Saturday, November 9, 2019
Bank of England Essay
The Bank of England, is the central bank of the United Kingdom . Established in 1694, it is the second oldest central bank in the world, and the world’s 8th oldest bank if you include commercial banks. It was established to act as the English Government’s banker, and to this day it still acts as the banker for the U.K Government, the Bank was privately owned and operated from its foundation in 1694 but it was nationalised in 1946. The bank of England has about  £156 billion pounds worth of gold ingots as a backup if people start to ask for their money back , the bank also acts a custodian for other counties gold, including Germanys and various other counties. History The establishment of the bank was devised by Charles Montagu, Earl of Halifax, in 1694.He suggested loan of  £1.2m to the government; in return the subscribers would be incorporated as The Governor and Company of the Bank of England with long-term banking privileges including the issue of notes. The Royal Charter was granted on 27 July through the passage of the Tonnage Act 1694. Public finances were in poor a condition at the time that the terms of the loan were that it was to be serviced at a rate of 8% per year, and there was also a service charge of  £4,000 per year for the management of the loan. The first governor was Sir John Houblon, who is depicted in the  £50 note issued in 1994. The Bank’s original home was in Walbrook in the City of London, unitl it moved to its current location on Threadneedle Street, and thereafter slowly acquired neighbouring land to create the bulding seen today. When the idea and reality of the National Debt came about during the 18th century this was also managed by the bank. By the charter renewal in 1781 it was also the bankers’ bank – keeping enough gold to pay its notes on demand until 26 February 1797 when war had so diminished gold reserves that the government prohibited the Bank from paying out in gold. This prohibition lasted until 1821. The 1844 Bank Charter Act tied the issue of notes to the gold reserves and gave the bank sole rights with regard to the issue of banknotes. Private banks which had previously had that right retained it, provided that their headquarters were outside London and that they deposited security against the notes that they issued. A few English banks continued to issue their own notes until the last of them was taken over in the 1930s During the period which lasted from 1920 to 1944, the Bank made deliberate efforts to move away from commercial banking and become a central bank. In 1946 the bank was nationalised by the Labour government. On 6 May 1997, following the 1997 general election which brought a Labour government to power for the first time since 1979, it was announced by the Chancellor of the Exchequer, Gordon Brown, that the Bank of England would be granted operational independence over monetary policy. Under the terms of the Bank of England Act 1998 which came into force on 1 June 1998. Location The Bank’s headquarters has been located in London’s main financial district, the City of London, on Threadneedle Street, since 1734. The busy road junction outside is known as Bank junction as well as the tube terminal called ‘Bank’. Employees The bank currently employees around 1900 people. Sir Mervyn King is the most executive figure within the bank, he then has two deputies under him who are called Charles Bean and Paul Tucker, there are then 10 directors under them responsible for the everyday decisions of the bank and its subsidiaries. Functions of the Bank The Bank of England performs all the functions of a central bank. The most important of these is supposed to be maintaining price stability and supporting the economic policies of the British Government, thus promoting economic growth. There are two main areas which are tackled by the Bank to ensure it carries out these functions efficiently. Monetary stability – stable prices and confidence in the currency are the two main criteria for monetary stability. Stable prices are maintained by making sure price increases meet the Government’s inflation target. The Bank aims to meet this target by adjusting the base interest rate, which is decided by the Monetary Policy Committee, and through its communications strategy, such as publishing yield curves. Financial stability -maintaining financial stability involves protecting against threats to the whole financial system. Threats are detected by the Bank’s surveillance and market intelligence functions. The threats are then dealt with through financial and other operations, both at home and abroad. In exceptional circumstances, the Bank may act as the lender of last resort by extending credit when no other institution will. The Bank of England has a monopoly on the issue of banknotes in England and Wales. Scottish and Northern Irish banks retain the right to issue their own banknotes, but they must be backed one to one with deposits in the Bank of England, excepting a few million pounds representing the value of notes they had in circulation in 1845. Since 1998, the Monetary Policy Committee (MPC) has had the responsibility for setting the official interest rate. However, with the decision to grant the Bank operational independence, responsibility for government debt management was transferred to the new UK Debt Management Office in 1998, which also took over government cash management in 2000. The Bank used to be responsible for the regulation and supervision of the banking and insurance industries, although this responsibility was transferred to the Financial Services Authority in June 1998. After the financial crises in 2008 new banking legislation transferred the responsibility for regulation and supervision of the banking and insurance industries back to the Bank.
