In Miltons paradise lost, god Essay

In Milton’s Paradise Lost, God is portrayed as having limited influence and contact with our world. This is perhaps a result of his respect for free will/conscience. This lack of contact is supported by one; God’s passiveness, there are several situations in the book in which God seems like he should be able to influence events but he simply doesn’t act. When he does act, he acts indirectly. God seems to execute his plans through either his angels or his son. Finally, perhaps the best indication of God’s limited connections is in the cases where God uses complicated, elaborate plans to do things that if he really had 100% power he would perform simply and immaculately. In the book Paradise Lost, God plays a relatively passive role considering that he is by far the most significant character in this book. He seems to sit up on his heavenly throne and observes rather than interact with his creations. A good case of this is in Book three lines 80-90, when God watches Satan ascending from hell. It would seem that when he was alerted by Uriel, the archangel would have been a good time to intervene and smite down Satan. It almost seems like Milton’s God wants the events of Paradise Lost to transpire because he yields so many times at so many opportunities to stop Satan. Satan should have been stopped at the very beginning. God must have foreseen this incident (the partaking of the forbidden fruit,) after all, does he not have sight of the future, past and present? (Book Three lines 75-80) Sadly, no-one will ever know what God was planning when he allowed Satan to run rampant in the garden. Or then again, maybe God wasn’t planning anything at all but rather leaving events to unfold without divine intervention, thus his seeming respect for free will. The only problem with that theory though, is that God punished Adam and Eve for making a decision with the free will that he gave them. Two other instances make cases against God’s absolute power. Why was hell so easily escaped by Satan? One would think that a Purgatory created by God himself would be impenetrable. Even stranger is the case with the Angelic War. Although for the most part symbolic, God either was not capable or at least unwilling to strike down Satan’s attempted conquest before it began. Instead, God chose once again to remain passive and allow things to go along for awhile. A good question to ask at this point is just what are God’s intentions? If he truly wanted a perfect heaven with conformist angels, what is stopping him from taking their free will? That leads to the point that possibly the reason why God’s influence is limited is his own conscience, based on his respect of free will. When God does act in the story, it is almost exclusively indirectly through his Son, (as in Book Nine,) or through his Archangels. The most well-known case where God acts through his Son is when God sent him down to be sacrificed upon the cross. Although this specific event does not occur within this story, the reasoning behind it is lengthily discussed especially in Book Three. This however, only supports the thesis if one believes that Jesus is the son of God rather than the Christian view that God IS Jesus. (John, 8:58) Based on how Milton writes, it shows that he is using the interpretation of the Bible in which Jesus was created by God. Jesus plays a sympathetic role when it comes to mankind and often influences God’s decisions on what to do about Adam and Eve. He persuades God to allow him to go down to heaven to inform Adam about his state of sin. In this case, Jesus actually influences God rather than the usual case with God giving orders and Jesus acting carrying them out. God also acts a lot through his throng of angels. The archangels are his main instruments of manipulation. Of the seven archangels, Raphael, Michael, and Gabriel are the three most influential. God acts through Raphael most often. Raphael plays a large role in starting in Book Five lines 246-249 â€Å"So spake th’Etetnal Father, and fulfill’d All Justice: nor delay’d the winged Saint (Raphael)after his charge receiv’d. † Raphael then proceeds down to the Garden to warn Adam and Eve of the impending danger posed by Satan. Raphael also spends Book Six and Book Seven informing Adam of the war in heaven and then telling him the nature of his own creation. Michael and Gabriel have slightly smaller roles than Raphael does; but they do get a chance to enact God’s will when he orders them to lead the faithful Angels in the war against Satan. The final case to prove God’s limited interactions and influence is when God seems to have to work around rules that he must have created himself. A good example of this is when he prepares for the redemption of man. It simply doesn’t make sense that if God desires to redeem his creations that he simply doesn’t grant them redemption from their sins. Instead he schemes up the elaborate plan to send his son down to receive punishment in place of man. Once again, this could be explained by God having to act within the parameters of what his conscience will allow, (regarding free will). Maybe God has to do these elaborate things so that he can justify to himself the redemption of man. Maybe he thinks that it is only right that somebody receives punishment. Cases similar in nature occur when God didn’t keep Satan from entering the Garden of Eden and when he had to send the great flood. If not for his respect for free will, God wouldn’t have had to allow mankind to sink so low. In Paradise Lost, Milton presents a God that is strangely limited in his actions and influence with his own creations. Whether through passiveness, indirectness, or a conscious â€Å"distancing of himself† God seems to allow many things to happen without direct intervention. However, this is not really a novel concept; people throughout history have questioned the concept of an all-powerful God in a very imperfect world.

