining techniques are both unsupervised and supervised. Unsupervised learning technique is not guided by variable or class label and does not create a model or hypothesis before analysis. Based on the results a model will be built. A common unsupervised technique is Clustering. In Supervised learning prior to the analysis a model will be built. To estimate the parameters of the model apply the algorithm to the data. The biomedical literatures focus on applications of supervised learning techniques. A common supervised techniques used in medical and clinical research is Classification, Statistical Regression and association rules. The learning techniques briefly described below as: Clustering Clustering is a dynamic field of research in data mining. Clustering is an unsupervised learning technique, is process of partitioning a set of data objects in a set of meaningful subclasses called clusters. It is revealing natural groupings in the data. A cluster include group of data objects similar to each other within the cluster but not similar in another cluster. The algorithms can be categorized into partitioning, hierarchical, density-based, and model-based methods. Clustering is also called unsupervised classification: no predefined classes. Association Rule Association rule in data mining is to find the relationships of items in a data base. A transaction t contains X, itemset in I, if X à  t. Where an itemset is a set of items. E.g., X = {milk, bread, cereal} is an itemset. An association rule is an implication of the form: X  ® Y, where X, Y ÃÅ' I, and X ÇY = Æ An association rules do not represent any sort of causality or correlation between the two item sets. X Þ Y does not mean X causes Y, so no Causality X Þ Y can be different from Y Þ X, unlike correlation Association rules assist in marketing, targeted advertising, floor planning, inventory control, churning management, homeland security, etc. Classification Classification is a supervised learning method. The classification goal is to predict the target class accurately for each case in the data. Classification is to develop accurate description for each class. Classification is a data mining function consists of assigning a class label of objects to a set of unclassified cases. Classification A Two-Step process show in figure 4. Data mining classification mechanisms such as Decision trees, K-Nearest Neighbor (KNN), Bayesian network, Neural networks, Fuzzy logic, Support vector machines, etc. Classification methods classified as follows: Decision tree: Decision trees are powerful classification algorithms. Popular decision tree algorithms include Quinlans ID3, C4.5, C5, and Breiman et al.s CART. As the name implies, this technique recursively separates observations in branches to construct a tree for the purpose of improving the prediction accuracy. Decision tree is widely used as it is easy to interpret and are restricted to functions that can be represented by rule If-then-else condition. Most decision tree classifiers perform classification in two phases: tree-growing (or building) and tree-pruning. The tree building is done in top-down manner. During this phase the tree is recursively partitioned till all the data items belong to the same class label. In the tree pruning phase the full grown tree is cut back to prevent over fitting and improve the accuracy of the tree in bottom up fashion. It is used to improve the prediction and classification accuracy of the algorithm by minimizing the over-fitting. Compared to other data mining techniques, it is widely applied in various areas since it is robust to data scales or distributions. Nearest-neighbor: K-Nearest Neighbor is one of the best known distance based algorithms, in the literature it has different version such as closest point, single link, complete link, K-Most Similar Neighbor etc. Nearest neighbors algorithm is considered as statistical learning algorithms and it is extremely simple to implement and leaves itself open to a wide variety of variations. Nearest-neighbor is a data mining technique that performs prediction by finding the prediction value of records (near neighbors) similar to the record to be predicted. The K-Nearest Neighbors algorithm is easy to understand. First the nearest-neighbor list is obtained; the test object is classified based on the majority class from the list. KNN has got a wide variety of applications in various fields such as Pattern recognition, Image databases, Internet marketing, Cluster analysis etc. Probabilistic (Bayesian Network) models: Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. Bayesian algorithms predict the class depending on the probability of belonging to that class. A Bayesian network is a graphical model. This Bayesian Network consists of two components. First component is mainly a directed acyclic graph (DAG) in which the nodes in the graph are called the random variables and the edges between the nodes or random variables represents the probabilistic dependencies among the corresponding random variables. Second component is a set of parameters that describe the conditional probability of each variable given its parents. The conditional dependencies in the graph are estimated by statistical and computational methods. Thus the BN combine the properties of computer science and statistics. Probabilistic models Predict multiple hypotheses, weighted by their probabilities[3]. The Table 1 below gives the theoretical comparison on classification techniques. Data mining is used in surveillance, artificial intelligence, marketing, fraud detection, scientific discovery and now gaining a broad way in other fields also. Experimental Work Experimental comparison on classification techniques is done in WEKA. Here we have used labor database for all the three techniques, easy to differentiate their parameters on a single instance. This labor database has 17 attributes ( attributes like duration, wage-increase-first-year, wage-increase-second-year, wage-increase-third-year, cost-of-living-adjustment, working-hours, pension, standby-pay, shift-differential, education-allowance, statutory-holiday, vacation, longterm-disability-assistance, contribution-to-dental-plan, bereavement-assistance, contribution-to-health-plan, class) and 