Against The Privacy Of Aids :: Free AIDS Essays

Last October, the case of Nushawn Williams hit the front pages. He is believed to have infected at least 13 girls and women in Jamestown, New York, with HIV, the virus that causes AIDS. His name and face appeared all over the media, shredding the accepted norm of keeping HIV stead confidential. In breaking this tradition, prevalent health officials sought to identify and reach the young women he may have infected. Due to this breaking of the silence and insurance coverage the name of the person with this infectious disease at least some women had a greater chance of living because they found out about the virus at an early state. Individuals who are set by name on disease reports can be contacted by health departments for sermon. Fear of being identified on disease reports could deter multitude from seeking medical examination sustainment or disease testing, therefore harming the individual by causing delays in care and threatening public health because such delays could r esults in further spread of disease. Name reporting of persons with infectious diseases has the potential to benefit both individual and public health.Name reporting of persons with infectious disease can benefit the individual person. It could enable health officials to find and counsel people who test positive, but do not return for their results or who are tested in venues that do not provide extensive educational opportunities. Such contacts could also lead to medical referrals and earlier viral loads and CD4 testing, resulting in more timely treatment and reductions in viral loads that could not only improve the individual health but, at least, theoretically, also improve the public health by reducing the infectiousness of individuals. Furthermore, there have been recent studies on new therapies that can keep HIV-positive people healthy for years. These therapies are a combination of drugs that effectively reduce the amount of HIV in the blood. People have the greatest chances of success with these therapies if they begin treatment early on, and they cant be treated if they dont know that they are infected and their names are not found someplace so that they could be at least consulted. To insure that the information recorded about an individual is not used to discriminate them a law could be passed. Something like this already exists, were you can not discriminate a person if they are gay, black, white, etc., therefore, you have to hire the person if they are fully qualified

Classical Period of Music Essay -- essays research papers

The inspirit of the genuine Era The Classical Era saw the convergence of two opposing schools of thought in society. The first was the unexpended from the Baroque Era, which said that the noblesse had absolute power of society. The second was from the philia class, who said that the nobility had gone too far with their power and should give some of their power over to the middle class. The result of this was many opportunities for composers. Not only could they have theirtraditional occupations as servents to various noblemen who served as their patrons, but they could also make a fair living playing their work at public concerts, an innovation of the time. This uprising from the bourgeousie was happening all around the world. The American and French Revolution showed that the short people of the world were not going to stand for being trampled underfoot and the Industrial Revolution allowed for a power transfer from the nobility to the middle class, who controlled the mines and factories. Thinkers of the Classical Era despised complexness and detail. They preferred beauty in simplicity and form. The Classical Era represented a throwback to ancient Rome and Greece. The chemical chain Quartet The string intravenous feeding represents one of the first musical forms that could be described as chamber music. In this style of music, the composer deals with a small pigeonholing of instruments and the emphasis is on the drop dead and interplay between the players. A string quartet usually consists ... Classical Period of Music Essay -- essays research papers The Spirit of the Classical Era The Classical Era saw the convergence of two opposing schools of thought in society. The first was the leftover from the Baroque Era, which said that the nobility had absolute power of society. The second was from the middle class, who said that the nobility had gone too far with their power and should give some of their power over to the middle class. The result of this was many opportunities for composers. Not only could they have theirtraditional occupations as servents to various noblemen who served as their patrons, but they could also make a fair living performing their work at public concerts, an innovation of the time. This uprising from the bourgeousie was happening all around the world. The American and French Revolution showed that the little people of the world were not going to stand for being trampled underfoot and the Industrial Revolution allowed for a power transfer from the nobility to the middle class, who controlled the mines and factories. Thinkers of the Classical Era despised complexity and detail. They preferred beauty in simplicity and form. The Classical Era represented a throwback to ancient Rome and Greece. The String Quartet The string quartet represents one of the first musical forms that could be described as chamber music. In this style of music, the composer deals with a small group of instruments and the emphasis is on the blend and interplay between the players. A string quartet usually consists ...

The Purpose of Education Essay -- Education Schooling

What is the purpose of education? What do we seek from it? How does it better our lives? What is the role of frequent schools? These are all important questions, but first I am going to give a brief summary of the text by timothy McMannon entitled The Public endeavor of Education and shoal. McMannon gives the reader plenty of reasons for why we need education and how it helps us and our society he starts by explaining that in other(prenominal) cultures schooling was not done in a formal school but in the community, the family, and the church (McMannon 1). Some cultures even turn overd that education was something that took a lifetime. The try out then progresses into explaining how education has evolved through the centuries into what it is today and why it has become what it is. Everyone has his or her own take on what they believe education is and should be. Your parents could think of it as a day care facility until you get to high school and then they might think of it as a place to earn a decimal point and soon move out. Your grandparents might think of it as a place where you go to learn things that you arent going to need in life because they never did. Political leaders may think of it as an economical advantage over another country. The list goes on, but as a student, I believe that there are many purposes of education it is more than one thing, but many things that combine into what education really is. One role of public schools is to prove the principles and standards of our society, which have been all but forgotten. Today we presume that school is a place where we go to learn history and mathematics, but it is much more than that. Schooling teaches us what our leaders are too busy to explain. By this, I mean that our teachers... ...u never acquired anything from becoming educated, why would you continue with it? In part, education moldiness be practical (McMannon 8) and we need to recognize that.Works CitedFulghum, Robert. All I Really Ne eded to Know I Learned in Kindergarten. New York Ballantine Books, 1986.Hudson, William E. and Robert H. Trudeau. scratch Journal of Community Service Learning. An Essay on the Institionalization of Service-Learning The Genesis of the Feinstein Institute for Public Service 2.1 (1995) 150-158.McMannon, Timothy. The Changing Purposes of Education and Schooling. McMannon, Timothy and John Goodlad. The Public Purpose of Education and Schooling. San Francisco Jossey-Bass, 1997.Nussbaum, Martha. Cultivating Imaginations Literature and the Arts. Not For Profit Why Democracy Needs the Humanities. Princeton Princeton University Press, 2010.

Boxing: Down For The Count :: essays research papers fc

Boxing Down for the CountThe tenth edition of Merriam-Websters Collegiate Dictionary defines boxing as"the art of attack and defense with the fists practiced as a sport." I could bemistaken, but there is a certain emphasis placed on the idea that boxing ispracticed as a sport. It is rather ambiguous. Is boxing a sport to begin with?Is boxing something else that is just practiced as a sport? Is it, can it, orshould it be practiced as something else rather than as a sport? Maybe I am justmaking too big a film out of a simple definition here. Nevertheless, this simpledefinition of boxing gives rise to one question we should all take some time to reply should boxing be practiced as a sport? Examination of medical findingsand statistics and re-examination of our views and goals as a modern societywill lead us to the one inevitable conclusion conside beleaguer boxing as arespectable sport just flies in the face of decency and civilization andtherefore, it should be banned. Someh ow, knickers and supporters have deludedthemselves into thinking that boxing, when properly conducted, is safe. Theclassic justification goes something like this "boxers are not two brawlingbrutes seeking to maim or kill each other. they are two closely matchedathletes seeking, through the use of such skills an footwork, timing, accuracy,punching, and feinting, to determine who is the better man in the ring" (Farley26). Unfortunately, dead boxers tell a unlike story. A study on dangerouscontact sports conducted by Patrick Malone of the Knight Ridder News Service in1980 revealed that from 1970 to 1978 in America, there was an average of 21deaths per stratum among 5,500 boxers, or 3.8 deaths per 1,000 participants,compared to college footballs 0.3 deaths per 1,000 and high school footballs0.1 deaths per 1,000 (Sammons 247). Another more recent study conducted by theNational Health and Medical Research Council (NHMRC) of Australia revealed that361 deaths have occurred in th e ring worldwide since 1945 (NHMRC 22). Deaths andserious injury suffered in boxing contests reveal only a small percentage of thepotential for danger. Unfortunately, the damaging effects of the "sport" arecumulative and difficult to diagnose, sometimes resulting in death, seriousillness, or blindness long after the boxer is out of the public limelight.However, convincing evidence has attach over the years to the effect thatchronic encephalopathy (a disease of the brain marked by personality changes,intellectual impairment, slurred speech, and motor deficits), Parkinsonssyndrome (a spooky disorder marked by tremors, drooling, muscle weakness, and

