Comparison On Classification Techniques Using Weka Computer Science Essay

Comparison On Classification Techniques Using Weka Computer Science Essay Computers have brought tremendous improvement in technologies especially the speed of computer and reduced data storage cost which lead to create huge volumes of data. Data itself has no value, unless data changed to information to become useful. In past two decade the data mining was invented to generate knowledge from database. Presently bioinformatics field created many databases, accumulated in speed and numeric or character data is no longer restricted. Data Base Management Systems allows the integration of the various high dimensional multimedia data under the same umbrella in different areas of bioinformatics. WEKA includes several machine learning algorithms for data mining. Weka contains general purpose environment tools for data pre-processing, regression, classification, association rules, clustering, feature selection and visualization. Also, contains an extensive collection of data pre-processing methods and machine learning algorithms complemented by GUI for different machine learning techniques experimental comparison and data exploration on the same problem. Main features of WEKA is 49 data preprocessing tools, 76 classification/regression algorithms, 8 clustering algorithms, 3 algorithms for finding association rules, 15 attribute/subset evaluators plus 10 search algorithms for feature selection. Main objectives of WEKA are extracting useful information from data and enable to identify a suitable algorithm for generating an accurate predictive model from it. This paper presents short notes on data mining, basic principles of data mining techniques, comparison on classification techniques using WEKA, Data mining in bioinformatics, discussion on WEKA. Introduction Computers have brought tremendous improvement in technologies especially the speed of computer and data storage cost which lead to create huge volumes of data. Data itself has no value, unless data can be changed to information to become useful. In past two decade the data mining was invented to generate knowledge from database. Data Mining is the method of finding the patterns, associations or correlations among data to present in a useful format or useful information or knowledge[1]. The advancement of the healthcare database management systems creates a huge number of data bases. Creating knowledge discovery methodology and management of the large amounts of heterogeneous data has become a major priority of research. Data mining is still a good area of scientific study and remains a promising and rich field for research. Data mining making sense of large amounts of unsupervised data in some domain[2]. Data mining techniques Data mining techniques are both unsupervised and supervised. Unsupervised learning technique is not guided by variable or class label and does not create a model or hypothesis before analysis. Based on the results a model will be built. A common unsupervised technique is Clustering. In Supervised learning prior to the analysis a model will be built. To estimate the parameters of the model apply the algorithm to the data. The biomedical literatures focus on applications of supervised learning techniques. A common supervised techniques used in medical and clinical research is Classification, Statistical Regression and association rules. The learning techniques briefly described below as: Clustering Clustering is a dynamic field of research in data mining. Clustering is an unsupervised learning technique, is process of partitioning a set of data objects in a set of meaningful subclasses called clusters. It is revealing natural groupings in the data. A cluster include group of data objects similar to each other within the cluster but not similar in another cluster. The algorithms can be categorized into partitioning, hierarchical, density-based, and model-based methods. Clustering is also called unsupervised classification: no predefined classes. Association Rule Association rule in data mining is to find the relationships of items in a data base. A transaction t contains X, itemset in I, if X à  t. Where an itemset is a set of items. E.g., X = {milk, bread, cereal} is an itemset. An association rule is an implication of the form: X  ® Y, where X, Y ÃÅ' I, and X ÇY = Æ An association rules do not represent any sort of causality or correlation between the two item sets. X Þ Y does not mean X causes Y, so no Causality X Þ Y can be different from Y Þ X, unlike correlation Association rules assist in marketing, targeted advertising, floor planning, inventory control, churning management, homeland security, etc. Classification Classification is a supervised learning method. The classification goal is to predict the target class accurately for each case in the data. Classification is to develop accurate description for each class. Classification is a data mining function consists of assigning a class label