Wednesday, November 6, 2019
Complete List The Smallest Colleges in the United States
Complete List The Smallest Colleges in the United States SAT / ACT Prep Online Guides and Tips You might be interested in going to a small college, but just how small? In general, schools labeled small have fewer than 5,000 students in total. However, quite a few schools are actually at least 10 times smaller than this. In this article I’ll describe the characteristics of small colleges and then provide a list of the smallest colleges in the nation by category. Why Are These Schools So Small? It might seem unorthodox for the enrollment of an entire college to be the same size as your high school class (or even smaller!). Though definitely uncommon, these schools usually have a solid rationale for keeping their student bodies so tiny.There are several reasons these colleges are particularly small: Extremely Specialized Curriculum Often, small colleges have a very specialized curriculum that caters to a narrow demographic of students.Many of the smallest colleges are religious schools of a particular denomination, art schools, or professional schools. The smallest liberal arts colleges usually have a curriculum that emphasizes certain modes of learning and exploration of subjects.For example, some of these schools have a â€Å"Great Books†curriculum,meaning that all students must read a collection of classic texts as part of the college’s universal academic requirements. Began as Parts of Larger Universities Some of these schools were once part of larger universities and then branched off to form their own communities.This goes along with their tendency to be more specialized and attract a much smaller group of prospective students. Dedicated to Personalizing Each Student's Academic Experience These schools are committed to keeping class sizes small and giving each student individualized attention.Often, students can design their own curricula and access a level of guidance and support from professors and advisors that's unheard of at larger schools.Students frequently collaborate with professors and are asked to give self-evaluations. Tiny schools treat the college experience as an evolving dialogue between students, their teachers, and their communities.This enables them to focus less on grades alone and more on learning as an ongoing interactive process. What Is the Tiny College Experience Like? So you can get a sense of what the smallest schools are actually like, I’ve compiled a few student testimonials that provide perspective on the pros and cons of attending these colleges. Thomas Aquinas College "They create an academic bubble of seclusion, quite literally." "The rules are a bit extreme, and never think that someone is not watching. At a school this small, everything gets out in the open." "I admit that this school does wonders with the mind. Thomas Aquinas delves into critical thinking and reading beyond the text." Thomas Aquinas College, Santa Paula California (Harold Litwiler/Flickr) Marlboro College "Marlboro is the best place for independent students who want to take a serious role in the pursuit of their education." "Marlboro does not have class requirements, [so]each student creates a course of study based on their interests and aspirations." "Marlboro College classes expect serious work ethic. Class sizes are small, so sleeping in and missing your 8 AM is not an option if you think your professor won't notice." Marlboro College Neumont University "There is always something to do and it is a very tight, close community who all are willing to help each other when/if someone asks." "There is no leeway for those who just want to coast on by and get a degree for something. This is an active learning environment." "There is no meal plan. You are expected to buy your own groceries and prepare your own meals." As you can see from all of these school quotes, the smallest colleges are often limited in their housing and dining options and campus activities. However, they might be the right fit for students who are interested in a specific academic field or mode of learning.One benefit you can count on is a close bond with professors and other students. List of the Smallest Colleges in the US These are the smallest four-year, non-profit colleges in the nation sorted by type and enrollment number. This list includes schools withfewer than 500 students but more than 50 students because colleges with fewer than 50 students are extremely rare and not relevant to enough students to merit inclusion. All enrollment data is from the College Board's Big Future website. Smallest Arts Colleges College Enrollment Oregon College of Art Craft 109 VanderCook College of Music 122 Visible Music College 127 Art Academy of Cincinnati 175 Pennsylvania Academy of the Fine Arts 188 San Francisco Conservatory of Music 205 Watkins College of Art, Design Film 205 Cleveland Institute of Music 227 Pennsylvania College of Art and Design 260 American Academy of Art 260 Johns Hopkins University Peabody Conservatory of Music 265 San Francisco Art Institute 299 New Hampshire Institute of Art 308 School of the Museum of Fine Arts 349 Moore College of Art and Design 373 McNally Smith College of Music 409 Pacific Northwest College of Art 419 Conservatory of Music of Puerto Rico 433 New England Conservatory of Music 436 Escuela de Artes Plasticas de Puerto Rico 441 Columbia College Hollywood 453 Manhattan School of Music 488 Smallest Religious Colleges For this list, I've focused on colleges that primarily identify as seminaries or Bible colleges. Also, I've excluded religious colleges that only train