Monday, September 30, 2019

The Third Leg in the Strategy Tripod †Institutional Based View

THE THIRD LEG IN THE STRATEGY TRIPOD – Institutional Based View Introduction Strategy has come to play a significant role in international business (IB) in recent times. This is predicated on the fact of complexities associated with globalisation. The interplay of various factors of production in an environment could have been sufficient for MNEs in taking investment decisions. However, experience has shown otherwise. In this light, strategising in the international business arena has been dominated by industry and resource based views, somewhat ignoring the magnitude of institutional impact on investment decisions.EVALUATION OF ‘THE THIRD LEG IN THE STRATEGY â€Å"TRIPOD†Ã¢â‚¬â„¢ According to Peng et al (2008), citing Porter(1980) Barney (1991) the industry-based view is rooted in the assumption that the strategy employed by MNEs is determined by the conditions within the industry of focus. Also, the resource based view tends to narrow performance and internation al business strategy to resources in specific firms. The aggregated views are functions of research findings carried out in environments whose institutions are seemingly standardised and stable.Meanwhile these views have not been able to deal with the nagging questions posed by strategy as regards investment locations. Recent research directions have established remarkable differences in the institutional frameworks of emerging economies relative to developed economies. This is largely due to long history of cultural, legal and political platforms that organise their businesses. According to Peng et al (2008), the effect of these formal and informal institutions are quite remarkable in shaping strategies and performance even in developed economies.This may not have come to the front burner without recent research on the relationship between institutions and organisational strategies in emerging economies. Peng et al (2008) are of the view that earlier approach to international IB st rategy did not offer institutions appropriate place in the strategy discourse. In furtherance to this, Peng et al (2008) argue that the institutions should actually be recognised as the third leg in the strategy tripod. The other legs being resource based and industry based views.Let us explore the institutions and how they impact IB strategies. In understanding an institution, the renowned Economist Searle (2005 pp. 21) defines an institution as ‘any collectively accepted system of rules (procedures, practices) that enable us to create institutional facts’. It follows that the institutions set the rules for doing business in any environment. As such an MNE is only going to be successful where it understands, assimilates and applies the rules of the environment for competitive advantage.Peng et al’s (2008) argument on the institutional view of strategy focuses on legal, social and political aspects as these have been found to change quite significantly in emergin g economies upon both internal and external impacts. EFFECTIVENESS OF THE FOUR CASES OFFERED IN SUPPORTING THE CENTRAL ARGUMENT Peng et al (2008) posit that in transacting business in a developed economy, the supporting institutions operate albeit silently at the background. This is in contrast to the situation in emerging economies where the weight of institutions plays visible roles in consummating transactions.These visible roles have tendencies to alter the business equation in favour of MNEs who have taken proactive steps in influencing the institutional outcomes. Place this fact side by side the growing importance of emerging economies, it becomes imperative that institutional view of strategy becomes as important as the traditional views. Emerging economies like China and India are great examples where institutions’ actions and inactions play great roles in balancing the IB strategy tripod. Growing The Firm In ChinaPeng et al (2008) note that it is puzzling to see Chin a growing economically in the midst of underdeveloped institutional framework. This growth could not have taken place on the strength of the industry and resource based views alone. Peering deeply, Peng et al (2008) Wong (2012) recognise the cultural influences that abound in Chinese business climate which border on social and family networks otherwise referred to as guanxi. This may have phenomenally replaced the formal institution, as such affects the success of businesses within the environment.Peng et al (2008) suggest that in economic environments where institutions are week, norms and cognition take over as game changers. This highlights the import of institution based view in IB strategies. Competing In And Out Of India The meteoric rise of India in the area of IT and Business Process Outsourcing (BPO) was attributed to resource and industry based view of IB strategy (Peng et al 2008). These views are based on the opinion that cheap labour, excellent skills and virtuality are major factors responsible for their growth.Nonetheless, institutional considerations are found to be quite relevant in India’s context. Considerations in the areas of education subsidy to top Universities and various economic reforms including liberalisation by government institutions played crucial roles in making India a competitive environment for investments (Peng et al 2008). These institutional references can conveniently stand as the third tripod of the IB strategy. Antidumping As Entry Barriers Market-based variables as noted by Peng et al (2008) have been alleged as the basic entry barriers in IB.Meanwhile non market based institutional variables have been largely ignored in IB startegies.. Trade barriers have come to play important roles in IB strategies. Countries in protecting their local business players against foreign competition usually set up institutional policies aimed at stemming imports by foreign MNEs. These MNEs are alleged to compete unfavourably thro ugh the process of dumping. Davis (2009) citing the 1947 GATT agreement defines dumping as a situation where goods originating from a country are imported into another country at less than normal values.The use of anti-dumping laws by developed countries against MNEs from developing countries is a good example of how an institutional policy could be a tool of trade barrier in IB. Western countries have used this tool of protectionism against competition from Asia quite effectively. The issue here is that when market-based forces fail, antidumping laws could be deployed by local firms to ward off MNEs, thus firming the position of institutional based view as the third leg in the IB strategy tripod. Governing The Corporation In Emerging Economies Corporate governance is highly a determinant in firm performance in developed countries.This is due to the fact that the agency theory focuses on separation of ownership and control. These are exemplified by principal-manager conflicts (Peng et al 2008) quite unlike a lot of emerging economies where principal-principal conflict is the norm. The ability of an MNE to invest in such an environment would mean understanding the value and complexities of concentrated ownership and strategising accordingly. CONCLUSION It is evident from the work of Peng et al (2008) and other researchers that institution-based view has come to occupy the third leg of the IB strategy discourse.It is pertinent to note that the emergence of the institution based view does not in any way diminish the importance of the traditional based views in IB strategy. Instead it complements the traditional views, thus balancing the IB strategy in form of a tripod. It is my opinion that this tripod leverages on industry-based view, resource-based view and institutional-based view for balance. While Peng et al (2008) referenced most of their arguments on developing economies, the institutional effects are found to be applicable in developed countries as well. References Davis L. 2009) ‘Ten years of anti-dumping in the EU: economic and political targeting’. ECIPE Working Paper †¢ No. 02/2009 (Online) Available from: http://www. ecipe. org/media/publication_pdfs/ten-years-of-anti-dumping-in-the-eu-economic-and-political-targeting. pdf (Accessed September 26, 2012) Peng, M. W. , Wang, D. Y. L. & Jiang, Y. (2008) ‘An institution-based view of international business strategy: A focus on emerging economies’,Journal of International Business Studies,  39 (5), July/August, pp. 920–936. (Online) Available at: http://dx. doi. org. ezproxy. liv. ac. k/doi:10. 1057/palgrave. jibs. 8400377 (Accessed September 23) Searle J. (2005) ‘What is an institution? ’ Journal of Institutional Economics (2005), 1: 1, 1–22 (Online) Available from: http://www. laisumedu. org/DESIN_Ibarra/desin/pdf-seminario2006/seminario-2006-04d. pdf (Accessed September 24, 2012) Wong J. (2012) ‘The Guanxi Strategie s of Taiwanese Firms in China’s Economic Reforms’ The Journal of Global Business Management Volume 8 * Number 1 * February 2012. (Online) Available at: http://www. jgbm. org/page/15%20Jeng-Min%20Wong. pdf (Accessed September 24, 2012)

Sunday, September 29, 2019

Key Features of Utilitarianism Essay

The theory of Utilitarianism is based on the concept of utility, a theory of usefulness. Utilitarianism is a system of morality that generates us with what the most useful thing to do in different situations and outcomes. Different Utilitarian approaches to morality have emerged each with their own theory of good and community of concerning individuals. Featuring the main influential contributors to this theory are Jeremy Bentham and John Stuart Mill. There are two types of theories, teleological and deontological theories. Firstly for the teleological theory, you would consider the ends, or the outcomes of your decision. It considers whether it is right or wrong depending on the different outcomes it might cause and not concerned with the motive or intention for an action. This is the most common thing to all Utilitarian, the teleological outlook. In this theory, the means justifies the ends. Whereas the deontological theory concentrates on the moral rules that can’t be broken. For this theory, the most important ethical thing isn’t the result or the consequence of the action, but the action itself. If by nature that the action is wrong, then don’t do it. For example, a deontologist could day, ‘You should never steal, this means by the act itself of stealing is wrong. This theory suggests that the end never justifies the means. Introducing Jeremy Bentham, where his theory focuses on weighing up pleasure and pain. In 1789, in Principles of Morals and Legislation, he wrote: Nature has placed mankind under the governance of two sovereign masters, pain and pleasure. It is for them alone to point out what we ought to do as well as what we shall do. This is when the hedonic calculus came into the equation. Its purpose is to weigh up pain and pleasure generated by the available moral actions to find the best option. There are 7 factors that needs to be considered in this calculus before making the decision, starting with its intensity, considering how deep or superficial the happiness is, duration, how temporary or permanent the happiness is, certainty, how sure the happiness is, propinquity, how near or remote the happiness is, fecundity, how likely the happiness is to recur or lead to future happiness, purity, how free from pain the happiness is and extent, how far the happiness-giving effects of action will spread. This suggestion will only help the majority and no the minority. It doesn’t give any protection to the minority, for example the sadistic guards, where the guards gain pleasure from torturing and the helpless prisoner gets pain but there are a higher number of guards comparing to one prisoner. So is it correct to say that what the guards are doing is right? For the calculas, what if someone doesn’t have all the available information for every 7 factors? Will the calculus still be put to use? There’s absolutely no guarantee in predicting the future because everyone is different. Each person has different views even if it’s on the same subject. The calculus is obviously flawed. J. S. Mill on the other hand, felt that Bentham had made a mistake in his assessment of what human beings desired the most. Mill thought that what was more important was that happiness will be most affectively gained when individuals seek their own needs. Mill knows that Bentham’s theory is based on quantitative level and that’s where he saw upcoming errors where human behaviours react to different things change everything. So, instead of focusing on quantity, Mill focuses on the qualitative pleasures. He developed a system of higher and lower pleasures where the higher pleasure would be taken into consideration first before the lower ones. Mill stood up on the fact that pleasures of the mind were higher than those of the body. For example, Mill thought that pure bodily pleasure like food, drink, drugs and sex was not as high an objective as those on an intellectual level. There are two types of utilitarianism, act and rule. For act, the principle of utility is faced individually. All of the acts are decided by the resulting consequences even if it might break the law. It has the benefit of being flexible where the section of justification may vary. Meaning of this is that, one day it might be the right thing to do and another day it might be the wrong thing to do. It is said that the act states the most number of good for the most number of people is generally good. For example, it can be in a form of satisfaction, pleasure and happiness. Act utilitarianism is the application on a case by case basis. It states that, when faced with a choice, we must first consider the likely consequences of potential actions and from that choose to do what we believe will generate the most pleasure.   The problem with this is that we have to take every single thing into account. By every dilemma that we face, we’d have to go through it first then make a decision to act upon it. It is closely associated with Bentham’s theory. For rule utilitarianism, it focuses on general rules that everyone should follow to bring about the greatest good for that community. For example, the rules are pursued by the whole community giving them convenience and reasonable rules to live by which ultimately brings the best result overall. This theory leans closer to Mill’s theory. In any situation, an individual must obey the rule even if it doesn’t lead to the greatest pleasure. By obeying all these rules, it brings the greatest good when everyone acts in the same manner. Now, there are the weak and the strong where the weak utilitarianism doesn’t have to stick by the rules cause they chose not to abide by it therefore when it comes to certain situations, they have a free choice to either follow it or not. For example, although rules should be framed on previous examples that benefit society, it is possible, under specific circumstances, to do what produces the greatest happiness and break that rule. On the other hand, strong utilitarianism is where people never break the rules. So a strong rule utilitarian might say the â€Å"Do not steal† rule must never be broken and they would stick to it even though in some situations, it might be better if they did steal but to them, it’s the wrong thing to do no matter whether it brings them to a better condition. Lastly, there are other forms of Utilitarianism such as the â€Å"Best Interest Utilitarianism† by Peter Singer where he tries to solve the problem that some pleasures are bad for us. Preference Utilitarianism by R. M. Hare where he tries to solve the problem that people have different ideas of pleasure even if it’s something very small and specific, not everyone wants and thinks the same. Ending with Motive Utilitarianism by Sidgwick where he tries to solve the problem of the difficulty of predicting consequences where he elaborates on the inaccuracy of an individual’s chances of guessing what’s going to happen in the future.

