kublai khan Essays - Marco Polo, Borjigin, , Term Papers

Marco Polo went on plenty of adventures well during his adventures he grew to know and study a man named Kublai Khan. Marco Poloheld Kublai in high regaurd he referred to him as Lord of lord. There was a great battle in which Kublai showed his swift yet calculated reaction. In no time at all Kublai assembled his army and got them ready. Marco respected kublai?s precaution and planning skills Nayan the enemy was taken by surprise he and his men were amazed at the precision and care that Kublai took to surround them on their homefront. The tradition is that the battle doesn?t begin until the horns and music have finished sounding the music is beautiful for the war that is about to take place. The battle was fought hard and kind of long it was from 9 to midday the arrows are described as black rain filling the sky horses and men lay across the ground fallen. A good amount of men were lost from both sides. Nayan realizing that there was a victor and it wasn?t him surrendered as well as t he rest of his men. He was put to death by being wrapped up in carpet and being tossed back and fourth until he passed It is said that The Khan did this so that his screams would not be heard on the ground and so his blood would not be spilled on the ground. I believe Marco really admired Kublai Khan?s military precision, experience, and intelligence. Religion Marco Polo observed how Kublai Khan thought about religion and his views on it. He had extraordinary views on religion Easter and Christmas Kublai would get 4 books of the prophets from the different treligions. Rewards Kublai rewarded his troops greatly for the battle against Naya which shows appreciation for his helper fighters. That is why he probably had such a strong Army and support sytem because he rewarded his troops you reward a child when they esxecute something properly rewarding your troops is a very good idea they strive to want to do better and are less likely to betray. He also gives them leadership independence. ? They can take whenever they please, and lead from place to place, the troops and horses of any prince or king. Kublai Kublai is from Marco Polo?s description a very beautiful man not too far gone with the looks. He has 4 wivesthat each have their title as emperesses they each have their own attendants. One of his wives has born a son which is to be the successor. Kublai has 22 sons 7 of which have their own kingdoms that they rule that are very large.