religious professionals because they are too specialized for most people. College Enrollment Mount Angel Seminary 51 St. Charles Borromeo Seminary- Overbrook 57 Baptist Missionary Association Theological Seminary 58 Southern California Seminary 62 American Jewish University 67 Kentucky Mountain Bible College 76 Hellenic College/Holy Cross 78 New Hope Christian College 87 Crossroads College 96 Clear Creek Baptist Bible College 98 Montana Bible College 100 Huntsville Bible College 8 Southwestern Christian College 123 Boise Bible College 130 Faith International University 144 Nebraska Christian College 145 Jewish Theological Seminary 162 Arlington Baptist University 162 Holy Apostles College and Seminary 164 Baptist University of the Americas 177 Johnson University- Florida 180 Kuyper College 184 Trinity College 189 Trinity Bible College 191 Mid-Atlantic Christian University 192 Criswel College 198 Dallas Christian College 213 Calvary University 223 Virginia Baptist College 227 Barclay College 229 Ecclesia College 232 Central Christian College of the Bible 239 Bethesda University of California 256 John Paul the Great Catholic University 260 Emmaus Bible College 269 Theological University of the Caribbean 271 Appalachian Bible College 274 Marygrove College 285 Beulah Heights University 288 Luther Rice College and Seminary 295 Faith Baptist Theological Seminary 300 Davis College 302 Northpoint Bible College 323 Grace Bible College 336 Piedmont International University 339 Welch College 348 Trinity Baptist College 353 Multnomah University 394 The King’s University 400 Southeastern Baptist Theological Seminary 406 Baptist College of Florida 427 College of Biblical Studies- Houston 428 Lincoln Christian University 464 Columbia International University 486 Williams Baptist University 493 Smallest Engineering, Medical, and Other Professional Colleges College Enrollment California Institute of Integral Studies 50 Northwestern Polytechnic University 52 Webb Institute 98 Rush University 109 Columbia College of Nursing 6 Lincoln University 120 St. John’s College 122 United States Sports Academy 124 Charles R. Drew University of Medicine and Science 169 Bastyr University 197 Saint Anthony College of Nursing 199 Trinity College 213 Amberton University 217 Blessing-Rieman College of Nursing Health Sciences 272 Maharishi University of Management 324 Allen College 329 University of Puerto Rico Medical Sciences 341 Boston Architectural College 343 Franklin W. Olin College of Engineering 380 Bellin College 397 St. Francis Medical Center College of Nursing 406 Cabarrus College of Health Sciences 419 Touro University Worldwide 484 Saint Luke’s College of Health Sciences 490 Smallest Liberal Arts Colleges All the colleges on this list offer a variety of degrees and a complete liberal arts education (but note that some of these schools are religiously affiliated). College Enrollment Thomas More College of Liberal Arts 90 Logan University 98 Antioch University 103 Antioch College 133 University of the West 185 New Saint Andrews College 141 Medaille College- Rochester 145 Sterling College 146 Marlboro College 183 Goddard College 189 College of St. Joseph in Vermont 237 Cottey College 270 Patrick Henry College 277 Alaska Pacific University 296 Randall University 304 Selma University 3 Aquinas College 312 Sweet Briar College 319 St. John’s College 322 Bryn Athyn College 326 Beacon College 348 College of the Atlantic 349 Southern Vermont College 361 Marylhurst University 364 Judson College 366 Thomas Aquinas College 370 Silver Lake College of the Holy Family 388 Bard College at Simon’s Rock 390 Prescott College 391 Warner Pacific University 400 Soka University of America 412 Pine Manor College 419 Naropa University 419 Pine College 426 York College 431 Sierra Nevada College 435 Principia College 455 Penn State- Wilkes-Barre 456 St. John’s College 458 Green Mountain College 468 Golden Gate University 470 Wells College 470 University of Minnesota- Rochester 472 Voorhees College 475 Penn State- Shenango 490 Bennett College for Women 493 Christendom College 493 Penn State- Greater Allegheny 497 What's Next? If you're just starting your college search, you might not be sure whether a big or small college is the best choice for you. Learn about the major differences between the two. Another factor to consider in the college search process is location. Do you want to stay close to home or start over somewhere new? Read this article to find out if a college close to home is the right choice for you. For more advice on how to conduct your college search, read my guide on how to choose the best college for you and my review ofthe top 10 college search websites. Want to improve your SAT score by 160 points or your ACT score by 4 points?We've written a guide for each test about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now:
Monday, November 4, 2019
IT in Supply Chain Management Essay Example | Topics and Well Written Essays - 2500 words