of objects to a set of unclassified cases. Classification A Two-Step process show in figure 4. Data mining classification mechanisms such as Decision trees, K-Nearest Neighbor (KNN), Bayesian network, Neural networks, Fuzzy logic, Support vector machines, etc. Classification methods classified as follows: Decision tree: Decision trees are powerful classification algorithms. Popular decision tree algorithms include Quinlans ID3, C4.5, C5, and Breiman et al.s CART. As the name implies, this technique recursively separates observations in branches to construct a tree for the purpose of improving the prediction accuracy. Decision tree is widely used as it is easy to interpret and are restricted to functions that can be represented by rule If-then-else condition. Most decision tree classifiers perform classification in two phases: tree-growing (or building) and tree-pruning. The tree building is done in top-down manner. During this phase the tree is recursively partitioned till all the data items belong to the same class label. In the tree pruning phase the full grown tree is cut back to prevent over fitting and improve the accuracy of the tree in bottom up fashion. It is used to improve the prediction and classification accuracy of the algorithm by minimizing the over-fitting. Compared to other data mining techniques, it is widely applied in various areas since it is robust to data scales or distributions. Nearest-neighbor: K-Nearest Neighbor is one of the best known distance based algorithms, in the literature it has different version such as closest point, single link, complete link, K-Most Similar Neighbor etc. Nearest neighbors algorithm is considered as statistical learning algorithms and it is extremely simple to implement and leaves itself open to a wide variety of variations. Nearest-neighbor is a data mining technique that performs prediction by finding the prediction value of records (near neighbors) similar to the record to be predicted. The K-Nearest Neighbors algorithm is easy to understand. First the nearest-neighbor list is obtained; the test object is classified based on the majority class from the list. KNN has got a wide variety of applications in various fields such as Pattern recognition, Image databases, Internet marketing, Cluster analysis etc. Probabilistic (Bayesian Network) models: Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. Bayesian algorithms predict the class depending on the probability of belonging to that class. A Bayesian network is a graphical model. This Bayesian Network consists of two components. First component is mainly a directed acyclic graph (DAG) in which the nodes in the graph are called the random variables and the edges between the nodes or random variables represents the probabilistic dependencies among the corresponding random variables. Second component is a set of parameters that describe the conditional probability of each variable given its parents. The conditional dependencies in the graph are estimated by statistical and computational methods. Thus the BN combine the properties of computer science and statistics. Probabilistic models Predict multiple hypotheses, weighted by their probabilities[3]. The Table 1 below gives the theoretical comparison on classification techniques. Data mining is used in surveillance, artificial intelligence, marketing, fraud detection, scientific discovery and now gaining a broad way in other fields also. Experimental Work Experimental comparison on classification techniques is done in WEKA. Here we have used labor database for all the three techniques, easy to differentiate their parameters on a single instance. This labor database has 17 attributes ( attributes like duration, wage-increase-first-year, wage-increase-second-year, wage-increase-third-year, cost-of-living-adjustment, working-hours, pension, standby-pay, shift-differential, education-allowance, statutory-holiday, vacation, longterm-disability-assistance, contribution-to-dental-plan, bereavement-assistance, contribution-to-health-plan, class) and 57 instances. Figure 5: WEKA 3.6.9 Explorer window Figure 5 shows the explorer window in WEKA tool with the labor dataset loaded; we can also analyze the data in the form of graph as shown above in visualization section with blue and red code. In WEKA, all data is considered as instances features (attributes) in the data. For easier analysis and evaluation the simulation results are partitioned into several sub items. First part, correctly and incorrectly classified instances will be partitioned in numeric and percentage value and subsequently Kappa statistic, mean absolute error and root mean squared error will be in numeric value only. Figure 6: Classifier Result This dataset is measured and analyzed with 10 folds cross validation under specified classifier as shown in figure 6. Here it computes all required parameters on given instances with the classifiers respective accuracy and prediction rate. Based on Table 2 we can clearly see that