How to Get a Literary Agent For Your Book

How to Get a Literary Agent For Your Book How to Get a Literary Agent for Your Book Many first-time authors want to find a literary agent as soon as they put the finishing touches on their debut masterpiece. And if you’re in a similar situation, it’s only natural to feel that way as well! But before we snap off a fresh roll of quarters and start dialing the numbers of some New York agencies (so to speak), let’s answer a few basic questions about what agents do and whether you actually need one. Then, once you’re ready, we’ll reveal a professionally endorsed process for finding and evaluating agents who could be perfect for you.Should I get a literary agent?Fiction writers who want to be traditionally published generally do need a literary agent, while only some non-fiction will need to pursue literary representation. Writers Artists editor Alyson Owen explains: "Nearly all fiction authors do require representation of a literary agent if their book is to be produced by a traditional publishing house, and that is also true with some forms of general non-fiction too - such as histories, memoirs, and biographies."For tips on how to write an effective book proposal, check out this article and its companion piece on submission best practices.Who doesn't need a literary agent?It’s important to note that not all literary forms typically require the representation of an agent. Poetry is a good example of this. Also, academic, professional, and the vast majority of educational books are usually commissioned direct from the publisher. If you’re writing in any of these fields, it is possible - provided you have the right credentials as an author, a well-argued proposal, and quality script - to get your non-fiction book accepted by a publisher without being represented by an agent.If you do fall into one of the categories above and you wish to pitch directly to publishers, it’s essential that your book has an easily identifiable market or niche so publishers know what they’re dealing with r ight away. You also need to know who actually publishes the type of book you’re offering and which of those publishers accept unsolicited proposals. Firing off emails to every publisher under the sun will not only waste your time but potentially earn you a reputation as someone who doesn’t understand the publishing game.And of course, if you’re choosing to self-publish, then you definitely don’t need a literary agent to get your book out into the world.What do literary agents do?A literary agent represents your book to acquiring editors who might want to buy it. In other words, they get an author's foot into the door of a publishing house.In addition to having regular contact with editors within the publishing houses and championing you and your writing to the hilt, agents will understand an editor's tastes and the types of books they are looking to add to their list - a crucial component in getting a publishing house to invest in your work.â€Å"Quite simply, being represented by a literary agent may be the only realistic route to getting a foot on the first rung of the publishing ladder,† says Owens. â€Å"Most publishers of fiction do not accept unsolicited manuscript submissions from debut writers.†In addition to getting your foot in the door, here's what a literary agent does:They are expert negotiators, combining financial acumen with a nose for the value of good, sellable writing;They act as useful buffers between you and your publisher (when you don’t want to taint your working relationship with an editor by talking brass tacks);They manage the financial and marketing side of things while you concentrate on the writing;They also tend to have strong editorial skills, working with you to help refine your manuscript before it’s even submitted to a publisher.All of this can only work to your advantage as an author, helping you secure a more lucrative deal with the most appropriate publisher - much m ore than you may have been able to acquire yourself, without any contacts or knowledge of the industry. Ideally, their negotiations alone should offset their commission (ideally).How to get a literary agent in 6 stepsWhen we say ‘finding a literary agent,’ we really mean finding the ideal representative for your book - someone who is perfectly primed to help you develop your writing career. You’re basically on the hunt for a long-term business partner and creative soulmate. Ready to start looking?1. Locate a pool of literary agents who work in your nicheMost agents are always on the lookout for new authors to represent. They’re dying for their dream client to reach out to them. To help make their dreams come true, it’s your job to find out a) who these agents are and b) how best to get through to them. So start putting together your shortlist.You can begin your search by going to agent databases such as Agent Query or Query Tracker and filtering by genre. You can also look through the acknowledgments pages of books similar to yours: you can bet that the authors thank their agents in there. Or, this being the 21st century, you can just Google † agent† and see what comes up.Reading recommendations: Writers’ Artists’ Yearbook (for the UK) and its US equivalent Writer’s Market are invaluable tools that you should consider putting on your Christmas list. Tip for finding an agent: use Agent Query and Query Track to start your search 2. Thoroughly research and evaluate each agentHere are the four main questions you want to answer in your research about individual literary agents:Do they represent books in my genre?Are they currently open to queries?Would I work well with them?Are they a scam artist?How do you go about answering these questions? Well, checking out the submission guidelines of the agent's website should answer #1 and #2 fairly easily. In regards to #3 and 4, Caitlin Jans, the  founding editor of Authors Publish, has a few tips:â€Å"I always research the agent outside of their website first. I find that one of the best ways to tackle this is to head again over to the Absolute Write Water Cooler Forum. I use it to vet agents and publishers. The forums are active and get a lot of use: if an author has a good or bad experience with an agent, they’ll share it.I can’t emphasize this enough: one of the most important steps is to make sure the agency isn't on Writer Beware’s Thumbs Down Agencies List. At this point, I almost have the list memorized. Writer Beware (a volunteer organization that works on behalf of writers) also has a terrific section on dishonest agents, so be sure to check that out.†No matter what, as an ironclad rule: avoid agents who ask for an upfront fee. It doesn’t matter whether they call it ‘professional development’ or say it’s to cover the costs of editing your manuscript: an agent who asks for preemptive payment is not legitimate.After due research, if you find an agent who seems to tick all the right boxes, then add them to your final shortlist and proceed to step three. Tip for finding an agent: avoid agents who ask for an upfront fee 3. Send a personalized query letterA good portion of every agent’s week is spent on ‘slush’ - what they call the never-ending pile of letters they get from hopeful authors seeking representation. On one hand, you might think, Geez! How do I stand a chance of being seen in a pile that big? But you should really look at it from the attitude of, Wow! These agents are really keen to find their next client!With that in mind, your query letter (your first point of contact with an agent) must be perfect. We have a detailed guide to writing query letters (that comes with a super-sweet template), but if you don’t want to head down the rabbit hole right this second, here are three top-line tips:Always read an agent’s submission guidelines. Some might ask for a sample chapter, others might not. One might want you to double-space, another might require single. Apart from ensuring that they get what they need to make an assessment, the submission guidelines also serve as a rudimentary idiot test: If this author doesn’t follow our standards, it either means that they haven’t read them (and is lazy) or they haven’t been able to comprehend simple instructions (which is not exactly what you want in a business partner).Make sure to personalize each query letter. You’ll want to contact as many agents as possible, and it’s tempting to create a standard query letter that starts with Dear Sirs and features no specific language whatsoever. But remember, agents have read countless boilerplate query letters and can sniff them out from a mile away. That’s why the research you do in step 2 will serve you so well: it’ll show each agent that you actually care. (For more detailed tips on personalizing your query letter, check out this transcripted replay of our webinar with former agent Rachel Stout.)Start with the hook. Ultimately, it’s the book that agents want to know about. While you might think i t’s best to introduce your book by first talking about your own background, the quicker you get to the exciting pitch for your book, the better. Tip for finding an agent: always personalize your query letters 4. Always follow up if you don’t hear anythingWe’ve all had that experience of looking at an email and thinking, â€Å"I’ll get to that later,† only to forget about it until someone reminds you. Well, the same thing happens to agents as well! If you don’t hear back from the after several weeks, it’s not impolite to follow up and ask whether your query may have been lost in the shuffle. Often, the agent will be grateful that you reminded them.Again, read the agent’s submission guidelines. In many cases, they’ll even tell you how many weeks to wait before you follow up. But if they don’t, then four to six weeks is a good rule of thumb (erring closer to six, perhaps). Tip for finding an agent: wait 4-6 weeks before following up 5. Don’t just say yes to the first agentAssuming agents dig your query letter, get intrigued by your sample, and adore your full manuscript after they request and read it†¦ what happens next? In broad strokes, the two of you will discuss how the agent might help develop your book and career, and then they’ll offer you representation.Very exciting, right? But before you frantically search for a pen to sign on all those dotted lines, make sure that they are the right agent for you. It’s not considered impolite to query multiple agents at the same time, so you don’t need to lie about it or cover it up. An agent will not be insulted that you’re daring to consider other options. After all, if they like your book enough to want to represent it, it stands to reason that others might as well.Of course, you can always choose to leave an agent at any time if things aren’t working out. But keep in mind that if you’re at the start of your car eer, you don’t want a reputation as someone who switches agents every book. It’ll make editors, agents, and other publishing gatekeepers assume that you’re hard to work with.So be polite, stay candid, and don’t rush into any decisions.Now that you have a rough idea of how to find a literary agent, we want to leave you with some resources that will help you in your search.