IT in Supply Chain Management - Essay Example The traditional supply chain had limitations caused by power structures, limited information processing ability, and limited coordination and communication paths (Christiaanse & Kumar, 2000). Today organizations are facing complex changes to combat which Mutsaers, Zee and Giertz (1998) proposed the Nolan and Crosson six-stage model. it has become essential for organizations to be flexible and deliver a wide and changing variety of products. This requires a shift from â€Å"make and sell†approach to an externally-oriented â€Å"sense and respond†structure. This in turn implies the need for real time information. Real time information can be feasible only with the application of information technology in the different processes and functions. To meet the changing market requirements, companies have decentralized their value-adding activities by outsourcing and developing virtual enterprises (Gunasekaran & Ngai, 2004). All these highlight the importance of integrating IT with supply chain partners in the virtual enterprise or the supply chain. Demand for new information services like query processing, knowledge sharing and data mining led to the extension of information system engineering to support new, flexible software architecture so that the information system could contain new as well as legacy data and software components (Mylopoulos, 1998). IT has been recognized as a critical factor in the supply chain as they have demonstrated positive contribution to the performance of the firm and the supply chain (Jin, 2006). Technology is essential as it provides direction to the procurement, production and supplies strategies. When suppliers are able to meet customer demands compatibility of exchanges has occurred (Halley & Nollet, 2002). Success of incorporating technology depends upon the personnel’s ability to extract information (Lin & Tseng, 2006). Hence firms rely on technology to increases the flow of information across organizational boundaries and
Saturday, November 2, 2019
Human Recourse Management and Personnel Issues Essay - 1
Human Recourse Management and Personnel Issues - Essay Example There are a number of occasions when assessment is done. Such as when a student is admitted into a high school, he is often required to take some test, likewise, during his educational, time to time, his educations is assessed under certain standards. Finally, when he wished to enter the professional life, there too, he is assessed for certain skills that the employer expects to be essentially present in an employee. Out of all these assessments, the pre-employment assessment is considered to be one of the crucial ones. This is so on the account of the fact that this test will enable him to enter into the professional life through the gate he wants. There is one complexity involved with the assessments. The complexity is that what factors should be tested in order to determine that who is the most deserving candidate is. This complexity is enhanced when it comes to the assessment for the recruitment. So far as the assessment for academic entry such as admission in masters is concerne d, it seems quite logical that you primarily test the academic skills and a bit of Intelligent Quotient etc. But, when we talk about employment, the purpose is to find the deserving candidate and what factors make a person a deserving candidate differs from person to person. From organization’s perspective too, it is also essential to filter the right person so as to make their organization run more effectively and efficiently.
Thursday, October 31, 2019
US Role Essay Example | Topics and Well Written Essays - 500 words
US Role - Essay Example When that was rejected, other presidents tried using diplomacy to settle the conflict like presidents W. Bush’s administration in the 1990s during the gulf war. The diplomacy path has enabled the United States gain confidence of the Arab nations and this facilitated the second camp David peace agreement that was also rejected. In the wake of September 11 terrorist attacks, the Bush administration started siding more with the Israeli because the Arab countries could not be trusted anymore. It also led to declaration of war on the terrorist groups which reside in the middle east and this put to an abrupt stop the peace talks and the mediation role of the United States. Even though this took place and is still taking place, the Obama administration has once again embarked on the peace talks for the sake of the energy products in the middle east. President Obama has constantly been advocating for peace in those Arab countries that are in conflict and once again has gained the trust of the Arab countries and the results of the peace talks are yet to be seen (Simon 2009). United States government has its reasons for seeking peace in the Arab countries. The Middle East is endowed with oil that is an essential commodity in the world and also has other minerals that are interest to the American government. The mediation by the US is a faà §ade so that their reputation as the superpowers and being more in control of the world than the Soviet Union is not questioned or even threatened (Mahler and Mahler 2010). In reality, war in the Arab countries benefits the United States by them pretending to side with no one and hence gaining the support of all the fighting parties and thus continuing to gain the oil and minerals without struggle. If the conflict between the Arab and Israeli ends without the face and hands of US being seen in the peace talks and
Tuesday, October 29, 2019
Outline for arts speech - story of Hamlet Essay Example for Free