the highest accuracy is 89.4737 % for Bayesian, 82.4561 % for KNN and lowest is 73.6842 % for Decision tree. In fact by this experimental comparison we can say that Bayesian is best among three as it is more accurate and less time consuming. Table 2 : Simulation Result of each Algorithm DATA MINING IN BIONFORMATICS Bioinformatics and Data mining provide challenging and exciting research for computation. Bioinformatics is conceptualizing biology in terms of molecules and then applying informatics techniques to understand and organize the information associated with these molecules on a large scale. It is MIS for molecular biology information. It is the science of managing, mining, and interpreting information from biological sequences and structures. Advances such as genome-sequencing initiatives, microarrays, proteomics and functional and structural genomics have pushed the frontiers of human knowledge. Data mining and machine learning have been advancing with high-impact applications from marketing to science. Although researchers have spent much effort on data mining for bioinformatics, the two areas have largely been developing separately. In classification or regression the task is to predict the outcome associated with a particular individual given a feature vector describing that individu al; in clustering, individuals are grouped together because they share certain properties; and in feature selection the task is to select those features that are important in predicting the outcome for an individual. We believe that data mining will provide the necessary tools for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics. Propose novel data mining techniques for tasks such as Gene expression analysis, Searching and understanding of protein mass spectroscopy data, 3D structural and functional analysis and mining of DNA and protein sequences for structural and functional motifs, drug design, and understanding of the origins of life, and Text mining for biological knowledge discovery. In todays world large quantities of data is being accumulated and seeking knowledge from massive data is one of the most fundamental attribute of Data Mining. It consists of more than just collecting and managing data but to analyze and predict also. Data could be large in size in dimension. Also there is a huge gap from the stored data to the knowledge that could be construed from the data. Here comes the classification technique and its sub-mechanisms to arrange or place the data at its appropriate class for ease of identification and searching. Thus classification can be outlined as inevitable part of data mining and is gaining more popularity. WEKA data mining software WEKA is data mining software developed by the University of Waikato in New Zealand. Weka includes several machine learning algorithms for data mining tasks. The algorithms can either call from your own Java code or be applied directly to a dataset, since WEKA implements algorithms using the JAVA language. Weka contains general purpose environment tools for data pre-processing, regression, classification, association rules, clustering, feature selection and visualization. The Weka data mining suite in the bioinformatics arena it has been used for probe selection for gene expression arrays[14], automated protein annotation[7][9], experiments with automatic cancer diagnosis[10], plant genotype discrimination[13], classifying gene expression profiles[11], developing a computational model for frame-shifting sites[8] and extracting rules from them[12]. Most of the algorithms in Weka are described in[15]. WEKA includes algorithms for learning different types of models (e.g. decision trees, rule sets, linear discriminants), feature selection schemes (fast filtering as well as wrapper approaches) and pre-processing methods (e.g. discretization, arbitrary mathematical transformations and combinations of attributes). Weka makes it easy to compare different solution strategies based on the same evaluation method and identify the one that is most appropriate for the problem at hand. It is implemented in Java and runs on almost any computing platform. The Weka Explorer Explorer is the main interface in Weka, shown in figure 1. Open fileà ¢Ã¢â€š ¬Ã‚ ¦ load data in various formats ARFF, CSV, C4.5, and Library. WEKA Explorer has six (6) tabs, which can be used to perform a certain task. The tabs are shown in figure 2. Preprocess: Preprocessing tools in WEKA are called Filters. The Preprocess retrieves data from a file, SQL database or URL (For very large datasets sub sampling may be required since all the data were stored in main memory). Data can be preprocessed using one of Wekas preprocessing tools. The Preprocess tab shows a histogram with statistics of the currently selected attribute. Histograms for all attributes can be viewed simultaneously in a separate window. Some of the filters behave differently depending on whether a class attribute has been set or not. Filter box is used for setting up the