Based Data Mining Approach for Quality Control

Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala| INDEX Sr No| Title| Page No. | 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre-Processing| | 5| Statistics Used In Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| | 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts.Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine quality based on physicochemical data. * This study was also conducted to identify outlier or anomaly in sample wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The present content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and Domain Theories is a free Internet repository of analytical datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from many scientists and are claimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASET|Title:| Wine Quality| Data Set Characteristics:| Multivariate| Number Of Instances:| WHITE-WINE : 4898 RED-WINE : 1599 | Area:| Business| Attrib ute Characteristic:| Real| Number Of Attribute:| 11 + Output Attribute| Missing Value:| N/A| * Attribute Information * Input variables (based on physicochemical tests) * Fixed Acidity: Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * Volatile Acidity: Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid: Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar: The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides: Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies in the brewing water. * Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as pres ervative in wine process. * Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. In kg per liter) * PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values) * Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol: Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source.Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason. * Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation=â€Å"† * Noisy : Containing errors or outliers. * E. g. Salary=â€Å"-10† * Inconsistent : Containing discrepancies in codes or names. * E. g. Age=â€Å"42† Birthday=â€Å"03/07/1997† * E. g. Was rating â€Å"1,2,3†, Now rating â€Å"A, B, C† * E. g. Discrepancy between duplicate records * No quality data, no quality mining results! Quality decisions must be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in done in the Data Preprocessing are, * Data Cleaning * Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization a nd aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numerical data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * Here, NumericToNominal, InterquartileRange and RemoveWithValues filters are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. attribute. NumericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute â€Å"Quality† in both dataset (Red-wine Quality, White-wine Quality) have a type †Å"Numeric†. So after applying this filter, class attribute â€Å"Quality† convert into type â€Å"Nominal†. * And Red-wine Quality dataset have class names 3, 4, 5 †¦ 8 and White-wine Quality dataset have class names 3, 4, 5 †¦ 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme values based on interquartile ranges. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds two more fields which names are â€Å"Outliers† and â€Å"ExtremeValue†. And this fields has two types of label â€Å"No† and â€Å"Yes†. Here â€Å"Yes† label indicates, there are outliers and extreme values in dataset. * In our dataset, there are 83 extreme values and 125 outliers i n White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are â€Å"AttributeIndex† and â€Å"NominalIndices†. * AttributeIndex choose attribute to be use for selection and NominalIndices choose range of label indices to be use for selection on nominal attribute. * In our dataset, AttributeIndex is â€Å"last† and NominalIndex is also â€Å"last†, so It will remove first 83 extreme values and then 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm| RED-WINE| RANKED| WHITE-WINE| Volatile_Acidity(2)| 0. 1248| 0. 0406| Volatile_Acidity(2)| Total_sulfer_Diox ide(7)| 0. 0695| 0. 0600| Citric_Acidity(3)| Sulphates(10)| 0. 1464| 0. 0740| Chlorides(5)| Alcohal(11)| 0. 2395| 0. 0462| Free_Sulfer_Dioxide(6)| | | 0. 1146| Density(8)| | | 0. 2081| Alcohal(11)| * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics MeasuresThere are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics measures. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or index which compares the agreement from the model with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our predicti on for class attribute for given dataset is how much near to actual values. * Values Range For Kappa Range| Result| lt;0| POOR| 0-0. 20| SLIGHT| 0. 21-0. 40| FAIR| 0. 41-0. 60| MODERATE| 0. 61-0. 80| SUBSTANTIAL| 0. 81-1. 0| ALMOST PERFECT| * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 5365| 0. 5294| J48| 0. 3813| 0. 3881| Multilayer Perceptron| 0. 2946| 0. 3784| * Mean absolute error (MAE) * Mean absolute error (MAE)  is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by, Mean absolute Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 1297| 0. 1381| J48| 0. 1245| 0. 1401| Multilayer Perceptron| 0. 1581| 0. 1576| * Root Mean Squared Erro r * If you have some data and try to make a curve (a formula) fit them, you can graph and see how close the curve is to the points. Another measure of how well the curve fits the data is Root Mean Squared Error. * For each data point, CalGraph calculates the value of  Ã‚  y from the formula. It subtracts this from the data's y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, Root Mean Square Error is Root Mean Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 2428| 0. 2592| J48| 0. 3194| 0. 3354| Multilayer Perceptron| 0. 2887| 0. 3023| * Root Relative Squared Error * The  root relative squared error  is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the to tal squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared error  one reduces the error to the same dimensions as the quantity being predicted. * Mathematically, the  root relative squared error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0.So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal. Root Relative Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 78. 1984 %| 79. 309 %| J48| 102. 9013 %| 102. 602 %| Multilayer Perceptron| 93. 0018 %| 92. 4895 %| * Relative Absolute Error * The  relative absolute error  is very similar to the  relative squared error  in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, the  relative absolute error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0. So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal.Relative Absolute Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality | K-Star| 67. 2423 %| 64. 5286 %| J48| 64. 577 %| 65. 4857 %| Multilayer Perceptron| 81. 9951 %| 73. 6593 %| * Various Rates * There are four possible outcomes from a classifier. * If the outcome from a prediction is  p  and the actual value is also  p, then it is called a  true positive  (TP). * However if the actual value is  n  then it is said to be a  false positive  (FP). * Conversely, a  true negative  (TN) has occurred when both the prediction outcome and the actual value are  n. And  false negative  (FN) is when the prediction outcome is  n while the actual value is  p. * Absolute Value | P| N| TOTAL| p’| True positive| false positive| P’| n’| false negative| True negative| N’| Total| P| N| | * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the top left corner of the ROC chart the better the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, Precision and F-Measure There are four possible results of classification. * Different combination of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of the se classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced. * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow : PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * Confusion Matrix * A matrix used to summarize the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-Nearest Neighbor Classifiers * Nearest neighbor classifiers are based on learning by analogy. * The training samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. * These k training samples are the k-nearest neighbors of the unknown sample. â€Å"Closeness† is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners can incur expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniqu es. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that time.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier -> lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0. 02 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 3307 | 70. 6624 % | Incorrectly Classified Instances| 1373 | 29. 3376 %| Kappa Statistic | 0. 5365| | Mean Absolute Error | 0. 1297| | Root Mean Squared Error| 0. 2428| | Relative Absolute Error | 67. 2423 %| | Root Relative Squared Error | 78. 1984 %| | Total Number Of Instances | 4680 | | * Detailed Accuracy By Class | TP Rate| FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0 | 0 | 0 | 0 | 0. 583 | 0. 004 | 3| | 0. 211 | 0. 002 | 0. 769 | 0. 211 | 0. 331 | 0. 884 | 0. 405 | 4| | 0. 672 | 0. 079 | 0. 777 | 0. 672 | 0. 721 | 0. 904 | 0. 826 | 5| | 0. 864 | 0. 378 | 0. 652 | 0. 864 | 0. 743 | 0. 84 | 0. 818 | 6| | 0. 536 | 0. 031 | 0. 797 | 0. 536 | 0. 641 | 0. 911 | 0. 772 | 7| | 0. 398 | 0. 002 | 0. 883 | 0. 398 | 0. 548 | 0. 913 | 0. 572 | 8| | 0 | 0 | 0 | 0 | 0 | 0. 84 | 0. 014 | 9| Weighted Avg. | 0. 707 | 0. 2 | 0. 725 | 0. 707 | 0. 695 | 0. 876 | 0. 787| | * Confusion Matrix| A | B | C | D | E | F| G | | Class| 0 | 0 | 4 | 9 | 0| 0 | 0 | | | A=3| 0| 30| 49| 62| 1 | 0 | 0| | | B=4| 0 | 7 | 919| 437| 5 | 0 | 0 | | | C=5| 0 | 2 | 201| 1822| 81 | 2 | 0 | || D=6| 0 | 0 | 9 | 389 | 468 | 7 | 0| || E=7| 0 | 0 | 0 | 73 | 30 | 68 | 0 | || F=8| 0 | 0 | 0 | 3 | 2 | 0 | 0 | || G=9| * Performance Of The Kstar With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 6581 %| 100 %| 70. 6624 %| 63. 9221 %| Kappa statistic| 0. 9949| 1| 0. 5365| 0. 4252| Mean Absolute Error| 0. 0575| 0. 0788| 0. 1297| 0. 1379| Root Mean Squared Error| 0. 1089| 0. 145| 0. 2428| 0. 2568| Relative Absolute Error| 29. 8022 %| | 67. 2423 %| 71. 2445 %| * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 1013 | 71. 379 %| Incorrectly Classified Instances| 413 | 28. 9621 %| Kappa Stat istic | 0. 5294| | Mean Absolute Error | 0. 1381| | Root Mean Squared Error | 0. 2592| | Relative Absolute Error | 64. 5286 %| | Root Relative Squared Error | 79. 309 %| | Total Number Of Instances | 1426 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0. 001 | 0 | 0 | 0 | 0. 574 | 0. 019 | 3| | 0 | 0. 003 | 0 | 0 | 0 | 0. 811 | 0. 114 | 4| | 0. 791| 0. 176 | 0. 67| 0. 791| 0. 779 | 0. 894 | 0. 867 | 5| | 0. 769 | 0. 26 | 0. 668 | 0. 769 | 0. 715 | 0. 834 | 0. 788 | 6| | 0. 511 | 0. 032 | 0. 692 | 0. 511 | 0. 588 | 0. 936 | 0. 722 | 7| | 0. 125 | 0. 001 | 0. 5 | 0. 125 | 0. 2 | 0. 896 | 0. 142 | 8| Weighted Avg. | 0. 71| 0. 184| 0. 685| 0. 71| 0. 693| 0. 871| 0. 78| | * Confusion Matrix | A | B | C | D | E | F| | Class| 0 | 1 | 4| 1 | 0 | 0 | | | A=3| 1 | 0 | 30| 17 | 0 | 0| | | B=4| 0 | 2| 477| 120 | 4 | 0| | | C=5| 0 | 1 | 103 | 444| 29 | 0| || D=6| 0 | 0 | 8 | 76 | 90 | 2 | || E=7| 0 | 0 | 0 | 7 | 7 | 2| || F=8| Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 7895 %| 100 % | 71. 0379 %| 70. 7216 %| Kappa statistic| 0. 9967| 1| 0. 5294| 0. 5154| Mean Absolute Error| 0. 0338| 0. 0436| 0. 1381| 0. 1439| Root Mean Squared Error| 0. 0675| 0. 0828 | 0. 2592| 0. 2646| Relative Absolute Error| 15. 8067 %| | 64. 5286 %| 67. 4903 %| * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute; the branches between the nodes tell us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (class ification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm: * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling wi thin its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. 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It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted an d fed simultaneously to a second layer of â€Å"neuronlike† units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to build model: 36. 22 seconds| Stratifi ed cross-validation| * Summary| Correctly Classified Instances | 2598 | 55. 5128 %| Incorrectly Classified Instances | 2082 | 44. 4872 %| Kappa statistic | 0. 2946| | Mean absolute error | 0. 1581| | Root mean squared error | 0. 2887| |Relative absolute error | 81. 9951 %| | Root relative squared error | 93. 0018 %| | Total Number of Instances | 4680 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 344 | 0. 002 | 3| | 0. 056 | 0. 004 | 0. 308 | 0. 056 | 0. 095 | 0. 732 | 0. 156 | 4| | 0. 594 | 0. 165 | 0. 597 | 0. 594 | 0. 595 | 0. 98 | 0. 584 | 5| | 0. 704 | 0. 482 | 0. 545 | 0. 704 | 0. 614 | 0. 647 | 0. 568 | 6| | 0. 326 | 0. 07 | 0. 517 | 0. 326 | 0. 4 | 0. 808 | 0. 474 | 7| | 0. 058 | 0. 002 | 0. 5 | 0. 058 | 0. 105 | 0. 8 | 0. 169 | 8| | 0 | 0 | 0| 0 | 0 | 0. 356 | 0. 001 | 9| Weighted Avg. | 0. 555 | 0. 279 | 0. 544 | 0. 555 | 0. 532 | 0. 728 | 0. 526| | * Confusion Matrix |A | B | C | D | E | F | G | | Class| 0 | 0 | 5 | 7 | 1 | 0 | 0 | | | A=3| 0 | 8 | 82 | 50 | 2 | 0 | 0 | | | B=4| 0 | 11 | 812 | 532 | 12 | 1 | 0 | | | C=5| 0 | 6 | 425 | 1483 | 188 | 6 | 0 | | | D=6| 0 | 1 | 33 | 551 | 285 | 3 | 0 | | | E=7| 0 | 0 | 3 | 98 | 60 | 10 | 0 | | | F=8| 0 | 0 | 0 | 2 | 3 | 0 | 0 | | | G=9| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 58. 1838 %| 50 %| 55. 5128 %| 51. 3514 %| Kappa statistic| 0. 3701| 0. 3671| 0. 2946| 0. 2454| Mean Absolute Error| 0. 1529| 0. 1746| 0. 1581| 0. 1628| Root Mean Squared Error| 0. 2808| 0. 3256| 0. 2887| 02972| Relative Absolute Error| 79. 2713 %| | 81. 9951 %| 84. 1402 %| * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model: 9. 14 seconds| Stratified cross-validation (10-Fold)| * Summary | Co rrectly Classified Instances | 880 | 61. 111 %| Incorrectly Classified Instances | 546 | 38. 2889 %| Kappa statistic | 0. 3784| | Mean absolute error | 0. 1576| | Root mean squared error | 0. 3023| | Relative absolute error | 73. 6593 %| | Root relative squared error | 92. 4895 %| | Total Number of Instances | 1426| | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 47 | 3| | 0. 42 | 0. 005 | 0. 222 | 0. 042 | 0. 070 | 0. 735 | 4| | 0. 723 | 0. 249 | 0. 680 | 0. 723 | 0. 701 | 0. 801 | 5| | 0. 640 | 0. 322 | 0. 575 | 0. 640 | 0. 605 | 0. 692 | 6| | 0. 415 | 0. 049 | 0. 545 | 0. 415 | 0. 471 | 0. 831 | 7| | 0 | 0 | 0 | 0 | 0 | 0. 853 | 8| Weighted Avg. | 0. 617 | 0. 242 | 0. 595 | 0. 617 | 0. 602 | 0. 758| | * Confusion Matrix | A | B | C | D | E | F | | Class| | 0 | 5 | 1 | 0 | 0| || A=3| 0 | 2 | 34 | 11 | 1 | 0 | | | B=4| 0 | 2 | 436 | 160 | 5 | 0 | | | C=5| 0 | 5 | 156 | 369 | 47 | 0 | | | D=6| 0 | 0 | 10 | 93 | 73 | 0 | | | E=7| 0 | 0 | 0 | 8 | 8 | 0 | | | F=8| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 68. 7237 %| 70 %| 61. 7111 %| 58. 7629 %| Kappa statistic| 0. 4895| 0. 5588| 0. 3784| 0. 327| Mean Absolute Error| 0. 426| 0. 1232| 0. 1576| 0. 1647| Root Mean Squared Error| 0. 2715| 0. 2424| 0. 3023| 0. 3029| Relative Absolute Error| 66. 6774 %| 51. 4904 %| 73. 6593 %| 77. 2484 %| * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality using  Kstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier ac hieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 1. 08| 35. 14| Kappa Statistics| 0. 5365| 0. 3813| 0. 29| Correctly Classified Instances (%)| 70. 6624| 58. 547| 55. 128| True Positive Rate (Avg)| 0. 707| 0. 585| 0. 555| False Positive Rate (Avg)| 0. 2| 0. 21| 0. 279| * Chart Shows The Best Suited Algorithm For Our Dataset (Measu res Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) | Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 0. 24| 9. 3| Kappa Statistics| 0. 5294| 0. 3881| 0. 3784| Correctly Classified Instances (%)| 71. 0379| 60. 6994| 61. 7111| True Positive Rate (Avg)| 0. 71| 0. 608| 0. 617| False Positive Rate (Avg)| 0. 184| 0. 214| 0. 242| * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET Step1: Load pre-processed dataset. Step2: Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3: Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ’? ’ in the cell of the corresponding raw ) as shown below. Step 4: Save this data set as . rff file. Step 5: From â€Å"tes t option† panel select â€Å"supplied test set†, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6: From â€Å"Result list panel† panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click â€Å"Re-evaluate model on current test set† Step 7: Again right click on Kstar algorithm and select â€Å"visualize classifier error† Step 8:Click on save button and then save your test model.Step 9: After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10: Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine productio n, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that d etermines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.