Saturday, September 28, 2019

Insurance Essay

There are many differences between insurance and social assistance programs. They differentiate in administration, financing, coverage and the basis of payments. Insurance is having coverage for the most important and costly things that need to be protected throughout life. Health insurance is used to help cover the cost of routine check-ups, immunization, medications, emergency visits, surgeries, and hospitalization. These costs increase over time due to the increase in the quality of care and prescription costs.Depending on what your income and insurance plan you have you will be opted to pay a deductible before your insurance company will pay their part. Auto insurance protects auto accidents, medical bills from accidents, and repairs such as collision coverage. Social assistance programs are solely for people who have a low income, or are unemployed due to disabilities, maternity leave, or have children and are unemployed.Auto and health insurance have many similarities as well a s differences. When it comes to auto insurance you are paying a predetermined amount to keep a coverage on your vehicle. Whether it be a car accident, theft, repairs or towing and protecting against an accident if you are at fault. Depending on what type of insurance you have and how much you are willing to pay for your coverage is all based on what you receive on your auto insurance.Health insurance is primarily to keep you in a good health. There are more than 51 million people throughout the US that have no health insurance. Having health insurance is so important in order to have routine check-ups, ER visits for illnesses, giving birth, medication and hospitalization which are all very costly matters. The similarity between these two types of insurance is to protect you against the financial hardships the happen throughout life to be able to afford them.

Friday, September 27, 2019

Water pollution Research Paper Example | Topics and Well Written Essays - 1750 words

Water pollution - Research Paper Example The most significant problem with water pollution is that it endangers amphibians (and other genus) and can lead to extinction of various species without adequate steps taken to mitigate water pollution’s reoccurrences. There are many different types of water pollutants which threaten species. These include introduction of pathogens and chemicals which are dispersed into bodies of water from many different sources. Pipes, storm drains, factor-based discharges and sewer systems maintain the capability to contaminate various bodies of water. Specific contaminants, and the most common, include sodium, iron, benzene, and harmful pathogens. One of the most significant problems is that water-polluting substances tend to deplete oxygen levels in a body of water or cause a phenomenon known as turbidity, a situation in which vital light is obstructed, thereby disrupting the capability of plants to grow and even causing blockage of a water-dwelling species’ gills. When harmful chemicals are introduced into these bodies of water, it can promote the production of various diseases, increase the acidity of the water, cause dramatic shifts in temperature which are not conducive to longevity for species, an d affect the general quality of the water which impacts the reproductive cycles of water-dwelling creatures. A common water pollutant is benzene, which is often introduced into bodies of water as a result of industrial activity and through waste disposal of different consumer products such as glue and detergents. To illustrate the potential harm that benzene causes, a study was conducted in an industrial environment dedicated to producing nanotechnologies. The researchers recruited 121 different workers and performed a longitudinal study over five years. The recruited sample population was asked to allow recurring blood samples to be taken

Thursday, September 26, 2019

Iran's Nuclear weapons Essay Example | Topics and Well Written Essays - 500 words

Iran's Nuclear weapons - Essay Example It is therefore very important for the US to carefully evaluate policy options available in order to take the most appropriate possible course of action on Iran’s issue. Iran is a signatory to the NPT and thus agreeable to international regulations with regard nuclear proliferation, Iran has always been open and permitted inspection of its nuclear facilities by the IAEA and has constantly maintained that its nuclear program is purely for peaceful reasons permitted in the NPT. There is no evidence to convince anyone that Iran’s program is intended at developing weapons to attack any nation or the US for that matter. Additionally we have seen our forces go into Iraq on grounds which were later to be proved false. Most important to consider is the fact that Iran is located in a region rich of oil which very crucial to the US and world economy hence an incursion on Iran will definitely interfere with the flow of oil at such a delicate time when a slight increase in oil price could take the world economy back to recession. With this background information in mind therefore the United States have four key policy options consider on approaching Iran. The first option is employ military action as the threat appears real and close. America should therefore take action on its own to stop Iran from producing nuclear weapons and passing to other nations. But the Iraq experience tells us that war is more costly that good and therefore our effort should be geared at ending the problem and not going to war. It should be noted that Iran does not have a history of aggression without provocation, it won’t be easy to eliminate the nuclear facilities, and military action will just compound the issue and affect oil flow in the gulf region. Another option is to use the American military and overthrow government in Iran since it is not possible to dissuade them from

IFRS and GAAP Convergence Research Paper Example | Topics and Well Written Essays - 1500 words

IFRS and GAAP Convergence - Research Paper Example The company has two main investments in Shanghai, China, and its Onstar branch gives the company safety, information services and security (Chandler, 1964). 2.0 The SEC’s Position on Convergence of GAAP and IFRS The main purpose of the Security Exchange Commission (SEC) is to safeguard the investors and sustain the integrity of the securities markets. The SEC stipulates that publicly traded firms in the United States should report important financial and other facts to the general public, which gives a universal set of information, on the basis of which investors can make decisions whether the securities of the firm are a viable source of venture. The firms must follow the IFRS procedures of accounting, which are beneficial to different firms in unique ways. Even though the schedule for the US firms to shift from GAAP to IFRS is not yet set, such a move, through convergence or conversion, is largely appreciated and accepted by majority of the multinational corporations. Genera l Motors has made some steps in relation to the convergence of GAAP to IFRS though other firms hesitated. International Financial Reporting Standards as they are commonly known are beneficial to the General Motors Company given that they provide principles for financial reporting. IFRS ensures the requirements and provisions under them are followed to the letter by the firm and its subsidiaries to ensure uniformity in reporting and production of financial statements. IFRS were adopted by the International Accounting Standard Boards to ensure transparency in the manner in which the financial statements are prepared and presented to the stakeholders. IFRS replicates a dominantly regulation-based approach to building accounting standards as opposed to the GAAP, which was based principally on rules approach. 3.0 IFRS for the financial statements Balance sheet After the IFRS in the balance sheet are adopted, the guidelines stipulated under the standards will enable General Motors to have a uniform method of reporting the assets, liabilities and the equities of the firm in all the subsidiaries and the parent company. Notably the guidelines are provided by the International Accounting Standards Committee, which is currently known as the International Accounting Standards Board. According to the guidelines, the balance sheet names and utilization rely upon the US policies and the type of organization. General Motors follows the standards set by the board, which enables the business to provide a summary of values for all the items included in the balance sheet. Cash Flow Statement There are various differences that come out under the US GAAP and IAS 7 principles for the cash flow statements. The IAS 7 stipulates that the cash flow statement must comprise of both cash and cash equivalents. The United States GAAP allows use of only cash or cash equivalents. IAS 7 allows bank overdraft in particular nations to be put in the cash equivalents as opposed to being taken as a section of financing activities. IAS 7 permits interests paid to be considered as an operating activity or financing activity. The United States GAAP stipulates that interest paid be considered as operating activity. Moreover, the US GAAP (FAS 95) provides that when the direct method is applied to project the operating activities of the statement of cash flow, an additional schedule must also project the statement of

Wednesday, September 25, 2019

Virtual Gallery Essay Example | Topics and Well Written Essays - 500 words

Virtual Gallery - Essay Example At worst, the blacks were made slaves. Slavery was a form of business in the United States at that time. The slaves were not free. They were considered property by the American traders and plantation owners. They were not granted civil and political rights. Both the south and north of America had exemplified this historical event. Nevertheless, the time came when the Americans realized that the black people should be granted freedom and be treated like humans. It was the Northern part of America which started granting liberty to the slaves. Nevertheless, the Southerners became persistent than ever to maintain slavery in their area. The opposing policies of the Northern and Southern part of America had resulted into a civil war. There were states that made their own confederacy and disintegrated from the American united states or the union. Significantly, the civil war was ended through the Emancipation Proclamation. The triumph of the Emancipation Proclamation could be reflected thro ugh A. A. Lamb’s painting, an American painter. The painting entitled Emancipation Proclamation perfectly illustrates the declaration as an important period of American history. It can be observed that there are four main characters in the painting. These are the following: the black people, the soldiers of the Union, the Statue of Liberty and Abraham Lincoln.

Tuesday, September 24, 2019

U.S. in the New World Order's Affairs Essay Example | Topics and Well Written Essays - 1500 words

U.S. in the New World Order's Affairs - Essay Example The very famous use of the term new world order was in the speech made by President George H. W. Bush Sr. on September 11, 1990. â€Å"The leitmotif of modern American presidential politics is unquestionably an imperial theme, most blatantly expressed in his slogan, The New World Order and for 1991, the pax universalis† ( Tarplay, and Chaitkin 9). The main theme of his presidency is the formation and unification of a solitary and widespread empire that very much reflects the different stages of the Roman kingdom. In his speech, George Bush senior said, â€Å"The war in Iraq is a rare opportunity to move toward an historic period of cooperation. Out of these troubled times...a New World Order can emerge" (National Archives). To add to that, in his September 21, 1992 speech addressed to the United Nations he also urged the nations to develop and train military units as peacekeepers. He added that to be able to achieve this goal, each nation should work, train together and have coordinated efforts. These efforts refer to having a centralized command, control and operations on all aspects of any operation and communication. Through G.H.W Bush’s speech the Commission on Global Governance was created by the United Nations. Through this organization, a controversial report, Our Global Neighbourhood, was brought out in 1995. The report states reforms that will give the United Nations absolute power. It was also predicted in the report that there would be a world court, a unified tax system and a global police force. In short, world leaders are calling for a one world government. These were greatly criticized by many people but also, many people are unaware of these facts. Amazingly, after a decade from the George H.W. Bush speech, the United States was attacked on September 11, 2001. Adding to the mystery, it was during the presidency of George Bush senior’s son, George W. Bush, that the attacks accord. To many people, this is the continuation of the goal to establish a new world order by world leaders. Aside from the political aspects of the new world order, economy is also put into focus. This includes the control of oil, an international currency which would replace the US dollar. Moreover, there would be a world development fund that would provide equal finances to communist and free nations. This is where the World Bank and the Bank of International Settlements come into play. Some say that the global monetary crisis we are experiencing now is intended to institute a worldwide debt-based currency organized by global investors and distributed to individuals alongside biometric identification cards. Religion is another factor that is included in this new world order. It has an aim of introducing a new world religion. Some would postulate that the establishment of the World Council of Churches and the Parliament World Religions is to gain control of the world’s religion. Consequently, Barack Obama has been popularly linked to a new world order as well. Authors suggest that his administration has been building an international order. This is through empowering certain institutions and connections across the globe. If the Bush administration was assessed through the infamous Gulf War, the Libyan War functioned as an assessment of Obama’s governance. â€Å"The current dangerous situation in Libya has become a serious test for the international community’s resolve and credibility, especially in the context