Nutrition Essay -- Health Nutrition Pyramid Diet

Nutrition is an important issue in my life for many reasons. The most important existence my major, dental consonant hygiene. Ive taken a couple nutrition courses already. I also simply care for the fact that what we eat is associated with our health and how long we may live. onwards I went to the website I didnt expect to see many healthy benefits to eating McDonalds so I guess I was prepared. It said that McDonalds victuals is link to serious diseases because of the high fat and low fiber content. I know from my nutrition classes in the past that a low fiber aliment is united obesity, diabetes, arteriosclerosis, colon cancer, and many more things. I find it amusing that they can say that their food can be a valuable vocalization of a balanced diet. I dont think that they should be allowed to promote that in any way. On the website I found a break down on evidence talking about how McDonalds is not being truthful to its customers. They lied about what goes into their Chicke n McNuggets. They claimed that it was just chicken converge and thigh meat but it is actually chicken meat mixed with chicken skin and cooked in high-beef lard, which is absorbed into it. They also lied about being the commencement exercise fast food chain to voluntarily give out the ingredients in their food. They didnt do it voluntarily they were made to but not without a struggle of course. So the question is why is McDonalds trying to deceive their customers? I went to McDonalds healthy eating policy on the website... Nutrition bear witness -- Health Nutrition Pyramid Diet Nutrition is an important issue in my life for many reasons. The most important being my major, dental hygiene. Ive taken a couple nutrition courses already. I also simply care for the fact that what we eat is associated with our health and how long we may live. Before I went to the website I didnt expect to see many healthy benefits to eating McDonalds so I guess I was prepared. It said t hat McDonalds food is linked to serious diseases because of the high fat and low fiber content. I know from my nutrition classes in the past that a low fiber diet is linked obesity, diabetes, arteriosclerosis, colon cancer, and many more things. I find it amusing that they can say that their food can be a valuable part of a balanced diet. I dont think that they should be allowed to promote that in any way. On the website I found a part on evidence talking about how McDonalds is not being truthful to its customers. They lied about what goes into their Chicken McNuggets. They claimed that it was just chicken breast and thigh meat but it is actually chicken meat mixed with chicken skin and cooked in high-beef lard, which is absorbed into it. They also lied about being the first fast food chain to voluntarily give out the ingredients in their food. They didnt do it voluntarily they were made to but not without a fight of course. So the question is why is McDonalds trying to deceive thei r customers? I went to McDonalds healthy eating policy on the website...

Nutrition Essay -- Health Nutrition Pyramid Diet

Nutrition is an alpha issue in my life for many reasons. The or so important being my major, dental hygiene. Ive taken a couple nutrition courses already. I also simply care for the fact that what we eat is associated with our health and how yearn we may live. Before I went to the website I didnt expect to see many healthy benefits to eating McDonalds so I guess I was prepared. It tell that McDonalds food is linked to serious diseases because of the high fat and funky fiber content. I know from my nutrition classes in the past that a low fiber diet is linked obesity, diabetes, arteriosclerosis, colon cancer, and many more things. I find it amusing that they can say that their food can be a valuable part of a balanced diet. I dont think that they should be allowed to promote that in any way. On the website I put a part on evidence talking about how McDonalds is not being truthful to its customers. They be about what goes into their grumbler McNuggets. They claimed that it w as on the dot chicken breast and thigh meat notwithstanding it is actually chicken meat mixed with chicken skin and cooked in high-beef lard, which is absorbed into it. They also lied about being the first fast food chain to voluntarily give out the ingredients in their food. They didnt do it voluntarily they were do to but not without a fight of course. So the question is why is McDonalds trying to deceive their customers? I went to McDonalds healthy eating policy on the website... Nutrition Essay -- Health Nutrition Pyramid Diet Nutrition is an important issue in my life for many reasons. The most important being my major, dental hygiene. Ive taken a couple nutrition courses already. I also simply care for the fact that what we eat is associated with our health and how long we may live. Before I went to the website I didnt expect to see many healthy benefits to eating McDonalds so I guess I was prepared. It said that McDonalds food is linked to serious diseases because of the high fat and low fiber content. I know from my nutrition classes in the past that a low fiber diet is linked obesity, diabetes, arteriosclerosis, colon cancer, and many more things. I find it amusing that they can say that their food can be a valuable part of a balanced diet. I dont think that they should be allowed to promote that in any way. On the website I found a part on evidence talking about how McDonalds is not being truthful to its customers. They lied about what goes into their Chicken McNuggets. They claimed that it was just chicken breast and thigh meat but it is actually chicken meat mixed with chicken skin and cooked in high-beef lard, which is absorbed into it. They also lied about being the first fast food chain to voluntarily give out the ingredients in their food. They didnt do it voluntarily they were made to but not without a fight of course. So the question is why is McDonalds trying to deceive their customers? I went to McDonalds healthy eating policy on the website...

Poem Analysis “Out, Out”

Poem Analysis Out, Out- In the poem, Out, Out-, author Robert Frost starts off his poem by giving an inanimate object, the buzzsaw, a sense of life. Using the literary device, Personification, the buzz saw is cosmos written with characteristics a curious and instead playful child. The buzzsaw acts the likes of once hears the young mans mother call for supper time, that it wants to eat, so eats the young mans hand. The buzzsaw takes (Cuts Off) the hand in a rather subtle demeanor, but in truth, it would be a very graphic to behold.Throughout the poem, everything is written in a peaceful and quite tone, even during the violent and disturbed ones to. To add to the fact of the buzzsaw is being personified in the story, the buzzsaw seems to only attack when the mother calls all for supper. The buzzsaw acts like it knows what the meaning of supper time is. Another literary device apply in this poem is the process of along with the use of otomotapia(s).Using repetition first to deliver emphasis to the reader of the sounds that buzzsaw would be making, and then the actual sound being written/sounded out in an otomotapia base. The otomotapia in the story would be the grinding sounds made the buzzsaw ripping the through the poor young mans arm. This plagiarize from the story pretty much sums all that I describe above The saw snarled and rattled, snarled and rattled and it continues about three more clock over and over.The use of the sound effects gives the once playful buzzsaw a more animalistic approach, making it seem like it is hungry after perceive the key word supper. To conclude, the literary devices used in Robert Frosts poem are mostly to emphasis and give life to once lifeless piece of machinery. The story, rather bloody and saddening, is a well written example of poetry and depth behind each and every letter/word. The analysis is pipe down to be assessed, but this all gives basic understanding as to what meant behind his more obvious literary devices.

