Sunday, January 26, 2020
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
Saturday, January 18, 2020
There are many indications within the poetry of Tony Harrison that he considers his work within the context of the canon
‘Whether one thinks of the canon as objectionable because formed at random or to serve some interests at the expense of others, or whether one supposes that the contents of canons are providentially chosen, there can be no doubt that we have not found ways of ordering our thoughts about the history of literature and art without recourse to them. ‘ (Kermode, p. 20). In what ways do you believe Tony Harrison to be affected by the canon. Use analyses of the poem to illustrate your answer. There are many indications within the poetry of Tony Harrison that he considers his work within the context of the canon. The repeated referencing of other poets1 and conscious emulation of the form of other poems (‘v. ‘ is an adaptation of Gray's ‘Elegy on a Country Churchyard' ) suggest that Harrison's work is heavily influenced by other poets, despite his seemingly original style. The way that he uses his referencing is not straightforward, however; it could be suggested that the more traditional references are ironic, as Harrison contrasts his brash modern style with the more ‘genteel' feel of the poets in the canon. The continual allusions to the opposition his poetry has faced, and his subsequent under confidence, can have said to have led to a need for the reassurance of the canon: using the models of other poets to validate the worth of his own poetry. Alternatively, Harrison may feel that the only way to express the voice he wishes to project, that of a working class northern man with authority is by using the â€Å"enemy's weapons†2, and establishing a scholastic side to his work, in order to be taken seriously by the ‘cultural elite'. It has been argued that Harrison uses other people's words and forms to justify his own work; that his feeling of social inferiority reveals itself as an insecurity in his poetry3. Whereas in Gray's ‘Elegy' the last stanza is a contemplation upon the life of the poet, filled with a sense of repose, Harrison ends his epic poem ‘v. ‘ still striving to justify his choice to become a poet. By placing it as a viable occupation alongside other more manual lifestyles, such as the production of â€Å"the beef, the beer, the bread†,4 and anticipating possible reactions: â€Å"How poems can grow from (beat you to it! ) SHIT†5, Harrison tries to protect himself from derision. Critics relate the closing stanzas of â€Å"Elegy on a Country Churchyard†to Gray's fears about his poetic destiny. Damien Grant states â€Å"The poet writes conscious of his own possible doom, to be ‘preserved beneath deep permaverse' like any other victim of evolution†6, but he is considering Harrison's ‘epitaph'. By using a recognised canonical poet such as Gray, Harrison has a model to explore his feelings about his own destiny, investigating his own experiences regarding death: â€Å"taking a short cut home through the graves here/ they reassert the glory of their team/ by spraying words on tombstones, pissed on beer†7, within a controlled and set form. The way that Harrison himself views the canon determines his reaction, and therefore his poetry. The canon could be construed as an enabling, useful force, giving Harrison ideas and structures to work with8, and Harrison himself admits to the influence of classical authors, such as Milton9. Altieri notes that â€Å"contemporary writers†¦ need to address specific canonical works and engage the same degree of emotional and intellectual energy that canonical works provide†10, and Harrison seems to have taken up this mantle, engaging it with his desire to keep poetry relevant to his experience and therefore, to him, alive. Harrison is not trying to be one of the classical authors; he is trying to respond to them in a way that is different but not necessarily inferior11. Indeed, Kermode agrees that â€Å"the best commentary on any verse is another verse, possibly placed very far away from it†. Harrison accepts that he writes from a different world perspective than many of the ‘canonical' authors, but to illustrate the similarities he uses similar forms and quotes them, either to show his awareness of their work or his reaction to it. â€Å"Mute ingloriousness†13, for instance, explores the theme of the difficulties of articulation, and is a direct quote from Gray. Harrison uses it to illustrate the difficulties he has found in developing his own poetic voice. Damien Grant draws comparisons between the symbolism in â€Å"v†and erotic images drawn by other, more traditionally ‘established' poets. The skinhead's addition of a â€Å"middle slit to one daubed v†14 is not an obscenity, it can be argued, but merely Harrison joining a long line of established authors invoking â€Å"the erotic image†¦ to serve public purposes†. 15 Another way of viewing the canon is that of â€Å"codified by a cultural elite, with power to influence the way the country thinks across a broad range of issues†. 16If the canon reflects simply a cultural emphasis, then Harrison should be considered part of that canon, as he is widely taught and studied, to a high level. If, however, the canon is set by the ‘cultural elite', then Harrison's use of some of the more standard forms and obscure classical references may be an attempt to be accepted by this elite, in order to propagate his own cultural emphasis and make his own stance widely known and acceptable. â€Å"Harrison is provoked by the persecution of an RP English teacher to fight back with the enemy's weapons, on the enemy's own ground†18; â€Å"So right, ye buggers, then! We'll occupy/ your lousy leasehold Poetry†. 