Monday, September 23, 2019

Critically Assess Government Policy on 'Bridging the Digital Divide' Essay

Critically Assess Government Policy on 'Bridging the Digital Divide' in U.K - Essay Example However, the relevant measures are often proved as inadequate; delays and failures are reported when trying to apply such policies in practices. Current paper focuses on the efforts of the British government to ‘bridge the digital divide’ so that the quality of services in the country’s private and public sector to be improved, since these services are highly based on ICTs. The aspects of this effort are analysed below. The review of the plans that the British government has promoted in this field has led to the following assumption: the effort of the British government ‘to bridge the digital divide’ can be characterized as successful, at least up to now. In accordance with a relevant report, the expansion of online services could lead to savings of about ? 1.3b each year (France 2011). However, in the future, the effectiveness of these plans would be in risk, taking into consideration the continuous advances of technology and the level of resources r equired for the realization of the relevant schemes. 2. Bridging the digital divide in Britain – governmental policy In order to understand the effectiveness of the efforts of the British government ‘to bridge the digital divide’ across the country, it would be necessary to understand the context of the specific effort. The phrase ‘bridging the digital divide’ reflects the efforts required for covering gaps in regard to the use of ICTs within a particular country. Most commonly, these gaps are covered through a series of initiatives focusing on different aspects of the use of ICTs. For example, emphasis is given on the physical access to online centres, the availability of appropriate Internet connection, the development of computer skills of people of different ages, the elimination of inequalities in regard to the access to ICTs and the development of effective e-government schemes in accordance with the sources available and the targets set, eithe r in the short or the long term. The efforts of the British government to ‘bridge the digital divide’ have been based on the avoidance of complex ICTs; by choosing ICTs which are easy to be managed and flexible, as of their potentials and their needs, the British government tries to increase the efficiency of its online services which have been characterized by difficulties as of their access and failures as of their performance (Cabinet Office 2011). The British government’s new strategy in regard to the promotion of ICTs across the country is presented in Graph 1 (Appendix). It is clear that the country’s government has set different priorities, compared to the past, for improving its online services. Simplicity and high speed have been preferred instead of complexity, since in this way the following target is achieved: the level of ICTs in the public sector of Britain is standardized, a fact that helps people to understand easier the use of ICTs for acc essing the country’s public sector. 2.1 Measures for supporting physical access The physical access to online services requires that online centres across UK are increased, in terms of their number. In this way, people in all regions will be able to access the government’s online services. At the same time, the quality of broadband services available in online centres in Britain should be improved, leading to the increase of the number of customers. Since problems are often reported across the countr

Sunday, September 22, 2019

Abortion Should Be Made Illegal Essay Example for Free

Abortion Should Be Made Illegal Essay Abortion is a huge issue that most Americans have their own opinion of. It is also a touchy subject where there is no right or wrong answer; it is all a matter of personal opinion. Ones opinion could be from believing abortion should be legal, and that a woman should be able to control what happens with their body. Ones opinion could also believe abortion should be illegal, and that if a woman can take the responsibility of having sex then she should be able to have the responsibility of having a child. Going through with an abortion has got to be the most painful, excruciating thing to go through, not only physically, but mentally. Abortion is murder, when you take the life of a being, whether it is in the whom or not, it is still murder. The day a child is conceived is the day it is living. Abortion is serious, and something I disagree with strongly. I have been researching this topic for over three months now, and the information I found is incredible and mind blowing. I have learned a lot from researching this topic and hope to further someone else’s knowledge about this very important issue. Abortion, in more than one way, is wrong, and throughout this report I plan to show my audience why this is what I believe. See more: Analysis of Starbucks coffee company employees essay Abortion is a huge issue throughout America. It is something people have different opinions about. Some people think abortion should be legal and that a woman should be able to choose what goes on with her body and that it is no one else’s business but her own. Then there are some people who believe abortion should be illegal, like me for example. I believe abortion should be illegal, I think women abuse this action, and don’t realize the consequences until it is too late. As it states in Brian Wilson’s Article Outlawing Abortion, â€Å"Abortion has taken the lives of more than 40 million babies since 1973. That’s 40 million lives taken because women are abusing this horrible issue. I believe we as a country should really try to make enough pro-life groups to show women what could happen, and what mentally happens, and physically. In Brian Wilson’s Article Outlawing Abortion, it states that â€Å"Abortion should be outlawed, but until it is, pro-lifers should work to change the hearts of women seeking an end to their pregnancies. † I agree one hundred percent with this statement, I believe that pro-lifers should take the time and try and change the hearts of women. I believe women should really be educated in the entire subject, women should know what they are going to go through, both physically, and mentally. The physical part of the entire abortion is the process itself. There are a couple different ways a doctor can surgically remove the fetus from a woman. The most popular way to remove a fetus is when the doctor dilates the women’s cervix, while he or she scrapes the uterine lining. Another process is a woman could take certain types of medication to terminate the pregnancy, but that is not as affective as a doctor surgically removing the fetus. So the process itself is painful, and traumatizing, if that is just the physical part of an abortion you can only imagine what the mental part is like. When a woman is thinking about getting an abortion they have to think of everything before they decide to go through with it. The act itself of an abortion really is not that big of a deal, it’s a rather simple procedure that in itself causes little anguish. The big issue that women are concerned about is living with themselves knowing that they ended the development of their unborn child, that as they sit looking back at the procedure there could have been a child sitting next to them. Women that go through with an abortion struggle through the emotional pain that an abortion causes. One of the most faulty arguments against abortions is that is causes serious depression and trauma to the mother. Some women even suffer from post traumatic stress disorder. A lot of women feel regret and sorrow after going through with the procedure, statistics show that women suffer major trauma when going through that. I think there should be doctors in every abortion clinic that does what the Article by Lynn-nore Chittom, and Heather Newton, Pro-Life Activists Need to Push the Anti-Abortion Agenda, do. Doctors in South Dakota were required to inform women seeking abortions that they will be â€Å"terminating the life of a whole, separate, unique, living human being. † Every women should be informed this, maybe it will make them step back and think about their next decision because that one decision could completely change there life forever. Many women after going through with an abortion feel sadness, guilt, anxiety, numbness, and shame. It’s not unusual for post-abortive women to experience all of those feelings plus much more, for example, women go through motional, spiritual, psychological, and physical side-effects for decades after their abortions. Abortion is a deadly and dangerous procedure. In every abortion the life of an innocent, unborn baby is ended, and its mother faces potentially life-long emotional consequences as a result of that decision. Abortion may be legal in the United States, but it is not the preferred choice. With proper education, women seeking abortions can come to understand their decision-making power and be persuaded to make a better choice. Abortion is becoming more and more common between younger girls. Girls need to be educated on this issue before the age gets extremely low. The fact that our country had to make it a law that minors can’t get an abortion without parental consent means girls are getting abortions way too young. This major issue is affecting girls of all ages, it should not be abused as much as it is. Women should be educated on the procedure, and the mental issues that come with the procedure. They say that a sonogram is what makes women change their minds about getting an abortion because the sound of a heart beat shows women there really is something living inside them and that shows them also that you would be terminating something that could one day be their pride and joy. Abortion is a major issue and should be stopped. Abortion should be illegal, I believe abortion should be banned completely. It is murder in one way or another. Someone could get two counts of murder for killing a pregnant woman but women are â€Å"murdering† their baby each and every day. How can someone go into work each and every day just knowing they are going to terminate someone’s unborn child? It has to be stopped and we need more pro-lifers to show women what it does to our bodies and how if affects us. Women don’t realize how traumatizing it really is until it is too late. Women need to be informed, and educated about the procedure and the guilt afterwards. The guilt itself eats women away, knowing they terminated something that could be their happiness one day. The issue of women not being ready for the responsibility, or just not wanting a child to begin with needs to stop. Some women need to take responsibility of their actions, and deal with what they started. Even if the woman is incapable of keeping a kid, they should at least give it up for adoption, if they do not want the responsibility they should also give him/her up for adoption. There are women who are completely unable to have kids and would be more than happy to take in a baby if only women would let their babies have the gift of life. A child is considered alive the minute it is conceived, abortion is usually done during the first trimester, but there have been some cases that it is done later. Abortion is still, and probably always will be a big issue. Women should not be allowed to get an abortion, no matter what. Women need to stop thinking about themselves and put their unborn babies’ life before there own. Everyone should be given the right to live, and no one should be able to hold that much power in their hands. All in all abortion is wrong, and just not something I could ever go through with nor would I ever think about going through with it. Abortion is almost like the easy way out of your mistakes, sometimes people need to own up and take up for their mistakes.