Small Talk

Small Talk Weve all engaged in some(a) sort of small clack either in the office, on campus, at a leavey, or other places where you mother yourself in the company of others. For some of us, participating in small babble out may come easily, opus for others it may be quite difficult. Some individuals may find small tattle to be irritating while others find it a necessity.In this piece I am going to explore what ex turn of eventsly defines small communion as such the reasons why people find the involve to engage in small call on the carpet the benefits, and disadvantages of small talk is there an ethical approach to small talk and provide tips on how to participate in small talkwithout it creating an uncomfortable atmosphere. What exactly defines small talk as such, segregating it from the normal conversations exchanged between two individuals? Small talk is defined as a good-off conversation it is typically polite and about matters of little importance, especially between p eople who do not know each other well.In these conversations general-interest topics argon commonly discussed such as movies, sports, food, travel, and music. Many find that those who engage in this form of conversation are approachable and friendly. By taking an dynamic part in these conversations you send a message that you are ready, willing and able to communicate. This may not seem like a difficult task, considering all human being communicate at various levels daily, but to engage in small talk may actually be very difficult, so when done volitionally and well it can say a forget me drug about that individual. Part of being in(predicate) at small talk is being an avid listenener.This is a very important part of conversation. Many get worried about being able to communicate their thoughts clearly that they neglect to listen. Listening carefully helps in understanding and encouraging those who are speaking to you. Franklin Roosevelt, the thirty-second president of the Uni ted States of America, believed that most people were poor listeners. He believed that this held true especially when engaging in small talk conversations. To prove his point and amuse himself, he would greet visiting guests with, I murdered my grandmother this morning. He was usually responded with a perplexing look yet a polite nod in approval. However, one evening he was impressed when one guest responded with a surprising response, Im sure she had it coming. Even so, Roosevelt did prove the common neglect individuals give towards small talk. So, why do people engage in small talk? Talking is the primary form of communication between all beings. The mere act of talking is a way to break the ice, relieve any tensions, and it helps individuals find a common ground amongst them.Small talk is most commonly found in an instance when there is an uncomfortable silence, or is used in a situation where a person is waiting for somethingit passes the time. There are many instances when pe ople looking they substantiate to engage in small talk simply not to be rude. We have been cultured in our society to view small talk as a politically correct act when in one of these instances. Those who do not openly converse with others are looked upon as rude and unapproachable. This leads to the topic of the benefits small talk has and its importance.An article from the Los Angeles Times entitled, Researchers reward a high view of idle chatter showed that an increase of social contact aided mental function. This affect is similar to those of solitaire games such as Sudoku and crossword puzzles, which have the same benefit when engaged in for the same amount of time. Good news for those who enjoy chatter, but not great for those who have difficulties with it. Striking a conversation with person gives him or her the opportunity to either accept or reject you. This is probably the main reason for its importance.Americans typically separate out to be accepted by their peers. W e want to be liked by our peers and when we engage in small talk we are being judged. A lot of the time it is the get-go impression we leave with someone. Another benefit of small talk is meeting new people and possibly new friends or devising new business relations, which could lead to a promotion or new business venture. Wendy Warman, co-author of Loud and Clear How to Prepare and Deliver Effective business enterprise and Technical Presentations, gives instructional seminars on how to effectively participate in small talk for big success.Warman discusses the importance of small talk in order to emend communication, boost sales, enhance customer service and increase profits in any organization or industry. These are all very important benefits, which I think everyone could find use for. Now that we more thoroughly understand the basic means of small talk and what small talk actually consists of, we need to be aware of the topics chosen for these conversations. There is an ethic al approach to small talk and ethical judgments need to be made.Very frequently small talk is shared between individuals whom do not know each other well, therefore there are many forbidden topics that should be avoided. When people do not know each other well it is not a wise decision to discuss personal information such as salaries or divorce. Two main topics that should be avoided are religion and politics. The foundationalists view should not be considered here since everyones justified beliefs on these topics might vary. The confined of small talk should basically be to get to know someone better, keeping in mind other peoples feelings.Raised emotion may raise if one of these topics is brought forth in a conversation. The consequence of these discussions may be a negative response, being that many people have different beliefs and opinions in the matter. An altruistic approach is definitely called for when engaging in small talk. The golden rule of Love your neighbor as yours elf should be mobiliseed in these situations. Placing the other persons feelings first will usually lead to a pleasant conversation with affirmative results. Thus one can also say a utilitarian approach is also necessary when engaging in small talk.There are some basic guidelines one can follow to take care in creating proficient small talk. The first step before going to a function or gathering is to prepare yourself. Here is a list of tips that will help in engaging in small talk 1. One should derive at least three topics to discuss as well as four questions that can be asked to others to spark conversation. If you know that there will be people there that you have met before, try to remember some things about them that you can bring up . . . maybe a charity theyre involved with or a hobby they partake in. 2. Be the first to say Hello. Offering your name when greeting someone will avoid an uncomfortable situation of him or her not remembering your name. 3. Make an effort to reme mber names and use them frequently. 4. Motivate the other person(s) to start talking by asking an open-ended question that is common ground, such as, How do you know the boniface? 5. Be an active listener and provide feedback. 6. You were given two ears and one mouth . . . you should listen twice as much as you talk. 7. Try to devote something interesting to the conversation. Stay away from negative or controversial topics, as mentioned above. . If you want to meet someone in particular, be introduced to him or her by someone they respect. A mutual friend should be asked to do so. 9. When accepting a business card, receive it with both hands, read it, and place it in a rise pocket, purse or wallet to show it is valued. 10. Be mindful of you body language, this can cause others to be uncomfortable. Act confident. 11. Observe and listen before submission a conversation that has already begun. 12. Always be prepared with a few exit lines so you can have a graceful spark from the conversation. Be bright. Be brief. Be gone. I chose this topic with the expectations of finding good reasons for small talk. Although I find small talk to be necessary and appropriate in certain situations, I find it unnecessary at time and frankly quite annoying. I still think talking about the weather with a fellow employee at the printer is unnecessary, but I dont see those instances being avoidable. However, I have learned if you are active in using the tips provided, you can obtain more control over the conversation and guide it so that it is more enjoyable. Being prepared for small talk is the best way to avoid lame conversation.If you go to a function with a select few topics to discuss, chances are the conversation will be good and others will be guided by your contributions. Another important matter in successful small talk is to think of others first. This is very important with successful small talk and getting positive results. You want to make people happy and to fee l goodeveryone likes to be around people that make them feel better about themselves. Reference List 12 Tips for Making Small Talk. CareerBuilder. com. 2005. CNN. com. 1 whitethorn 2009 Hoekman, Laurel. The Benefits of Small Talk. Gray Center SUN News. 2008. Gray Center. 2 May 2009 Murphy, Peter. How to Master the Art of Small Talk. Relationships/Communication. 2007. Ezinarticles. 1 May 2009 Rosenstand, Nina. The Moral of the Story An Introduction to Ethics (Sixth Edition). New York, NY The McGraw-Hill Companies, Inc. , 19942009. Small Talk. Encarta World Dictionary. 2009. EncartaMsn. 2 May 2009 Small Talk Who, What, Where, When, Why?. EnglishClub. com. 19972009. nglichClub. com. 1 May 2009 Wendy Warman. World Class Speakers & Entertainers wcSpeakers. com. 20052009. wcSpeakers. com. 2 May 2009.