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Dementia Research Paper Example | Topics and Well Written Essays - 1500 words

Dementia - Research Paper Example Dementia is a condition that affects the nervous system of the individual. It is mainly labeled to be a disease of the old people but it may occur in the younger age groups as well. Dementia mainly presents with loss of memory and the condition deteriorates with time. The functioning of the cerebral hemisphere is affected in this condition and thus the day to day performance of the individual greatly suffers as a result of dementia. Dementia can result owing to many underlying pathologies which include Alzheimer's disease, Huntington’s disease and Creutzfeldt-Jacob disease. There is no definitive cure for this disease but early diagnosis can make a difference. Dementia Dementia is a degenerative neurological disorder. Previously dementia was considered to be a normal part of aging but researches and studies have proved that it is a pathological condition that results from varying illnesses or disorders present in the body.As a number of disorders or factors can be responsible for causing dementia,it is better known as a syndrome rather than a disease.It can also present as a clinical sign or symptom of an underlying disorder.Dementia is sudden or spontaneous in onset and progresses with time and age.The risk of onset of dementia is most likely at the age of 60 and it is seldom seen before this age.The risk of onset increases with the advancement in age. Dementia seriously affects the emotional behavior and social attitude of the victims of this disorder and sometimes it can also be associated with life threatening consequences. It is a pathological condition of the brain which causes impairment of normal mental activity and deterioration of cerebral functions with difficulties in carrying out the routine chores due to regression of certain areas of brain that maintain and regulate the normal functions of life. The individual suffering from dementia faces memory loss and the magnitude of loss is directly related to the severity of the disease. The disease affects the patient's personality and alters the emotional and social behavior. (Cox, 2007; Jacques & Jackson, 2000) Dementia involves gradual deteriorating changes in the brain of an individual which results in the decline of cerebral functions with time. The patient undergoes these changes for quite a long time before presenting with some solid differences in his or her personality. The patient can present with complaints which include deterioration of memory, decreased rate of performance at any type of work, compromised skills, mismanagement of personal or business affairs, uncertain and unreliable attitude, the decline in social activities, varying moods and delirium. The dementia is divided into two types depending on the time of its onset. These two types are pre-senile and senile dementia. The two groups differ as the pre-senile dementia is seen in mostly young patients while senile is in much older individuals. Both the disease processes however, follow the same course of development with very little differences in the signs and symptoms shown by the patients suffering from pre-senile or senile dementia (Boon & Davidson, 2006; Jacques & Jackson, 2000). The causes of dementia vary according to the type. Dementia may either be resulting from vascular pathologies which include disease of the small blood vessels, numerous emboli in the vessels or inflammation of the vessels in the brain. Degenerative or the inherited type of dementia results due to pathologies which include Alzheimer’s disease, Huntington’s disease, Wilson's disease, cortical Lewy body disease and mitochondrial encephalopathies. Dementia may also be associated with cancerous conditions. The tumors may spread from distant sites and lead to dementia or there may be tumors originating with the brain itself for example the primary cerebral tumor. Sarcoidosis and Multiple Sclerosis lead to inflammation and thus they are classified as inflammatory causes of dementia. Trauma can a lso be an underlying cause of dementia and it may be due to

Entry into Foreign Market Essay Example | Topics and Well Written Essays - 1500 words

Entry into Foreign Market - Essay Example This desire for expansion outside initial business setups seeks to enhance production and supply of goods to more potential consumers. Increase in consumer population translates into a corresponding increase in revenue generation. This means that international corporations wanted to increase their customer base; hence established business facilities within international markets. Tielmann (2010) says that despite the fact that most business environments are present within a free market settings, other factors still play a significant role in starting and developing production and sales activities within such settings. Therefore, business organizations wishing to expand into new markets should adopt appropriate strategies in order to achieve marketing success. From a practical perspective, entry into a new market needs systematic approaches depending on the internal and external factors surrounding the company. Typical internal factors include a company’s efficiency in managemen t, availability of resources for expansion and the culture of its business operations. On the other hand, external factors include those elements lying outside the control of company’s management. According to Erkan (2011), these include social, political and economic factors prevailing within the new market. The nature of internal and external factors acknowledged above could either smoothen or toughen a company’s entry into a new market. In the context of marketing terminologies, effects on the environment could narrow down to aspects of risk, cost and magnitude of control that an organization experiences upon entering a market segment. In the process of determining as to whether entry into a new market will be successful, company managers select appropriate strategies that will produce the best desired outcomes. Upon thorough analysis, some organizations may decide to use indirect market entry, which involves export of manufactured products into the new markets usin g existing supply channels. On the other hand, a product manufacture may use direct entry method by a partnership with agents already present within a new market environment. In this regard, the operational thesis statement postulates that Myanmar oil industry is a potential market for Cameron’s production equipment. Institutional Strengths and Risks Having appreciated the theoretical framework of new market entry, we will conduct a real life analysis on Cameron International Corporation. This organization has successfully entered other new market in the past. Erkan (2011) says that currently, Cameron International Corporation is undertaking its business operations in approximately 100 countries around the globe. Its expansion strategies have yielded fruits; hence there is a growing desire to venture into other virgin territories. This time, Cameron International Corporation has identified Myanmar, a reforming South Asian nation formerly known as Burma, as its target market. In the year 2010, the company posted a profit of approximately $ 500 million. This profit resulted mainly from its operation within the US market. Cameron Corporation deals with production and supply of equipment used in oil and gas productions around the globe. In this case, the organization decided to venture into the South Asian nation in subject since Myanmar has prospectus profile in terms of oil and gas resources. In the past, the nation had a dented history of human