Saturday, September 21, 2019

Enver Pasha and the Britain

Enver Pasha and the Britain Ä °smail Ä °lker Yà ¼rà ¼yen Ismail Enver was born in near Constantinople, Istanbul, on 23 November 1881 to a working-class family from Monastir, todays Macedonia. His father, Ahmed, was a Turk, who rose from being a porter to a railway official and acquired the honorable title Bey. Envers mother, Aisha, was an Albanian from the Monastir region. He was an Ottoman general and commander in chief, a hero of the Young Turk Revolution of 1908, and a leading member of the Ottoman government from 1913 to 1918. He became the main leader of the Ottoman Empire in both the Balkan Wars in 1912-13 and in World War I in 1914-18. In the course of his career, he was known by increasingly elevated titles as he rose through military ranks, including Enver Efendi, Enver Bey, and finally Enver Pasha. By January 13, 1914, Enver had made himself Minister of War and played a key role in the Ottoman entry into World War I on the side of Germany. He influenced his associates into an alliance with Germany signed secretly on August 2. Subsequently, he approved the German bombardment of Odessa and Sevastopol, which precipitated the Ottoman Empires entry into World War I. An organizer of the Young Turk Revolution, Enver joined General Mahmud Ã…Å ¾evket, under whose command an Army of Deliverance advanced to Constantinople to depose the Ottoman sultan Abdà ¼lhamid II. In 1911, when warfare broke out between Italy and the Ottoman Empire, he organized the Ottoman resistance in Libya, and in 1912 he was appointed the governor of Benghazi. Back in Constantinople, he participated in the politics of the Committee of Union and Progress, leading the coup dà ©tat of January 23, 1913, which restored his party to power. In the Second Balkan War of 1913, Enver was chief of the general staff of the Ottoman army. On July 22, 1913, he recaptured Edirne from the Bulgars; and until 1918, the empire was dominated by the triumvirate of Enver, Talat PaÃ…Å ¸a, and Cemal PaÃ…Å ¸a. In 1914, Enver, as minister of war, was instrumental in the signing of a defensive alliance with Germany against Russia. When the Ottoman Empire entered World War I on the side of the Central Powers in November 1914, Enver cooperated closely with German officers serving in the Ottoman army. His military plans included Pan-Turkic, or Pan-Turanian, schemes for uniting the Turkic people of Russian Central Asia with the Ottoman Turks. These plans resulted in the disastrous defeat in December 1914 at SarÄ ±kamÄ ±Ãƒâ€¦Ã… ¸, where he lost most of the 3rd Army. However, he recovered his prestige when the Allied forces withdrew from the Dardanelles in 1915-16. In 1918, following the Russian Revolution of 1917 and Russias withdrawal from the war, he occupied Bakà ¼. After the Armistice in Europe, Enver fled to Germany on November 1918. In Berlin, he met the Bolshevik leader Karl Radek, and in 1920 he went to Moscow. He proposed the idea of overthrowing the regime of Mustafa Kemal in Turkey with Soviet aid, but this plan received no support from Moscow. Though the Russian leaders became suspicious of him, Enver was nevertheless allowed to go to Turkistan with a plan for helping to organize the Central Asian republics. Yet, in 1921, the revolt of the Basmachi in Bukhara against the Soviet regime flared up, and Enver joined the insurgents. He was killed in action against the Red Army. After having provided Enver Pashas short biography, this essay will try to state Envers relation with the Britain by considering the situation of his being minister of war, the person who is responsible of the whole Ottoman army. In 1909 or 1910, Enver Bey was sent to London to fetch military goods. The British media announced him as the Turkish Garibaldi because Garibaldi was an Italian general who revolutionized Italy and made it what it is today. Its clearly understood that the British media admired Enver PaÃ…Å ¸a and considered him equal to Garibaldi. Enver PaÃ…Å ¸a was persona non grata for Britain. By 1908, Britain joined the alliance between France and Russia against the alliance of Germany, Austria, and Italia. Thus, Britain started to welcome Russias interests on Istanbul and Frances interests on Syria.   Enver Bey, who is considered to be the leader of the unionists, had no choice but to ask for Germanys help. Since France and Britain rejected the Ottomans demand for money for its own debts, it was Germany that helped Ottoman Empire. As Enver Bey joined the alliance with Germany, a powerful country, he was always disliked by Britain, for he blocked their interests. During the Italo-Turkish War in 1911-12, Enver Bey went to Libya to defend the Ottomans territory along with Mustafa Kemal. He was a major and had the highest rank there. Since the previously overthrown Sultan, Abdà ¼lhamid II, made the navy dysfunctional, Enver needed army troops. Ottoman Empire couldnt send the necessary troops since Britain had invaded Egypt and she didnt allow Ottoman troops go through Egypt. Eventually, Ottoman Empire lost Tripolitania mostly because of Britain. The Mesopotamian campaign was a campaign in the Middle Eastern theatre of World War I fought between the Allies represented by the British Empire, mostly troops from Britain and the Indian Empire, and the Central Powers, mostly of the Ottoman Empire. It took place between 6 November 1914 and 14 November 1918. Enver PaÃ…Å ¸a was the minister of war and Ottoman forces was defeated in this campaign. It was resulted in allied victory and the Treaty of Sà ¨vres. The only success for Ottoman Empire during this campaign was the Siege of Kut. Halil Bey, uncle of Enver Pasha, managed to capture the British general Townshend along with his garrison. British leaders attempted to buy their troops out. Aubrey Herbert and T. E. Lawrence were part of a team of officers sent to negotiate a secret deal with the Ottomans. The British offered  £2 million ( £122,300,000 today) and promised they would not fight the Ottomans again, in exchange for Townshends troops. Enver Pasha ordered that this offer be rejected. Historian Christopher Catherwood has called the siege the worst defeat of the Allies in World War I. The Raids on the Suez Canal, also known as Actions on the Suez Canal, took place between 26 January and 5 August 1916 after a German-led Ottoman Army force advanced from Southern Palestine to attack the British Empire-protected Suez Canal, before the beginning of the Sinai and Palestine Campaign of World War I. Ottoman troops were led by Cemal PaÃ…Å ¸a, Minister of the Navy, who was under the command of Enver PaÃ…Å ¸a, Minister of War. These two raids resulted in failure for Ottoman Empire. As conjectured in the story published in the Telegraph, Britain offered $5 million to the Ottomans to allow them to cross the straits of Çanakkale, and another $2 million to the Ottomans to get them to pull their troops out of Palestine. The story also says that an international arms dealer by the name of Basil Zaharoff was responsible for the talks between the two sides. The Telegraph story also says that Enver Pasha was contacted by a civil servant by the name of Kerim Bey in Vienna, who was working for the Ottoman Empires Loan Bureau there, and that the suggested bribe later went up to the astonishing figure of $10 million dollars. It can be easily understood these bribe offers were made before the outbreak of the WWI because it outbroke on July 28 and Ottoman Empire joined the war five days later on August 2 in alliance with Germany. Britain wouldnt make such an offer when Ottoman Empire was already her enemy. Enver PaÃ…Å ¸a could accept these offers for the sake of his countrys economy but he rejected them because he knew that his country would be under the risk of Russians if they received aid from the British. Thus, he prevented British navy from sailing to Black Sea. Although it seems to be win for Britain and lose for Ottoman between the relations of the British and Enver PaÃ…Å ¸a, his great struggles for both Ottoman Empire and Turkic people havent been forgotten; thus, his funeral was brought to Turkey by the Republic of Turkey and buried in Istanbul on his death anniversary in 1996. Rest in peace.

Friday, September 20, 2019

The Central Dogma of Molecular Biology

The Central Dogma of Molecular Biology The molecule we know today as deoxyribonucleic acid was first observed in 1869 by Swiss biologist Friedrich Miescher, who stumbled upon a substance which was resistant to protein digestion. At the time he referred to the molecule as nuclein (Pray, 2008). Though Miescher remained in obscurity, Russian biochemist Phoebus Levene continued work with this substance and in 1919 discovered the three major components of a nucleotide: phosphate, sugar, and base. He noted that the sugar component was ribose for RNA and deoxyribose for DNA, and he proposed that nucleotides were made up of a chain of nucleic acids (Levene, 1919). He was largely correct, and in 1950 Erwin Chargaff, after reading a paper by Oswald Avery in which Avery identified the gene as the unit of hereditary material (Avery, 1944), set out to discover whether the deoxyribonucleic acid molecule differed among species. He found that although, in contrast to Levenes proposal that nucleotides are always repeated in the same order , nucleotides appear in different orders in different organisms, these molecules maintained certain characteristics. This led him to develop a set of rules (known as Chargaffs Rules) in which he states that the total number of purines (Adenine and Guanine) and the total number of pyrimidines (Cytosine and Thymine) are almost always equal in an organisms genetic material. In 1952 Rosalind Franklin and Maurice Wilkins used X-ray crystallography to capture the first image of the molecules shape, and in 1953 James Watson and Francis Crick finally proposed the three dimensional model for DNA (Watson, 1953). The four main tenants of their discovery still hold true today: 1) DNA is a double-stranded helix, 2) the majority of these helices are right-handed, 3) the helices are anti-parallel, and 4) the DNA base pairs within the helix are joined by hydrogen bonding, and the bases can hydrogen bond with other molecules such as proteins. The Central Dogma of Molecular Biology, first proposed by Francis Crick (Crick, 1958), describes the directional processes of conversion from DNA to RNA and from RNA to protein. This gene expression process starts with DNA, a double-stranded molecule consisting of base-paired nucleic acids adenine (A), cytosine (C), guanine (G), and thymine (T) on a sugar-phosphate backbone. This genetic material serves as the information storage  for life, a dictionary of sorts that provides all of the necessary tools for an organism to create the components of itself. During the process of transcription, the DNA molecule is used to make messenger RNA (mRNA), which carries a specific instance  of the DNA instructions to the machinery that will make protein. Proteins are synthesized during translation  using the mRNA molecule as a guide. Gene expression is a deterministic process during which each molecule is manufactured using the product of the previous step. The end result is a conversion fr om the genetic code into a functional unit which can be used to perform the work of the cell. As you can imagine, this process must be controlled by an organism in order to make efficient use of resources, respond to environmental changes, and differentiate cells within the body. Gene regulation, as it is sometimes called, occurs at all stages along the way from DNA to protein. Regulation falls into four categories: 1) epigenetic (methylation of DNA or protein, acetylation), 2) transcriptional (involves proteins called transcription factors), 3) post-transcriptional (sequestration of RNA, alternative splicing of mRNA, microRNA (miRNA) and small interfering RNA (siRNA)), and 4) post-translational modification (phosphorylation, acetylation, methylation, ubiquitination, etc. of protein products). Epigenetic regulation of DNA involves a reversible, heritable change that does not alter the sequence itself. DNA methylation occurs on the nucleic acid cytosine. Arginine and lysine are the most commonly methylated amino acids. When proteins called histones) contain certain methylated residues, these proteins can repress or activate gene expression. Often this occurs on the transcriptional level, and thus prevents the cell from manufacturing messenger RNA (mRNA), the precursor to proteins. Proteins are often referred to as the workhorse of the cell and are responsibl e for everything from catalyzing chemical reactions to providing the building blocks for skeletal muscles. Some proteins, called transcription factors), help to up- or down-regulate gene expression levels. These proteins can act alone or in conjunction with other transcription factors and bind to DNA bases near gene coding regions. This is a general schema for gene expression. DNA is a double-stranded molecule consisting of base-paired nucleic acids A, C, G, and T on a sugar-phosphate backbone and is used as information storage. mRNA is made during transcription and carries a specific instance of the DNA instructions to the machinery that will make the protein. Proteins are synthesized during translation using the information in mRNA as a template. This is a deterministic process during which each molecule is manufactured using the product of the previous step. mRNA requires a 5 cap and a 3 poly(A) tail in order to be exported out of the nucleus. The cap is critical for recognition by the ribosome and protection from enzymes called RNases that will break down the molecule. The poly(A) tail and the protein bound to it aid in protecting mRNA from degradation by other enzymes called exonucleases. What can be gained by studying gene regulation? In general, it allows us to understand how an organism evolves and develops, both on a local scale (Choe, 2006,Wilson, 2008), and on a more global network level. There are, however, more specific reasons to investigate this process more closely. Failure in gene regulation has been shown to be a key factor in disease (Stranger, 2007). Additionally, learning how to interrupt gene regulation may lead to the development of drugs to fight bacteria and viruses (McCauley, 2008). A clearer understanding of this process in microorganisms may lead to possible solutions to the problem of antimicrobial resistance (Courvalin, 2005). There are two major factors that motivate the studies herein. Firstly, the size and quality of biological data sets has increased dramatically in the last several years. This is due to high-throughput experimental techniques and technology, both of which have provided large amounts of interaction data, along with X-ray crystallography and nuclear magnetic resonance (NMR) experiments which have given us the solved three-dimensional structure of proteins. Secondly, machine learning has become an increasingly popular tool in bioinformatics research because it allows for more sound gene and protein annotation without relying solely on sequence similarity. If a collection of attributes which distinguish between two classes of proteins can be assembled, function can be predicted. In this work we focus mainly on regulation at the transcriptional level and the components which play a commanding role in this operation. So-called nucleic acid-binding (NA-binding) proteins, which includes transcription factors, are involved in this and many other cellular processes. Disruption or malfunction of transcriptional regulation may result in disease. We identify these proteins from representative data sets which include many categories of proteins. Additionally, in order to understand the underlying mechanisms, we predict the specific residues involved in nucleic acid binding using machine learning algorithms. Identification of these residues can provide practical assistance in the functional annotation of NA-binding proteins. These predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Toward the ultimate goal of attaining a deeper understanding of how nucleic acid-binding proteins facilitate the regulation of gene expression within the cell, the research described here focuses on three particular aspects of this problem. We begin by examining the nucleic acid-binding proteins themselves, both on the protein and residue levels. Next, we turn our attention toward protein binding sites on DNA molecules and a particular type of modification of DNA that can affect protein binding. We then take a global perspective and study human molecular networks in the context of disease, focusing on regulatory and protein-protein interaction networks. We examine the number of partnership interactions between transcription factors and how it scales with the number of target genes regulated. In several model organisms, we find that the distribution of the number of partners vs. the number of target genes appears to follow an exponential saturation curve. We also find that our generat ive transcriptional network model follows a similar distribution in this comparison. We show that cancer- and other disease-related genes preferentially occupy particular positions in conserved motifs and find that more ubiquitously expressed disease genes have more disease associations. We also predict disease genes in the protein-protein interaction network with 79% area under the ROC curve (AUC) using ADTree, which identifies important attributes for prediction such as degree and disease neighbor ratio. Finally, we create a co-occurrence matrix for 1854 diseases based on shared gene uniqueness and find both previously known and potentially undiscovered disease relationships. The goal for this project is to predict nucleic acid-binding on both the protein and residue levels using machine learning. Both sequence- and structure-based features are used to distinguish nucleic acid-binding proteins from non-binding proteins, and nucleic acid-binding residues from non-binding residues. A novel application of a costing algorithm is used for residue-level binding prediction in order to achieve high, balanced accuracy when working with imbalanced data sets. During the past few decades, the amount of biological data available for analysis has grown exponentially. Along with this vast amount of information comes the challenge to make sense of it all. One subject of immediate concern to us as humans is health and disease. Why do we get sick, and how? Where do our bodies fail on a molecular level in order for this to happen? How are diseases related to each other, and do they have similar modes of action? These questions will require many researchers from multiple disciplines to answer, but where do we start? We take a bioinformatics approach and examine disease genes in a network context. In this chapter we analyze human disease and its relationship to two molecular networks. First, we find conserved motifs in the human transcription factor network and identify the location of disease- and cancer-related genes within these structures. We find that both cancer and disease genes occupy certain positions more frequently. Next, we examine the human protein-protein interaction (PPI) network as it relates to disease. We find that we are able to predict disease genes with 79% AUC using ADTree with 10 topological features. Additionally, we find that a combination of several network characteristics including degree centrality and disease neighbor ratio help distinguish between these two classes. Furthermore, an alternating decision tree (ADTree) classifier allows us to see which combinations of strongly predictive attributes contribute most to protein-disease classification. Finally, we build a matrix of diseases based on shared genes. Instead of using the raw count of genes, we use a uniqueness) score for each disease gene that relates to the number of diseases with which a gene is involved. We show several interesting examples of disease relationships for which there is some clinical evidence and some for which the information is lacking. We believe this matrix will be useful in finding relationships between diseases with v ery different phenotypes, or for those disease connections which may not be obvious. It could also be helpful in identifying new potential drug targets through drug repositioning.