19 Harrison wishes for his voice to be heard, and is not afraid of using techniques supposedly alien to his class to achieve this. Harrison takes canonical influences and makes them seemingly more accessible to a wider cross section of society, introducing more modern themes such as the problems of the Thatcher era. This is in part to make poetry more relevant and acceptable to those he seeks the approval of the most: the uneducated and the cynical, such as his parents. Catherine Packham suggests that the canon may seem oppressive and intimidating to Harrison; his feeling of insecurity may have led him to feel that all of the timeless themes that he wishes to cover have been explored extensively, by people who are better educated and suitable to be ‘poets'20. Harrison's poetry is full of the issue of self doubt and self worth: â€Å"Poetry's the speech of kings. You're one of those/ Shakespeare gives the comic bits to: prose! â€Å"21 , and seems at times to want to distance his writing from the recognisable canon to show a progression of attitudes and innovation, and perhaps attempting to demonstrate that he is not competing with the established canon. This can be seen in the fact that of the many â€Å"versus†couplings in ‘v. , a major one is that of Harrison's version versus Gray's. The very title of another poem, â€Å"On Not Being Milton†, shows that Harrison is aware of the canon and embraces his differences to it, but the poem itself, with its lyricism and innovative use of language in fact recalls the epic poetry of Milton himself; this is an irony that the poet seems to enjoy. Harrison obviously appreciates the fine crafting of established authors, and wishes to learn from them, whilst staying true to his e arthy subject matter. The touch of some of the word handling may hint at Miltonesque heights, but the subject matter of a man returning to his roots (â€Å"my growing black enough to fit my boots†22) and the outsider becoming a hero (Tidd the Cato Street Conspirator), with his â€Å"Sir, I Ham a Very Bad Hand at Righting†23 indicates that Harrison believes that education is not everything; this, in a poem littered with reference to historical figures and epic literature, hints at play. The theme of articulation is prevalent24: Harrison is concerned with the way things are said, and who they are said by, as he is aware of the impact that other works have had upon him. It would be impossible to ascertain exactly what sway the canon has had upon Harrison's poetry: nevertheless, if we are to judge his work within the context of the canon, then we must consider his literary intentions. We must ask whether his intentions are to be considered within the same school of those that he references and quotes so copiously, or if in fact these references were designed to show the vast differences between their worlds. I believe Harrison to be stuck in between the two worlds, but supremely in command. He is aware that to gain a recognition as a poet, certain rules must be followed; and he adapts these rules to suit his own purposes. Harrison incorporates enough traditional ideas and forms not his work to stay credible, but he fills his poetry with subjects and contexts unfamiliar to the ‘cultural elite'. These are the subjects and contexts that he wishes to bring into the public domain and make issues of, and by taking on the timeless element of the canonical works, Harrison ensures that he pushes poetry forward: into unfamiliar territory, and to unfamiliar readers.
Friday, January 10, 2020
The Nuiances of Horror Story Essay Topics
The Nuiances of Horror Story Essay Topics Personal narrative essays are about personal experience that's presented in the very first person. A kid's imaginary friend isn't imaginary. To have the ability to move your characters along, you have to be aware of what they're up against. Tell your story with the use of emotional language and proper information. Do not appear through the topics before you opt for. Essay has to be in MLA format. Short essays are still spend the sort of formal essay because the parts want to get included in it. Of all Of the kinds of essay, writing a quick essay may appear to be the easiest. Scroll down the page in order to see extra essay samples which can help you in producing your very own literary essay. As soon as you feel you realize the question, reread the part of literature, making notes. To see all of the commentary, you can want to click the arrow multiple times. The very first and the most essential suggestion is to keep in mind that you're writing a story, not only an essay. You should start your work with the analysis of the topic, on the grounds of the analysis of the subject of the essay, you opt for the material, the principal facts, and the critical points of your paper. To compose an effective important analysis, you must first be positive that you know the question that's been posed, and all literary terms that you've been requested to deal with. The conclusion is just one of the essential components of the narrative essay. It should contain a synthesis or a brief summary. A significant part the narrative essay is that the writer experienced the events described. A better part of the moment, writers find it tough to answer that question. The very first story happens in a little town of Myrgorod in Ukraine. The stories written by our writers aren't only precise and clear, but give a crystal clear view of the whole story in a brief period of time. Explain yourself as you cooperate, rather than attempting to refer your reader back to a prior statement. Telling facts isn't a hard job, and it may also be fun. This e-mail has two lies. Narration is telling a story from a particular viewpoint, and there is generally a reason behind the telling. Horror Story Essay Topics Can Be Fun for Everyone This room consists of the most coveted table in the area. Attempt not to compose the conclusion in a rush at the previous moment, because it leaves the general impression of your work. Other things start to happen too. The issue with our world is that people don't learn to listen to one another. Write legibly in ink so the evaluators will have the ability to read your writing. 8 essential elements of an effective training plan. Get in contact with our specialist to observe how it's going with the analysis and to task about any amendment that may need to get performed. In order to construct a logical chain, you are in need of a plan of writing. The very first room, through the door, is the principal portion of the restaurant. Thus, our primary feature is to supply quality stories. The primary aim of the introduction is to bring the reader to the major part. A lyrical introduction is among the universal means, and connects the topic of the work by means of your life experience. Therefore, as you should naturally don't hesitate to build on what you've already written this semester in blogs or other informal writing (or that which we have discussed in class), do not just repeat what you've previously stated elsewhere. There are drawbacks, obviously. Don't hesitate to browse the webpage and click on any individual download hyperlink button below a sample which you like. When the whole payment is released, the order is completed. If you are in need of a website that will supply you with an extensive collection of samples, then you're at the appropriate place. Today, there are several on-line sites which provide sample papers.