Huckleberry Finn Essay Example for Free

Huckleberry Finn Essay The Adventures of Huckleberry Finn, written by Mark Twain, takes place during the antebellum era, and revolves around a young boy, named Huck. The antebellum era was the years right before the Civil War, so Huck was living in a dark and murky time in American History. Huck starts off by living with The Widow Douglas and her sister, Miss Watson, who is trying to civilize him or make him to be what the perfect child should look like and make him act how a perfect child should act. Huck does not want that. He just wants to live how he wants, just like most youth want. In the novel, Huckleberry Finn befriends a runaway slave, Jim, and his adventures begin. According to Dennis Puopard, Mark Twain exposed many of the dark problems of antebellum United States. Some say Mark Twain wrote this episodic novel as a boys adventure story and that Huck is a character that children should look up to. (422) Modern readers do not see Huckleberry Finn as a childrens book because the book is racist, there a themes of lying, and characters object and criticize authority. Because, modern readers see the book as improper for children The Adventures of Huckleberry Finn is on the banned books list on many school in the United States. In The Adventures of Huckleberry Finn there are racial slurs, lies, and profanity. The Adventures of Huckleberry Finn is not a childrens book in todays society because of the prominent theme of race. The topic of race and racism is strong in todays society. If a modern American citizen uses racial slurs against another race in a hurtful way that citizen would be convicted with a criminal offense. A racial slur such as the word nigger is not tolerable todays society. The word nigger was used to belittle and dehumanize African American slaves, such as Jim, in antebellum United States. Through out the book, The Adventures of Huckleberry Finn, author Mark Twain includes racial slurs such as the word, nigger toward African American characters, such as Jim and other slaves. Good gracious! anybody hurt? Nom. Killed a nigger. Well, its lucky; because sometimes people do get hurt. ( Twain 109). This quote shows how the white society views Jim different then themselves. They view Jim as property rather than a human with a living breathing heart. This dialoged between two white characters just shows how hurtful and cruelly someone can sound just by taking. Barbra L. Jackson professor at Fordham University in New York City says, It is hard to teach The Adventures of Huckleberry Finn in a diverse class because of its racial views. (63). If a college professor has a hard time teaching the book, The Adventures of Huckleberry Finn, to her class, how can it be easy for high school students who are studding the novel, or even young boys whom pick up the book and start reading it? Also, Barbra L. Jackson says, I always see a lack in participation, when studying the book, the students do not want to read out loud, (64). The students do not feel right saying nigger out loud because they do not want to offend any of their classmates. The students know that the word, nigger is a taboo in modern society. The Adventures of Huckleberry Finn should not be taught or read to children because of the racial slurs. The type of racial language that Mark Twain uses in the book is offensive and crude. The exposure of the racial slurs to young children would be harmful. The young children will think it is okay to say the new words they discover from reading The Adventures of Huckleberry Finn, which would get them into trouble in the future.

United colors of benetton Essay Example for Free

United colors of benetton Essay Society permits ever individual to live their own identity and style. This new trend of freedom has developed more in the past few years. The color trends for fall 2009 can be divided into three categories including: Chic, Classic and Casual colors reflecting the general mood of the people. CHIC: These shades are mostly reflected by digital prints. This trend highlights the solid and cold tones including mid night blues, earthly tones and deep pinks which creates a world of playful and attractive patterns. The combination of blues and browns create a turbulent and attractive combination. CLASSIC: This theme includes mainly greens and earthly colors in combinations. These are mainly the diluted color effects. These two colors mixed together give the effect of foggy and gray shades. CASUAL: These combinations reflect the shady technologies. Juicy orange, scarlet and neutral nuances combine in a tremendously attractive manner. This creates a magic formula reflecting the soothing whether of winter and the cloudy climate of the fall season. Chic Classic Casual Fashion Trendsetter. (n. d. ) The browns greens and blues reflect the cozy environment for winter however the fabric trends for winter 2009 include silk, knitwear, wool, cotton and some of the functional fabrics (Fashion Trendsetter, n. d. ). Decoration is a special fashion impulse and thus this winter it is represented more discreetly by the seductive effect given by silk. To enhance the beauty and attractiveness of a female silk is re introduced this winter. It will however the coming season strive for a more decent and sophisticated effect of graphic prints and motifs. The touch of embroideries enhances the beauty of silk, satin and crape. They enrich the out fits with feminine touch. The embroideries include dark bronze oxidized and copper shades. This gives them a lavish complexity. To interpret the winter season both for men and women, knit wear made a new entry for them. Comfortable and cozy as the season of winter is, it is complimented by the woolen knit wear and some Milano- jersey as a contemporary out fit. Cotton re defines it self this winter. To reflect the casual attitude slightly over dyed compressed cotton is going to be in fashion again. The cotton prints include mixed and blurred checks and stripes. The artistic design includes ornamental patterns, pictorial designs and fast abstract paintings. Cotton Artistic Designs Cosmo Worlds (n. d. ). Silhouette is a picture in which the outline of the subject is visible. It involves two colors the subject is white and the background is black. This image is used by all the designers in United colors of Benetton to reinforce the design of the fabric and to enhance the color combination and color scheme. The merchandise assortment reinforces the over all design details. The different shades of colors arranged and displayed on the silhouettes enhance the overall affect of the merchandise and fabric arranged (CosmoWorlds,n. d. ). The type of fabric and color combination combined together enhances the effect of the design. As the silhouette gives the overall resemblance of the subject with no prominent features the color scheme used and the designs and fabrics used are very important, this will enhance the credibility of the work. The design and the forecasted trend of color and fabric will be understood well by the customers and the clients as the display will be effective. References: CosmoWorlds(n.. d. ). Fabric Trends Winter 2008/09. Retrieved August 21,2008 from http://www. cosmoworlds. com/trends/trends_2008_2009-texworld_fabric_trends-08122007. htm Fashion Trendsetter. (n. d. ) TFL Fashion Color Inspirations Autumn/Winter 2009/2010. Retrieved August 21, 2008 from http://www. fashiontrendsetter. com/content/color_trends/2008/TFL-Color-Trends. Krom, Peter. (2008). How to photograph silhouettes

Waste management in India Essay

Waste management in India Essay INTRODUCTION India is the second most populated country a second fastest growing economy in the world. From the period of 2001-2026 the population of India is to increase from 1030 million to 1400 million, if we consider the increase rate to be 1.2 % annually then there will be an increase of 36% in 2026.accordingly about 285 million live in urban areas and about 742 million live in rural areas. (Census of India, 2001).In India urbanisation is becoming more because people are moving from villages to cities and there is a rapid increase in population in the metropolitan cities .Mumbai is the largest populated city followed by New Delhi and Kolkata. Generally, the greater the economic prosperity and the higher the percentage of urban population, the greater is the amount of solid waste produced (Hoornweg and Laura, 1999). In Hoornweg and Laura, 1999 1996 about 114,576 tonnes/day of municipal solid waste was generated by the urban population of India, by the end of 2026 it is predicted to increase to 440,460tonnes/day This great increase in the amount of MSW generated is due to changing lifestyle and living standards urban population(Hoornweg and Laura, 1999). STUDY AREA Delhi is a very densely populated area and is the capital of India. Since Delhi is an urbanised city the annual growth rate is increasing very rapidly in the last decade the growth rate has increased by 3.85%. Delhi is the capital of India this tells us that it is the centre for commerce trade and power, since it is one of the largest cities and the capital it produces excellent job opportunities, which account for its rapid increase in its population and increased pace of urbanization. Due to the fast urbanisation and the growing population the production of municipal solid waste is also increasing very rapidly. According to a survey Delhi generates about 7000 tonnes/day of municipal solid waste and this municipal waste is to ride about 17000-25000 tonnes/day by the year 2026.due to the rapid increase in the population and municipal solid waste the disposal of the waste has become a great head ache for the municipality in Delhi. Out of the waste gathered only 70-80% of municipal sol id waste is collected while the remaining is dumped onto streets or open ditches. Out of the 70-805 collected only 9% of the collected municipal solid waste is treated by composting the remaining is sent to the land fill sites. New Delhi Municipal Corporation (NDMC), The Municipal Corporation of Delhi (MCD) and Delhi Cantonment Board (DCB) are three municipal entities responsible for MSW management in Delhi. (Vikash Talyan, R.P. Dahiya, 2008). IDENTIFICATION OF SOURCES, TYPES AND COMPOSITION OF MUNCIPAL SOLID WASTE IN DELHI Sources and types of solid waste in Delhi: Residential:-the residence might be single family or multiple family dwellers the types of waste they produce are paper, food wastes , cardboard , leather, yard wastes, textiles, glass, special wastes, metals, plastics , ashes, wood and household hazardous wastes. Industrial: industries produce ashes, food wastes, packaging, special wastes, housekeeping wastes, construction and demolition materials and hazardous wastes. Commercial Institutional: they produce wood, metals, cardboard, glass, special wastes, Paper, food wastes, hazardous wastes. Municipal services: landscape and tree trimmings, Street sweepings, general wastes from beaches, parks, and other recreational areas, sludge. (Hoornweg, Daniel with Laura Thomas. 1999) Composition of waste: The population of Delhi is 13.9 million, and they produce 7000 tonnes/day of municipal solid waste at the rate of 0.500 kg/capital/day and accordingly the population as well as the MSW in increasing by 2026 the municipal solid waste generated will increase to 17,000-25,000 tonnes/day. Because of the increase in the MSW the municipal body will face a lot of problem after composting and incineration they would still have to deal with a lot of waste and this waste would generally go to landfill sites. The characterisation of the waste by its type, composition and source is important this will make monitoring and management of solid waste easy. Based on this we can use different types of processes to dispose the solid waste. The following information will tell about the generation of MSW from various sources is Delhi in the year 2004. Source wise generation of the MSW (tonnes/day) in Delhi Sources MSW(Tonnes/day) Residential waste 3010 Industrial waste 502 Hospital waste 107 Main shopping centres 1017 Construction waste 382 Vegetable and fruit markets 538 Source 🙠 MCD, 2004) The Tata Energy Research Institute conducted a study in 2002 in Delhi to determine the physical and chemical composition of municipal solid waste. This study in 2002 tells us that the composition of MSW is not changed that much from the past decade. According to the study the major part of the MSW consists of biodegradables fallowed by other wastes. Physical composition (as wt. %) of MSW Chemical composition (as wt. %) of MSW Parameters 2002 Biodegradable 38.6 Inert 34.7 Glass and Crockery 1.0 Paper 5.6 Non-biodegradable 13.9 Plastic 6.0 Parameters 2002 Moisture 43.8 Phosphorus as P2O5 0.3 Organic carbon 20.5 nitrogen 0.9 C/N ratio 24.1 Calorific value (kCal/kg) 713.0 Source 🙠 TERI, 2002) The composition of MSW of an urban population depends on various factors like place location, climate, commercial activities, population, cultural activities, economic status if the residence and urban structure .Before we do anything we need to know the composition of the MSW so we can determine the best suited operations and equipment for the facilities that dispose of the MSW. There was a survey conducted by Municipal Corporation of Delhi to evaluate the composition and properties of MSW. This study involved the different places in Delhi where MSW was produces. The following table tells the details of the study Composition (as wt. %) of MSW generating from various sources in Delhi Parameters Food waste Recyclables Inert Others Moisture Ash content C/N ratio Lower CV (kcal/kg) Higher CV Residential waste                            1.low income group 58.4 15.7 22.8 3.1 54 21.8 39 754-2226 2238-4844 2. Middle income group 76.6 21.2 0.5 1.7 65 6.3 30 732-1939 3415-6307 3.High income group 71.9 23.1 0.3 4.7 59 10.9 31 1300-1887 4503-5359 4. JJ Clusters (Slums) 69.4 14.1 15.8 0.7 63 15.6 46 204-1548 1582-4912 Vegetable markets 97.2 2.3 0.5 76 3.3 16 0-1309 3083-4442 Institutional areas 59.7 33.8 4 2.5 50 6.7 35 129-3778 2642-5459 Streets 28.4 12 56.1 3.5 19 56.7 51 1007-2041 1188-3289 Commercial areas 15.6 68 16.4 18 8.8 158 1815-4593 3373-6185 Landfills 73.7 9.2 10.8 6.3 47 15.3 38 191-4495 2042-5315 Source :- (MCD, 2004) RELEVANT REGULATIONS FOR MUNICIPAL SOLID WASTE MANAGEMENT IN INDIA The major policies and legislative frameworks for the municipal solid waste management in Delhi are Municipal Solid Waste (Management and Handling) Rules, 2000: according to this policy there is a set process for the collection, sorting, storage, transportation and disposal of the MSW. The Bio-Medical Waste (Management and Handling) Rules, 1998 and Amendment Rules, 2003:- bio-medical waste should be treated according to the standards of schedule v. The Delhi plastic bag (Manufacture, Sales and Usage) And Non-Biodegradable Garbage (Control) Act, 2000: according to this plastic bags should be recycled and non-degradable plastic bags should not be dumped in public drains. Hazardous Wastes (Management and Handling) Rules, 1989 and Amendment Rules, 2000 and 2003:-there are limitations for the import and export of hazardous wastes and there should be proper handling and management of hazardous waste. (Ministry of Environment and Forests, 2000) MUNICIPAL SOLID WASTE MANAGENENT IN DELHI Primary collection and storage of MSW in Delhi According to the Delhi municipal corporation act 1957 the owners, tenants or the person who is occupying the residence, commercial or industrial area is responsible for the disposal of the MSW at a particular area provided by the municipal corporation. But this rule was changed in 2000 which stated that the collection of MSW would be from house to house because of this rule the municipality cooperation faced a lot of problem due to the rise in population as well as residential houses so doth the systems are being applied to collect MSW. The municipal cooperation of Delhi is getting awareness programs to help the citizens understand the need of segregating the municipal solid waste by placing two separate bins one for recycling materials and the other non-recycling materials. By doing this the municipality is reducing the work load and they can dispose of the material in an easy way without any fuss. The municipal authority has a schedule for the collection of the waste example a part icular area will have a particular day for the collection of MSW. The Delhi municipal authority provides a primary storage facilities like dustbins, metal containers that have different capacities ranging from 1m3 ,4m3,10m3 to 12-15 tonnes these containers are placed in locations that are easily accusable to people. The size of the containers that are place at a primary storage location depends on the amount of MSW being produced by the area and the population of the area. These metal containers and bins are emptied with the help of modern hydraulic collection trucks. In Delhi on an average there are 3-4 collection sites. The MCD has employed about 50,000 people for primary storage collection, 2600 for secondary storage collection and about 370 people foe sweeping the streets. (Ministry of Environment and Forests, 2000) Transportation The MCD has many vehicles for the collection of primary and secondary storage waste. The MSD in its fleet contains refuse removal trucks, tractors and loaders they have about 100 vehicles to do the job. What these vehicles do is they collect the waste and take them to the landfill sites. Recycling and re-use Recycling and re-use of MSW is done in a widespread manner where waste pickers are employed as well as there are self employed waste pickers who collect the waste and sell them. How the system works is that these waste pickers and waste collectors gather waste from the residential areas, commercial areas, streets and landfill sites and they sell them to the dealers these dealers range from small, medium and large dealers. After the dealers purchase the materials they are sent to the recycling plant that is established by the government. The following table tells us at what rate the materials are sold (Ankit agarwal, Ashish Singhmar, 2004) Prices of recyclable materials at different recycling levels Recyclable material Recyclable material Price at small recyclable dealer (Rs.) Price at medium recyclable dealer (Rs.) Price at large recyclable dealer (Rs.) Value added in the Process (%) Plastic             PET bottles 1.75 2.25-2.50 3.75-4 121 Milk packets 5.5-6.5 6-7 8-8.50 37.5 Hard plastic like shampoo bottles, caps 7-7.25 9 10-10.5 41 Plastic thread, fibres, ropes, chair cane 6-7 8-8.50 10 67 Plastic cups and glasses 7-8 10-12 13-14 80 Paper             White paper 3-3.25 3.75-4 5-6 76 Mix shredded paper 2-2.25 2.25-2.50 3-3.25 47 Cartons and brown packing Papers 2.25 2.50 3 33 Fresh newspaper 3-3.50 3.25-3.75 4.50-4.75 42 Tetra pack 1.75-2 2-2.25 2.75-3 53 Glass             Broken glass 0.50 0.90-1    90 Bottles 2 2.25-2.50    19 Aluminium             Beer and cold drink cans 40-45 43-48 75-85 88 Deodorant, scent cans 42-45 55-60 90-95 113 Aluminium foil 20-22 25-27 30-32 48 Other metals             Steel utensils 20-22 25-27 30 43 Copper wire 70-75 80-85 95-100 35 Source 🙠 Ankit agarwal, Ashish Singhmar, 2004) Composting: Coming to composting only 9% of the total MSW is composted the remaining 91% is sent to landfill sites. There are three places set up by the Delhi municipal authority for composting MSW where as two plants are set up at Okhala and the other one is set up at Bhalswa .These plant has a treatment capacity of 150 tonnes/day but they are not utilised to the fullest because of the cost. The treatment capacity of the plant at Bhalswa is 500 tonnes/day. (Vikash Talyan, R.P. Dahiya, 2008) Incineration: The municipal cooperation of Delhi also tried incarnation they built an incineration plant with the help of a foreign company. But this was shut down immediately because the MSW did not have enough calorific value the minimum calorific value is between 1200-1400 kcal/kg. (Vikash Talyan, R.P. Dahiya, 2008). Final disposal of MSW: Of the total amount of MSW collected 91% is sent to landfill. These landfill sites are located at the outskirts of the city. The land fill sites are the nearest available low line area or waste lands. The transfer of the MSW to these sites is by the vehicles that the Delhi municipality has. These landfill sites are chosen based only on availability and not on any other reason. These landfill sites are poorly maintained which arises a problem of health and safety as well as environmental concerns. There is another big issue because of the poor maintenance of the landfill sites there is a lot of leachate that is being produced mostly in the rainy season due to which the ground water as well as the river next to the landfill sites is getting contaminated. At these landfill sites with the help of bulldozers the MSW is levelled and compressed. The MSW is compressed to a layer of 2-5m and a covering is provided. At the binging there were 20 landfill sites that were created by the Delhi mun icipality out of which 15 are exhausted already. At present there are 3 landfill sites that are being operated one is at Gazipur it was started in 1984 , the other landfill site is located at Bhalswa it was started in 1993 ,the last operating land fill site is located in Okhala it was started in 1994. (Vikash Talyan, R.P. Dahiya, 2008). HEALTH AND SAFETY AND ENVIRONMENTAL RISKS Health and safety and environmental risks are a major concern in the MSW management in Delhi. The workers as well as the waste pickers are not provided with proper health and safety equipment like boots and gloves. The working conditions are unhygienic .the chance of transfer of infection is high and because of this if a worker gets sick he loses his wages. The workers are also not provided with medical insurance. The environmental risk is also high because the landfill sites are not maintained properly and the leachate gets leaked into the underground water as well as the river Yamuna .these issues should be looked into very carefully. IMPROVEMENT We can improve these poor conditions by privatisation. We can let the private sectors help in the disposal of MSW. The Delhi municipal authorities can open the incineration plant and dispose the waste. They can also involve the local communities as well as the NGOs to help in the disposal of waste. The municipal authorities should identify a proper treatment technology. The authorities should increase standards of reuse and recycling of waste mainly composting. CONCLUSION With the rapid increase in population and fast urbanisation of Delhi the current policies and regulations want be sufficient for controlling the rapid increase in the MSW. Due to this the health and safety as well as the environmental risks are increasing .The municipal authorities of Delhi cannot keep up with the MSW that is being produced now but according to a prediction the MSW by 2026 is going to increase 4 folds if this happens the municipal authorities will be facing a lot of problem. Even the Delhi government has realised this and they are making changes in the form of master plans. The government is also approaching the public and private sectors for help like the citizens and the NGOs. First of all people should be educated on proper disposal of MSW. The government should see to that the master plans are being properly followed at all levels. Only by doing this the Delhi municipal authorities can keep the MSW in control. Referencing Ankit agarwal, Ashish Singhmar, 2004. Municipal solid waste recycling and associated markets in Delhi, India. Resources, Conservation and Recycling Census of India,.2001 . Ministry of Home Affairs, Government of India (GoI). [Online].available http://www.censusindia.net Hoornweg, Daniel with Laura Thomas. 1999. Working Paper Series Nr. 1. Urban Development Sector Unit. East Asia and Pacific Region. Page 5. [Online] http://web.mit.edu/urbanupgrading/urbanenvironment/sectors/solid-waste-sources.html. Hoornweg, D., Laura, T., 1999. What a waste: solid management in Asia. Working Paper Series No. 1. Urban Development Sector Unit, East Asia and Pacific Region, the World Bank, Washington, DC MCD, 2004. Feasibility study and master plan report for optimal solid waste treatment and disposal for the entire state of Delhi based on public and private partnership solution, Municipal Corporation of Delhi, Delhi, India. Ministry of Environment and Forests, 2000.the gazette of India. [Online]. Available http://envfor.nic.in/legis/hsm/mswmhr.html TERI, 2002.Performance Measurements of Pilot Cities, Tata Energy Research Institute, New Delhi, India. Vikash Talyan, R.P. Dahiya, 2008. State of municipal solid waste management in Delhi, the capital of India, Waste ManagementVolume 28, Issue 7, 2008, Pages 1276-1287 waste management essay in 150 words