Thursday, September 19, 2019

Understanding Kissinger’s Actions Toward Chile Essay -- Foreign Policy

Understanding Kissinger’s Actions Toward Chile Can an individual influence foreign policy? Based upon the eight years that Henry Kissinger was the Secretary of State it is clear that an individual can (Starr 466). It has become apparent through recently released classified documents that Kissinger played a large role in allowing the brutal Pinochet dictatorship over Chile to take place and allowed massive human rights violations to continually occur during the Pinochet regime. What is continually being attempted to understand is why Kissinger acted as he did towards Chile. The goal of stopping the spread of communism to Latin America is obvious in Kissinger’s actions, but why allow Pinochet to continue to receive United States support while he breaks International Human Rights Laws (Kornbluh 5)? To understand Kissinger’s actions toward Chile it is necessary to analyze his background. Considering that he was born in Germany and fled to the United States to escape the Nazi’s, there most certainly was previous life experiences that contributed to his foreign policy beliefs (Crapol 260). As a politician, Kissinger gained an extraordinarily high level of popularity for a man of his position. Thus, his dealings with Chile may have been more of an effort to uphold his reputation than an eagerness to do what is best for Chile and the United States. Therefore, it can be considered that Kissinger’s strict realist view and constant attempt to create stability and order was derived from his past experiences as a Jewish Refugee and his actions as a Secretary of State may have been skewed by his desire to keep his popular status in the United States media and public (Starr 467 and 477; Crapol 260-265). Kissinger’s past experi... ...ton: Houghton Mifflin, 1957. Kornbluh, Peter. â€Å"Kissinger and Pinochet.† The Nation. 268. 12 (1999) p. 5. 1 March 2005. . Starr, Harvey. â€Å"The Kissinger Years: Studying Individuals and Foreign Policy.† International Studies Quarterly. Vol. 24, No. 4. (Dec., 1980), p. 465-496. 10 April 2005. . Walker, Stephen G. â€Å"The Interface between Beliefs and Behavior: Henry Kissinger's Operational Code and the Vietnam War.† The Journal of Conflict Resolution. Vol. 21, No. 1. (Mar., 1977), p. 129-168. 10 April 2005. .

Wednesday, September 18, 2019

Analysis of The Rocking Horse Winner Essay -- Literary Analysis, D. H.

Certain individuals have a drive that can lead them to achieve what they desire most. In the Short story â€Å"The Rocking Horse Winner†, D.H Lawrence showcases this through character motivation and symbolism. He further this using pursuit of desire, and how if you take it to a certain extent it can result in tragedy if the individual chooses not to conform. Paul wants to please his mother because his mother feels that there family has no luck, but Paul proclaims that he is lucky. Paul suddenly becomes consumed with this sudden spree of good luck and feels this is the only way he will be able to gain to the affection of his mother. D.H Lawrence reveals that Paul has a certain flaw that turns him to believe that the only way he will be able to gain his mothers love and affection is by winning money in the horse races. He leads this pursuit of desire to the standards he thought he wanted to, but not to the standards that would have achieved what he wanted, which leads to his dow n fall. When individuals desire love from another, they may choose to conform their beliefs and actions to that person. At first they may feel successful, however if they sacrifice everything, in pursuing this kind of goal, they may pay a heavy price instead of gaining there hearts desire. Paul desire his mother’s love more than anything. However he believes he needs to prove he is lucky. He struggles to prove that he is to make his mother happy. Paul wants his mother to love him more than anything. For Paul’s mother money equals luck, and this will gain her love. â€Å"There must be more money†(19). He hears these voices throughout the house. They hear these voices when hey are receiving items of joy, especially around Christmas time. The other children can h... ...eparate paths, one for the money, and another for his mother. This ultimately leads to Paul’s demise. â€Å"Mother, did I ever tell you/No you did not†(34) He never told his mother what he was doing for her. He wanted to gain her love more than anything. He conformed to a certain path that he thought would be able to achieve his ultimate desire. Paul wanted to gain the love his mother more than anything. He chose to conform to the path of luck. While pursing this personal desire, he became overwhelmed with in it and this ultimately led to his demise. If he would have been able to control his desire it may have been able to save him from tragedy. When an individual chooses to conform to meet the ideals of another individual in order to achieve there own personal desire, they can not sacrifice everything for that persons ideals because it can often result in tragedy.

Tuesday, September 17, 2019

Columbus and Western Civilization Analyzation Essay

The four critical duties as a writer described in Rebecca Howard’s â€Å"Writing Matters,† are your responsibility; to your readers, your topic, other writers, and to yourself. Howard Zinn achieved these task’s by first, defining his target view of history. Howard did not want to tell the story of Columbus, in the same tainted overview that is most known to the multitudes. He desired to gaze at it from all directions, and determine the accuracy behind Columbus’s story and how it should be perceived. Howard Zinn was a historian, who incidentally, had little knowledge of Columbus and his story. He plainly stated this from the beginning of his essay, explaining to the audience his credentials and intensions. Zinn collected his information from the diaries of the men who were truly there, to witness what took place. He made this known by quoting these witnesses, letting the reader know where he was obtaining his truths. He appears to see the duties of the historian and citizen as, â€Å"to widen the spectrum of ideas, to take in new books, new approaches, new information, and new views of history.† Once reading â€Å"Columbus and Western Civilization,† my view of Columbus hasn’t changed much because, honestly, growing up, I don’t remember much about Columbus and his journey. It is like I’m learning of his journey for the first time. From what I can remember, Columbus’ story was one of the first I had been educated in school. From kindergarten, all through middle school, I never understood his nature or his motives, nor did I care at the time. â€Å"In 1492, Columbus sailed the ocean blue.† This quotation is the only piece of info I can remember about Columbus from my previous education. As far as I am concerned, Columbus was that of a national icon. He was a Hero, master of the seas, without his bravery and determination, our universe as we know it would never have been imaginable. It is a safe bet that none of would be here today if not for him and his determination.