Electrical and Electronics

This effect can be used to build an galvanising function reference, such as the star described in this paper. A roller attached to a shaft spins within the attractivenessized stadium ofa U shaped magnet. Three conveniently designed conductive disks allow the electrical load of the generator to be fed every with alternating current or direct current. the loop terminals is sinusoidal with nonentity mean value (Fig. 2). Its frequency is equal to the number of revolutions per second exe fadeded by the loop. Each terminal of the loop is connected to a metallic ring. The contacts with peal are made by means of fixed brushes.If the brushes are onnected to an electrical load, an alternating current will be established in the circuit. Keywords. Alternating Current, orient Current, Generator, Magnetic Field, Induced Voltage. 1. Introduction Although diverse forms of energy (mechanical, thermal, chemical etc. ) can be converted into electrical energy, the expression electric generato r is reserved, in the industry, energy into electrical energy. The generators that produce direct current (DC) are called dynamos and the ones that produce alternating current (AC) are called alternators.The device described in this paper is a generator capable of supplying an electrical load ith the desired type of current alternating current or direct current. s AC outturn Figure 1. AC generator. 2. AC generator principle of operation. Figure 1 illustrates the principle of operation of an AC generator. A wire loop rotates within the magnetic field generated by a magnet, which induces an AC potential between the loop terminals. The periodic change of the voltage polarity is due to the change of the position of the wave relatively to the magnetic poles.The amplitude of the voltage depends on the magnetic field strength and is also directly proportional to the rotating speed 1, 2, 3, 4. If he magnetic field is uniform and the rotation speed is constant, the voltage induced between Figure 2. AC generator output. 3. DC generator principle of operation. The described AC generator may be transformed into a DC generator, substituting the contact rings by a mechanical switch. As illustrated on Fig. 3, a dim-witted switch may be done with a metal ring divided into two isolated halves ( parts), which are attach in the axis.This type of commutator is denominated aggregator. segment of the collector. When the loop rotates, an AC voltage is induced in the draw in, exactly as in the AC generator. But, before r each(prenominal)ing the oad, the induced voltage is transformed into a DC voltage by the collector (Fig. 4), which works as a mechanical rectifier. The contact segments of the collector move to a different brush each half turn of the loop, keeping a unidirectional current flowing through the electrical load of the circuit 1. The rotation speed has to be strong determined so that the final result is the expected one.As stated before, the rotation speed influenc es the induced voltage amplitude and frequency. U shaped strong aeonian magnet, shown in Fig. 6. The most challenging part to build was a contact rings and collector unit (Fig. 7). It was ade of three printed circuit board disks, coaxially mounted on the rotating axis. The two smaller disks were kept with their entire conductive layer and were intended to supply the generated AC voltage. The conductive layer of the larger disk was cut into two halves, in order to implement the collector, which mechanically rectifies the generated AC voltage.Figure 5. Coil with iron core. DC output Figure 3. DC generator. Figure 6. Permanent magnet used to induce a voltage in the coil. Figure 4. DC generator output. 4. Generator description Instead of a simple loop, an iron core coil with 1241 turns of O,16mm2 varnished copper ire was used. The iron core and its windings are shown in Fig. 5. The magnetic field used to induce a voltage between the coil terminals was provided by a Figure 7. Three coa xial printed circuit board disks with coil on top. 45 Fig. 8 and Fig. 9 illustrate how the rings and collector unit was built in a more comprehensive way.In Fig. 8, a cross-section of this unit is shown, revealing how electrical connections were made one terminal of the coil was connected to one of the smaller disks and to one of the halves of the larger disk (collector) the other terminal was connected to the other smaller disk and to the ther half of the larger one. Fig. 9 shows a panoramic view of the assembly and the generator outputs responsible electrical load. In order to make the generator operate properly, the DC output brushes positions must(prenominal) be displaced by 1800 from each other.The AC output brushes may be placed anywhere on the respective disks. coil Copper wire dielectric Copper Solder Figure 8. Connecting the coil to the three coaxial printed circuit board disks. power could be easily measured, some sort of mechanical power meter was needed and it was not available. There are always mechanical and electrical power losses in the process of ransforming mechanical energy into electric energy. Mechanical losses may be reduced by lubricating friction points. The generator was put to rotate at 3000RPM the measured induced voltage was 1,2V peak-topeak, with a 50Hz frequency. . Conclusions Spinning a wire loop within a uniform magnetic field in a convenient fashion induces a voltage between the loop terminals. Rotation speed influences the induced voltage amplitude and frequency. If an electrical load is connected to the loop terminals, a current will be established in the circuit. The current generated by a basic electrical generator is alternating current. If the generator s intended to supply direct current, it must have a device working as a mechanical rectifier the collector.A device capable of generating both AC voltage and DC voltage has been presented. A coil attached to a shaft spins within the magnetic field ofa U shaped magnet. T hree conveniently designed conductive disks allow either with alternating current or direct current. This device is very useful to illustrate the principles of electrical energy generation. It also shows the main similarities and differences between AC and DC generators the working principle is the same for both machines, but the AC generator has contact rings and the DC enerator has a collector.

Pride And Prejudice Diary Entry

Dear Diary,Little did I know about Mr.Bingley and Mr.Darcy. I heard some good things about them however nothing truly relevant. But it was my time to find out everything I needed to know about them at a small dinner fierceowship that some ane organised. It doesnt matter how the party will be, I want to know who is going to be there, and most importantly, what will happen. I spent the whole solar day shopping for my dress, and some accessories. I bought a diamond necklace and I was ready to gossip about the new gentlemans in town. I enter the dinner without socialize to much, I didnt want to get distracted, I was keeping my prize.I saw one of the two dancing, how did I know it was them? Every single one of my ace was spying on those two guys. It must have been them. I get closer and closer and I just cant avoid to notice how one of the two looks so arrogant and bored. His face had no emotion, the little emotion he portrayed was negative. He was bored and wasnt even dancing. How ever, the other one had an endless smile on his face and he was undeniably having fun like a real gentleman. I go there and find out that the boring, arrogant looking one was Mr.Darcy. So obviously the other one was Mr.Bingley who was flirting with a girl. I couldnt be bothered to even look at the girl because I was so distracted from this amazingly fascinating man.Mr.Bingley stops dancing for a while and speaks to Mr.Darcy but Im not near enough to listen to what they are saying. I get out Mr.Darcy pointing at Lizzy and, Lizzy offended. Her eyes were full of tears. What did he say? Why did she cry?She is tolerant but not handsome enough to tempt me. These are the manner of speaking Mr.Darcy said about Lizzy. No wonder she got offended.His character was decided, he is an arrogant and exuberant person. I stop concentrating on Mr Darcy because he is too boring for me, I need some interesting things. Some gossip.I run to my girlfriends and they immediately point at Mr.Bingley. Yes, I knew he was dancing with a girl, but I looked at his eyes and I was shocked. His eyes fell in love. No wonder he had a huge smile on his face, he fell in love with Lydia. Meanwhile I notice that Mr.Bennet announces that he had already met Mr.Bingley. He didnt tell the wife or the daughters. Why? Well I dont really care. He probably did it to contradict his family. He is a nice man but he is always playing jokes and tricks on everyone. Especially the wife. I cant remember these two individuals are married, they are like day and night, two complete different things. I see the two of them discussing about inviting Mr.Bingley to dinner. The night was over.What will croak Next? Only I know And who am I? Thats a secret Ill never tell, the only one.

A Million Little Pieces

Is James Frey the next Great American author? A Million Little Pieces is the supposed bread and butter of James Freys six week rehabilitation from alcohol addiction, when he was 23 years old.The graphic results caused by Freys complex, along with the deeply root relationship America has with alcoholism allowed this novel to become a best seller. This is despite the fact that the majority of instances presented in this book to be actual occurrences are, in fact, fiction.The story follows James Freys struggle with addiction. It begins with him in dire straights. In this essay, I allow prove and show why James Frey is a aspect for possibly being considered the greatest writer of the new generation.Frey wakes up on an airplane half dead with absolutely no recollection of how he got there. With a hole in his cheek, his nose broken and barely able to walk, Freys family picking him up from the airport has absolutely no choice but to getting even him to rehab. In rehab, Frey is forced to quit alcohol and drugs cold-turkey, which would be reasonably difficult for anyone in his assumed position.He claims to be addicted to practically e really drug, on eliminate of his addiction to alcohol he claims to have an extensive criminal record, which makes it rattling(prenominal) clear to the reader that he was a self destructive youth. But, I in person feel some of his depictions are a bit over the top. The signature line from this book is, I am an Alcoholic and I am a Drug Addict and I am a Criminal. This also seems to be more of a persona played out by the main constituent as apposed to an actual depiction of Frey himself.The fact that Frey has dental surgery without any anesthetics, and the idea that he gets into numerous fights with whomever crosses his path, these are all examples of how Frey tries to create a defective persona for himself. As an epic hero, Frey serves well, but once it is discovered that his book is fictional, I think it becomes even more complex and actually makes a very saying statement about the author.I do commend Frey on his writing style. It is very unpredictable and unique to him. His linesThe fact that it is known that Frey fabricated the truth, and that the book is very loosely based on fact, it leaves very little room for sociological interpretations. There are moments in Freys rehabilitation when he meets with other fire addicts.He compares their addictions to his own, and in turn forms a rational perception of his own problem. He eventually uses this new self perception for the purpose of his own rehabilitation.This is a very trying and uplifting moment for the reader, whether it is fictional or not. The six week experience draws a lot of emotion out of Frey, and if you feel for the character you will embody these emotions along with him. Freys realization about addiction is a sincere take that is universally true.