Family Violence Law Case Study Example | Topics and Well Written Essays - 1500 words

Family Violence Law - Case Study Example This paper analyzes the arguments in favor of and against treating Marcia as guilty. The Case Marcia Norman, 39, and her husband, Mitchell, had been married for 25 years and had several children. Mitchell did not work. He forced Marcia to make money by prostitution, and he made fun of that fact before family and friends. He beat her if she resisted going to a truck stop and offering her body. On a few occasions, he made her eat out of the pets’ bowls and insisted that she bark like a dog. He threatened to kill or maim her numerous times.   Marcia and Mitchell got along very well when he was sober. But early one morning, he was intoxicated when he went to a highway rest area where Marcia was prostituting herself and assaulted her. He continued beating her all day after they got home. She called the police and was told to come in and file a complaint, but she was afraid that he would kill her if she had him arrested. She ingested a bottle of pills, then panicked and called EMS . Her husband cursed her as the paramedics treated her and urged them to let her die. After they left, Mitchell continued to slap, kick and throw objects at Marcia. At one point, he put a cigarette out on her upper torso, causing a small burn. After he fell asleep, Marcia took their baby to her mother’s so that she would not wake him, returned with a pistol and killed him.   At trial, Marcia pleads self-defense, which is defined as â€Å"the necessity, real or reasonably apparent, of killing an unlawful aggressor to save oneself from imminent death or great bodily harm† (Given Case). When we consider the case North Carolina v. Judy Ann Laws Norman, we can argue that Marcia is not guilty even though she killed her sleeping husband. Judy Norman suffered a lot from the hands of her husband. Her husband forced her into prostitution and when opposed, she suffered a great deal of physical and psychological abuse from her husband. It was difficult for her to defend herself when she and her husband were in a physical encounter. So she killed him while he was sleeping. â€Å"A three judge panel of the intermediate appellate court, in a unanimous opinion held that there was sufficient evidence to support a charge of reasonable self-defense† (Angel 21). The above verdict is applicable in the case of Marcia also. She had no other way to escape from the brutalities of her husband. It should be noted that Mitchel (husband), disrespected her individuality and identity and forced her to live in accordance with the guidelines given by him. He used his physical and domineering strength to attack her both physically and mentally. Mitchel was denying Marcia the right to live on an equitable level as him. If Marcia had failed to kill him, he would have killed her. Using the â€Å"Self-Defense† argument could be a reasonable defense within this case. Under these circumstances, Marcia can argue that she tried to save her life from a certain death. Accor ding to U.S. Supreme Court Justice Samuel Alito â€Å"self-defense is a basic right, recognized by many legal systems from ancient times to the present† (Killing in Self-Defense). â€Å"The general criminal law allows for the use of deadly force anytime a faultless victim reasonably believes that unlawful force which will cause death or grievous bodily harm is about to be used on him†(Hobart). Marcia believes that her life is in danger and her husband may kill her at any time. It is reasonable for Marcia