Monday, September 16, 2019

Obligation and Contracts Reviewer

OBLIGATIONS AND CONTRACTS REVIEWER TITLE I – OBLIGATIONS CHAPTER 1 GENERAL PROVISIONS 1156. An obligation is a juridical necessity to give, to do, or not to do. JURIDICAL NECESSITY – juridical tie; connotes that in case of noncompliance, there will be legal sanctions. – An obligation is nothing more than the duty of a person (obligor) to satisfy a specific demandable claim of another person (obligee) which, if breached, is enforceable in court. – A contract necessarily gives rise to an obligation but an obligation does not always need to have a contract.KINDS OF OBLIGATION A. From the viewpoint of â€Å"sanction† – 1. CIVIL OBLIGATION – that defined in Article 1156; an obligation, if not fulfilled when it becomes due and demandable, may be enforced in court through action; based on law; the sanction is  judicial due process 2. NATURAL OBLIGATION – defined in Article 1423; a special kind of obligation which cannot be enforced i n court but which authorizes the retention of the voluntary payment or performance made by the debtor; based on equity and natural law. (i. e. hen there is prescription of duty to pay, still, the obligor paid his dues to the obligee – the obligor cannot recover his payment even there is prescription) the sanction is the law, but only conscience had originally motivated the payment. 3. MORAL OBLIGATION – the sanction is conscience or morality, or the law of the church. (Note: If a Catholic promises to hear mass for 10 consecutive Sundays in order to receive P1,000, this obligation becomes a civil one. ) B. From the viewpoint of subject matter – 1. REAL OBLIGATION – the obligation to give 2. PERSONAL OBLIGATION – the obligation to do or not to do (e. . the duty to paint a house, or to refrain from committing a nuisance) C. From the affirmativeness and negativeness of the obligation – 1. POSITIVE OR AFFIRMATIVE OBLIGATION – the obligatio n to give or to do 2. NEGATIVE OBLIGATION – the obligation not to do (which naturally inludes not to give) D. From the viewpoint of persons obliged – â€Å"sanction† – 1. UNILATERAL – where only one of the parties is bound (e. g. Plato owes Socrates P1,000. Plato must pay Socrates. ) 2. BILATERAL – where both parties are bound (e. g. In a contract of sale, the buyer is obliged to deliver) – may be: (b. ) reciprocal (b. 2) non-reciprocal – where performance by one is non-dependent upon performance by the other ELEMENTS OF OBLIGATION a)ACTIVE SUBJECT – (Creditor / Obligee) the person who is demanding the performance of the obligation; b)PASSIVE SUBJECT – (Debtor / Obligor) the one bound to perform the prestation or to fulfill the obligation or duty; c)PRESTATION – (to give, to do, or not to do) object; subject matter of the obligation; conduct required to be observed by the debtor; d)EFFICIENT CAUSE – the JURIDICAL TIE which binds the parties to the obligation; source of the obligation.PRESTATION (Object) 1. TO GIVE – delivery of a thing to the creditor (in sale, deposit, pledge, donation); 2. TO DO – covers all kinds of works or services (contract for professional services); 3. NOT TO DO – consists of refraining from doing some acts (in following rules and regulations). Requisites of Prestation / Object: 1)licit (if illicit, it is void) 2)possible (if impossible, it is void) 3)determinate or determinable (or else, void) 4)pecuniary value †¢INJURY – wrongful act or omission which causes loss or harm to another †¢DAMAGE – result of injury (loss, hurt, harm) 157. Obligation arises from – (1) law; (2) contracts; (3) quasi-contracts; (4) acts or omissions punished by law; (5) quasi-delicts. (1) LAW (Obligation ex lege) – imposed by law itself; must be expressly or impliedly set forth and cannot be presumed – [See Artic le 1158] (2) CONTRACTS (Obligation ex contractu) – arise from stipulations of the parties: meeting of the minds / formal agreement – must be complied with in good faith because it is the â€Å"law† between parties; neither party may nilaterally evade his obligation in the contract, unless: a)contract authorizes it b)other party assents Note: Parties may freely enter into any stipulations, provided they are not contrary to law, morals, good customs, public order or public     policy – [See Article 1159] (3) QUASI-CONTRACTS (Obligation ex quasi-contractu) – arise from lawful, voluntary and unilateral acts and which are enforceable to the end that no one shall be unjustly enriched or benefited at the expense of another – 2 kinds: 1.Negotiorum gestio – unauthorized management; This takes place when a person voluntarily takes charge of   another’s abandoned business or property without the owner’s authority 2. Solutio i ndebiti – undue payment; This takes place when something is received when there is no right to demand it, and it was unduly delivered thru mistake – [See Article 1160] (4) DELICTS (Obligation ex maleficio or ex delicto) – arise from civil liability which is the consequence of a criminal offense – Governing rules: 1.Pertinent provisions of the RPC and other penal laws subject to Art 2177 Civil Code [Art 100, RPC – Every person criminally liable for a felony is also civilly liable] 2. Chapter 2, Preliminary title, on Human Relations ( Civil Code ) 3. Title 18 of Book IV of the Civil Code – on damages – [See Article 1161] (5) QUASI-DELICTS / TORTS (Obligation ex quasi-delicto or ex quasi-maleficio) – arise from damage caused to another through an act or omission, there being no fault or negligence, but no contractual relation exists between the parties – [See Article 1162] 158. Obligations from law are not presumed. Only th ose (1) expressly determined in this code or (2) in special laws are demandable, and shall be regulated by the precepts of the law which establishes them; and as to what has not been foreseen, by the provisions of this code. †¢Unless such obligations are EXPRESSLY provided by law, they are not demandable and enforceable, and cannot be presumed to exist. †¢The Civil Code can be applicable suppletorily to obligations arising from laws other than the Civil Code itself. Special laws – refer to all other laws not contained in the Civil Code. 1159. Obligations arising from contracts have the force of law between the contracting parties and should be complied with in good faith. CONTRACT – meeting of minds between two persons whereby one binds himself, with respect to the other, to give, to do something or to render some service; governed primarily by the agreement of the contracting parties. VALID CONTRACT – it should not be against the law, contrary to mora ls, good customs, public order, and public policy. In the eyes of law, a void contract does not exist and no obligation will arise from it. OBLIGATIONS ARISING FROM CONTRACTS – primarily governed by the stipulations, clauses, terms and conditions of their agreements. †¢If a contract’s prestation is unconscionable (unfair) or unreasonable, even if it does not violate morals, law, etc. , it may not be enforced totally. †¢Interpretation of contract involves a question of law. COMPLIANCE IN GOOD FAITH – compliance or performance in accordance with the stipulations or terms of the contract or agreement.FALSIFICATION OF A VALID CONTRACT – only the unauthorized insertions will be disregarded; the original terms and stipulations should be considered valid and subsisting for the partied to fulfill. 1160. Obligations derived from quasi-contracts shall be subject to the provisions of chapter 1, title 17 of this book. QUASI-CONTRACT – juridical relat ion resulting from lawful, voluntary and unilateral acts by virtue of which, both parties become bound to each other, to the end that no one will be unjustly enriched or benefited at the expense of the other. (See Article 2142) 1)NEGOTIORUM GESTIO – juridical relation which takes place when somebody voluntarily manages the property affairs of another without the knowledge or consent of the latter; owner shall reimburse the gestor for necessary and useful expenses incurred by the latter for the performance of his function as gestor. (2)SOLUTIO INDEBITI – something is received when there is no right to demand it and it was unduly delivered through mistake; obligation to return the thing arises on the part of the recipient. (e. g. If I let a storekeeper change my P500 bill and by error he gives me P560, I have the duty to return the extra P60) 1161.Civil obligations arising from criminal offenses shall be governed by the penal laws, subject to the provisions of Article 21 77, and of the pertinent provisions of Chapter 2, Preliminary in Human Relations, and of Title 18 of this book, regulating damages. Governing rules: 1. Pertinent provisions of the RPC and other penal laws subject to Art 2177 Civil Code [Art 100, RPC – Every person criminally liable for a felony is also civilly liable] 2. Chapter 2, Preliminary title, on Human Relations ( Civil Code ) 3. Title 18 of Book IV of the Civil Code – on damages †¢Every person criminally liable for a felony is also criminally liable (art. 00, RPC) CRIMINAL LIABILITY INCLUDES: (a)RESTITUTION – restoration of property previously taken away; the thing itself shall be restored, even though it be found in the possession of a third person who has acquired it by lawful means, saving to the latter his action against the proper person who may be liable to him. (b)REPARATION OF THE DAMAGE CAUSED – court determines the amount of damage: price of a thing, sentimental value, etc. (c)INDEM NIFICATION FOR CONSEQUENTIAL DAMAGES – includes damages suffered by the family of the injured party or by a third person by reason of the crime. Effect of acquittal in criminal case: . when acquittal is due to reasonable doubt – no civil liability b. when acquittal is due to exempting circumstances – there is civil liability c. when there is preponderance of evidence – there is civil liability 1162. Obligations derived from quasi-delicts shall be governed by the provisions of chapter 2, title 17 of this book, and by special laws. QUASI-DELICT (culpa aquiliana) – an act or omission by a person which causes damage to another giving rise to an obligation to pay for the damage done, there being fault or negligence but there is no pre-existing contractual relation between parties. (See Article 2176)REQUISITES: a. omission b. negligence c. damage caused to the plaintiff d. direct relation of omission, being the cause, and the damage, being the effect e. no pre-existing contractual relations between parties Fault or Negligence – consists in the omission of that diligence which is required by the nature of the obligation and corresponds with the circumstances of the person, time, and of the place. BASIS DELICTS QUASI-DELICTS 1. INTENT Criminal/ malicious Negligence 2. INTEREST Affects PUBLIC interest Affects PRIVATE interest 3.LIABILITY Criminal and civil liabilities Civil liability 4. PURPOSE Purpose – punishment Indemnification 5. COMPROMISE Cannot be comprised Can be compromised 6. GUILT Proved beyond reasonable doubt Preponderance of evidence CHAPTER 2 NATURE AND EFFECT OF OBLIGATIONS 1163. Every person obliged to give something is also obliged to take care of it with the proper diligence of a good father of a family, unless the law or the stipulation of the parties requires another standard of care. Speaks of an obligation to care of a DETERMINATE thing (that is one which is specific; a thing identified by its indi viduality) which an obligor is supposed to deliver to another. †¢Reason: the obligor cannot take care of the whole class/genus DUTIES OF DEBTOR: †¢Preserve or take care of the things due. ? ~DILIGENCE OF A GOOD FATHER – a good father does not abandon his family, he is always ready to provide and protect his family; ordinary care which an average and reasonably prudent man would do. -Defined in the negative in Article 1173 ~ANOTHER STANDARD OF CARE – extraordinary diligence provided in the stipulation of parties. ? ~FACTORS TO BE CONSIDERED – diligence depends on the nature of obligation and corresponds with the circumstances of the person, time, and place. ** Debtor is not liable if his failure to deliver the thing is due to fortuitous events or force majeure†¦ without negligence or fault in his part. †¢Deliver the fruits of a thing †¢Deliver the accessions/accessories †¢Deliver the thing itself †¢Answer for damages in case of non-fulfillment or breach

Sunday, September 15, 2019

Malcom X and Amy Tan

Hide Course Menu Menu Management Options Refresh Display Course Menu in a Window Course Menu: PREP 108: Introduction to College Writing Houses Entry Page Announcements Syllabus and Course Schedule Instructor BIO Unit 1 unit 2 My Grades Tools Course Evaluation Email My Class Student Help Reading Blob #2: Malcolm X and Tan Actions for Content Page Create Blob Entry View Drafts Content Please answer the following questions as thoroughly as possible. While these entries are due Wednesday September 3 before class, you are welcome to take until Friday to complete them. Malcolm X, â€Å"Learning to Read† Questions (from 50 Essays): .How did the process by which Malcolm learned to read differ from the typical way people learn to read? 2. Though Malcolm changed many of his views after the time covered in this portion of his autobiography, the project of recovering African history remained important to him and remains important to many African Americans. How do you react to his claims a bout African history? Tan, â€Å"Mother Tongue† 1 . List the different English Tan describes, defining each. 2. Do you use different languages yourself? Even if English is your sole language, consider how your use of it hanged depending on circumstances and audience.Write an essay in which you describe the different ways you speak and the meaning of these differences. Friday, September 5, 2014 Malcolm X and Tan Posted by Access the profile card for user: Alexis Gang September 5, 2014 AM KODAK Alexis Gang Proof. Day PREP AWAY 5 September 2014 Alexis Gang at Friday, 1 . Malcolm X learned how to read different from many other people, he learned how to read at the Norfolk Prison. At the prison he would read the dictionary to get a better understand of how to read a book and know the meaning of every word.Malcolm was so interested in the dictionary he would spend three to four hours sitting on his cell floor Just for the light to read constantly. Malcolm read during late hours all the time to the point he knew when the guards did a night walk through of each cell hallway. 2. Malcolm Ax's view on African American history was shocking and made me think that some points he made did actually make sense in some parts. One point that was interesting was when he said † If you started with a black man, a white man could be produced; but starting with a Whitman, you never could produce a black man- cause the white gene is recessive† (peg. 77). Malcolm made sense of the views of African American history to the point where reading this autobiography has me thinking that maybe the world did start off with a black man instead of a white man. His views did give me a outlook on the history and makes me want to read more into black history and see if it is true that we started with a black man instead of white man. Tan † Mother Tongue† 1 . Tan describes standard English as a form of English where the grammar is perfect along with the tenses.Along wit h standard English she also talked about the way she alas to her family and husband where that is called English of intimacy. The English that made an impact on her was her mothers English where some would call it † limited English† (peg. 419) where their English is unclear or not perfect. 2. English has been my first language. I was born in Fairbanks, Ak where everyone just speaks English and no terms or slang Just regular good ole English of yes ma'am and yes sir. As a child I learned how to talk from my parents, because as babies you repeat words and sounds like a parrot.I have really never put thought into how my English is use, but thinking of it now my English has changed from being a little kid to and adult now. As a kid the way I talked I would always use anti and consider it a word and would argue if it was a word or not to everyone. Looking back at it anti wasn't a word and I wasn't using standard English my English was limited at the time to where some people could not understand what I was talking about half the time. As time went on my English got better with more knowledge and words I had learned throughout my life in school to where I became great at standard English.