required filter. WEKA contains filters for Discretization, normalization, resampling, attribute selection, attribute combination, Classify: Classify tools can be used to perform further analysis on preprocessed data. If the data demands a classification or regression problem, it can be processed in the Classify tab. Classify provides an interface to learning algorithms for classification and regression models (both are called classifiers in Weka), and evaluation tools for analyzing the outcome of the learning process. Classification model produced on the full trained data. WEKA consists of all major learning techniques for classification and regression: Bayesian classifiers, decision trees, rule sets, support vector machines, logistic and multi-layer perceptrons, linear regression, and nearest-neighbor methods. It also contains metalearners like bagging, stacking, boosting, and schemes that perform automatic parameter tuning using cross-validation, cost-sensitive classification, etc. Learning algorithms can be evaluated using cross-validation or a hold-out set, and Weka provides standard numeric performance mea sures (e.g. accuracy, root mean squared error), as well as graphical means for visualizing classifier performance (e.g. ROC curves and precision-recall curves). It is possible to visualize the predictions of a classification or regression model, enabling the identification of outliers, and to load and save models that have been generated. Cluster: WEKA contains clusterers for finding groups of instances in a datasets. Cluster tools gives access to Wekas clustering algorithms such as k-means, a heuristic incremental hierarchical clustering scheme and mixtures of normal distributions with diagonal co-variance matrices estimated using EM. Cluster assignments can be visualized and compared to actual clusters defined by one of the attributes in the data. Associate: Associate tools having generating association rules algorithms. It can be used to identify relationships between groups of attributes in the data. Select attributes: More interesting in the context of bioinformatics is the fifth tab, which offers methods for identifying those subsets of attributes that are predictive of another (target) attribute in the data. Weka contains several methods for searching through the space of attribute subsets, evaluation measures for attributes and attribute subsets. Search methods such as best-first search, genetic algorithms, forward selection, and a simple ranking of attributes. Evaluation measures include correlation- and entropy based criteria as well as the performance of a selected learning scheme (e.g. a decision tree learner) for a particular subset of attributes. Different search and evaluation methods can be combined, making the system very flexible. Visualize: Visualization tools shows a matrix of scatter plots for all pairs of attributes in the data. Practically visualization is very much useful which helps to determine learning problem difficulties. WEKA visualize single dimension (1D) for single attributes and two-dimension (2D) for pairs of attributes. It is to visualize the current relation in 2D plots. Any matrix element can be selected and enlarged in a separate window, where one can zoom in on subsets of the data and retrieve information about individual data points. A Jitter option to deal with nominal attributes for exposing obscured data points is also provided. interfaces to Weka All the learning techniques in Weka can be accessed from the simple command line (CLI), as part of shell scripts, or from within other Java programs using the Weka API. WEKA commands directly execute using CLI. Weka also contains an alternative graphical user interface, called Knowledge Flow, that can be used instead of the Explorer. Knowledge Flow is a drag-and-drop interface and supports incremental learning. It caters for a more process-oriented view of data mining, where individual learning components (represented by Java beans) can be connected graphically to create a flow of information. Finally, there is a third graphical user interface-the Experimenter-which is designed for experiments that compare the performance of (multiple) learning schemes on (multiple) datasets. Experiments can be distributed across multiple computers running remote experiment servers and conducting statistical tests between learning scheme. Conclusion Classification is one of the most popular techniques in data mining. In this paper we compared algorithms based on their accuracy, learning time and error rate. We observed that, there is a direct relationship between execution time in building the tree model and the volume of data records and also there is an indirect relationship between execution time in building the model and attribute size of the data sets. Through our experiment we conclude that Bayesian algorithms have good classification accuracy over above compared algorithms. To make bioinformatics lively research areas broaden to include new techniques.