Stops, Frisks, and Terry Stops Research Paper Example | Topics and Well Written Essays - 3750 words

Stops, Frisks, and Terry Stops - Research Paper Example This paper will analyze the Terry stop legal issue and its impact in the society. The paper will also look at constitutional issues that touch on the matter and the impact that they have had. Introduction The issue of stop, frisk and Terry stop is very controversial and has been debated for a long period of time. A Terry stop involves stopping of an individual by a law enforcement officer for frisking and in some cases a short detention. An individual may be stopped for frisking if they are suspected of having committed or being likely to commit a crime by a law enforcement officer. As one undergoes a Terry stop, the police officer frisks and individual (the suspect) to ensure that the officer is not in danger or other members of the public (Acker & Brody, 2012). A Terry stop is different from an arrest since it takes less time and is accompanied by less legal procedures. During a Terry Stop, a police officer usually confirm the details of the suspect and finds out whether they have a criminal record or outstanding fines or arrest warrants that have been issued against them. Groups that represent both sides of the argument present their debates which sound justified. Records of crime statistics show that there has been an increase in crimes that are committed all over the country. People all over the world feel that is up to the government to introduce measures that lead to a reduction of crime levels. Stop, frisks and Terry stops is one of the ways that was introduced by the government to try and curb crime levels all over the country. Law enforcers feel that prevention of crimes consumes fewer resources and saves more lives for minimal inconvenience if any to those that undergo the Terry stops. Discussions Stops, Frisks, and Terry Stops affect many individuals in the United States and perceptions on the government and law enforcement officers. This constitutional issue also affects the constitutional rights of individuals. Terry stop and frisk involves detent ion by police if they are suspicious that an individual has been involved in committing a crime. The Terry stop and frisk law was introduced in 1968 after the Terry v. Ohio, 392 U.S 1 (1968) court decision that was made by the Supreme court in the United States. The decision also authorized the police to conduct searches on the outer garments of the suspect if they have articulable suspicion that the suspect is armed and dangerous. The court authorized law enforcers to being able to detain an individual who they suspect is involved in committing a crime. Law enforcement officers also have the legal right to search the individual that they have detained. In some cases, the law enforcers might feel that the suspect might be in possession of a weapon, and may be planning to use in order to commit a crime. The rationale that causes the law enforcers to feel that the individual is likely to commit a crime should be reasonable. For example, a suspect might look overly nervous yet there is no evident reason why they should appear so. Such behavior might be an indication that such an individual is about to commit a crime. The law authorizes police officers to seize any form of contraband that they would find on a suspect while conducting the frisking. However there are some conditions in the frisking of the suspect (Acker & Brody, 2012). The frisking should be limited to the outer clothing of the

Legal Risk and Opportunity in Employment Essay Example | Topics and Well Written Essays - 1250 words

