Today, in the hasty advancement epoch of technology, allotting and gathering of information are imperative. Readers enthrall with an undersized edition of copious prolonged text documents. In this paper, we represent our approach which we used in our Automated Text Summarization System known as MDSS (Multiple Documents Summarization System). We elucidate a new fangled approach which is based on statistical (rather than semantic) factors. In contrast to single document summarization, the issues of compression, speediness, superfluous and passage opting are more decisive in multiple documents summarization. For sentence comparison, Jaccard"s coefficient is used to improve the worth and quality of the summarization. Resemblance exists between our algorithms and dynamic time warping. Our experimental domino effects indicate that it is useful and effectual to enhance the quality of multiple documents summarization via Jaccard"s coefficient. Our system MDSS is implemented in Java (jdk 1.6).
In this paper, an automatic diagnosis system based on Neural Network for hepatitis virus is introduced. This automatic diagnosis system deals with the mixture of feature extraction and classification. The system has two stages, which are feature extraction -reduction and classification stages. In the feature extraction -diminution stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Missing values of the instances are adjusted using local mean method. Then, the number of these features was reduced to 6 from 19 due to relative significance of fields. In the classification stage, these reduced features are given as inputs Neural Network classifier. The classification accuracy of this ANN diagnosis system for the diagnosis of hepatitis virus was obtained, this accuracy was around 99.1% for training data and 100% for testing data.
In this paper, we scrutinize factors that dole out significantly to augmenting the risk of hepatitis-C virus. The dataset has been taken from the machine learning warehouse of University of California. It contains nineteen features along with a class feature having binary classification. There is a total of 15 binary attributes together with a class attribute and 5 continuous attributes. The dataset contains 155 records. In order to prevail over the missing values problem, data normalization techniques are applied. First, the dimension of the problem is trimmed down. Next binary logistic regression is applied to classify the cases by using qualitative and quantitative approaches for data reduction. The three stage procedure has produced more than 89% accurate classification. Our proposed approach has a low feature complexity with a good classification rate as it is working by using only 37% of the total fields.
Business strategies portray the measures that should be taken with the intention of achieving enduring objectives. Above and beyond, enduring objectives characterize the result anticipated from taking up particular strategies. Nevertheless, opting strategies which go well with organization is not an undemanding task. In this research paper, on the basis of diverse organizations' data, a novel methodology to get top 3 strategies for a business is presented. For this purpose, a dummy dataset of different organizations has been generated. The dummy dataset contains 134 influential variables as well as the successful strategies adopted by the considered organizations. Two different similarity measures namely, Jaccard coefficient and Dice coefficient have been applied. Besides, Pearson correlation coefficient is also applied on the dummy dataset. It is predicted that by means of our novel approach, a business strategist would obtain the suitable business strategies for his or her organization in an efficient and quite tranquil way.
One of the important endeavors of Computer Science is its dealing with data and performing different responsibilities regarding analysis. In this paper, an ontology based automated score evaluation of unstructured text in the domain of text mining is presented. The use of ontologies in this respect is not old. For this research, we have dealt with different approaches and have also represented those methods which provide less optimized score as compared to our finally opted method. For our experimental work, we have collected real answers of students and then compared them with the model answer. We have found that our ultimate approach gives much more optimized end result as compared to other approaches which were carried out throughout our delve process. Moreover, the efficiency of result depends on the ontologies stored in the dataset. General TermsText Mining
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