57 instances. Figure 5: WEKA 3.6.9 Explorer window Figure 5 shows the explorer window in WEKA tool with the labor dataset loaded; we can also analyze the data in the form of graph as shown above in visualization section with blue and red code. In WEKA, all data is considered as instances features (attributes) in the data. For easier analysis and evaluation the simulation results are partitioned into several sub items. First part, correctly and incorrectly classified instances will be partitioned in numeric and percentage value and subsequently Kappa statistic, mean absolute error and root mean squared error will be in numeric value only. Figure 6: Classifier Result This dataset is measured and analyzed with 10 folds cross validation under specified classifier as shown in figure 6. Here it computes all required parameters on given instances with the classifiers respective accuracy and prediction rate. Based on Table 2 we can clearly see that the highest accuracy is 89.4737 % for Bayesian, 82.4561 % for KNN and lowest is 73.6842 % for Decision tree. In fact by this experimental comparison we can say that Bayesian is best among three as it is more accurate and less time consuming. Table 2 : Simulation Result of each Algorithm DATA MINING IN BIONFORMATICS Bioinformatics and Data mining provide challenging and exciting research for computation. Bioinformatics is conceptualizing biology in terms of molecules and then applying informatics techniques to understand and organize the information associated with these molecules on a large scale. It is MIS for molecular biology information. It is the science of managing, mining, and interpreting information from biological sequences and structures. Advances such as genome-sequencing initiatives, microarrays, proteomics and functional and structural genomics have pushed the frontiers of human knowledge. Data mining and machine learning have been advancing with high-impact applications from marketing to science. Although researchers have spent much effort on data mining for bioinformatics, the two areas have largely been developing separately. In classification or regression the task is to predict the outcome associated with a particular individual given a feature vector describing that individu al; in clustering, individuals are grouped together because they share certain properties; and in feature selection the task is to select those features that are important in predicting the outcome for an individual. We believe that data mining will provide the necessary tools for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics. Propose novel data mining techniques for tasks such as Gene expression analysis, Searching and understanding of protein mass spectroscopy data, 3D structural and functional analysis and mining of DNA and protein sequences for structural and functional motifs, drug design, and understanding of the origins of life, and Text mining for biological knowledge discovery. In todays world large quantities of data is being accumulated and seeking knowledge from massive data is one of the most fundamental attribute of Data Mining. It consists of more than just collecting and managing data but to analyze and predict also. Data could be large in size in dimension. Also there is a huge gap from the stored data to the knowledge that could be construed from the data. Here comes the classification technique and its sub-mechanisms to arrange or place the data at its appropriate class for ease of identification and searching. Thus classification can be outlined as inevitable part of data mining and is gaining more popularity. WEKA data mining software WEKA is data mining software developed by the University of Waikato in New Zealand. Weka includes several machine learning algorithms for data mining tasks. The algorithms can either call from your own Java code or be applied directly to a dataset, since WEKA implements algorithms using the JAVA language. Weka contains general purpose environment tools for data pre-processing, regression, classification, association rules, clustering, feature selection and visualization. The Weka data mining suite in the bioinformatics arena it has been used for probe selection for gene expression arrays[14], automated protein annotation[7][9], experiments with automatic cancer diagnosis[10], plant genotype discrimination[13], classifying gene expression profiles[11], developing a computational model for frame-shifting sites[8] and extracting rules from them[12]. Most of the algorithms in Weka are described in[15]. WEKA includes algorithms for learning different types of models (e.g. decision trees, rule sets, linear discriminants), feature selection schemes (fast filtering as well as wrapper approaches) and pre-processing methods (e.g. discretization, arbitrary mathematical transformations and combinations of attributes). Weka makes it easy to compare different solution strategies based on the same evaluation method and identify the one that is most appropriate for the problem at hand. It is implemented in Java and runs on almost any computing platform. The Weka Explorer Explorer is the main interface in Weka, shown in figure 1. Open fileà ¢Ã¢â€š ¬Ã‚ ¦ load data in various formats ARFF, CSV, C4.5, and Library. WEKA Explorer has six (6) tabs, which can be used to perform a certain task. The tabs are shown in figure 2. Preprocess: Preprocessing tools in WEKA are called Filters. The Preprocess retrieves data from a file, SQL database or URL (For very large datasets sub sampling may be required since all the data were stored in main memory). Data can be preprocessed using one of Wekas preprocessing tools. The Preprocess tab shows a histogram with statistics of the currently selected attribute. Histograms for all attributes can be viewed simultaneously in a separate window. Some of the filters behave differently depending on whether a class attribute has been set or not. Filter box is used for setting up the required filter. WEKA contains