Nature vs. Culture in Odyssey Essay

The imagery of nature and culture connects every scene from Book 9 to Book 12 in Odyssey. On the land of Cyclopes, Odysseuss encounter with the one-eyed uncivilized giant unveils Odysseuss cleverness as a civilized gentlemans gentleman being Nevertheless, Odysseus eventually fails to overcome the flaw of valet de chambre beings characteristic, as he tells Polyphemus about his real mortal identity when he is sailing away, which ultimately brings Poseidons revenge to Achaeans. At one point homophile civilization teaches Odysseus the skill of using wooden staff and wine to fight with Polyphemuss strength, but the nature of superpower can easily surpass human beings wisdom. When Circe in Book 11 turns Odysseuss men into pigs, when the blind prophet Tiresias foretells Odysseuss fate, and when Zeus punishes Odysseus with another storm, Odysseus and his men are powerless but take their destiny.The conflict between nature and culture connects humans world with gods world, thus makes eve ry story interesting to follow as uncivilized creatures possess human characteristics and civilized human beings possess limited superpower. For example, in book 10, when Circe turns Odysseuss men into pig, Odysseus can only overpower Circe by following Hermes instruction submit this herb, which is one of great virtue, and keep it about you when you go to Circes house, it will be a talisman to you against every kind of mischief(Book X, 54). When Odysseus rushed at her with sword drawn, Odysseus is in a position where he is even able to overcome the power of an heavenly creature.Both Polyphemus and Circe represent the power of nature, as their world does not have any law or morality, while odysseuss journey represents the power of culture, as the man of culture defeats powerful immortal creatures through cleverness and self-restraint.

Crank Mechanism

Name Monish Kumar (S11065194) The University of the South Pacific MM313 dynamical Systems experiment 2- Crank Mechanism Aim To investigate the relationship between piston work shift and ball slant for different ratios between the connecting rod and the water ice. Also to look at the relationship between the turn of events moment on the crank sleep with and crank angle for a given force on the piston. Equipment and Instrument Introduction A crank is an arm link up at right angles to a rotating shaft by which reciprocating motion is imparted to or received from the shaft. It is used to convert circular motion into reciprocating motion, or vice-versa.The arm may be a bent portion of the shaft, or a separate arm attached to it. Attached to the kibosh of the crank by a pivot is a rod, ordinarily called a connecting rod. The end of the rod attached to the crank moves in a circular motion, while the other end is usually constrained to move in a linear sliding motion. Theory Figur e 1. 0 skidder crank mechanism The slider crank mechanism as shown in figure 1. 0 is a kinematic mechanism. The piston displacement from the top dead centre, x, can be determined from the geometry of the mechanism, in terms of the lengths of the connecting rod, L, and crank, R, and the crank angle, ? can be expressed as x=L+R-(Lcos? -Rcos? ) Also from the geometry, it can be seen that R lousiness? =Lsin? And sin? =sin? n Hence cos? =1+sin? n21/2 Where n is a ratio n=LR Procedure Part A 1) No weights and hangers required, the unit initial starting position 0 in the protractor is setup and 90? and 270? protractor positions to be in line with the level lines in each side. 2) The unit is to be setup in its highest point, Top dead centre point was used to work out the displacement value 3) The mounted disc was turned 30? nd the displacement was noted on the results table, this meter was again repeated for different angles and different crank positions. Part B Results PART A accede 1 R esults of Piston Displacement Crank angle Displacement P1 (mm) experiment P1 (mm) possible action P2 (mm) experiment P2 (mm) theory P3 (mm) experiment P3 (mm) theory 0 0 0 0 0 0 0 30 3 3. clxxx748214 5 4. 252344481 7 5. 324742758 45 7 6. 86291501 10 9. 20565874 13 11. 55001055 60 12 11. 51142198 17 15. 51081741 20 19. 51263112 90 22 22. 02041029 31 30. 01960212 39 38. 2202662 120 31 31. 51142198 45 43. 51081741 53 55. 51263112 cxxxv 35 35. 14718626 50 48. 80363849 63 62. 4616988 150 38 37. 82176437 53 52. 74976709 68 67. 67857183 one hundred eighty 39 40 56 56 71 72 confuse 2 calculation of the angle ? Crank angle ? 0 0 30 5. 73917 45 8. 130102 60 9. 974222 90 11. 53696 120 9. 974222 cxxxv 8. 130102 150 5. 73917 180 1. 40E-15 interpret of Displacement (mm) vs. Crank angle position (? ) Sample Calculation For Displacement P1 at 30? crank angle. To find, ? , n = 5 sin? =sin? n ?=sin-1sin? n=sin-1sin305=5. 73917?To calculate the notional displacement, x x=r1-cos? +nr(1-cos? ) x= 201-cos30+nr1-cos5. 73917=3. 180748214 mm Discussion 1. After plotting the chart of Displacement versus the crank angle position, the graph show that the experimental values and the theoretical displacement can be compared, the experimental plot and the theoretical plot are almost same. 2. From the results graph the graph show that the measured displacement follows the theoretical rick very well. The maximum difference between the experimental and theoretical displacement is 2 mm. 3. For full revolution i. e. 60? the motion of the piston is close to simple harmonic, after 180? the displacement will piecemeal decrease to 0, it will form a cosine graph. PART B Piston Balance and Forces Table 3 Piston balance and forces Angle (? ) No added Piston Weight P3 (N) 4N Added Piston Weight P3 (N) LHS RHS LHS RHS 0 4. 9 4. 9 4. 9 4. 9 30 5. 3 4. 9 5. 8 4. 9 45 5. 5 4. 9 6. 1 4. 9 60 5. 7 4. 9 6. 3 4. 9 90 5. 8 4. 9 6. 2 4. 9 120 5. 5 4. 9 5. 8 4. 9 135 5. 3 4. 9 5. 6 4. 9 150 5. 1 4. 9 5. 5 4. 9 180 4. 9 4. 9 4. 9 5. 3 225 4. 9 5. 3 4. 6. 5 270 4. 9 5. 4 4. 9 6 315 4. 9 5. 5 4. 9 5. 7 Graph of Weights vs. Angle (No added Piston Weight P3 (N)) Graph of Weights vs. Angle (4N added Piston Weight P3 (N)) Discussion 1) Experimental results was not satisfactory, there was some errors made which was ascribable to friction between the mounted disc and the protractor. 2) After looking at the results graph the greatest measuring stick of force approximately at 60? to 90? for no added piston weight. The weight is 5. 8 N at LHS whereas for 4N added piston weight the greatest amount of force is 6. 5 N at 225? RHS. cobblers lastThe kinematic motion of the crank mechanism can be expressed in terms of the lengths of the crank and the conrod, and the displacement of the crankshaft. The experimental measurements of piston displacement agree with the prediction of a theoretical model of the piston motion. ascribable to friction errors were made in the second part of the experiment but still manage to pay back the results to find out the greatest amount of force being exerted on crank mechanism. Reference Experiment 2 Crank Mechanism. (2013). Suva, Fiji Islands. Kearney, M. (2005, August 15). Kinematics of a Slider- crank mechanism.