Legal Risk and Opportunity in Employment - Essay Example (Morris, 2008). If Paul had been fired for being black, he would definitely have a case against Newcorp. However, if he was fired for his voiced opinion at the school board meeting, this would not be protected. While the First Amendment protects freedom of speech, it makes no note that employers have to â€Å"put up with it.† (Morris, 2008). Pat is wrong in his belief that his employee manual’s described process of the corrective action plan means that he cannot be fired without warning. In the case of Toussaint v. Blue Cross & Blue Shield, the employee handbook stated that employees could only be fired after warning, notice, and hearing; Toussaint had been fired without warning. The court ruled in favor of Toussaint, and declared that an employee handbook creates contract. (Samuelson, 2007). However, since then, employers have been very careful in taking steps to protect themselves from this sort of liability. (Morris, 2008). Newcorp having Pat sign the statement of at-will employment was a proper way to prevent liability. And if Pat had looked carefully at his handbook, he probably would have discovered some sort of language within it expressing that it was not to be relied upon as a contract. (Morris, 2008). Paula is correct in believing that it is illegal for her not to be transferred to wire-coating based on her sex. Sex discrimination in the workplace involves the different treatment of an employee based on sex that negatively affects things like pay, employment, and advancement or training opportunities. Title VII of the 1964 Civil Rights Act prohibits sex discrimination in the workplace and applies to â€Å"private employers, state and local government employers, labor organizations, employment agencies, and joint employer-union apprenticeship programs with 15 or more employees.† (Equal Rights Advocates, 2008). Although Paula is young and could

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   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   X set A of objects {a1, a2,à ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦an}   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   Distance function is d1 and d2   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   For j=1 to n   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   dj={aj}   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   end for   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   D= {d1, d2,à ¢Ã¢â€š ¬Ã‚ ¦..dn}   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   Y=n+1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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:   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   d (Xi,Yi)=   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   (1)   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   Where   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   - (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:   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   Entropy () =   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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‚¬  Ã¢â‚¬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   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   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

Claudia Jones and Ella Baker :: Essays Papers

Claudia Jones and Ella Baker On Christmas day 1964, Claudia Jones, only forty-nine years old, died alone in her London apartment. Over three hundred people attended her funeral on January 9, 1965 to commemorate the woman who spent her entire adult life agitating against oppression. â€Å"Visitors who come to London’s Highgate Cemetery see that next to the grave of Karl Marx there is the tombstone of Claudia Jones. Many wonder what earned her the honour of being buried beside the founder of scientific communism.† [1] On the other side of the globe, Ella Baker, a leading African-American Civil Rights leader, was defending her theories of decentralized leadership. Tensions mounted in the movement when grassroots organizations rejected the ideas of central leadership and non-violence. One such organization, the Student Non-Violent Coordinating Committee (SNCC), founded in part, by the efforts of Ella Baker, became dedicated to Ella’s ideals of decentralized leadership, challenging the auth ority of high profile individuals in the Civil Rights Movement. In this paper I will examine the experiences of these two radicals. Both Ella Baker and Claudia Jones spent their entire adult lives writing, speaking and debating the issues that African-Americans faced. These issues included racist oppression, class hierarchy and the roles of women. However, although they both confronted the same issues, they had divergent philosophies that shaped their political careers. Their individual ideas can be examined in terms of Winston James’ definition of radicalism and Cedric Robinson’s theory of the development of the Black Radical tradition. Although the radicalism of both Ella Baker and Claudia Jones fits within Robinson and James’ definitions, their unique experiences as women helped define their ideas and theories, and transform the role of women in the Black Radical tradition. In Winston James’, Holding Aloft the Banner of Ethiopia, he defines radicalism or radical politics as, â€Å"the challenging of the status quo either on the basis of social class, race (or ethnicity), or a combination of the two.† [2] He goes on to articulate, in terms of the above definition, radicals. According to James radicals, therefore, â€Å"are avowed anti-capitalists, as well as adherents of varieties of Black Nationalism.† [3] Included in this definition are those who have attempted to unite anti-capitalist and nationalist thought. Though James examined Black Radicalism in terms of Caribbean migrants in the United States, his definition could be applied to native-born African-Americans as well.