1970’s American Culture and the Impact on Dance Essay -- Sociology Ess

1970’s American Culture and the Impact on Dance The freedom of the American life and culture of the 1970’s overflowed to make a major impact on music and dance during this period. American culture flourished. The events of the times were reflected in and became the inspiration for much of the music, literature, entertainment, and even fashion of the decade. Choreographers wanted to motivate the dancers to leap into the unknown and experience the contact of dance in their own way. Free love and the idea surrounding it helped break down barriers from traditional dance movements. Men and women began using their bodies to portray physical acts, built on to each other creating unique and interesting positions. They were working with the physical forces of gravity, momentum, and inertia. They would use the force of one body hurling into another to find out what would happen. For the first time in history, it was not unusual for a man and woman to depict the act of making love on the dance floor. The term â€Å"bump and grind† did start during this era of free love. Another ...

Integratron and Giant Rock, Mojave Desert, California

If you stand directly under the hole in the center of the room and say something the sound reverberates through your head and body and sounds like peaking to a large outdoor or indoor audience with lots of echoes and large speakers. Very strange no doubt. If you whisper from one side of the room, people on the opposite side can hear you clearly, which Is why I got strange looks when I said my watch was running backwards from the electrical effects. There was a presentation that covered some of the history of the builder and the bulging.When they got to the part about aliens I sort of lost interest. Everyone was Invited to take a blanket and lay out on the floor for meditation and a sound massage session with sounds provided by vibrations from crystal bowls and goblets. Remember how you can run your finger around the rim of a crystal goblet and get a nice humming sound? Well here in the Integration it really is magnified and enhanced creating a peaceful and relaxing interlude while la ying on the blankets. I however sat in a plastic chair. Several fell asleep based on snoring sounds.Founded in 1954 by aeronautical engineer and biologist George Van Tassel, the Integration offers something called a â€Å"sound bath†, and though we were not quite sure what a â€Å"sound bath† would entail, we were pleasantly surprised to find that while the energy vortex may not have been subjugating our poor abused livers, the experience was Indeed quite relaxing. For 30 minutes, our host â€Å"Torn† played varying tones on a set of quartz crystal slaying bowls as we meditated, listened to each other breathe, cough, and In some cases, nap.As It turns out, Van Tassel was not your run-of-the-mill OF;chasing desert eccentric. An aeronautical engineer and test pilot who worked for both Lockheed and alongside Howard Hughes at Hughes Aviation, he moved to the Mojave Desert in 1947 to operate an airport and inn. It was there that he claimed to be contacted ellipticall y by the Venusians, who were entrusting to him the secrets of cell rejuvenation. Acting on these instructions, Van Tassel began building the Integration, a 38 Ft. High dome inspired by Moses' Tabernacle and the writings of Tests.While not the rejuvenation center and time machine that Van Tassel had intended, it Is does serve as the only all-wood, acoustically perfect sound chamber in the U. S. According to Van Tassel, the site of the Integration Is actually a magnetic vortex, an Intersection of geometric forces that would amplify energy required for human cell rejuvenation ND healing. All that was needed to harness this great gift to the human race was a parabolic dome was designed to focus that energy, much like it focuses sound, toward the center with it's spherical shape.Not meeting the standards of life-saving rejuvenation chamber Just yet, the Integration still serves a purpose as a meditation spot, event location, and unusual desert stop to feel closer to our Venusians friends in the stars. Outside of the dome itself there is a dry garden with a clump of hammocks serving as â€Å"Hammock Village†, and tons of interesting folk art and alien-themed knick- knacks such as the â€Å"Alien Clings to Rock† piece you see here. To enjoy our sound bath, we found a blanket or yoga mat and claimed a spot in the circle, feet facing out.Our host described how the sound chamber works, and demonstrated how if we heard someone breathing or coughing as if they were right next to our ear, it was actually the person directly across from us on the other side of the dome. After a unusually long and stern warning regarding snoring during the bath, we closed our eyes as our host played the singing bowls. Bending the sound in ways that made it feel like it was owing in and out of our heads in waves, the intense sound was both soothing and unsettling.There was surprisingly a lot of snoring, and the earlier warning no longer seemed frivolous. After 30 minutes of hypn otic sound, we felt refreshed and ready to take on the rest of our desert adventure. Recently honored with a dedication and historical monument by the Ancient and Honorable Order of E Clamp's Vitas, Billy Holcomb Chapter, the Integration today receives many visitors drawn to experience the Integration's enhanced energy fields. An overnight stay at the Integration is said o result in waves of peace, heightened awareness, and relaxation of the mind and body.

The Novotel Value Chain Essays

The Novotel Value Chain Essays The Novotel Value Chain Essay The Novotel Value Chain Essay Novel gains its competitive advantage by ensuring customer expectations are met with personal warmth and consistent service standards are delivered. Their multi- killing approach allows staff to work as a team to perform tasks and duties flexibly. This will reduce core staff levels and it creates a more resourceful workforce. They have standardized procedures that are delivered to all locations worldwide. The use of the value chain analysis can provide a number of benefits to the management of Novel by identifying linkages between value activities within the organization. A system was set up to monitor standard procedures which became a template for learning and every staff was given a booklet with all the procedures. Standards were monitored by mystery shoppers. Nonvoters capabilities are valuable due to its hospitality concept being customer responsive welcoming their customers. They have the capabilities to attract and retain their customers by targeted approach to sales and marketing and this will win over their competitors. Nonvoters resources and capabilities are not rare although Novel Is a well-known hotel. It would not be hard to Imitate them on the basic level since hotel Industries have similar marketing and promotion schemes as well as partnership programs. But it would be extremely hard to Imitate them In terms of size, the number of hotels they already have worldwide and name brand Image. The organization Is strong and stable as they have been established for a long time and they managed to change system In order to maintain universal quality standards. They can exploit new areas fairly quickly and easily, largely due to the fact that they have management control.