Outline for arts speech story of Hamlet Essay IB TOK R3 1. Story of Hamlet A. Hamlet son of late king Claudius, mother remarried less than 2 months after her husbands death. B. Ghost of late king visits Hamlet and tells him that the new king murdered him. C. Hamlet lashes out at everyone around him, including his love Ophelia. D. Hamlet plots to kill king E. Hamlet stages a play called The Mousetrap, in which a king is murdered by his brother, who then takes up with his wife, Claudius freaked out and Hamlet Claudius is guilty. F. Hamlet visited his mother and derides her for taking up such man. G. Polonius, Ophelias father, hid himself in Gertrudes, Hamlets mother, room behind a curtain. When he calls out for help, Hamlet kills him thinking that it is the king. See more: outline format for essay H. Because of the murder, Hamlet is sent to England and when he returns to Elsinore, he sees a funeral-taking place, he finds that Ophelia has drowned. Her brother Laertes, blaming Hamlet for the death his father and sister, challenges Hamlet to a duel. I. At the duel, Laertes poisons his blade to make sure Hamlet will die. At the same time, Claudius inserts a poison pearl into a wine cup in hope that Hamlet will drink it. J. Every important character dies: Gertrude gets to the cup first, and dies. Laertes wounds Hamlet with the poison blade, Hamlet mortally wounds Laertes. Hamlet then finds out that Claudius put poison in the cup and he goes after the king and kills him. Then Hamlet lies down and dies. K. This play is often referred to as the one in which everybody dies. 2. Differences between the two A. Mel Gibson version directed by Franco Zeffirelli 1. Starts differently 2. Only 135 min. cut out huge sections. 3. High number of extra- King actually seems to have power 4. Color -can relate 5. Play-in-play with spoken words- much more importance- used by hamlet as proof. 6. Seemed to be made to retell an old story 7. Switched around to make it more entertaining 8. Said by a critic to be written for the masses a. short b. cut out dialogue- easier for common people to understand c. fun to watch B. Olivier as hamlet directed by himself 1. Starts as the play does 2. Less extras- King seems to rule no one 3. 155 min -missing huge sections 4. Play-in-play in mime less importance done to jab at the king and queen for what they have done 5. New interpretation of an old story 6. Black and White cannot relate to lack of color as well 7. Friends of Hamlet left out a. Rosencrantz and Guildenstern not in b. Allows Hamlet to be less insane 8. Better sword play- build more suspense as to who will win 9. Cinematography changes feeling Darker a. castle is dark and so is the sky, see more of the dark sky b. Humor is cut out 3. Differences in Hamlet A. Soliloquies in Olivier to self allows him to get closer to subjects, inner turmoil, in Zeffirelli it is out loud, insanity 1. Kill Claudius when praying 2. To be or not to be B. In Zeffirelli- acts much more insane- wild eyed, over the top. C. In Olivier- much more reserved, caustic when speaking. 4. Other differences in characters A. Queen Gertrude 1. In Olivier a. actress is 29 years old Olivier is 41- looks strange b. drinks the poison knowing that she will die- to save Hamlet, a noble death 2. In Zeffirelli a. Devastating and tragic death- didnt knew that the cup was poisoned, more of an impact on viewers. B. Ophelia 1. In Zeffirelli a. Completely mad, gives out bones and sticks and calls them flowers 2. Olivier a. Semi-mad, more out of it than insane C. Dead King 1. Zeffirelli a. King looks like he is alive and is just back visiting not freighting 2. Olivier a. Never see the face of the king, comes surrounded in fog b. Scary c. See the murder acted out 5. Conclusion A. Way the director influenced my perception of the story 1. Same story, many of the same lines, same characters, but different feelings emitted from both. a. Zeffirelli humorous, have fun watching it makes the ending more tragic more of a dramatic change b. Olivier is dark always fell that something terrible is going to happen end not as devastating c. Polonius: Words, words, words B. Olivier version constitutes what I believe to be a masterpiece when following Clarks definition from unit four of our book. 1. follows all of the guidelines a. The original play by Shakespeare that it is based off of fills the first 5 requirements as well as the last. b. Oliviers version creates the feeling of complete supremacy of the artists art. Whereas Zeffirellis is entertaining but not a masterpiece. c. Oliviers version won 5 Oscars, Zeffirellis, none. Show preview only
Sunday, October 27, 2019
Lazy, Decision Tree classifier and Multilayer Perceptron
Lazy, Decision Tree classifier and Multilayer Perceptron Performance Evaluation of Lazy, Decision Tree classifier and Multilayer Perceptron on Traffic Accident Analysis Abstract. Traffic and road accident are a big issue in every country. Road accident influence on many things such as property damage, different injury level as well as a large amount of death. Data science has such capability to assist us to analyze different factors behind traffic and road accident such as weather, road, time etc. In this paper, we proposed different clustering and classification techniques to analyze data. We implemented different classification techniques such as Decision Tree, Lazy classifier, and Multilayer perceptron classifier to classify dataset based on casualty class as well as clustering techniques which are k-means and Hierarchical clustering techniques to cluster dataset. Firstly we analyzed dataset by using these classifiers and we achieved accuracy at some level and later, we applied clustering techniques and then applied classification techniques on that clustered data. Our accuracy level increased at some level by using clustering techniques on datas et compared to a dataset which was classified without clustering. Keywords: Decision tree, Lazy classifier, Multilayer perceptron, K-means, Hierarchical clustering INTRODUCTION Traffic and road accident are one of the important problem across the world. Diminishing accident ratio is most effective way to improve traffic safety. There are many type of research has been done in many countries in traffic accident