EXTRACTING ACCURATE DATA FROM MULTIPLE CONFLICTING INFORMATION ON WEB SOURCES

For The World-Wide Web has become the most important information source for most of us. As different websites often provide conflicting information there is no guarantee for the correctness of the data. Among multiple conflict results, can we automatically identify which one is likely the true fact?, In this paper our experiments show that Fact finder, a supporter for user to resolve the problem, successfully finds true facts among conflicting information, and identifies Trust worthy websites better than the popular search engines. In our paper we give ratings based on two things- popularity or the hits & number of occurrences of same data. As we can’t give preference only to popularity, we have considered another rating i.e. about number of occurrences of same data in several other websites, which are less popular. This paper helps user to get resolved by conflicting facts from multiple websites on two basis. Further by considering few more relations we can develop a search engine that truly helps the user to resolve the Veracity problem

A Comparative Analysis of Lexical/NLP Method with WEKA's Bayes Classifier

Various websites are available as source of microblogs. This is due to nature of microblogs on which people post real time messages about their attitudes on a various topics, talk about present issues, criticize, and articulate positive or negative sentiment for products they use in daily life. That?s why, manufacturing companies of such products have started to take these microblogs to get a sense of general sentiment for their product. Reply can be given by the companies on microblogs for the reactions of the users. Thus challenge is to build a technique to detect and summarize an overall sentiment. The proposed methodology examines sentiments on Twitter data contextually. Sentiment Analysis is the major aspect of present day NLP. Also, Twitter has emerged as the most important data source for present day NLP. In the work carried out, tweets are extracted from Twitter using Twitter API after authentication, a fine pre-processing is dealt and provided for further processing. Later, tag each word with their respective parts of speech using Part-Of-Speech (POS) tagger. SentiWordNet, WordNet and NLP weight assignment policies are used to assign weights and provide results. The analysis of same data set is also done with Na?ve Bayes classifier using WEKA - the data mining tool. Then results of both ? the proposed method and Na?ve Bayes are compared. (Then finally comparison between the results of proposed method with Na?ve Bayes classier is done.) The investigation proved that our method i.e. NLP technique works better than that of Na?ve Bayes Classifier. And this study also proves that the training set to the classier matters a lot in Machine Learning - ?Expected output can be accurate if and only if the training of a classifier is better?

Adaptive Energy-Optimized Consolidation Algorithm

We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud.  The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance