Anatomy of a Restaurant Promo Coursework - 1100 Words

Anatomy of a Restaurant Promo (Coursework Sample) Content: Anatomy of a Restaurant PromoYour nameUniversity TitleAnatomy of a Restaurant PromoIntroductionAn anatomy of something is fundamentally its structural outlay and in this case it is the structure of a restaurant promo. The anatomy of a restaurant promo is one of the major important factors considered in the hospitality industry. This is because customers are inclined to like or despise the restaurantà ¢Ã¢â€š ¬s services based on their first impression derived from the structure of the promo. A good structure entices the clients and catches their attention, which makes them buy from the restaurant. Poor restaurant promo anatomy on the hand makes customers walk away and make negative generalizations on the restaurant. This makes it an important factor in determining the business performance of any given restaurant.Case 1In the first case study on menu development, Kelly comes up with a number of guiding questions. The main problem in his situation is how best to structu re the menu for it to give customers maximum information in the simplest possible way.A good menu should have a cover well designed but simple for the guests to easily notice it is a menu. The material of the cover is dependent on the type of the restaurant. A leather cover is official and suitable to restaurants in five star hotels (Maria, 2008). Minimal writings on the cover help the guest save time and go direct to the contents of the menu. The color is an important factor as it expresses the theme of the restaurant. Vibrant colors like red, green, turquoise, and green are perfect for a restaurant with a Mexican theme, however, the same colors may not be good for a French themed restaurant.The panel of a menu would depend on the number of items the restaurant wants to display to the customers. Where the items are few, a two panel menu would present the items simply and completely. A restaurant with many items and new foods should have a detailed menu displaying prices and food pi ctures and thus a four panel menu is efficient (Maria, 2008). For a menu to be efficient, it should display all the foods the restaurant has to offer. At the same time this has to be done in brief and simple way for the customer to easily identify what they will buy. This makes a book menu not efficient as it may put off some clients who are not comfortable with excess details.The sequence for best positioning on a restaurant menu is; begin with appetizers to mouth water the guest, then proceed to desserts, salads, beverages, entrees, and lastly soups. To make the menu short and precise, a specialty menu is important for especially for alcoholic beverages. This helps the non alcoholic clients to easily identify what is good for them. It also helps clients with health conditions to make good choice of healthy meals for them. The font of the menu should not be too small to read. Italics may be used for sporty restaurants that are less formal while other restaurants that are formal sh ould adopt the bold face print to match with the theme of the restaurant.Descriptions make the menu lengthy for the clients to go through. Therefore, to avoid such pictures are important as they give the guest a clear idea of what to expect will be served. It is important for the menu to present the heart healthy items to help clients with heart complications choose healthy meals.Case 2This case study is on the Schultzie, a 30 unit casual dining chain meeting attended by various managers of different units of Schultzie. The meeting is about the new policy of updating the menu with numerous new items each quarter.It was right for the corporate headquarters to introduce the new menu items because it encourages innovation and attracts more clients to try the new items. This is clear as the manager next to Seymour whispers back to him that his clients have been asking for new and interesting items. The managers acknowledges that he is happy because the headquarter is responding to guest sà ¢Ã¢â€š ¬ requests. However, initially, Seymour has a different view on the same and sees the new policy as exotic. Later on it works well for Seymour and he comes to terms that the new items are beneficial to the performance of his unit.What Seymour did right is his decision to finally use more materials he had to train the evening servers. He uses videos and provides meal samples as well to the servers. This leads to promising and positive outcomes. The new items move faster in the evening more than lunch time and Seymour regrets he should have used more materials in the training at lunch time. Seymour Tidwell did one mistake while training the servers at lunch time. He decides not to use all the materials provided by the headquarters in training Jennifer, Steve, and Rollo about the new items for the menu (Maria, 2008). Tidwell uses only video material and lets the kitchen do some samples. The manager also does not keenly follow the head quarterà ¢Ã¢â€š ¬s directives and inst ead of using lemon sauce he lets them use white sauce in its place and pretend its lemon sauce. Mike brings to his attention the availability of only two burners that are insufficient incase customer orders are many. Seymour tells the head cook not to worry about the burners as he does not anticipate guests to go for the shrimp in a big way. All these sum up to the...

Analysis Of The Movie I re Your Problem Annie

â€Å"You’re your problem Annie, and you’re also your solution,† is something surprisingly deep from the R-rated Comedy Bridesmaids (1:37:13). This movie is about a girl, Annie, who along with falling on hard times has to plan her best friend, Lillian’s wedding while dealing with a crazy group of bridesmaids: Helen, the rich perfectionist, Rita, the mother who hates her three boys, Becca, the newly wed, and Megan, the overtalkative nutcase. Bridesmaids has a lot of truths about money, success, and overall happiness. The main themes of the movie are that money will not always make someone happy and that money does not make anyone better than anyone else. With those themes there are also a lot of comparisons that could be made with the movie including how Helen, the very rich, and Annie, the very poor, constantly struggle to prove who would be a better maid of honor, and how Annie could be an embodiment of the struggles millennials faced during and after the stock market collapse. Two characters that really show that money does not buy happiness: Megan and Rhodes, a cop who becomes Annie’s love interest. Megan is a very interesting character in this movie because while she is very odd she really has her life put together. She was able to overcome the obstacles that she talked about with Annie and how she loves what she does (1:35:54-1:36:24). While Megan does make a lot of money, she would most certainly be happy without and be able to have a fulfilled life as long as she did whatShow MoreRelatedEast Is East, Directed By Damien O Donnell913 Words   |  4 Pages This analysis will examine the film, East is East, directed by Damien O’Donnell. There are three focal points this analysis will examine. First, the multicultural marriage between a Pakistani man (George Khan) and his English wife (Ella Khan). Second, focus on the parenting between these parents and their seven children created from the marriage, half Pakistan and half English. 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Supervised Learning Model and Algorithm-Free-Samples for Students

Question: Discuss about the Machine Learning. Answer: Introduction Machine Learning (ML) could be termed as a branch of Artificial Intelligence, as they contain the methods, which allows the computers and systems to act more smartly. The result is a unique way of cumulative working of various functions in an orderly manner, rather than simple data insertion and retrieval from applications like database and others, making the machines take better decision are different situations with minimal input from the user. Machine learning is a branch of study, grouped from various different fields, such as computer science, statistics, biology, and psychology. The base functionality of Machine Learning is to identify the best Predictor model for making decisions, by analyzing and learning from previous scenarios, which is the job of a Classifier. The job of Classification is the prediction of unknown scenarios (output) by analyzing known scenarios (input). The process of classification is performed over data set D comprising of the following objects: Let the set size be {X1, X2, |X|} where |X| signifies the attributes count of the set X Class label is Y then the target attribute; ?= y1, y2, |Y|} where Y is the number of classes and Y { 2. Then the basic goal of the machine learning is prediction or classification over the dataset D, such that it relates the attributes in X and classes in Y (Mohri et al, 2012). Classification of Machine Learning is on the basis of the type of input signal or feedback received by the learning system. These are as follows: Supervised learning: System is presented with a set of input condition and an instructor providing the desired output, thus making the system understand what to expect for an answer in a certain scenario, provides the desired outputs (Zhang et al, 2011). Unsupervised learning: There are no markers in the learning methodology, thus stranding the system to discover patterns in the inputs. This type of learning can become a target in itself (unraveling pattern in data) or the end game (feature learning). Reinforcement learning: System is challenged with an unprecedented environment to perform (for instance driving or playing against an opponent). The system is either rewarded or punished, based on the performance it displayed while navigating in the problem sphere. This paper deals with Supervised Learning, the various processes and all the functionalities. Supervised learning This type of learning is based on understanding the mapping of certain attributes and functions in a predefined scenario, then using the knowledge gathered in this process to make a decision based on the mapping learned, in unprecedented scenarios. This way of learning is very important and is a key functionality in multimedia processing (Mohri et al, 2012). In supervised learning a computer model represents a learner system which contains two set of data namely the training data set and the other set is the testing data set. For example consider a system for classification of a particular disease. In such scenario a system will use a data set which contains records of the patients with their diseases. This record is split into training data set and the testing data set. The idea behind this type of learning is to train the learner system with all possible outcomes in the training set, so it can perform with the highest percentage of accuracy in the test set. Which means, the target of a learner is to make out the pattern in the inputs provided in the test set and find a solution from what it has learned in the training set? The classification then can be the categories of diseases for the given example. Similarly the training set might include pictures of dogs like terrier and spaniel, along with the identification of each, now the test set would include another group of unidentified images but of the same set. The target for the learner is to design a rule to guide itself towards a solution in un-known scenarios (Mohri et al, 2012). In supervised learning the training set comprises of various ordered pairs like (a1, b1), (a2, b2), ...,(an, bn), where each ai is represents the set of measurements of a single example data point, and bi is the label for that data point. Consider an example where, a ai might be a group of five attributes for a cricket match, such as run-rate, wickets in hand, strike rate, fielding plan and individual performance. In such case the corresponding bi would be a classification of the game as a win or loose. Generally a testing data is comprises of data but without labels: (an+1, an+2... an+m). As discussed earlier, the target is to make an educated guess in the test set about win or loose by using the learning achieved in training set (Mohri et al, 2012). Supervised learning model and algorithm Following are the steps performed in order to solve a problem of Supervised Learning: Classifying the type of training set. Before proceeding further, an engineer must decide what type of training set he must use for his system. It could be a single unit, a group of it or a bunch of it (Rambhajani et al.,2015). Collect a set. The training set should model the real world entities, so a training set is gathered according. Along with this, possible outcomes are collected to form a set, either through experience or through some empirical measurements (Mohri et al, 2012). Ascertain the input predictive model of the educated function. Learned function's accuracy is directly dependent on the representation of the input. Basically, the input is converted to a feature vector, comprising of various features to model the object. The features should neither be too large in number to confuse the prediction nor should they be too small in number to strangle the decision-making capabilities (Mohri et al, 2012). Ascertain the model of the predictor function and the method employed. The choice may be any model of support vector machine, neural network, decision tree etc. Design finalization. Repetitive use of the carved out method to sharpen the accuracy. Some of the methods may require strengthening of a feature by practicing over a subset or through cross-referencing (Rambhajani et al.,2015). Calculate method's accuracy. Once the accuracy is achieved on the training set, the system must be presented with a test set to ascertain the final accuracy in real world scenario (Mohri et al, 2012). Figure 1: Supervised learning model The base step in supervised learning is handling the dataset. A subject matter expert could be employed to help with feature selection from a given data set. When a subject matter expert is not available then the second best method to ascertain the feature is "Brute Force" method, the use of empirical measurements, judging every possible scenario with all factors in mind and using statistical techniques for arriving at a conclusion, i.e. a feature. This method although is not applicable in all the cases like for a perfect induction, as in all of these cases this feature comprises of significant noise which is to be removed before final induction, thus creating a requirement of over-head pre-processing of the data set. The next step would deal with information preparation and pre-process to be used in Assisted Machine Learning (AML) (Rambhajani et al.,2015). There are various techniques available from several researchers to deal with missing data. Rambhajani et al.(2015), performed a survey and deducted a method to remove noise from the system. Zaremba et al (2016), have also deduced another method for noise removal and is used in several other systems. Konkol (2014), has made a comparative study on six different noise removal techniques by working over base data sets and using a hypothetical test data set (Rambhajani et al.,2015). Issues to be considered in supervised learning The main issues which are to be considered while designing the supervised learning method are as follows: Trade-off between the bias and the variance. Consider a scenario having different set of training data. In such scenario if the learning model tends to be biased to a particular variable y then while training on these data set the model gives incorrect prediction to the exact value for y. Similarly if the model has high variance for a input y then it predicts different output values for that variable for different training sets. Generally the prediction error of any learning model is sum of the bias and the variance of the model. Thus, there exists a tradeoff between the bias and the variance of the model. In case the learning model is too flexible in nature then it will make arrangements within the training data sets differently and thus shall exhibit high variance. The complexity of the function of the classifier and the relative quantity of the training data is the second issue. In case the function of the classifier is a simple function then it tends to be inflexible in nature and this result in the learning model having high bias and low variance values and thus shall be capable to learn from a very small quantity of the training data. On the other hand if the function is complex in nature than it requires high number of data set as the learning model shall be flexible with low bias and high variance making (Marsland, 2015). The dimensionality of the input space also poses challenges to the learning model. In case the input feature vectors comprise of high dimensions than it becomes difficult for the learning model to work upon as high dimensions causes the model to have high variances. Thus the input dimensions need to have low variances and high bias for correct learning model of the classifier (Marsland, 2015). Another issue which arises is the noise coming from the output values. In case the desired output values comprise of error due to human error or errors caused by sensors then the learning model should never try to compute a function that exactly matches the training examples. Doing such execution may lead to over fitting of the function (Marsland, 2015). Algorithm of supervised learning Supervised learning has attracted researchers and thus many types of efficient algorithms have been designed for supervised learning. Many of these algorithms have been used for wide range of applications like for classification of diseases and non-disease attributes, pattern recognition, speech recognition, etc (Ling et al, 2015; Nguyen et al ,2014; Marsland, 2015). Each algorithm of supervised learning has its own set of advantages and disadvantages. Still, there exists no single algorithm that can be used on all supervised learning problem. Some of the popular supervised learning algorithms are as follows: Decision Trees: Decision trees classify the input instances by arranging the instances based on feature values and forms a tree like structure where the tree node represents the feature of the input instance which is to be classified and the branch of the tree signifies a value that can be assigned to the node vector. Classification of the input instances starts at the root node and these instances are arranged based on their assigned feature values (Karimi et al, 2011). Example: Figure 2: Decision tree and data In the figure above there is a decision tree for the data given in the table on the right side. In this example an input instance comprising of at1 with value a1 at2 with a2 etc. can be arranged and classified as a class of Yes or No (Kami?ski et al, 2017). Decision trees are widely used in the classification of input data as they are flexible to be used for wide range of classification problems. Generally, the tree path or the resulting set of the rules of the tree are mutually exclusive which makes each data of the input to be covered in a single rule. This enhances the accuracy of the predictive systems and makes them more scalable (Kami?ski et al, 2017). Nave Bayes Classifier: Nave Bayes classifier comes under the group of Bayesian network based classifier which is generally statistical learning algorithms (Huang et al, 2014). The Nave Bayes Classifier networks comprise of directed acyclic graphs where there is one parent which represents the un observed node and many children nodes representing the observed nodes. There is a sting assumption of independence among the children nodes (Archana et al, 2014). The Nave Bayes makes use of the Bayes theorem to calculate the probability by counting the value frequencies and the combination of values of the historical data (Cohen et al, 2014). The posterior probability is calculated using the Bayes formula as given below: Where, P (c|x) is the posterior probability of class (target) given predictor (attribute). P(c) is the prior probability of class. P (x|c) is the likelihood which is the probability of predictor given class. P(x) is the prior probability of predictor For example, the frequency table is given below with the likelihood table. Figure 3: Frequency table and likelihood table The posterior probability now can be calculated using the above equation and the class with the highest probability becomes the result of the prediction. The advantage of this method is that it requires a very small amount of input training data and is fast to predict the outcome. But the biggest challenge while using this method is that getting an independent set of predictors is not possible always in a real life situation (Ghahramani et al, 2015). K-Nearest Neighbor This method of the supervised learning algorithm is used for classification when there is no prior or very less information about the distribution of the input data. Thus, this method is a good choice where it is required to perform the discriminate analysis when the probability densities are unknown. The classification in this method is done on the majority of the k-nearest neighbor category (Hamid et al., 2010). The input is classified as a new object based on the training samples and the attributes. The classification uses majority vote for the classification of the k objects. For example, consider the given sample data: X1 X2 Result 7 7 NO 7 4 NO 3 4 Yes 1 4 Yes Table 1: sample table The values of the variables x1 and x2 drive the outcome for the data as Yes or No. Now suppose the input value of x1 and x2 are 4 and 7 respectively, then there is no need to perform a lengthy survey in such scenarios this method can be used and the nearest k value shall be the result of the given input and thus the outcome shall be yes (Michalski et al, 2013). Support Vector Machine Support Vector Machine model represents the examples as a point in a given space and these examples than are separated into classes by a clear gap or the hyper plane. The new instances of the examples are then mapped into the same input space and their classes are predicted such that they fall on the either side of the hyper plane (Karamizadeh et al, 2014). Thus, the hyper plane is used for classification and regression. Thus, Support Vector Machine is a linear binary classifier which first classifies the entire input into two categories divided by a wide gap of the hyper plane based on some function criteria. SVM has been widely used for classification as they are effective in high dimensional spaces, is memory efficient and versatile in nature for holding different kernel functions which can be used for decision making. The only challenge this method poses is the refinement to be done for multi variable classification which requires linear binary classifier to be executed recursive ly thereby consuming more time (Liu et al, 2012). Artificial neural network and Deep Learning: Supervised learning can be efficiently implemented using the artificial neural network algorithms like back propagation algorithm. Neural Networks are machine model of human neurons and have a set of weighted inputs. The neuron produces an output based on the threshold function. In the training phase, various combinations of input and weights are used to train the network to give an output. The error from the output obtained is used for learning via back propagation and thus the network gets trained from the error (LeCun et al, 2015).Deep artificial neural networks are a variant of neural networks which have become popular in pattern recognition and machine learning. In the deep Learning model, neural networks with many layers are trained in a layer wise manner. Each layer by learning enhances the quality of learning and the accuracy of the output and henceforth this is used in many applications like computer vision, speech recognition, handwriting recognition, natural language proce ssing etc (Ruslan et al. , 2012; Dalessandro, 2013). Today Deep learning has been implemented in various to products of the company like Microsoft, Google, and Apple for data analysis and natural language processing. Google Voice search and Apple Siri make use of Deep learning method for natural language processing and thus are able to recognize the spoken text (Vincent et al,2010). These applications learn from the user usage every time and thus enhance their quality over the time of their usage (Dahl et al. , 2012). Applications of supervised learning Supervised learning has been used for various applications. There are many algorithms for supervised learning and many of them have proven to be useful in various applications. Any researchers have successfully implemented applications which make use of supervised learning for prediction, for classification for recognition etc. The main popularity of the usage of the supervised learning algorithms is due to its simplicity and that it helps in the development of the application with the available input sets and expected output sets. This makes the automation process to be an easier one. Supervised learning has been used for pattern recognition, speech recognition, handwriting recognition, optical character recognition, image classification, data mining, knowledge mining, data and text classification, spam filtering, single filtering, intrusion detection system, automated systems for traffic lights, flight scheduling, congestion control systems, disease classification and prediction sy stems etc. Some of the notable works are given below Disease classification prediction Supervised learning can be used for diagnosis of any disease where the existing disease patterns and symptoms of the previously known patients are given as the training inputs. After the training is done the new set of inputs for prediction are supplied to the model and the model predicts the probability of the disease i.e. it is present or not (Jordan et al, 2015). It classifies the diseases in the given class of present or absent. Such work has been done for predicting diseases like Breast cancer, Diabetes, heart diseases etc (Yugowati et al ,2013). In a research by Vembandasamy et al. (2015), the authors used Nave Bayes supervised learning algorithm to predict the heart disease of the patients. The data set comprised of 500 patients and the classification of the data was done using 70 % rules. The system accuracy was 87%. Similarly, Iyer et al (2015), performed the prediction of diabetes diseases using decision tree and Nave Bayes classifier. Pattern Recognition Supervised learning algorithms can be used to classify different patterns of images, shapes, handwriting characters, etc. The model is trained with the existing set of input and output patterns after the training the model is used to classify the other trusted input pattern into the various classes identified in the training phase. Pattern recognition abilities have helped in the development of computer vision applications using supervised learning (Murty et al, 2011). Many applications have been devised and been successfully implemented which work on the supervised learning model for image processing and pattern recognition system. For example automated smartcard recharge system is able to recognize the dollars through pattern recognition of the dollars submitted to the machine. Many researchers have used algorithms like Neural networks, Nave Bayes, SVM, K-Means to classify and identify patterns of handwriting, speech images, shapes etc (Smith et al, 2011; Sharma et al, 2015). Intrusion detection: The signature of the intrusions and their consequences are given as the training input to a supervised model. Based on the learning the model then classifies the new set of inputs into intrusion category or safe category. In this way, the supervised learning helps in instruction detection applications. The supervised learning methods are also helping in detecting signatures of cyber-attacks and viruses for the networks. Many types of research have been carried out in this area using various algorithms like neural networks, SVM etc (Poojitha et al, 2010). Conclusion One of the main objectives of the machine learning process is to impart computer an ability to learn from the data or the past experiences and thereby help in solving problems of classification and prediction. Machine learning can be done in 3 different ways i.e. supervised learning, unsupervised learning and reinforcement learning. The main aim of the supervised learning method is to build a model based on the trained input classes and their predicted features. The result is a classifier which assigns a label to the tested class instances with eh class labels formed in the training phase. Supervised learning has proven to be one of the efficient and easier methods of machines learning and hence it is widely used in various applications like speech recognition, pattern recognition, computer vision, intrusion detection, medical diagnosis systems etc. Supervised learning algorithms are more powerful than the other machine learning model like the unsupervised method because in the super vised learning the training data availability provides clear criteria for the optimization of the model. References Alpaydin, E., (2014). Introduction to machine learning. MIT press. Alsheikh, M.A., Lin, S., Niyato, D. and Tan, H.P., (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys Tutorials, 16(4), pp.1996-2018. Archana, S. and DR Elangovan, K. (2014) Survey of Classification Techniques in Data Mining. International Journal of Computer Science and Mobile Applications, 2, 65-71 Cohen, P.R., and Feigenbaum, E.A. eds., (2014). The handbook of artificial intelligence (Vol. 3). Butterworth-Heinemann. Dahl, G.; Yu, D.; Deng, L.; Acero, A. (2012). "Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition". IEEE Transactions on Audio, Speech, and Language Processing. 20 (1): 3042. Dalessandro, B., (2013). Bring the noise: Embracing randomness is the key to scaling up machine learning algorithms. Big Data, 1(2), pp.110-112. Deng, H.; Runger, G.; Tuv, E. (2011). The bias of important measures for multivalued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN). Ghahramani, Z., (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), p.452. Hamid, P, Alizadeh, H., and Minati, B. (2010). A Modification on K-Nearest Neighbor Classifier, Global Journal of Computer Science and Technology, Vol. 10, No. 14 (Ver.1.0), pp. 37-41. Huang, G., Huang, G.B., Song, S. and You, K., (2015). Trends in extreme learning machines: A review. Neural Networks, 61, pp.32-48. Huang, G., Song, S., Gupta, J.N. and Wu, C., (2014). Semi-supervised and unsupervised extreme learning machines. IEEE transactions on cybernetics, 44(12), pp.2405-2417. Iyer, A., Jeyalatha, S. and Sumbaly, R. (2015) Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining Knowledge Management Process (IJDKP), 5, 1-14. Jordan, M.I., and Mitchell, T.M., (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), pp.255-260. Kami?ski, B.; Jakubczyk, M.; Szufel, P. (2017). "A framework for sensitivity analysis of decision trees". Central European Journal of Operations Research. doi:10.1007/s10100-017-0479-6 Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J. and Rajabi, M.J. (2014) Advantage and Drawback of Support Vector Machine Functionality. 2014 IEEE International Conference on Computer, Communication and Control Technology (I4CT), Langkawi, 2-4 September 2014, 64-65 Karimi, K. and Hamilton, H.J. (2011). "Generation and Interpretation of Temporal Decision Rules", International Journal of Computer Information Systems and Industrial Management Applications, Volume 3. Konkol, M., (2014),. Brainy: A machine learning library. In International Conference on Artificial Intelligence and Soft Computing (pp. 490-499). Springer, Cham. LeCun, Y., Bengio, Y. and Hinton, G., (2015). Deep learning. Nature, 521(7553), pp.436-444. Ling, J. and Templeton, J., (2015). Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids, 27(8), p.085103. Liu, X. Y., Gao, C. H.and P. Li, (2012). A comparative analysis of support vector machines and extreme learning machines, Neural Network., vol. 33, pp. 5866. Marsland, S., (2015). Machine learning: an algorithmic perspective. CRC press. Michalski, R.S., Carbonell, J.G. and Mitchell, T.M. eds., (2013). Machine learning: An artificial intelligence approach. Springer Science Business Media. Mohri, M, Rostamizadeh, A., Talwalkar, A. (2012). Foundations of Machine Learning. USA, Massachusetts: MIT Press. ISBN 9780262018258. Murty, N.M.; Susheela Devi, V. (2011). Pattern Recognition: An Algorithmic Approach. ISBN 0857294946. Nguyen, D.H., and Patrick, J.D.,( 2014). Supervised machine learning and active learning in the classification of radiology reports. Journal of the American Medical Informatics Association, 21(5), pp.893-901. Poojitha, G. Kumar, K. N., and Reddy, P.J. (2010). Intrusion Detection Using Artificial Neural Network, International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-7. Rambhajani, M., Deepanker, W. and Pathak, N. (2015). A Survey on Implementation of Machine Learning Techniques for Dermatology Diseases Classification. International Journal of Advances in Engineering Technology, 8, 194-195. Ruslan, S, Joshua, T., (2012). "Learning with Hierarchical-Deep Models". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35: 195871. doi:10.1109/TPAMI.2012.269 Sharma, P. and Kaur, M. (2013) Classification in Pattern Recognition: A Review. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 298. Singh, Y., Bhatia, P.K., and Sangwan, O. (2007). A Review of Studies on Machine Learning Techniques. International Journal of Computer Science and Security, 1, 70-84. Smith, M.R, and Martinez, T. (2011). 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Research Paper Sample Outline

Research Paper Sample OutlineWriting a research paper is not the easiest thing to do but can be quite rewarding when you have completed it and are presenting it at your exam. You should know how to create a research paper outline so that it will help you when you are writing your paper. This article will show you how to do this.The first thing that you should do when writing a research paper outline is to make sure that you start off with a topic that you are already familiar with. You do not want to write about something that you are not familiar with. You also do not want to write about something that you do not care about. You want to know what your main topic is before you begin to write your paper.When you go to find a research paper sample outline, you may come across some that say things like: 'create a topic for the paper.' You should avoid doing this. Instead, you should write a sentence or two that explains what you want to talk about in your paper.Then, write the main poin ts of your paper in the following paragraph. Once you have finished your main points, you can then move on to explaining your summary. Remember to always come back to your main points throughout your paper. A good outline will help you in this process because it will keep you from having to type out so many sentences.If you use an outline to help you while writing your paper, you will quickly find that you have less errors and less mistakes because you will be saving a lot of time writing your paper and doing so quickly. If you are having trouble writing, take advantage of what you can do by taking advantage of an outline.After you have written your research paper outline, you can then start to write the body of your paper. Remember that it is very important that you use the information from your outline to help you with your writing. For example, you could use the information from your outline to explain your main points. If you are having trouble coming up with ideas for your pape r, take advantage of your outline.In addition, you want to try to remain as concise as possible when writing your paper. Try to only include the important points in your paper and leave out all the irrelevant stuff. When you follow this advice, you will find that you have a much better chance of writing your paper correctly.When you are finished writing your research paper sample outline, you should check that you have everything that you need for your paper. When you have completed this step, you can now begin to write your paper. It is a good idea to have someone help you while you are writing so that you will not have to start from scratch if you run into trouble.

Case Study Assignment Can Be Fun for Everyone

Case Study Assignment Can Be Fun for Everyone Experts in a specific area of study or customers that have implemented a tool or service which is the topic of the study will supply the ideal information. Furthermore, a case study contains not just a description of the unusual or useful circumstance but an analysis and some theoretical information regarding the topic. Likewise research for the question isn't difficult but you have to attempt to focus relevant and present information. All you will need is identifying the appropriate research materials necessary to explore such difficulties. Life After Case Study Assignment Experts will collect all of the necessary evidences that are absolutely relevant and essential to the topic of your study. Students must begin with relevant facts and by underlying key troubles. Students, in latter instance, may want to only explain the circumstance and supply their viewpoint. The 5-Minute Rule for Case Study Assignment Once you are finishe d with your case study, you will get a vital writing tool. A case study is among the many kinds of written assignments you have to face throughout your college years. Writing a case study isn't simple. If you were directed to compose a case study for a psychology training course, be certain to check with your instructor for any particular guidelines that you are needed to follow. After gathering all of the information, you can begin writing the essay. Our writers take part in writing quality and outstanding assignment for you with extensive study. Step two Draft a list of interview questions and opt upon how you are going to conduct your study. Finally, to be able to compose a case study assignment, the author ought to carefully approach the variety of the issue and stick to the formal plan. It is essential that you've got everything documented, should you should return to some point of your study. Don't forget that each and every excellent case study demands an excellent design, and it can be useful to bring in a designer to bring some visual interest to the piece. If you're finding it hard to conduct an extremely impressive case study then we're here to help save you from the strain and anxiety of completing a case study successfully. Therefore, you can write a study about almost everything it's possible to imagine. Virtually every thriving online company utilizes case studies, and Express Writers is not any different! As stated by the BIFA report, success for the test business was achieved through a partnership effort by making use of their clients and suppliers. As soon as it's a business which publishes a case study, the whole issue is devoted to recounting a customer's experience. Our service includes a guarantee. If as much is discussed by the organization, it's possible to learn more issues in case study analysisthan straining to locate any information for the exact same. You'll also need to debate the advantages and disadvantages of the company strategy. Another way to check at it is to think about case studies as stories told from the point of view of a happy customer. Various forms of case studies imply using a certain writing style, and the type you want to choose is dependent upon your purposes. The principal issue is to dig out appropriate information in accordance with your question. A case study is a paper that presents a deep analysis of a particular situation and provides the reader some probable methods for solving a situation. If you've written a superior scenario, they will have sufficient info to comprehend the circumstance and have a lively class discussion. If you choose a case problem too soon, you risk writing a biased essay since you'll only use the research to reply to your problem. Introducing Case Study Assignment Case study is essentially a hypothetical circumstance or a true life incident that may be analysed and studied from the theoretical viewpoint. It is different from t he other rigid protocols to understand the real scenario. Intrinsic case studies are a kind of case study where the researcher has a personal interest in the instance. Exploratory case studies are like a preliminary case study that makes it possible to identify questions and discover the focus of your principal case study. Case Study Assignment Help! Should you need law assignment help, you can get in touch with without hesitation to my assignment help anytime. The last portion of your assignment is going to be the conclusion. Assignments are a nightmare for those students. Nowadays you have understood that writing law assignment isn't easy like other assignments. What Is So Fascinating About Case Study Assignment? The government doesn't have any sufficient interest in the comprehensive ban of such advertisement. Large Multinational Corporation represents the majority of entrepreneurs on either side of export and import transactions. A study from 2007 proved the benefits of producing a company greener', and also reported on the quantity of time that it took to finish the transition. Thus, developing a case study takes a lot of patience and most importantly plenty of time. There's no sure shot recipe for writing an ideal case study, but your efforts are what you are able to put in to allow it to be perfect. If you feel you lack knowledge and can't cope with your written assignment in nursing by yourself, you can employ a professional writer on the custom made paper writing service. You need to concentrate on many of the subjects that are relevant to the true world and making mindful of the circumstance of the nation and society we live in. The Little-Known Secrets to Case Study Assignment Case Studies are primarily records of specific entity which could be a circumstance, an individual, or company organization. Our organization is enthusiastic to aid you in the best way possible. The staff is quite enthusiastic and eager to support one anot her's efforts. The staff were acknowledged and given a part in the procedure.

Business Prompt Essay Samples - Important Tips For Success

Business Prompt Essay Samples - Important Tips For SuccessPrompt essay samples are the most important part of the writing process. The information required for a prompt is often very important and a workable outline is essential for the writing process. Although the idea of writing prompt essay samples may seem fairly simple, it can be overwhelming when you first begin.An outline provides a starting point and the student can use this to move on to something more challenging but equally relevant to the academic level of study. It helps to have a starting point and outline to help guide your writing forward. To determine the difficulty of a prompt, it is important to read the assignment thoroughly. Some topics may require a lot of detail, while others are more apt to be succinct.For example, a vocabulary prompt for an essay samples may involve little to no background in the English language. This will be a more specific essay which will require lots of thought as well as reading compre hension skills. Some other examples include phrases such as: development of corporate culture, advertising agency, media relations, branding strategies, service provision strategies, project management and CRM (customer resource management).The essay sample will also be based around one of the skills that relates to business, for example customer service or strategy development. It is important to understand the areas of focus when completing the writing because the assignment should be able to relate to the particular goals and objectives of the company.As mentioned above, the outline is crucial when completing a writing sample. An outline will help to illustrate the needed skills and areas of focus and will also highlight which questions you need to ask to start writing. The document will also provide valuable research material, examples and ideas that can help students learn and achieve success.The initial goal should be to outline your topic and the writing samples that should b e included. You will then need to go over the outline and make any changes that need to be made. As with many other assignments, students will be able to give input into the final draft and ensure that it is balanced and does not contain spelling errors. As mentioned, writing prompt essay samples can be quite detailed and they will depend on the specific problems being addressed.During the writing process, students can still practice essay writing but through practice instead of just writing an essay. It is recommended that they take a good look at their writing samples and ensure that they follow the outline and demonstrate the desired skills. The challenge for teachers is that students will not necessarily be able to show their skills as they do in a written essay. It is however, a good way for students to explore the ability and demonstrate strengths and weaknesses.Business prompt essay samples can help students write well and also demonstrate that they are capable of solving dif ficult problems. They provide a solid base upon which the students can build upon and work on improving their writing skills. Therefore, prompt essay samples will provide the basis of the student's essay and will be used for all essays for the duration of the course.

The Most Popular Definition Essay Samples

The Most Popular Definition Essay Samples What Definition Essay Samples Is - and What it Is Not The very first step it to go for an intriguing word or a phrase you want to do a little bit of research about. To begin with, your key to winning essay is a crystal clear comprehension of what it is that you're likely to take care of. It's important the writer can engage the audience in a battle to specify the genuine significance of a word from a personal and academic perspective. Like the attention getter, it is necessary for the writer to have the capability to come up with a thesis statement which draws the readers to wanting to know more on the topic of the word. You want to set up facts, possess the confidence and demonstrate the crystal clear evidence of your private viewpoint to certain phenomenon. Instead, you should pick a notion that connects with your intended audience. The point is, so as to write in a professional manner about some type of phenomenon, you have to have at least basic comprehension of it. In the majority of instances, you are going to be defining terms your readers will already have a simple comprehension of. Sometimes people just can't begin writing their essay without looking at an entire instance of somebody else, seeing the structure and the way it's written. Therefore, a lot of people utilize beauty as something to address outer appearance in the world today. Perhaps it's safe to say that most men and women wish to be happy. 1 person might say a hipster is a person who follows all the hottest trends, while another might think that it is someone who has his own distinct style. Among the different sorts of essay, there's a definition essay that could appear to be among the most simple assignments. Mention the method by which the definition you were speaking about affected you. Thus, there's not any reason to include things like a dictionary definition. Similar to any other essay, a definition one has its peculiarities together with content requirements that should be met to be successful in the writing. Interestingly, one particular paragraph of about five lines is all that you'll want to finish your project. There are an endless number of words which can be defined in the period of an essay. Your real thesis statement should define the term in your words. When deciding on the best word for the ideal essay, there are a few elements to consider which include using a word that's abstract and has an intricate meaning. In such a situation, you may use various interpretations from your sources to spell out the expression. In the era of the web, different sources may offer a wide variety of information which might be confusing to you, especially on the best way to include information from the sources in your essay towards inventing a logical bit of writing. It is preferable to write about special terms because it will make it possible for you to analyze credible sources to create a comprehensive description. To acquire the maximum grade for your essay, you need not just to fill it with pertinent info and dependable facts or examples from your own personal life. Do everything you can to prevent part of the defined term in your definition. Bear in mind that before you opt for any topic, you ought to take your choice seriously, if you need your essay to be a well-written one and, sequentially, receive a high mark. Needless to say, here, you'll discover a wide scope of topics and essay ideas. There is an extensive array of definition essay ideas a student could utilize to compose an essay about. Characteristics of Definition Essay Samples The majority of the moment, tutors provide you topics to write about. Writing a quick draft is extremely effective, and a few teachers even require students to submit drafts to ensure that they've written the essay themselves. There are a significant variety of essays that students should learn in high school and in college. Yes, sometimes students need some excess inspiration to begin their own work and that's fine. It's very beneficial to obtain the use of the word in media, for instance, in movies, magazines. You may turn into a word artist with the aid of your pen or computer only! It's important to select the word which offers you enough to research and write about. The word looks interesting enough so as a prudent student you choose to look this up in the dictionary. Body paragraphs First, the expression hipster isn't clearly defined. In some instances, words aren't important. Anyway, below, you'll find topic that are great for both situations. In the event you can't figure out how to decide on the topic yourself, don't get upset too soon.