analysis by using different type of data mining techniques. Many researcher proposed their work in order to reduce the accident ratio by identifying risk factors which particularly impact in the accident [1-5]. There are also different techniques used to analyze traffic accident but its stated that data mining technique is more advance technique and shown better results as compared to statistical analysis. However, both methods provide appreciable outcome which is helpful to reduce accident ratio [6-13, 28, 29]. From the experimental point of view, mostly studies tried to find out the risk factors which affect the severity levels. Among most of studies explained that drinking alcoholic beverage and driving influenced more in accident [14]. It identified that drinking alcoholic beverage and driving seriously increase the accident ratio. There are various studies which have focused on restraint devices like helmet, seat belts influence the severity level of accident and if these devices would have been used to accident ratio had decreased at certain level [15]. In addition, few studies have focused on identifying the group of drivers who are mostly involved in accident. Elderly drivers whose age are more than 60 years, they are identified mostly in road accident [16]. Many studies provided different level of risk factors which influenced more in severity level of accident. Lee C [17] stated that statistical approaches were good option to analyze the relation between in various risk factors and accident. Although, Chen and Jovanis [18] identified that there are some problem like large contingency table during analyzing big dimensional dataset by using statistical techniques. As well as statistical approach also have their own violation and assumption which can bring some error results [30-33]. Because of these limitation in statistical approach, Data techniques came into existence to analyze data of road accident. Data mining often called as knowledge or data discovery. This is set of techniques to achieve hidden information from large amount of data. It is shown that there are many implementation of data mining in transportation system like pavement analysis, roughness analysis of road and road accident analysis. Data mining techniques has been the most widely used techniques in field like agriculture, medical, transportation, business, industries, engineering and many other scientific fields [21-23]. There are many diverse data mining methodologies such as classification, association rules and clustering has been extensivally used for analyzing dataset of road accident [19-20]. Geurts K [24] analyzed dataset by using association rule mining to know the different factors that happens at very high frequency road accident areas on Belgium road. Depaire [25] analyzed dataset of road accident in Belgium by using different clustering techniques and stated that clustered based data can extract better information as compared without clustered data. Kwon analyzed dataset by using Decision Tree and NB classifiers to factors which is affecting more in road accident. Kashani [27] analyzed dataset by using classification and regression algorithm to analyze accident ratio in Iran and achieved that there a re factors such as wrong overtaking, not using seat belts, and badly speeding affected the severity level of accident. METHODOLOGY This research work focus on casualty class based classification of road accident. The paper describe the k-means and Hierarchical clustering techniques for cluster analysis. Moreover, Decision Tree, Lazy classifier and Multilayer perceptron used in this paper to classify the accident data. Clustering Techniques Hierarchical Clustering Hierarchical clustering is also known as HCS (Hierarchical cluster analysis). It is unsupervised clustering techniques which attempt to make clusters hierarchy. It is divided into two categories which are Divisive and Agglomerative clustering. Divisive Clustering: In this clustering technique, we allocate all of the inspection to one cluster and later, partition that single cluster into two similar clusters. Finally, we continue repeatedly on every cluster till there would be one cluster for every inspection. Agglomerative method: It is bottom up approach. We allocate every inspection to their own cluster. Later, evaluate the distance between every clusters and then amalgamate the most two similar clusters. Repeat steps second and third until there could be one cluster left. The algorithm is given below            X set A of objects {a1, a2,à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦an}            Distance function is d1 and d2            For j=1 to n           dj={aj}          end for          D= {d1, d2,à ¢Ã¢â€š ¬Ã‚ ¦..dn}         Y=n+1         while D.size>1 do -(dmin1, dmin2)=minimum distance (dj, dk) for all dj, dk in all D -Delete dmin1 and dmin2 from D -Add (dmin1, dmin2) to D -Y=Y+1          end while K-modes clustering Clustering is an data mining technique which use unsupervised learning, whose major aim is to categorize the data features into a distinct type of clusters in such a way that features inside a group are more alike than the features in different clusters. K-means technique is an extensively used clustering technique for large numerical data analysis. In this, the dataset is grouped into k-clusters. There are diverse clustering techniques available but the assortment of appropriate clustering algorithm rely on the nature and type of data. Our major objective of this work is to differentiate the accident places on their frequency occurrence. Lets assume thatX and Y is a