How to Develop and Organize a Classification Essay

How to Develop and Organize a Classification Essay Classification is a method of developing an essay by arranging people, objects, or ideas with shared characteristics into particular classes or groups. After you have settled on a topic for a classification essay*  and explored it through various prewriting strategies, you should be ready to attempt a first draft. Here is how to develop and organize a five-paragraph classification essay. Introductory Paragraph In your introduction, clearly identify your subject - in this case, the group you are classifying. If you have narrowed your subject in any way (for example, types of bad drivers, rock guitarists, or annoying moviegoers), make this clear from the start. You may also want to provide some specific descriptive or informative details to attract the interest of your readers and suggest the purpose of the essay. Finally, include a thesis sentence (usually at the end of the introduction) that briefly identifies the main types or approaches that youre about to examine.   Heres an example of a short but effective introductory paragraph to a classification essay: Its a warm evening in July, and all across the country Americans are gathering to watch a game of professional baseball. Armed with hot dogs and cold drinks, they stroll to their seats, some in grand stadiums, others in cozy minor-league parks. But no matter where the game is played, you will find the same three types of baseball fan: the Party Rooter, the Sunshine Supporter, and the Diehard Fan. Notice how this introduction creates certain expectations. The specific details provide a setting (a ballpark on a warm evening in July) in which we expect to see the various fans described. In addition, the labels assigned to these fans (the Party Rooter, the Sunshine Supporter, and the Diehard Fan) lead us to expect descriptions of each type in the order theyre given. A good writer will go on to fulfill these expectations in the body of the essay. Body Paragraphs Begin each body paragraph with a topic sentence that identifies a particular type of approach. Then  illustrate each type with specific details. Arrange your body paragraphs in whatever order strikes you as clear and logical - say, from the least effective approach to the most effective, or from the most common type to the least familiar (or the other way around). Just make sure that the order of your body paragraphs matches the arrangement promised in your thesis sentence. Here, in the body of the essay on baseball fans, you can see that the writer has fulfilled the expectations set up in the introduction. (In each body paragraph, the topic sentence is in italics.) The Party Rooter goes to games for the hot dogs, the gimmicks, the giveaways, and the companionship; hes not really that interested in the ballgame itself. The Party Rooter is the sort of fan who shows up on Buck-a-Brew Night, often with a gang of fellow partiers. He cracks jokes, hurls peanuts at the team mascot, applauds the exploding scoreboard, blasts an electronic horn whenever he pleases- and occasionally nudges a companion and asks, Hey, whos winning? The Party Rooter often wanders out of the park in the sixth or seventh inning to continue his celebrations in the car on the way home.The Sunshine Supporter, usually a more common type than the Party Rooter, goes to the park to cheer on a winning team and bask in its glory. When the home side is on a winning streak and still in contention for a playoff spot, the stadium will be packed with this sort of fan. As long as her team is winning, the Sunshine Supporter will be roaring at every play, waving her pennant and shouting out th e names of her heroes. However, as the name implies, the Sunshine Supporter is a fickle fan, and her cheers quickly turn to boos when a hero strikes out or drops a line drive. She will stay around until the end of the game to celebrate a victory, but should her team fall a few runs behind, shes likely to slip out to the parking lot during the seventh-inning stretch.​Diehard Fans are also strong supporters of the local team, but they go to the park to watch good baseball, not just to root for a winner.  More attentive to the game than other fans, Diehards will study the stance of a power hitter, note the finesse of a quick fielder, and anticipate the strategy of a pitcher who has fallen behind in the count. While the Party Rooter is chugging a beer or dropping wisecracks, Diehards may be filling in a scorecard or commenting on a players RBI tally over the past few months. And when a Sunshine Supporter boos an opposing player for tagging out a local hero, Diehards may be quie tly applauding the expert moves of this enemy infielder. No matter what the score is, Diehard Fans remain in their seats until the last batter is out, and they may still be talking about the game long after its over.​ Notice how the writer uses comparisons to ensure cohesion in the body of the essay. The topic sentence in both  the second and third paragraphs refers to the preceding paragraph. Likewise, in the third body paragraph, the writer draws explicit contrasts between the Diehards and the other two types of baseball fans. Such comparisons not only provide smooth transitions from one paragraph to the next but also reveal the sympathies of the writer. He begins with the type of fan he likes the least and ends with the one he most admires. We now expect the writer to justify his attitudes in the conclusion. Concluding Paragraph The concluding paragraph gives you an opportunity to draw together the various types and approaches you have been examining in the body of the essay. You may choose to offer a final brief comment on each one, summarizing its value or its limitations. Or you may want to recommend one approach over the others and explain why. In any case, make sure that your conclusion clearly emphasizes the purpose of your classification. In the concluding paragraph to Baseball Fans, consider whether the author has been successful in his effort to tie his observations together. Professional baseball would have trouble surviving without all three types of fans. The Party Rooters provide much of the money that owners need to hire talented players. The Sunshine Supporters bring a stadium to life and help boost the morale of the home team. But only the Diehard Fans maintain their support all season long, year in and year out. By late September in most ballparks, enduring chilly winds, rain delays, and sometimes humiliating losses, only the Diehards remain. Notice how the writer hooks his conclusion back to the introduction by contrasting the chilly night in September with the warm evening in July. Connections such as this help to unify an essay and give it a sense of completeness. As you develop and organize your draft, experiment with various strategies, but keep this basic format in mind: an introduction that identifies your subject and the different types of approaches; three (or more) body paragraphs that rely on specific details to describe or illustrate the types; and a conclusion that draws your points together and makes the overall purpose of the classification clear.