Saturday, September 14, 2019

Based Data Mining Approach for Quality Control

Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala| INDEX Sr No| Title| Page No. | 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre-Processing| | 5| Statistics Used In Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| | 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts.Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine quality based on physicochemical data. * This study was also conducted to identify outlier or anomaly in sample wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The present content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and Domain Theories is a free Internet repository of analytical datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from many scientists and are claimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASET|Title:| Wine Quality| Data Set Characteristics:| Multivariate| Number Of Instances:| WHITE-WINE : 4898 RED-WINE : 1599 | Area:| Business| Attrib ute Characteristic:| Real| Number Of Attribute:| 11 + Output Attribute| Missing Value:| N/A| * Attribute Information * Input variables (based on physicochemical tests) * Fixed Acidity: Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * Volatile Acidity: Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid: Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar: The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides: Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies in the brewing water. * Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as pres ervative in wine process. * Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. In kg per liter) * PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values) * Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol: Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source.Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason. * Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation=â€Å"† * Noisy : Containing errors or outliers. * E. g. Salary=â€Å"-10† * Inconsistent : Containing discrepancies in codes or names. * E. g. Age=â€Å"42† Birthday=â€Å"03/07/1997† * E. g. Was rating â€Å"1,2,3†, Now rating â€Å"A, B, C† * E. g. Discrepancy between duplicate records * No quality data, no quality mining results! Quality decisions must be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in done in the Data Preprocessing are, * Data Cleaning * Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization a nd aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numerical data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * Here, NumericToNominal, InterquartileRange and RemoveWithValues filters are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. attribute. NumericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute â€Å"Quality† in both dataset (Red-wine Quality, White-wine Quality) have a type †Å"Numeric†. So after applying this filter, class attribute â€Å"Quality† convert into type â€Å"Nominal†. * And Red-wine Quality dataset have class names 3, 4, 5 †¦ 8 and White-wine Quality dataset have class names 3, 4, 5 †¦ 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme values based on interquartile ranges. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds two more fields which names are â€Å"Outliers† and â€Å"ExtremeValue†. And this fields has two types of label â€Å"No† and â€Å"Yes†. Here â€Å"Yes† label indicates, there are outliers and extreme values in dataset. * In our dataset, there are 83 extreme values and 125 outliers i n White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are â€Å"AttributeIndex† and â€Å"NominalIndices†. * AttributeIndex choose attribute to be use for selection and NominalIndices choose range of label indices to be use for selection on nominal attribute. * In our dataset, AttributeIndex is â€Å"last† and NominalIndex is also â€Å"last†, so It will remove first 83 extreme values and then 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm| RED-WINE| RANKED| WHITE-WINE| Volatile_Acidity(2)| 0. 1248| 0. 0406| Volatile_Acidity(2)| Total_sulfer_Diox ide(7)| 0. 0695| 0. 0600| Citric_Acidity(3)| Sulphates(10)| 0. 1464| 0. 0740| Chlorides(5)| Alcohal(11)| 0. 2395| 0. 0462| Free_Sulfer_Dioxide(6)| | | 0. 1146| Density(8)| | | 0. 2081| Alcohal(11)| * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics MeasuresThere are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics measures. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or index which compares the agreement from the model with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our predicti on for class attribute for given dataset is how much near to actual values. * Values Range For Kappa Range| Result| lt;0| POOR| 0-0. 20| SLIGHT| 0. 21-0. 40| FAIR| 0. 41-0. 60| MODERATE| 0. 61-0. 80| SUBSTANTIAL| 0. 81-1. 0| ALMOST PERFECT| * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 5365| 0. 5294| J48| 0. 3813| 0. 3881| Multilayer Perceptron| 0. 2946| 0. 3784| * Mean absolute error (MAE) * Mean absolute error (MAE)  is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by, Mean absolute Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 1297| 0. 1381| J48| 0. 1245| 0. 1401| Multilayer Perceptron| 0. 1581| 0. 1576| * Root Mean Squared Erro r * If you have some data and try to make a curve (a formula) fit them, you can graph and see how close the curve is to the points. Another measure of how well the curve fits the data is Root Mean Squared Error. * For each data point, CalGraph calculates the value of  Ã‚  y from the formula. It subtracts this from the data's y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, Root Mean Square Error is Root Mean Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 2428| 0. 2592| J48| 0. 3194| 0. 3354| Multilayer Perceptron| 0. 2887| 0. 3023| * Root Relative Squared Error * The  root relative squared error  is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the to tal squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared error  one reduces the error to the same dimensions as the quantity being predicted. * Mathematically, the  root relative squared error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0.So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal. Root Relative Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 78. 1984 %| 79. 309 %| J48| 102. 9013 %| 102. 602 %| Multilayer Perceptron| 93. 0018 %| 92. 4895 %| * Relative Absolute Error * The  relative absolute error  is very similar to the  relative squared error  in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, the  relative absolute error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0. So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal.Relative Absolute Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality | K-Star| 67. 2423 %| 64. 5286 %| J48| 64. 577 %| 65. 4857 %| Multilayer Perceptron| 81. 9951 %| 73. 6593 %| * Various Rates * There are four possible outcomes from a classifier. * If the outcome from a prediction is  p  and the actual value is also  p, then it is called a  true positive  (TP). * However if the actual value is  n  then it is said to be a  false positive  (FP). * Conversely, a  true negative  (TN) has occurred when both the prediction outcome and the actual value are  n. And  false negative  (FN) is when the prediction outcome is  n while the actual value is  p. * Absolute Value | P| N| TOTAL| p’| True positive| false positive| P’| n’| false negative| True negative| N’| Total| P| N| | * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the top left corner of the ROC chart the better the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, Precision and F-Measure There are four possible results of classification. * Different combination of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of the se classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced. * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow : PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * Confusion Matrix * A matrix used to summarize the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-Nearest Neighbor Classifiers * Nearest neighbor classifiers are based on learning by analogy. * The training samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. * These k training samples are the k-nearest neighbors of the unknown sample. â€Å"Closeness† is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners can incur expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniqu es. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that time.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier -> lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0. 02 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 3307 | 70. 6624 % | Incorrectly Classified Instances| 1373 | 29. 3376 %| Kappa Statistic | 0. 5365| | Mean Absolute Error | 0. 1297| | Root Mean Squared Error| 0. 2428| | Relative Absolute Error | 67. 2423 %| | Root Relative Squared Error | 78. 1984 %| | Total Number Of Instances | 4680 | | * Detailed Accuracy By Class | TP Rate| FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0 | 0 | 0 | 0 | 0. 583 | 0. 004 | 3| | 0. 211 | 0. 002 | 0. 769 | 0. 211 | 0. 331 | 0. 884 | 0. 405 | 4| | 0. 672 | 0. 079 | 0. 777 | 0. 672 | 0. 721 | 0. 904 | 0. 826 | 5| | 0. 864 | 0. 378 | 0. 652 | 0. 864 | 0. 743 | 0. 84 | 0. 818 | 6| | 0. 536 | 0. 031 | 0. 797 | 0. 536 | 0. 641 | 0. 911 | 0. 772 | 7| | 0. 398 | 0. 002 | 0. 883 | 0. 398 | 0. 548 | 0. 913 | 0. 572 | 8| | 0 | 0 | 0 | 0 | 0 | 0. 84 | 0. 014 | 9| Weighted Avg. | 0. 707 | 0. 2 | 0. 725 | 0. 707 | 0. 695 | 0. 876 | 0. 787| | * Confusion Matrix| A | B | C | D | E | F| G | | Class| 0 | 0 | 4 | 9 | 0| 0 | 0 | | | A=3| 0| 30| 49| 62| 1 | 0 | 0| | | B=4| 0 | 7 | 919| 437| 5 | 0 | 0 | | | C=5| 0 | 2 | 201| 1822| 81 | 2 | 0 | || D=6| 0 | 0 | 9 | 389 | 468 | 7 | 0| || E=7| 0 | 0 | 0 | 73 | 30 | 68 | 0 | || F=8| 0 | 0 | 0 | 3 | 2 | 0 | 0 | || G=9| * Performance Of The Kstar With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 6581 %| 100 %| 70. 6624 %| 63. 