matrix of m by n matrix of categorical data. The straightforward closeness coordinating measure amongst X and Y is the quantity of coordinating quality estimations of the two values. The more noteworthy the quantity of matches is more the comparability of two items. K-modes algorithm can be explained as:                             d (Xi,Yi)=                   (1)                Where           - (2) Classification Techniques Lazy Classifier Lazy classifier save the training instances and do no genuine work until classification time. Lazy classifier is a learning strategy in which speculation past the preparation information is postponed until a question is made to the framework where the framework tries to sum up the training data before getting queries. The main advantage of utilizing a lazy classification strategy is that the objective scope will be exacted locally, for example, in the k-nearest neighbor. Since the target capacity is approximated locally for each question to the framework, lazy classifier frameworks can simultaneously take care of various issues and arrangement effectively with changes in the issue field. The burdens with lazy classifier incorporate the extensive space necessity to store the total preparing dataset. For the most part boisterous preparing information expands the case bolster pointlessly, in light of the fact that no idea is made amid the preparation stage and another detriment is that lazy classification strategies are generally slower to assess, however this is joined with a quicker preparing stage. K Star The K star can be characterized as a strategy for cluster examination which fundamentally goes for the partition of n perception into k-clusters, where every perception has a location with the group to the closest mean. We can depict K star as an occurrence based learner which utilizes entropy as a separation measure. The advantages are that it gives a predictable way to deal with treatment of genuine esteemed attributes, typical attributes and missing attributes. K star is a basic, instance based classifier, like K Nearest Neighbor (K-NN). New data instance, x, are doled out to the class that happens most every now and again among the k closest information focuses, yj, where j = 1, 2à ¢Ã¢â€š ¬Ã‚ ¦ k. Entropic separation is then used to recover the most comparable occasions from the informational index. By method for entropic remove as a metric has a number of advantages including treatment of genuine esteemed qualities and missing qualities. The K star function can be ascertained a s: K*(yi, x)=-ln P*(yi, x) Where P* is the likelihood of all transformational means from instance x to y. It can be valuable to comprehend this as the likelihood that x will touch base at y by means of an arbitrary stroll in IC highlight space. It will performed streamlining over the percent mixing proportion parameter which is closely resembling K-NN sphere of influence, before appraisal with other Machine Learning strategies. IBK (K Nearest Neighbor) Its a k-closest neighbor classifier technique that utilize a similar separation metric. The quantity of closest neighbors may be illustrated unequivocally in the object editor or determined consequently utilizing blow one cross-approval center to a maximum point of confinement provided by the predetermined esteem. IBK is the knearest-neighbor classifier. A sort of divorce pursuit calculations might be used to quicken the errand of identifying the closest neighbors. A direct inquiry is the default yet promote decision blend ball trees, KD-trees, thus called cover trees. The dissolution work used is a parameter of the inquiry strategy. The rest of the thing is alike one the basis of IBL-which is called Euclidean separation; different alternatives blend Chebyshev, Manhattan, and Minkowski separations. Forecasts higher than one neighbor may be weighted by their distance from the test occurrence and two unique equations are implemented for altering over the distance into a weight. The qua ntity of preparing occasions kept by the classifier can be limited by setting the window estimate choice. As new preparing occasions are included, the most seasoned ones are segregated to keep up the quantity of preparing cases at this size. Decision Tree Random decision forests or random forest are a package learning techniques for regression, classification and other tasks, that perform by building a legion of decision trees at training time and resulting the class which would be the mode of the mean prediction (regression) or classes (classification) of the separate trees. Random decision forests good for decision trees routime of overfitting to their training set. In different calculations, the classification is executed recursively till each and every leaf is clean or pure, that is the order of the data ought to be as impeccable as would be prudent. The goal is dynamically speculation of a choice tree until it picks up the balance of adaptability and exactness. This technique utilized the Entropy that is the computation of disorder data. Here Entropy is measured by:                                 Entropy () =                                  Entropy () = Hence so total gain = Entropy () Entropy () Here the goal is to increase the total gain by dividing total entropy because of diverging arguments by value i. Multilayer Perceptron An MLP might be observed as a logistic regression classifier in which input data is firstly altered utilizing a non-linear transformation. This alteration deal the input dataset into space, and the place where this turn into linearly separable. This layer as an intermediate layer