The History of the Dog :: essays research papers

the dog has been around for many years they are careing loving animals but they also have a darkside. Dogs have been considered mans best friend for many many years; but do they really fit in the category as mans friend. DOgs have been known to turn on there owners and cause chaos throughout homes even neighborhoods. should they really be let in our homes and if so haow close can we really get to the vicious creatures? Domestic Dog, mammal generally considered to be the first domesticated animal. This trusted work partner and beloved pet learned to live with humans more than 14,000 years ago. A direct descendant of the wolves that once roamed Europe, Asia, and North America, the domestic dog belongs to the dog family, which includes wolves, coyotes, foxes, and jackals. Dog ancestry has been traced to small, civet-like mammals, called miacis, which had short legs and a long body and lived approximately 40 million years ago. The evolving relationship between the domestic dog and humans has been documented in fossil evidence, artifacts, and records left by earlier civilizations. Prehistoric dog skeletal remains, excavated from sites in Denmark, England, Germany, Japan, and China, indicate the early coexistence of dogs with people. An ancient Persian cemetery, dating to the 5th century BC, contained thousands of dog skeletons. Their formal burial and the positioning of the dog remains reveal the esteem in which the ancient Persians held their dogs. The relationship shared by dogs and humans also is evident in cave drawings, early pottery, and Asian ivory carvings that depict dogs. A statue of Anubis, the half dog, half jackal Egyptian god, was discovered inside King Tutankhamen's tomb, constructed in about 1400 BC. Literary references to the dog include those found in the Bible and in the Greek classic the Odyssey by Homer. In 1576 an English physician and dog fancier, John Caius, wrote a detailed text on dog breeds, Of English Dogges. Dogs are featured in tapestries that were created in the Middle Ages (5th century to 15th century), and in the work of many artists, including 17th- and 18th-century European painters Peter Paul Rubens and Thomas Gainsborough. Although it is not known how humans and dogs first learned to coexist, people soon discovered the many ways dogs could enrich their lives. Dogs have been used to hunt for food, herd animals, guard livestock and property, destroy rats and other vermin, pull carts and sleds, perform rescues, and apprehend lawbreakers.

The Family in Franz Kafkas The Metamorphosis :: Metamorphosis essays

The Family in Metamorphosis The Metamorphosis by Franz Kafka, is about a young man, Gregor Samsa, who is transformed overnight into a bug. He soon becomes a disgrace to his family. After his metamorphosis, his family goes through an even bigger metamorphosis than Gregor, himself. Therefore, the real metamorphosis occurs to the family rather than Gregor. One of the family members who goes through significant metamorphosis is Gregor's sister, Grete. She maybe the person that he cared the most about. After he turned into a bug, her love and care gave him a reason to live, but when she stopped caring it killed him. Grete turned from this loving, caring, and warm person into this dark, uncaring, and selfish person. After Gregor turns into a bug, Grete seems like the only one who cares about her brother, even in the body of a giant bug She keeps his room clean and brings him things to eat twice a day. She worries about what he might like to eat "But, he would of never been able to guess what his sister, in the goodness of her heart, actually did. To find out his likes and dislikes, she brought him an assortment of foods"(24). . Her kindness, even when she is afraid of his appearance, touches Gregor deeply. She gives him a reason to live. She is the only human that he has contact with. So, he doesn't feel as much alienated as he already is. This shows that after Gregor turned into a bug, she still cared about him. Her warmth gave him a reason to live. But, this would not last for long much longer. As the time passed, Grete practically stops caring about her brother. She starts to treat him differently. "No longer considering what she can do to give Gregor a special treat, his sister, before running to business every morning and afternoon, hurriedly shoved any old food into Gregor's room with her foot" (43). Grete is not thinking about Gregor like; this makes her uncaring. Shoving things with her foot is an example of her showing him that he is a bug because bugs are usually stomped on with feet. Grete gets a job to help pay for expenses, she no longer wants takes care Gregor makes her selfish.

Competition :: essays research papers

INTRODUCTION: Competition occurs between any organisms living in a mutual habitat. Whether it is for food, water, shelter, or a mate, competition can be harmful or helpful to each organism. There are two basic types of competition; intraspecific and interspecific. These terms refer to competition within a specific species and the competition between different species, respectively. In this lab, we conducted 3 basic experiments. Our goal was to observe the effects of the competition in each instance.The first one was to observe the intraspecific competition between the wheat plant species, the second was for the intraspecific competition between the mustard plant species. The third was the interspecific competition of the wheat and mustard species together. The latter experiment's data was divided into two sub groups of high density and low density, for purposes of graphing Dewitt diagrams. Dewitt diagrams are a way of expressing % yield and total productivity data so it can be evaluated and compar ed effectively. It has been noted that intraspecific competitions tend to be more intense than interspecific ones (Ciara, 1993). This is because members of the same species need the same types and amounts of nutrients. When these similar species are in the same habitat with fixed resources, then they consequently have to "fight " for their needs. This is was basis for our hypothesis. We hypopthesized that the species that were involved with the interspecific competitions would have greater production (by ave. weight of grams) than their counterparts involved in the intraspecific competitions. Furthermore, we hypothesized that as the density of the intraspecific and interspecific competition species increased, then the production of the plants (by ave, weight in grams) would go down. MATERIALS AND METHODS: Six weeks previous to the conductance of this lab, Biology 108 section,planted wheat and mustard plants according to table#1 on page 3 of the Principles of Biology 108 Lab Manual . This table depicts all of the total pots and number and type of seeds planted in the pots. It accounts for the experiments of the intraspecific competition and interspecific competition. Replicates of each pot were planted to add precision and more acceptable statistics. Therefore, there were 40 pots, that is, 20 treatments conducted twice(Ciara, 1993). Each Biology 108 section planted these pots and the data from every section was to be combined for an overall data sheet. Our group in section 6 had the role of planting 5 of the experimental pots with the assigned number of wheat seeds or mustard seeds or both.