9221 %| Kappa statistic| 0. 9949| 1| 0. 5365| 0. 4252| Mean Absolute Error| 0. 0575| 0. 0788| 0. 1297| 0. 1379| Root Mean Squared Error| 0. 1089| 0. 145| 0. 2428| 0. 2568| Relative Absolute Error| 29. 8022 %| | 67. 2423 %| 71. 2445 %| * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 1013 | 71. 379 %| Incorrectly Classified Instances| 413 | 28. 9621 %| Kappa Stat istic | 0. 5294| | Mean Absolute Error | 0. 1381| | Root Mean Squared Error | 0. 2592| | Relative Absolute Error | 64. 5286 %| | Root Relative Squared Error | 79. 309 %| | Total Number Of Instances | 1426 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0. 001 | 0 | 0 | 0 | 0. 574 | 0. 019 | 3| | 0 | 0. 003 | 0 | 0 | 0 | 0. 811 | 0. 114 | 4| | 0. 791| 0. 176 | 0. 67| 0. 791| 0. 779 | 0. 894 | 0. 867 | 5| | 0. 769 | 0. 26 | 0. 668 | 0. 769 | 0. 715 | 0. 834 | 0. 788 | 6| | 0. 511 | 0. 032 | 0. 692 | 0. 511 | 0. 588 | 0. 936 | 0. 722 | 7| | 0. 125 | 0. 001 | 0. 5 | 0. 125 | 0. 2 | 0. 896 | 0. 142 | 8| Weighted Avg. | 0. 71| 0. 184| 0. 685| 0. 71| 0. 693| 0. 871| 0. 78| | * Confusion Matrix | A | B | C | D | E | F| | Class| 0 | 1 | 4| 1 | 0 | 0 | | | A=3| 1 | 0 | 30| 17 | 0 | 0| | | B=4| 0 | 2| 477| 120 | 4 | 0| | | C=5| 0 | 1 | 103 | 444| 29 | 0| || D=6| 0 | 0 | 8 | 76 | 90 | 2 | || E=7| 0 | 0 | 0 | 7 | 7 | 2| || F=8| Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 7895 %| 100 % | 71. 0379 %| 70. 7216 %| Kappa statistic| 0. 9967| 1| 0. 5294| 0. 5154| Mean Absolute Error| 0. 0338| 0. 0436| 0. 1381| 0. 1439| Root Mean Squared Error| 0. 0675| 0. 0828 | 0. 2592| 0. 2646| Relative Absolute Error| 15. 8067 %| | 64. 5286 %| 67. 4903 %| * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute; the branches between the nodes tell us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (class ification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm: * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling wi thin its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the respective attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model: 1. 4 Seconds| Stratified Cross-Validation (10-Fold) | * Summary| | | Correctly Classified Instances| 2740 | 58. 547 %| Incorrectly Classified Instances | 1940 | 41. 453 %| Kappa Statistic | 0. 3813| | Mean Absolute Error | 0. 1245| | Root Mean Squared Error | 0. 3194| | Relative Absolute Error | 64. 5770 %| | Root Relative Squared Error| 102. 9013 %| | Total Number Of Instances | 4680| | * Detailed Accuracy By Class| | TP Rate| FP Rate| Precision| Recall| F-Measure| ROC Area| Class| | 0| 0. 002| 0| 0| 0| 0. 30| 3| | 0. 239| 0. 020| 0. 270| 0. 239| 0. 254| 0. 699| 4| | 0. 605| 0. 169| 0. 597| 0. 605| 0. 601| 0. 763| 5| | 0. 644| 0. 312| 0. 628| 0. 644| 0. 636| 0. 689| 6| | 0. 526| 0. 099| 0. 549| 0. 526| 0. 537| 0. 766| 7| | 0. 363| 0. 022| 0. 388| 0. 363| 0. 375| 0. 75| 8| | 0| 0| 0| 0| 0| 0. 496| 9| Weighted Avg. | 0. 585 | 0. 21 | 0. 582 | 0. 585 | 0. 584 | 0. 727| | * Confusion Matrix | A| B| C| D| E| F| G| || Class| 0| 2| 6| 5| 0| 0| 0| || A=3| 1| 34| 55| 44| 6| 2| 0| || B=4| 5| 50| 828| 418| 60| 7| 0| || C=5| 2| 32| 413| 1357| 261| 43| 0| || D=6| | 7| 76| 286| 459| 44| 0| || E=7| 1| 1| 10| 49| 48| 62| 0| || F=8| 0| 0| 0| 1| 2| 2| 0| || G=9| * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 90. 1923 %| 70 %| 58. 547 %| 54. 8083 %| Kappa statistic| 0. 854| 0. 6296| 0. 3813| 0. 33| Mean Absolute Error| 0. 0426| 0. 0961| 0. 1245| 0. 1347| Root Mean Squared Error| 0. 1429| 0. 2756| 0. 3194| 0. 3397| Relative Absolute Error| 22. 0695 %| | 64. 577 %| 69. 84 %| * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model: 0. 17 Seconds| Stratified Cross-Validation| * Summary| Correctly Classified Instances | 867 | 60. 7994 %| Incorrectly Classified Instances | 559 | 39. 2006 %| Kappa Statistic | 0. 3881| | Mean Absolute Error | 0. 1401| | Root Mean Squa red Error | 0. 3354| | Relative Absolute Error | 65. 4857 %| | Root Relative Squared Error | 102. 602 %| |Total Number Of Instances | 1426 | | * Detailed Accuracy By Class| | Tp Rate | Fp Rate | Precision | Recall | F-measure | Roc Area | Class| | 0 | 0. 004 | 0 | 0 | 0 | 0. 573 | 3| | 0. 063 | 0. 037 | 0. 056 | 0. 063 | 0. 059 | 0. 578 | 4| | 0. 721 | 0. 258 | 0. 672 | 0. 721 | 0. 696 | 0. 749 | 5| | 0. 57 | 0. 238 | 0. 62 | 0. 57 | 0. 594 | 0. 674 | 6| | 0. 563 | 0. 64 | 0. 553 | 0. 563 | 0. 558 | 0. 8 | 7| | 0. 063 | 0. 006 | 0. 1 | 0. 063 | 0. 077 | 0. 691 | 8| Weighted Avg. | 0. 608 | 0. 214 | 0. 606 | 0. 608 | 0. 606 | 0. 718 | | * Confusion Matrix | A | B | C | D | E | F | | Class| 0 | 2 | 1 | 2 | 1 | 0 | | | A=3| 2 | 3 | 25 | 15 | 3 | 0 | | | B=4| 1 | 26 | 435 | 122 | 17 | 2 | | | C=5| 2 | 21 | 167 | 329 | 53 | 5 | | | D=6| 0 | 2 | 16 | 57 | 99 | 2 | | | E=7| 0 | 0 | 3 | 6 | 6 | 1 | | | F=8| Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Qual ity Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 91. 1641 %| 80 %| 60. 7994 %| 62. 4742 %| Kappa statistic| 0. 8616| 0. 6875| 0. 3881| 0. 3994| Mean Absolute Error| 0. 0461| 0. 0942| 0. 1401| 0. 1323| Root Mean Squared Error| 0. 1518| 0. 2618| 0. 3354| 0. 3262| Relative Absolute Error| 21. 5362 %| 39. 3598 %| 65. 4857 %| 62. 052 %| * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted an d fed simultaneously to a second layer of â€Å"neuronlike† units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to build model: 36. 22 seconds| Stratifi ed cross-validation| * Summary| Correctly Classified Instances | 2598 | 55. 5128 %| Incorrectly Classified Instances | 2082 | 44. 4872 %| Kappa statistic | 0. 2946| | Mean absolute error | 0. 1581| | Root mean squared error | 0. 2887| |Relative absolute error | 81. 9951 %| | Root relative squared error | 93. 0018 %| | Total Number of Instances | 4680 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 344 | 0. 002 | 3| | 0. 056 | 0. 004 | 0. 308 | 0. 056 | 0. 095 | 0. 732 | 0. 156 | 4| | 0. 594 | 0. 165 | 0. 597 | 0. 594 | 0. 595 | 0. 98 | 0. 584 | 5| | 0. 704 | 0. 482 | 0. 545 | 0. 704 | 0. 614 | 0. 647 | 0. 568 | 6| | 0. 326 | 0. 07 | 0. 517 | 0. 326 | 0. 4 | 0. 808 | 0. 474 | 7| | 0. 058 | 0. 002 | 0. 5 | 0. 058 | 0. 105 | 0. 8 | 0. 169 | 8| | 0 | 0 | 0| 0 | 0 | 0. 356 | 0. 001 | 9| Weighted Avg. | 0. 555 | 0. 279 | 0. 544 | 0. 555 | 0. 532 | 0. 728 | 0. 526| | * Confusion Matrix |A | B | C | D | E | F | G | | Class| 0 | 0 | 5 | 7 | 1 | 0 | 0 | | | A=3| 0 | 8 | 82 | 50 | 2 | 0 | 0 | | | B=4| 0 | 11 | 812 | 532 | 12 | 1 | 0 | | | C=5| 0 | 6 | 425 | 1483 | 188 | 6 | 0 | | | D=6| 0 | 1 | 33 | 551 | 285 | 3 | 0 | | | E=7| 0 | 0 | 3 | 98 | 60 | 10 | 0 | | | F=8| 0 | 0 | 0 | 2 | 3 | 0 | 0 | | | G=9| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 58. 1838 %| 50 %| 55. 5128 %| 51. 3514 %| Kappa statistic| 0. 3701| 0. 3671| 0. 2946| 0. 2454| Mean Absolute Error| 0. 1529| 0. 1746| 0. 1581| 0. 1628| Root Mean Squared Error| 0. 2808| 0. 3256| 0. 2887| 02972| Relative Absolute Error| 79. 2713 %| | 81. 9951 %| 84. 1402 %| * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model: 9. 14 seconds| Stratified cross-validation (10-Fold)| * Summary | Co rrectly Classified Instances | 880 | 61. 111 %| Incorrectly Classified Instances | 546 | 38. 2889 %| Kappa statistic | 0. 3784| | Mean absolute error | 0. 1576| | Root mean squared error | 0. 3023| | Relative absolute error | 73. 6593 %| | Root relative squared error | 92. 4895 %| | Total Number of Instances | 1426| | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 47 | 3| | 0. 42 | 0. 005 | 0. 222 | 0. 042 | 0. 070 | 0. 735 | 4| | 0. 723 | 0. 249 | 0. 680 | 0. 723 | 0. 701 | 0. 801 | 5| | 0. 640 | 0. 322 | 0. 575 | 0. 640 | 0. 605 | 0. 692 | 6| | 0. 415 | 0. 049 | 0. 545 | 0. 415 | 0. 471 | 0. 831 | 7| | 0 | 0 | 0 | 0 | 0 | 0. 853 | 8| Weighted Avg. | 0. 617 | 0. 242 | 0. 595 | 0. 617 | 0. 602 | 0. 758| | * Confusion Matrix | A | B | C | D | E | F | | Class| | 0 | 5 | 1 | 0 | 0| || A=3| 0 | 2 | 34 | 11 | 1 | 0 | | | B=4| 0 | 2 | 436 | 160 | 5 | 0 | | | C=5| 0 | 5 | 156 | 369 | 47 | 0 | | | D=6| 0 | 0 | 10 | 93 | 73 | 0 | | | E=7| 0 | 0 | 0 | 8 | 8 | 0 | | | F=8| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 68. 7237 %| 70 %| 61. 7111 %| 58. 7629 %| Kappa statistic| 0. 4895| 0. 5588| 0. 3784| 0. 327| Mean Absolute Error| 0. 426| 0. 1232| 0. 1576| 0. 1647| Root Mean Squared Error| 0. 2715| 0. 2424| 0. 3023| 0. 3029| Relative Absolute Error| 66. 6774 %| 51. 4904 %| 73. 6593 %| 77. 2484 %| * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality using  Kstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier ac hieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 1. 08| 35. 14| Kappa Statistics| 0. 5365| 0. 3813| 0. 29| Correctly Classified Instances (%)| 70. 6624| 58. 547| 55. 128| True Positive Rate (Avg)| 0. 707| 0. 585| 0. 555| False Positive Rate (Avg)| 0. 2| 0. 21| 0. 279| * Chart Shows The Best Suited Algorithm For Our Dataset (Measu res Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) | Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 0. 24| 9. 3| Kappa Statistics| 0. 5294| 0. 3881| 0. 3784| Correctly Classified Instances (%)| 71. 0379| 60. 6994| 61. 7111| True Positive Rate (Avg)| 0. 71| 0. 608| 0. 617| False Positive Rate (Avg)| 0. 184| 0. 214| 0. 242| * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET Step1: Load pre-processed dataset. Step2: Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3: Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ’? ’ in the cell of the corresponding raw ) as shown below. Step 4: Save this data set as . rff file. Step 5: From â€Å"tes t option† panel select â€Å"supplied test set†, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6: From â€Å"Result list panel† panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click â€Å"Re-evaluate model on current test set† Step 7: Again right click on Kstar algorithm and select â€Å"visualize classifier error† Step 8:Click on save button and then save your test model.Step 9: After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10: Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine productio n, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that d etermines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.