Piaget and early childhood

Truss Excelsior College Even though Jean Paging passed over thirty years ago his work is still seen in the classroom today. There are three educational principles that are derived from Piglets supposition that continue to have a major impact on both teacher training and classroom practices, particularly during early churlhood. Discovery learning, aesthesia to childrens courtesy to learn and acceptance of individual differences are the three educational principles that are still impacting the educational atmosphere (Beer, 2010).Discovery learning hikes children to learn through discovery by spontaneous interaction with the surroundings. Teachers place items in their classroom that students can use for exploration and discovery. Children can explore art supplies, measuring tools, puzzles, table games, expression blocks, etc. To enhance learning. Teachers dont readily present verbal knowledge in this setting but encourage discovery by these tactile means (Beer, 2010). Sensitivit y to childrens readiness to learn is a nonher principle derived from Pages theory.In this environment teachers introduce activities that build on childrens current intending, challenging their incorrect ways of viewing the world and enabling them to practice newly discovered themes. However if the child doesnt not show interest or readiness the teachers ordain not teach them until they show interest or readiness. Lastly acceptance of individual differences, gives reliance to Piglets theory that children undergo the same stages of development, they Just do it at different rates. For this reason teachers must fancy activities for small groups and not the whole class.Evaluations must be related to the childs previous development rather than an mediocre based on normative standards or related to peers in the same age group. This allows for learning orient to individual differences (Beer, 2010). Although there are three main principles of Piglets theory still found in the classroo m today, her also theorized that there are limitations to early childhood thinking. According to Jean Pigged, egocentrics, conservation, concentration and reversibility and the lack of class-conscious classification, are limitations to early childhood thinking.These limitations are aspects in the operational stage of his cognitive development theory (Beer, 2010). Egocentrics, deals with childrens ability to see things form anothers point of view. Pigged conducted a three mountains problem, in which a doll was displace behind three distinctive mountains with the larger one facing the doll and the smaller ones facing the child. When asked to separate a picture from the dolls point of view they would only chose the picture that represented what they saw from their point of view.Conservation is explained as physical characteristics of objects remaining the same even when their outward appearances change. In a demonstration a child is shown devil glasses with equal amounts of liquidn ess. The child acknowledges that the two glasses have the same volume of liquid. He then pours the liquid of one glass into a taller glass. Children on the operational phase of thinking will say that the glass that is taller has more liquid even though they didnt see any additional liquid poured to increase volume or any liquid removed to decrease volume.This task also explains two other aspects of his theory, concentration and reversibility. In this experiment the children focus, or center on the height of the glass. They do not process the fact that the changes in height and width are what make the liquid appear taller. This is the predate behind concentration. Irreversibility is also at play here. The children are not able to reverse the process and think that if she pours the taller glass of liquid back into the same glass it was poured out of it would take on the original crop from the original glass..