is known as a hidden layer. One hidden layer is enough to create MLPs. Formally, a single hidden layer Multilayer Perceptron (MLP) is a function of f: YIà ¢Ã¢â‚¬ ’YO, where I would be the input size vector x and O is the size of output vector f(x), such that, in matrix notation                                                F(x) = g(ÃŽÂ ¸(2)+W(2)(s(ÃŽÂ ¸(1)+W(1)x))) DESCRIPTION OF DATASET The traffic accident data is obtained from online data source for Leeds UK [8]. This data set comprises 13062 accident which happened since last 5 years from 2011 to 2015. After carefully analyzed this data, there are 11 attributes discovered for this study. The dataset consist attributes which are Number of vehicles, time, road surface, weather conditions, lightening conditions, casualty class, sex of casualty, age, type of vehicle, day and month and these attributes have different features like casualty class has driver, pedestrian, passenger as well as same with other attributes with having different features which was given in data set. These data are shown briefly in table 2 ACCURACY MEASUREMENT The accuracy is defined by different classifiers of provided dataset and that is achieved a percentage of dataset tuples which is classified precisely by help of different classifiers. The confusion matrix is also called as error matrix which is just layout table that enables to visualize the behavior of an algorithm. Here confusing matrix provides also an important role to achieve the efficiency of different classifiers. There are two class labels given and each cell consist prediction by a classifier which comes into that cell. Table 1 Confusion Matrix                             Correct Labels Negative Positive Negative TN (True negative) FN (False negative) Positive FP (False positive) TP (True positive) Now, there are many factors like Accuracy, sensitivity, specificity, error rate, precision, f-measures, recall and so on. TPR (Accuracy or True Positive Rate) = FPR (False Positive Rate) = Precision = Sensitivity = And there are also other factors which can find out to classify the dataset correctly. RESULTS AND DISCUSSION Table 2 describe all the attributes available in the road accident dataset. There are 11 attributes mentioned and their code, values, total and other factors included. We divided total accident value on the basis of casualty class which is Driver, Passenger, and Pedestrian by the help of SQL. Table 2 S.NO. Attribute Code Value Total        Casualty Class Driver Passenger Pedestrian 1. No. of vehicles 1 1 vehicle 3334 763 817 753 2 2 vehicle 7991 5676 2215 99 3+ >3 vehicle 5214 1218 510 10 2. Time T1 [0-4] 630 269 250 110 T2 [4-8] 903 698 133 71 T3 [6-12] 2720 1701 644 374 T4 [12-16] 3342 1812 1027 502 T5 [16-20] 3976 2387 990 598 T6 [20-24] 1496 790 498 207 3. Road Surface OTR Other 106 62 30 13 DR Dry 9828 5687 2695 1445 WT Wet 3063 1858 803 401 SNW Snow 157 101 39 16 FLD Flood 17 11 5 0 4. Lightening Condition DLGT Day Light 9020 5422 2348 1249 NLGT No Light 1446 858 389 198 SLGT Street Light 2598 1377 805 415 5. Weather Condition CLR Clear 11584 6770 3140 1666 FG Fog 37 26 7 3 SNY Snowy 63 41 15 6 RNY Rainy 1276 751 350 174 6. Casualty Class DR Driver PSG Passenger PDT Pedestrian 7. Sex of Casualty M Male 7758 5223 1460 1074 F Female 5305 2434 2082 788 8. Age Minor 1976 454 855 667 Youth 18-30 years 4267 2646 1158 462 Adult 30-60 years 4254 3152 742 359 Senior >60 years 2567 1405 787 374 9. Type of Vehicle BS Bus 842 52 687 102 CR Car 9208 4959 2692 1556 GDV GoodsVehicle 449 245 86 117 BCL Bicycle 1512 1476 11 24 PTV PTWW 977 876 48 52 OTR Other 79 49 18 11 10. Day WKD Weekday 9884 5980 2499 1404 WND Weekend 3179 1677 1043 458 11. Month Q1 Jan-March 3017 1731 803 482 Q2 April-June 3220 1887 907 425 Q3 July-September 3376 2021 948 406 Q4 Oct-December 3452 2018 884 549 Direct Classification Analysis We utilized different approaches to classify this bunch of dataset on the basis of casualty class. We used classifier which are Decision Tree, Lazy classifier and Multilayer perceptron. We attained some result to few level as shown in table 3 Table 3 Classifiers Accuracy Lazy classifier(K-Star) 67.7324% Lazy classifier (IBK) 68.5634% Decision Tree 70.7566% Multilayer perceptron 69.3031% We achieved some results to this given level by using these three approaches and then later we utilized different clustering techniques which are Hierarchical clustering and K-modes. Figure 1 Direct classified Accuracy Analysis by using clustering techniques In this analysis, we utilized two clustering techniques which are Hierarchical and K-modes techniques, Later we divided dataset into 9 clusters. We achieved better results by using Hierarchical as compared to K-modes techniques. Lazy Classifier Output K Star: In this, our classified result increased from 67.7324 % to 82.352%. Its sharp improvement in result after clustering. Table 4 TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.956 0.320 0.809 0.956 0.876 0.679 0.928 0.947 Driver 0.529 0.029 0.873 0.529 0.659 0.600 0.917 0.824 Passenger 0.839 0.027 0.837 0.839 0.838 0.811 0.981 0.906 Pedestrian IBK: In this, our classified result increased from 68.5634% to 84.4729%. Its sharp improvement in result after clustering. Table 5 TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.945 0.254 0.840 0.945 0.890 0.717 0.950 0.964 Driver 0.644 0.048 0.833 0.644 0.726 0.651 0.940 0.867 Passenger 0.816 0.018 0.884 0.816 0.849 0.826 0.990 0.946 Pedestrian Decision Tree Output In this study, we used Decision Tree classifier which improved the accuracy better than ear
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