Future Threats to Public Safety and Success in the War on Terror Research Paper

Future Threats to Public Safety and Success in the War on Terror - Research Paper Example Antonio Maria Costa can be quoted saying (2008) â€Å"Your citizens indeed say that what they fear the most is not terrorism, not climate change, not a financial crisis. It is public safety. And in the Americas, the biggest threat to public safety comes from drug trafficking and the violence perpetrated by organized crime†. Furthermore, Mr Costa stated that violence, gangs, kidnappings, brutality, and insurgences happening in different parts of the globe are drug related. Countless lives have been lost within in this gruesome problem. To make matters worse, innocent lives fall victims to it. From 2001 up to this date, it is still uncertain if we have really achieved success on the war on terror. The capture and execution of Saddam Hussein and the successful assassination of Osama bin Laden can be considered two of the greatest successes in what we call the war on terror. Assessing the war on terror is complex. We get different opinions from different people. Some way that it is a success, others say it is a depressing failure. We can read lots of resources which give us mixed views of the war’s status. One would be the fact that since 2001 there were no further terror attacks on American soil (Dart, 2012). This may mean that the war has been successful or it means that a terrorist plan has not been brought out yet. Although the war on terrorism may be hard to calculate, we cannot ignore that many nations are invested in the war. In spite of criticisms or compliments, everyone is pushing talks to counter

Discussion paper on New actors in international and global health Essay - 1

Discussion paper on New actors in international and global health focuse on NGOs - Essay Example piece of discussion I will also put light on all kinds of loop holes present in current and emerging NGOs and would end up with number of suitable suggestions and recommendations for the improvement of these supporting bodies within a country. NGOs plays a significant role in improving the struggling masses present in the third world countries and in developing nations to an extent for which, I would use various references from books based on facts and figures to make this discussion qualitative and reasonable for the reader to understand the original essence of the topic. This paper analysis and discusses about the issues raise in Non Governmental Organizations which are non- profitable at the same time around the globe. In this era, where communication is not an issue and distances are no more a deal, all the NGOs have worked together and settled down together with civil society organizations to make a remarkable difference. Some of the facts which are eye catching, as it is getting into power rapidly. â€Å"Nationally, the non-profit arts and culture industry generates $166.2 billion in economic activity every year:   $63.1 billion in  spending by organizations and an additional $103.1 billion in event-related spending by their audiences†. Non-profit sectors/non-governmental organizations work in every community which includes rebuilding cities, schools for primary education, supporting the workforce,  medical assistance, old age masses, elevating the arts, mentoring our youth, protecting natural resources, and much more. In this deal, many new names of business industry and actors are coming up with great deals to offer. They are working for humanity not in a particular region but throughout and around the globe. All these points will be discussed in detail later in this paper. As the masses of the globe situated at different corners were dipping more and more into crises like Health, lack of education, insecurity, poverty and hunger, and when these issues