8 mile film analysis Essay

The 2009 get hold of, 8 air mile, parallels the legitimate sprightlinessspan story of multi-platinum artist, Eminem. Directed by Curtis Hanson, the cinema take a leaks place in 1995, a time where work stoppage melody was growing and Detroit, Michigan had hit an all time low. Eminem (formally referred to as Marshall Mathers) plays prize, a hopeful rapper from the poor side of Detroit and stars interchangeable Brittany Murphy, Kim Basinger, and Mekhi Phifer all play key roles in the film. The film follows the lyrically twaddlented prize, also known as Rabbit by his friends, and his struggle to become a cleanned rapper in a predominately black field. He also struggles with dealing with his alky mother, poor affable status, and his rival gang who call themselves The Free globe (8 ml, Wikipedia.com). The film effectively portrays the rap competition in Detroit and Jimmys fight to be on top. 8 land miles urban tale raises questions ab kayoed stereotypes, clan separation , and segregation, while gaining the viewers respect for blame music and successfully developing flakes and victimization real life matters to reproduce a true story.The film begins with Jimmy Smith (Eminem), a issue and unhappy blue-collar worker from a poor family, struggling with different aspects of his life. He has moved endorse north of 8 land mile Road to the run floor laggard home in Detroit, Michigan of his alcoholic mother, Stephanie (Kim Basinger), his little sister Lily (Chloe Greenfield), and Stephanies abusive live-in boyfriend Greg (Michael Shannon). Jimmy is focused on brookting his music carg one and solitary(prenominal)(a)r started, only when he gain vigorms unable to catch a break. Jimmy comes to realize that his life has remained generally the same since he graduated high school. (8 Mile, rottentomatoes.com) At first, he considers himself a victim of his circumstances and blames others for his problems. Over time, though, Jimmy begins to take more(p renominal) responsibility for the direction of his life.His newly established descent with Alex (played by Brittany Murphy) ends when Jimmy walks in on his friend Wink (Eugene Byrd) having sex with her. Jimmy beats up Wink, which later causes Wink to join forces with Jimmys enemies, a gang of rappers known as the leaders of the Free World. Later, Wink and the Free World gang jump him startside of his mothers trailer, but when one member pulls a gun on Jimmy, Wink stops him and they leave. Jimmysfriends run through hailed him throughout the film as an incredible rapper, and his friend Future (Mekhi Phifer) puts pressure on Jimmy to get his revenge by competing against the Leaders of the Free World at the next rap battle. (8 Mile, wikipedia.com) The battle acts as a final conflict with the Leaders of the Free World gang who extradite harassed Jimmy throughout the film. It has three rounds, and in each of them Jimmy confronts a member of the gang. Jimmy wins both of the first two r ounds and in the last round, he is paired against Papa Doc (Anthony Mackie), the tourneys most feared battler and Jimmys main thwarter throughout the film. Jimmy is informed that Doc knows all his weak points, so he decides to address them with his freestyle.Jimmy acknowledges without shame his lower-class sporting trash roots and the humiliations the Free World gang contain inflicted on him, and then uses the difficult life he has had as basis to reveal the truth about Papa Doc. With little to say in rebuttal, Papa Doc gives the microphone back to Future and Jimmy wins the battle. As Jimmy leaves the venue, Future suggests that he stay and celebrate his victory while also offering a position that would allow him to host battles at The Shelter. Jimmy turns him down, claiming he has to get back to work and to find success his own way. He then starts walking back to work, feeling more confident about his future. (8 Mile, wikipedia.com) The depiction is titled after a main street in Michigan, 8 Mile pathway. 8 Mile Road has carried major cultural significance it has served as a physical and cultural dividing line surrounded by the wealthier, predominantly white northern suburbs of Detroit and the poorer, predominantly black urban center (Michigan highway). The road plays a major man in the film and is the reason for the two different rap crews. One rap assemblage, Three One-Third is the one that Jimmy and his friends, Dr. Iz, Cheddar Bob, Future and Sol are a part of. Their group name is a representation of the slums they live in, with their area code beingness 313. Most of them are black, have old cars and live in run down homes. Jimmy lives in a trailer park with his alcoholic mother, and is often stereotyped as white trash. Throughout the cinema the grammatical cases consultation the road to one a nonher aware of its cultural meaning. Jimmy raps about it repeating in one of his freestyles, repeating, Everybody from the 313 put your motherf***ing hands up and follow me (The 10 Most Memorable Rap Lines From 8 Mile) He also raps Im gonna turn around with a great smile, and walkmy white ass back across 8 Mile (8 mile lyrics).The Free World, the second group in the film, lives on the northern side of 8 Mile. They are part of a middle class Detroit and their crew includes Papa Doc, Wink, Lyckity Splyt, and Lotto. They own guns, dress better, and have more control over the Detroit area imputable to their higher social status. They have the upper hand in the hip-hop game and are convinced they have the better connections and are gonna make it before Rabbit does. Papa Doc even owns an escalade in which they all pull up in when press release to beat up Rabbit, proving they are the antagonist in the film. 8 Mile is non only the title of the film but also the racial boundary that sets imaginary lines in class separation in the movie and in Detroit today.The notorious road is not the only racial dispute in the film. Rabbit struggles multiple times throughout the movie simply because he is white. In the opening scene, you see Rabbit in a club sewer preparing to rap battle. As he leaves the bathroom he passes the bouncer to proceed backstage. He quickly gets halt by the bouncer and the large black man asks Where the hell do you think youre going? Rabbit looks startled and begins to argue, claiming that the bouncer had just seen him leave to go the bathroom. When thebouncer replies, I didnt see nothin Rabbit gets worked up and screams Man, you just fuckin seen me I just went to the bathroom He continues to fight with the bouncer and is not let in until his black friend, Future, who is respected in the Detroit area, grabs him and lets the bouncer know that hes cool off. (8 mile, imbd.com) When Rabbit goes to rap, the camera focuses on him, then on the crowd where viewers take a mental note of the all black mob he is about to rap to. When the camera focuses back Rabbit, he looks nervous, chokes and walks off st age. This scene really portrays how difficult it is for Rabbit to overcome his disadvantage of being white in, what it seems to be, an all black competition. The opening scene makes Rabbit seem like a panicked white boy, fearful of the judgement of the blacks, but as the movie continues, Rabbits character grows and becomes less afraid. Not only do you see Rabbit becoming less fearful, but he also reveals his soft contact for children. After Rabbit flees from the rap battle, he goes backto live with his mother since he recently broke up with his lady friendfriend whom he lived with. Once he meets his mothers new boyfriend, they get into a verbal argument where Jimmy throws a beer bottle at him. The argument wakes up Jimmys little sister, Lily.Once Lily comes out, even the tone of Rabbits voice changes. He becomes higher pitch and looks at her with a softness in his eyes, and when requested to sing her to sleep, he does so, singing to her a stock he comes up with on the spot. This scene illustrates the complexity of Jimmys character and shows that he is more than just a white trash rapper. Another key moment in the film that shows Rabbit growing as a person is a work scene. When Rabbits car breaks down, hes late to work and upon questioning, Rabbit quickly says its not his fault. A few scenes later, when some other(prenominal) on the job dispute happens, he stops himself from saying that it wasnt his fault and assures his boss that it wint happen again. The audience can realize that Rabbit is maturing and taking responsibility for his actions. Another complex character is Rabbits friend, Dr. Iz. Although his do-rag and large clothing say otherwise, he is a complex character who often looks deeply into things. He says when looking around the city, Man, do you know how many abandoned buildings we have in Detroit?I mean, how are you supposed to take pride in your neighborhood with shit like that next door? And does the city tear them down? No, they too busy b uilding casinos and taking bills from the people. When his friends make it clear that they dont care he says, Did you care when that crackhead raped that little little girl? You think that woulda happened if he didnt have an abandoned house to take her to (8 Mile Quotes)?. He convinces Rabbit to help them burn down the building by evoking feelings of sympathy and saying It could have been Lily. Jimmys quick urge to help shows the compassion that Jimmy has for his sister and how Dr. Iz and the friends who helped burn the house down view in whats right, have morals, and wanted to erase the memory of a helpless girl getting raped.The actors in the film act tremendously well. Eminems portrayal of Jimmy was simple for him to perform considering the character is based on himself. Although the raps are scripted, Eminem delivers them with such strength. The veins in his arm pop out as he raps and his eyes bulge out of his head, truly showing his passion for rap music. The way Kim Basinge r takes the roleas an alcohol dependent and emotionally unstable is brilliant. She carries out her lines with a shaky, uncertain undertone that truly brings out how emotionally unsure she is. Brittany Murphy plays a seductive and sassy young adult seeking to be a model. She administers her lines with her head slightly down and her big eyes looking up. She shakes her head often and closely seems to be on drugs, but it works for her character. Overall, I think the acting was well done, but not too impressive considering the roles they played werent impossibly hard.The music choice in this film is another aspect to take note of. The song opens up with The Shook Ones by Mobb Deep. The famous line in the song, Cause aint no such thing as a halfway twist is played while Jimmy gets ready to rap, and in his final freestyle that line is used again, but by Jimmy this time. Whenever you see Jimmy coming up with lyrics, the instrumental beat comes on and only the haggling that he is rhyming are heard. While he is in the car with his friends, Biggie Smalls productive is playing. The song is an iconic tune that comes to mind when one thinks about rap music. I also believe it is foreshadowing that like Biggie, Jimmy will also become a legend in the hip-hop industry. When Rabbit is getting beat up by The Free World members, the song Gang Stories by South Central Cartel is playing which has a specific line dont be another sucker on my hit list and Jimmy was definitely on the Free Worlds hit list. The music in the movie really adds on the the hip hop, gangster oscillation of the film, and enhances its effectiveness to show what 1995 Detroit was like.Although the movie reviewing website, Rottentomatoes.com only rated the movie a 6.7/10, I would rate the film a 10. I believe that the actors were very into their characters and all of their roles were extremely believable. The scenery is not staged and was actually record right on 8 Mile, furthering the movies credibility. I also believe the music choice magnifies the movies energy. The film rids itself of subplots and complexities, making it a light film to watch, even though its urban and inner city settings weigh it down. I think people who rate it any lower than a seven out of ten lack an ability to see the artistic side of the film and expect it to be grander, when in reality the movie was not created with intent tobe criticized, or make millions. (8 mile chicagoreader.com). Rather, the film was created to give viewers a deeper insight into what Grammy award winning rapper, Eminem, had to overcome to become the amazing artist he is now and although some may not have received a deeper understanding of the life of a wannabe rapper, I certainly did.Works Cited8 Mile. IMDb. IMDb.com, n.d. Web. 30 Jan. 2014.8 Mile (2002). 8 Mile. N.p., 16 Feb. 2002. Web. 30 Jan. 2014. .8 Mile (film). Wikipedia. Wikimedia Foundation, 29 Jan. 2014. Web. 30 Jan. 2014. .8 Mile . Chicago Reader. N.p., n.d. Web. 30 Jan. 2014. .8 mile lyrics. IMDb. IMDb.com, n.d. Web. 30 Jan. 2014. .Eminem. Wikipedia. Wikimedia Foundation, 29 Jan. 2014. Web. 30 Jan. 2014. .Full Cast & Crew. IMDb. IMDb.com, n.d. Web. 30 Jan. 2014. .8 Mile Quotes. Quotefully Browse Your Favorite TV Show and image Quotes. Quotefully Browse Your Favorite TV Show and Movie Quotes. N.p., n.d. Web. 30 Jan. 2014. .The 10 Most Memorable Rap Lines From 8 Mile. Vibe. N.p., n.d. Web. 30 Jan. 2014. .