Wednesday, January 1, 2020
The World Moves Forward Into The Digital Age Essay
As the world moves forward into the digital age, American institutions must adapt to serve the needs of a digital society. The Newspaper industry has not entered the digital age unscathed; several issues of the current business model must be addressing if Newspapers are to survive the century. Americans and especially the younger generations are more skeptical of the Mass Media and less likely to believe that they are non-biases and question more than ever of the legitimacy of the news stories put out by the press. And as the world becomes a smaller community people are more concerned with international affairs and less local issues unless directly affecting them. Once one of the only outlets for advertisers to reach, large audiences Newspapers are heavily dependent on ad sales, which can cause an issue if every person in America has a portable device that spews ads at them all day. During the 2008 financial crisis, newspapers saw the largest drop in revenue from advertising sales since the great depression. The drop in sales affected every sector of the Industry’s market. Revenue from advertisements is the largest portion of the total income for newspapers; the dramatic decline was and is devastating for not only local but also national newspapers. In 2009 the total amount of spending on advertisements fell by 12.9% across all industries. Newspapers alone saw their advertisement revenue decline by 26%, a rate that was 50% steeper than in than in 2008 when revenues fell byShow MoreRelatedKodaks Faulty Decision Making: A Case Study619 Words  | 3 Pagescaused the downfall if the Kodak company is that they have refused to recognize that the new wave of digital photography would become so popular. Kodak bet that they would be able to survive with film cameras despite the incredible success of digital photography. 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With world events proceeding in an alarming direction, such as the recent missile strike by President Trump against Syria (CNNRead MoreImpact Of Big Data On The Future1364 Words  | 6 PagesAbstract The continuous flow of data and information flooding our lives in conjunction with further increases in technology, has created a world of endless possibilities in this day and age. The impact and influences that have been created through big data will shape our lives not only today, but well into the future. This report examines the benefits of big data and the impact it has currently having in our lives as we speak. It also explores the correlation between the lack of knowledge, securityRead MoreBusiness Proposal1436 Words  | 6 Pages(University of Phoenix, 2014) has developed a digital reading application that is simple to use, and necessary in today’s world of academics, business, and pleasure use. It is the intention of this business proposal to acquire the financing needed to begin launch of this new application and the following in-depth information will show that the numbers support Mr. Bury’s proposal. Market Analysis In the growing technological age and with the introduction of digital reading devices like the iPad ® fromRead MoreCharacteristics of Business Leadership1692 Words  | 7 Pagesled a cultural transformation in the way music, movies and mobile communications were experienced in the digital age. Jobs mastered digital technology and capitalized on his intuitive marketing sense. He largely came to define the personal computer industry and an array of digital consumer and entertainment businesses centered on the Internet. Coming on the scene just as computing began to move beyond the walls of research laboratories and corporations in the 1970s, Mr. Jobs saw that computing wasRead MoreAnalysis Of George Orwell s Dystopian Nineteen Eighty Four1725 Words  | 7 Pagesaccelerate as we move into the coming years. The decentralization of knowledge, brought about by expanding broadband penetration, will lead to a wider access to educational resources for a growing number of people. Thes e developments will produce a higher degree of democracy in education; students will take control over their own learning. How will this democratization come about? There are a variety of ideas. However, all of them share in common a recognition that the digital world is the frontierRead MoreThe Effects Of Digital And Traditional On The Classroom1586 Words  | 7 PagesMany adults think that because children and young adults have immersed in technology at a young age, they are naturally literate or skilled in using technology. Younger generations have been labeled as â€Å"digital native†or â€Å"tech savvy†individuals. Unfortunately, this labeling is not as true as most believe. Today’s students are no more literate with digital devices than their parents are. Digital literacy requires a much broader spectrum of skills than traditional reading requires. Skills that must
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