filters for Discretization, normalization, resampling, attribute selection, attribute combination, Classify: Classify tools can be used to perform further analysis on preprocessed data. If the data demands a classification or regression problem, it can be processed in the Classify tab. Classify provides an interface to learning algorithms for classification and regression models (both are called classifiers in Weka), and evaluation tools for analyzing the outcome of the learning process. Classification model produced on the full trained data. WEKA consists of all major learning techniques for classification and regression: Bayesian classifiers, decision trees, rule sets, support vector machines, logistic and multi-layer perceptrons, linear regression, and nearest-neighbor methods. It also contains metalearners like bagging, stacking, boosting, and schemes that perform automatic parameter tuning using cross-validation, cost-sensitive classification, etc. Learning algorithms can be evaluated using cross-validation or a hold-out set, and Weka provides standard numeric performance mea sures (e.g. accuracy, root mean squared error), as well as graphical means for visualizing classifier performance (e.g. ROC curves and precision-recall curves). It is possible to visualize the predictions of a classification or regression model, enabling the identification of outliers, and to load and save models that have been generated. Cluster: WEKA contains clusterers for finding groups of instances in a datasets. Cluster tools gives access to Wekas clustering algorithms such as k-means, a heuristic incremental hierarchical clustering scheme and mixtures of normal distributions with diagonal co-variance matrices estimated using EM. Cluster assignments can be visualized and compared to actual clusters defined by one of the attributes in the data. Associate: Associate tools having generating association rules algorithms. It can be used to identify relationships between groups of attributes in the data. Select attributes: More interesting in the context of bioinformatics is the fifth tab, which offers methods for identifying those subsets of attributes that are predictive of another (target) attribute in the data. Weka contains several methods for searching through the space of attribute subsets, evaluation measures for attributes and attribute subsets. Search methods such as best-first search, genetic algorithms, forward selection, and a simple ranking of attributes. Evaluation measures include correlation- and entropy based criteria as well as the performance of a selected learning scheme (e.g. a decision tree learner) for a particular subset of attributes. Different search and evaluation methods can be combined, making the system very flexible. Visualize: Visualization tools shows a matrix of scatter plots for all pairs of attributes in the data. Practically visualization is very much useful which helps to determine learning problem difficulties. WEKA visualize single dimension (1D) for single attributes and two-dimension (2D) for pairs of attributes. It is to visualize the current relation in 2D plots. Any matrix element can be selected and enlarged in a separate window, where one can zoom in on subsets of the data and retrieve information about individual data points. A Jitter option to deal with nominal attributes for exposing obscured data points is also provided. interfaces to Weka All the learning techniques in Weka can be accessed from the simple command line (CLI), as part of shell scripts, or from within other Java programs using the Weka API. WEKA commands directly execute using CLI. Weka also contains an alternative graphical user interface, called Knowledge Flow, that can be used instead of the Explorer. Knowledge Flow is a drag-and-drop interface and supports incremental learning. It caters for a more process-oriented view of data mining, where individual learning components (represented by Java beans) can be connected graphically to create a flow of information. Finally, there is a third graphical user interface-the Experimenter-which is designed for experiments that compare the performance of (multiple) learning schemes on (multiple) datasets. Experiments can be distributed across multiple computers running remote experiment servers and conducting statistical tests between learning scheme. Conclusion Classification is one of the most popular techniques in data mining. In this paper we compared algorithms based on their accuracy, learning time and error rate. We observed that, there is a direct relationship between execution time in building the tree model and the volume of data records and also there is an indirect relationship between execution time in building the model and attribute size of the data sets. Through our experiment we conclude that Bayesian algorithms have good classification accuracy over above compared algorithms. To make bioinformatics lively research areas broaden to include new techniques.

1970’s American Culture and the Impact on Dance Essay -- Sociology Ess

1970’s American Culture and the Impact on Dance The freedom of the American life and culture of the 1970’s overflowed to make a major impact on music and dance during this period. American culture flourished. The events of the times were reflected in and became the inspiration for much of the music, literature, entertainment, and even fashion of the decade. Choreographers wanted to motivate the dancers to leap into the unknown and experience the contact of dance in their own way. Free love and the idea surrounding it helped break down barriers from traditional dance movements. Men and women began using their bodies to portray physical acts, built on to each other creating unique and interesting positions. They were working with the physical forces of gravity, momentum, and inertia. They would use the force of one body hurling into another to find out what would happen. For the first time in history, it was not unusual for a man and woman to depict the act of making love on the dance floor. The term â€Å"bump and grind† did start during this era of free love. Another ...