Based Data Mining Approach for Quality Control

sieveification-Based entropy Mining Approach For feeling harbour In Wine Production GUIDED BY SUBMITTED BY Jayshri Patel Hardik Barfiwala INDEX Sr No salmagundi of address Page No. 1 Introduction Wine Production 2 Objectives 3 Introduction To entropyset 4 Pre-Processing 5 Statistics Used In algorithmic programic programic rules 6 Algorithms Applied On Dataset 7 Comparison Of Applied Algorithm 8 Applying examination Dataset 9 Achievements 1. inst tot every last(predicate)yation TO WINE PRODUCTION * Wine diligence is modernly growing come up in the market since the make it decade. However, the prime(prenominal) factor in booze has become the important contend in vino making and selling. * To meet the increasing demand, assessing the bore of fuddle is necessary for the wine-colored application to prevent tampering of wine whole step as well as maintaining it. * To remain competitive, wine industry is investing in juvenile technologies like data mining for analyzing taste and opposite properties in wine. Data mining proficiencys provide more than spiritmary, just now worth(predicate)(predicate) entropy such as patterns and relationships mingled with wine properties and human taste, all of which earth-closet be apply to improve termination making and optimize chances of victor in both marketing and selling. * Two key elements in wine industry be wine certification and fictional character assessment, which atomic number 18 usually conducted via physicochemical and sensorial rises. * Physicochemical tests argon lab-based and are employ to characterize physicochemical properties in wine such as its density, alcohol or pH ranges. * pie-eyedwhile, sensory tests such as taste preference are performed by human experts.Taste is a contingent lieu that indicates quality in wine, the success of wine industry leave behind be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are ali ke piece useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * casting the complex human taste is an important focus in wine industries. * The main purpose of this chew over was to predict wine quality based on physicochemical data. * This study was also conducted to identify bug outlier or anomaly in consume 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 array 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 ground Theories is a free Internet repository of analytical datasets from several areas. All datasets are in textbook files format provided with a short description. These datasets received recognition from many scientists and are cl aimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASETTitle Wine woodland Data engraft Characteristics Multivariate Number Of Instances WHITE-WINE 4898 RED-WINE 1599 theatre Business Attribute Characteristic Real Number Of Attribute 11 + Output Attribute wanting(p) Value N/A * Attribute Information * Input variable quantitys (based on physicochemical tests) * Fixed acidulousness Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * fickle 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 i n the brewing water. * issue Sulfur Dioxide Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide Amount of free and combined south dioxide present in wine. (In mg per liter) Used mainly as preservative in wine process. * constriction The density of wine is close to that of water, dry wine is less and sweet wine is high(prenominal). 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 time value) * Sulphates Amount of sodium metabisulphite or kibibyte metabisulphite present in wine. (In mg per liter) * Alcohol Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * feel (score between 0 and 10) White Wine 3 to 9 rubor 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 unalike lacks of the information in the data so urce.Following different process are carried out in the preprocessing reasons to make the dataset supple to perform miscellanea process. * Data in the real world is dirty because of the following reason. * Incomplete scatty specify values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation= * Noisy Containing wrongful conducts or outliers. * E. g. recompense=-10 * Inconsistent Containing discrepancies in codes or grade. * E. g. Age=42 Birthday=03/07/1997 * E. g. Was rating 1,2,3, Now rating A, B, C * E. g. Discrepancy between duplicate records * No quality data, no quality mining results Quality decisions essential be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in d unrivaled in the Data Preprocessing are, * Data Cleaning * Fill in deficient values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of threefold da tabases, 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 and 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 numericalal data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only requisite in data pre-processing. * Here, NumericTonominal, InterquartileRange and RemoveWithValues penetrates are used for data pre-processing. * NumericToNominal deform weka. sieves. unsupervised. attribute. Nu mericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute Quality in both dataset (Red-wine Quality, White-wine Quality) prevail a type Numeric. So after applying this filter, class attribute Quality convert into type Nominal. * And Red-wine Quality dataset remove 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 fundamental values based on interquartile commences. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds cardinal more fields which names are Outliers and ExtremeValue. And this fields has two types of label No and Yes. Here Yes label indicates, there are outliers and extre me values in dataset. * In our dataset, there are 83 extreme values and one hundred twenty-five outliers in 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 AttributeIndex and NominalIndices. * AttributeIndex choose attribute to be use for alternative and NominalIndices choose range of label indices to be use for filling on nominal attribute. * In our dataset, AttributeIndex is last and NominalIndex is also last, so It will remove first 83 extreme values and whence 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 graded WHITE-WINE Volatile_Acidity(2) 0. 1 248 0. 0406 Volatile_Acidity(2) Total_sulfer_Dioxide(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 recogniseion of attributes is performed automatically by WEKA use 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 incompatible algorithms that whoremonger be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics circulars. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa co high-octane, is a performance criterion or index which canvass the agreement from the lesson with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance ag reement. * Kappa statistic describe that our prediction for class attribute for given dataset is how much near to effective values. * Values Range For Kappa Range settlement lt0 POOR 0-0. 20 SLIGHT 0. 21-0. 40 plum 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 * humble authoritative misunderstanding (MAE) * Mean imperative misplay (MAE)is a quantity used to measure how close forecasts or predictions are to the eventual essences. The mean absolute error is given by, Mean absolute geological fault For Wine Quality DataSet Algorithm White-wine Quality Red-wine Quality K-Star 0. 1297 0. 1381 J48 0. 1245 0. 1401 Mult ilayer Perceptron 0. 1581 0. 1576 * extraction Mean square up defect * If you have some data and try to make a curve (a formula) fit them, you butt graph and see how close the curve is to the capitulums. Another measure of how well the curve fits the data is get-go Mean square up misconduct. * For each data point, CalGraph calculates the value ofy from the formula. It subtracts this from the datas 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, start Mean Square break is Root Mean Squared erroneous belief 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 copulation Squared Error * Theroot sexual congress shape erroris sexual intercourse to what it would have been if a ingenuous predictor had been used. More specifically, this simpl e predictor is just the average of the actual values. Thus, the relative square error takes the nub squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared errorone reduces the error to the same dimensions as the quantity being predicted. * Mathematically, theroot relative squared errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0.So, theEiindex ranges from 0 to infinity, with 0 ticking to the ideal. Root recounting 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 % * coitus unattackable Error * Therelative absolute erroris very s imilar to therelative squared errorin 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, therelative absolute errorEiof an individual programiis evaluated by the equation * whereP(ij)is the value predicted by the individual programifor sample casej(out ofnsample cases)Tjis the target value for sample casej andis given by the formula * For a perfect fit, the numerator is equal to 0 andEi= 0. So, theEiindex ranges from 0 to infinity, with 0 corresponding to the ideal.Relative infinite 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 % * di fferent swans * There are four achievable outcomes from a classifier. * If the outcome from a prediction ispand the actual value is alsop, then it is called atrue controlling(TP). * However if the actual value isnthen it is said to be afalse positive(FP). * Conversely, atrue invalidating(TN) has occurred when both the prediction outcome and the actual value aren. Andfalse negatively charged(FN) is when the prediction outcome isn while the actual value isp. * secure 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 immediate the ROC curve is to the t op left corner of the ROC graph the better the performance of the classifier. * Sample ROC curve (squares with the economic consumption 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 relevant e. g. in decision maneuver models or rule sets. * disavow, precision and F-Measure There are four possible results of classification. * Different conspiracy of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of these 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. * muddiness intercellular substance * A matrix used to summarise 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 acquisition by analogy. * The pedagogy 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 unk nown sample. * These k training samples are the k-nearest neighbors of the unknown sample. Closeness is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is allegeed 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 reach a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners washbasin amaze 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 techniques. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that ti me.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This clean-livingthorn 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 chassis Model 0. 02 Seconds Stratified Cross- organisation (10-Fold) * Summary justly class Instances 3307 70. 6624 % incorrectly classified advertisement 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 * small Accuracy By Class TP pace FP Rate Precision Recall F-Measure ROC domain 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 * feat Of The Kstar With Respect To A Testing manikin For The White-wine Quality DatasetTesting rule T raining Set Testing Set 10-Fold Cross Validation 66% fragmented 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 Statistic 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 acting Training Set Testing Set 10-Fold Cross Validation 66% discontinue 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 secern us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (classification) 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 avai lable 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 better(p) 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 within 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 Squared 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 Quality 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 comment layer, one or more hidden layers, and an sidetrack 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 distribute through the input layer and are then heavy and fed simultaneously to a twinkling layer of neuronlike units, known as a hidden layer. The outturns 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 networks 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-win e Quality Dataset Time taken to build model 36. 22 seconds Stratified cross- brass * 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. cv 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 Correctly 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 one hundred sixty 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 usingKstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier achieved 61. 7111% accuracy. For the egg 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 compulsive Rate (Avg) 0. 707 0. 585 0. 555 False coercive Rate (Avg) 0. 2 0. 21 0. 279 * Chart Shows The Best Suited Algorithm For Our Dataset (Measures 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 cha rt 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 exam DATASET Step1 Load pre-processed dataset. Step2 Go to classify tab. Click on choose button and select lazy brochure 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 crude ) as shown below. Step 4 Save this data set as . rff file. Step 5 From test option display board select supplied test set, click on to the set button and capable the test dataset file which was lastly created by you from the disk. Step 6 From Result list panel panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click Re-evaluate model on current test set Step 7 Again right click on Kstar algorithm and select v isualize classifier error Step 8Click on fork up 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 production, 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 hint 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 class-conscious alcohol as the highest in both datasets, hence the alcohol level is the main attribute that determines 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.